Systemic Issues

Systemic Issues

from Part III - Topics

Published online by Cambridge University Press: 15 February 2019

Edited by

Sally A. Fincher and

Anthony V. Robins

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Sally A. Fincher: Affiliation:
University of Kent, Canterbury
Anthony V. Robins: Affiliation:
University of Otago, New Zealand

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Type: Chapter
Information: The Cambridge Handbook of Computing Education Research , pp. 325 - 510

DOI: https://doi.org/10.1017/9781108654555 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2019

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References

ACM/IEEE–CS Joint Task Force on Computing Curricula (2013). Computer Science Curricula 2013. New York: ACM Press and IEEE Computer Society Press.Google Scholar

Alaoutinen, S., & Smolander, K. (2010). Student self-assessment in a programming course using Bloom’s Revised Taxonomy. In Proceedings of the Fifteenth Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘10) (pp. 155–159). New York: ACM.Google Scholar

Alexandron, G., Armoni, M., Gordon, M., & Harel, D. (2014). Scenario-based programming: Reducing the cognitive load, fostering abstract thinking. In Companion Proceedings of the 36th International Conference on Software Engineering (pp. 311–320). New York: ACM.Google Scholar

Allan, V. H., & Kolesar, M. V. (1997). Teaching computer science: A problem solving approach that works. ACM SIGCUE Outlook, 25(1–2), 2–10.CrossRef Google Scholar

Altadmri, A., & Brown, N. C. (2015). 37 million compilations: Investigating novice programming mistakes in large-scale student data. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 522–527). New York: ACM.CrossRef Google Scholar

Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., Raths, J., & Wittrock, M. C. (Eds.) (2001). A Taxonomy for Learning and Teaching and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. New York: Addison Wesley Longman.Google Scholar

Assessment Reform Group (1999). Assessment for Learning: Beyond the Black Box. Cambridge, UK: Cambridge University Press.Google Scholar

Barker, R. J., & Unger, E. A. (1983). A predictor for success in an introductory programming class based upon abstract reasoning development. ACM SIGCSE Bulletin, 15(1), 154–158.CrossRef Google Scholar

Bateman, C. R. (1973). Predicting performance in a basic computer course. In Proceedings of the Fifth Annual Meeting of the American Institute for Decision Sciences (pp. 130–133). Atlanta, GO: AIDS Press.Google Scholar

Bau, D., Gray, J., Kelleher, C., Sheldon, J., & Turbak, F. (2017). Learnable programming: Blocks and beyond. Communications of the ACM, 60(6), 72–80.Google Scholar

Bauer, R., Mehrens, W. A., & Vinsonhaler, J. F. (1968). Predicting performance in a computer programming course. Educational and Psychological Measurement, 28, 1159–1164.Google Scholar

Beaubouef, T. B., & Mason, J. (2005). Why the high attrition rate for computer science students: Some thoughts and observations. Inroads – The SIGCSE Bulletin, 37(2), 103–106.CrossRef Google Scholar

Bennedsen, J., & Caspersen, M. E. (2007). Failure rates in introductory programming. ACM SIGCSE Bulletin, 39(2), 32–36.CrossRef Google Scholar

Bennedsen, J., & Caspersen, M. E. (2005). An investigation of potential success factors for an introductory model-driven programming course. In Proceedings of the First International Workshop on Computing Education Research (ICER ‘05) (pp. 155–163). New York: ACM.Google Scholar

Bennedsen, J., & Caspersen, M. E. (2006). Abstraction ability as an indicator of success for learning object-oriented programming? SIGCSE Bulletin, 38(2), 39–43.Google Scholar

Bergin, S., & Reilly, R. (2006). Predicting introductory programming performance: A multi-institutional multivariate study. Computer Science Education, 16(4), 303–323.CrossRef Google Scholar

Berland, M., Martin, T., Benton, T., Petrick Smith, C., & Davis, D. (2013). Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences, 22(4), 564–599.Google Scholar

Berry, M., & Kölling, M. (2014). The state of play: A notional machine for learning programming. In Proceedings of the 2014 Conference on Innovation and Technology in Computer Science Education (pp. 21–26). New York: ACM.Google Scholar

Bhuiyan, S., Greer, J. E., & McCalla, G. I. (1992). Learning recursion through the use of a mental model-based programming environment. In International Conference on Intelligent Tutoring Systems (pp. 50–57). Berlin, Germany: Springer.Google Scholar

Biggs, J. B., & Collis, K. F. (1982). Evaluating the Quality of Learning: The SOLO Taxonomy (Structure of the Observed Learning Outcome). New York: Academic Press.Google Scholar

Bloom, B., Englehart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. (1956). Taxonomy of Educational Objectives: Handbook I: Cognitive Domain. New York: Longmans.Google Scholar

Bornat, R. (2014). Camels and humps: A retraction. Retrieved from: http://eis.sla.mdx.ac.uk/staffpages/r_bornat/papers/camel_hump_retraction.pdf Google Scholar

Bornat, R., Dehnadi, S., & Simon, (2008). Mental models, consistency and programming aptitude. In Proceedings of the Tenth Australasian Computing Education Conference (ACE 2008) (pp. 53–62). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Boustedt, J., Eckerdal, A., McCartney, R., Moström, J. E., Ratcliffe, M., Sanders, K., & Zander, C. (2007). Threshold concepts in computer science: Do they exist and are they useful? ACM SIGCSE Bulletin, 39(1), 504–508.CrossRef Google Scholar

Bower, M. (2008). A taxonomy of task types in computing. ACM SIGCSE Bulletin, 40(3), 281–285.CrossRef Google Scholar

Brabrand, C., & Dahl, B. (2009). Using the SOLO taxonomy to analyze competence progression of university science curricula. Higher Education, 58(4), 531–549.Google Scholar

Brooks, F. P. (1975). The Mythical Man-Month: Essays on Software Engineering. New York: Addison-Wesley.Google Scholar

Brooks, R. E. (1977). Towards a theory of the cognitive processes in computer programming. International Journal of Man–Machine Studies, 9, 737–751.Google Scholar

Brooks, R. E. (1983). Towards a theory of the comprehension of computer programs. International Journal of Man–Machine Studies, 18, 543–554.Google Scholar

Brooks, R. E. (1990). Categories of programming knowledge and their application. International Journal of Man–Machine Studies, 33(3), 241–246.Google Scholar

Bruce, K. B. (2004). Controversy on how to teach CS 1: A discussion on the SIGCSE-members mailing list. ACM SIGCSE Bulletin, 36(4), 29–34.Google Scholar

Bruner, J. S. (1960). The Process of Education. Cambridge, MA: Harvard University Press.Google Scholar

Buck, D., & Stucki, D. J. (2000). Design early considered harmful: Graduated exposure to complexity and structure based on levels of cognitive development. ACM SIGCSE Bulletin, 32(1), 75–79.Google Scholar

Burkhardt, J. M., Détienne, F., & Wiedenbeck, S. (2002). Object-oriented program comprehension: Effect of expertise, task and phase. Empirical Software Engineering, 7(2), 115–156.Google Scholar

Burkhardt, J. M., Détienne, F., & Wiedenbeck, S. (1997). Mental representations constructed by experts and novices in object-oriented program comprehension. In Proceedings of the IFIP TC13 International Conference on Human–Computer Interaction (INTERACT ‘97) (pp. 339–346). London, UK: Chapman & Hall.Google Scholar

Busjahn, T., & Schulte, C. (2013). The use of code reading in teaching programming. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research (pp. 3–11). New York: ACM.Google Scholar

Cañas, J. J., Bajo, M. T., & Gonzalvo, P. (1994). Mental models and computer programming. International Journal of Human–Computer Studies, 40(5), 795–811.Google Scholar

Carter, A. S., Hundhausen, C. D., & Adesope, O. (2017). Blending measures of programming and social behavior into predictive models of student achievement in early computing courses. ACM Transactions on Computing Education (TOCE), 17(3), 12.Google Scholar

Caspersen, M. E., & Bennedsen, J. (2007). Instructional design of a programming course: a learning theoretic approach. In Proceedings of the Third International Workshop on Computing Education Research (pp. 111–122). New York: ACM.Google Scholar

Caspersen, M. E., Larsen, K. D., & Bennedsen, J. (2007). Mental models and programming aptitude. ACM SIGCSE Bulletin, 39(3), 206–210.CrossRef Google Scholar

Castro, F. E. V., & Fisler, K. (2017). Designing a multi-faceted SOLO taxonomy to track program design skills through an entire course. In Proceedings of the 17th Koli Calling International Conference on Computing Education Research (Koli Calling ‘17) (pp. 10–19). New York: ACM.Google Scholar

Clancy, M. J., & Linn, M. C. (1999). Patterns and pedagogy. ACM SIGCSE Bulletin, 31(1), 37–42.Google Scholar

Clear, T., Whalley, J., Lister, R. F., Carbone, A., Hu, M., Sheard, J., Simon, B., & Thompson, E. (2008). Reliably classifying novice programmer exam responses using the SOLO taxonomy. In 21st Annual Conference of the National Advisory Committee on Computing Qualifications (NACCQ 2008) (pp. 23–30). Auckland, New Zealand: National Advisory Committee on Computing Qualifications.Google Scholar

Corney, M., Teague, D., & Thomas, R. N. (2010). Engaging students in programming. In Proceedings of the Twelfth Australasian Conference on Computing Education Volume 103 (pp. 63–72). Darlinghurst, Australia: Australian Computer Society,Google Scholar

Corney, M., Teague, D., Ahadi, A., & Lister, R. (2012). Some empirical results for neo-Piagetian reasoning in novice programmers and the relationship to code explanation questions. In Proceedings of the Fourteenth Australasian Computing Education Conference Volume 123 (pp. 77–86). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Corritore, C. L., & Wiedenbeck, S. (1991). What do novices learn during program comprehension? International Journal of Human–Computer Interaction, 3, 199–222.CrossRef Google Scholar

Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–185.Google Scholar

Cronan, T. P., Embry, P. R., & White, S. D. (1989). Identifying factors that influence performance of non-computing majors in the business computer information systems course. Journal of Research on Computing in Education, 21 (4), 431–441.Google Scholar

CSTA (2017). About the CSTA K–12 Computer Science Standards. Retrieved from www.csteachers.org/page/standards Google Scholar

Curtis, B. (1984). Fifteen years of psychology in software engineering: Individual differences and cognitive science. In Proceedings of the 7th International Conference on Software Engineering (pp. 97–106). New York: IEEE.Google Scholar

Dauw, D. (1967). Vocational interests of highly creative computer personnel. Personnel Journal, 46(10), 653–659.Google Scholar

Davies, S. P. (1993). Models and theories of programming strategy. International Journal of Man–Machine Studies, 39, 237–267.Google Scholar

Davies, S. P. (2008). The effects of emphasizing computational thinking in an introductory programming course. In Frontiers in Education Conference (FIE 2008) (p. T2C-3). New York: IEEE.Google Scholar

Dehnadi, S. (2006). Abstract for Dehnadi & Bornat (2006). Retrieved from www.eis.mdx.ac.uk/research/PhDArea/saeed Google Scholar

Dehnadi, S., & Bornat, R. (2006). The camel has two humps (working title). Retrieved from www.eis.mdx.ac.uk/research/PhDArea/saeed/paper1.pdf Google Scholar

Dijkstra, E. W. (1989). On the cruelty of really teaching computer science. Communications of the ACM, 32(12), 1398–1404.Google Scholar

du Boulay, B., O’Shea, T., & Monk, J. (1989). The black box inside the glass box: Presenting computing concepts to novices. In Soloway, E. & Spohrer, J. C. (Eds.), Studying the Novice Programmer (pp. 431–446). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

du Boulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57–73.Google Scholar

du Boulay, B. (1989). Some difficulties of learning to program. In Soloway, E. & Spohrer, J. C. (Eds.), Studying the Novice Programmer (pp. 283–299). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Ebrahimi, A. (1994). Novice programmer errors: Language constructs and plan composition. International Journal of Human–Computer Studies, 41(4), 457–480.Google Scholar

Eckerdal, A. (2009). Novice Programming Students’ Learning of Concepts and Practice (doctoral dissertation). Acta Universitatis Upsaliensis.Google Scholar

Eckerdal, A., Thuné, M., & Berglund, A. (2005). What does it take to learn “programming thinking”? In Proceedings of the First International Workshop on Computing Education Research (pp. 135–142). New York: ACM.Google Scholar

Elarde, J. (2016). Toward improving introductory programming student course success rates: experiences with a modified cohort model to student success sessions. Journal of Computing Sciences in Colleges, 32(2), 113–119.Google Scholar

Ensmenger, N. L. (2010). The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise. Cambridge, MA: MIT Press.Google Scholar

Evans, G. E., & Simkin, M. G. (1989). What best predicts computer proficiency? Communications of the ACM, 32(11), 1322–1327.Google Scholar

Falkner, K., Vivian, R., & Falkner, N. J. (2013). Neo-Piagetian forms of reasoning in software development process construction. In Learning and Teaching in Computing and Engineering (LaTiCE) (pp. 31–38). New York: IEEE.Google Scholar

Feldman, D. H. (2004). Piaget’s stages: The unfinished symphony of cognitive development. New Ideas in Psychology, 22, 175–231.Google Scholar

Felleisen, M., Findler, R. B., Flatt, M., & Krishnamurthi, S. (2001). How to Design Programs: An Introduction to Programming and Computing. Cambridge, MA: MIT Press.Google Scholar

Fincher, S. (1999). What are we doing when we teach programming? In Frontiers in Education Conference (FIE’99) Volume 1 (pp. 12A4-1–12A4-5). New York: IEEE.Google Scholar

Findler, R. B., Clements, J., Flanagan, C., Flatt, M., Krishnamurthi, S., Steckler, P., & Felleisen, M. (2002). DrScheme: A programming environment for Scheme. Journal of Functional Programming, 12(2), 159–182.Google Scholar

Fisler, K. (2014). The recurring Rainfall Problem. In Proceedings of the Tenth Annual Conference on International Computing Education Research (pp. 35–42). New York: ACM.Google Scholar

Freeman, E., Robson, E., Bates, B., & Sierra, K. (2004). Head First Design Patterns: A Brain-Friendly Guide. Sebastopol, CA: O’Reilly Media.Google Scholar

Fuller, U., Johnson, C. G., Ahoniemi, T., Cukierman, D., Hernán-Losada, I., Jackova, , Lahtinen, J., Lewis, E., Thompson, T. L., Riedesel, D. M., , C., & Thompson, E. (2007). Developing a computer science-specific learning taxonomy. ACM SIGCSE Bulletin, 39(4), 152–170.Google Scholar

Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1995). Design Patterns: Elements of Reusable Object-Oriented Software. New York: Addison-Wesley.Google Scholar

Garcia, R. A. (1987). Identifying the Academic Factors that Predict the Success of Entering Freshmen in a Beginning Computer Science Course (doctoral dissertation). Texas Tech University.Google Scholar

Garner, S. (2002). Reducing the cognitive load on novice programmers. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications (pp. 578–583). Chesapeake, VA: AACE.Google Scholar

Garner, S., Haden, P., & Robins, A. (2005). My program is correct but it doesn’t run: A preliminary investigation of novice programmers’ problems. In Proceedings of the Seventh Australasian Computing Education Conference (ACE2005) CRPIT 42 (pp. 173–180). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Gentner, D. (2002). Mental models, psychology of. In Smelser, N. & Bates, P. B. (Eds.), International Encyclopedia of the Social and Behavioral Sciences (pp. 9683–9687). Amsterdam, The Netherlands: Elsevier Science.Google Scholar

Gentner, D., & Stevens, A. L. (Eds.) (1983). Mental Models. Hillsdale, NJ: Erlbaum.Google Scholar

Gibbs, W. W. (1994). Software’s chronic crisis. Scientific American, 271(3), 86–95.CrossRef Google Scholar

Ginat, D., & Menashe, E. (2015). SOLO taxonomy for assessing novices’ algorithmic design. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 452–457). New York: ACM.Google Scholar

Gluga, R., Kay, J., Lister, R., Kleitman, S., & Lever, T. (2012). Coming to terms with Bloom: an online tutorial for teachers of programming fundamentals. In Proceedings of the Fourteenth Australasian Computing Education Conference Volume 123 (pp. 147–156). Darlinghurst, Australia: Australian Computer Society,Google Scholar

Gray, W. D., & Anderson, J. R. (1987). Change-episodes in coding: When and how do programmers change their code? In Olson, G. M., Sheppard, S., & Soloway, E. (Eds.), Empirical Studies of Programmers: Second Workshop (pp. 185–197). Norwood, NJ: Ablex.Google Scholar

Gray, S., St Clair, C., James, R., & Mead, J. (2007). Suggestions for graduated exposure to programming concepts using fading worked examples. In Proceedings of the Third International Workshop on Computing Education Research (pp. 99–110). New York: ACM.Google Scholar

Green, T. R. G. (1990). Programming languages as information structures. In Hoc, J. M., Green, T. R. G., Samurçay, R., & Gillmore, D. J. (Eds.), Psychology of Programming (pp. 117–137). London, UK: Academic Press.Google Scholar

Guzdial, M. (2007) What makes programming so hard? Retrieved from http://home.cc.gatech.edu/csl/uploads/6/Guzdial-blog-pieces-on-what-is-CSEd.pdf Google Scholar

Guzdial, M. (2010). Why is it so hard to learn to program? In Oram, A. & Wilson, G. (Eds.), Making Software: What Really Works, and Why We Believe It (pp. 111–124). Sebastopol, CA: O’Reilly Media.Google Scholar

Guzdial, M., & Soloway, E. (2002). Teaching the Nintendo generation to program. Communications of the ACM, 45(4), 17–21.Google Scholar

Hattie, J. A. (2009). Visible Learning: A Synthesis of 800+ Meta-Analyses on Achievement. Abingdon, UK: Routledge.Google Scholar

Hattie, J. (2012). Visible Learning for Teachers: Maximizing Impact on Learning. Abingdon, UK: Routledge.Google Scholar

Hiebert, J., & Lefevre, P. (1986). Conceptual and procedural knowledge in mathematics: An introductory analysis. In Hiebert, J. (Ed.), Conceptual and Procedural Knowledge: The Case of Mathematics, 2 (pp. 1–27). Hillsdale, NJ: Erlbaum.Google Scholar

Hill, G. J. (2016). Review of a problems-first approach to first year undergraduate programming. In Kassel, S. & Wu, B. (Eds.), Software Engineering Education Going Agile (pp. 73–80). Basel, Switzerland: Springer International Publishing.Google Scholar

Hoc, J. M., & Nguyen-Xuan, A. (1990). Language semantics, mental models and analogy. In Hoc, J. M., Green, T. R. G., Samurçay, R., & Gillmore, D. J. (Eds.), Psychology of Programming (pp. 139–156). London, UK: Academic Press.Google Scholar

Hoda, R., & Andreae, P. (2014). It’s not them, it’s us! Why computer science fails to impress many first years. In Proceedings of the Sixteenth Australasian Computing Education Conference Volume 148 (pp. 159–162). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Höök, L. J., & Eckerdal, A. (2015). On the bimodality in an introductory programming course: An analysis of student performance factors. In Learning and Teaching in Computing and Engineering (LaTiCE 2015) (pp. 79–86). New York: IEEE.Google Scholar

Howles, T. (2009). A study of attrition and the use of student learning communities in the computer science introductory programming sequence. Computer Science Education, 19(1), 1–13.Google Scholar

Hudak, M. A., & Anderson, D. E. (1990). Formal operations and learning style predict success in statistics and computer science courses. Teaching of Psychology, 17(4), 231–234.Google Scholar

Hundley, J. (2008). A review of using design patterns in CS1. In Proceedings of the 46th Annual Southeast Regional Conference (ACM SE’08) (pp. 30–33). New York: ACM.Google Scholar

Izu, C., Weerasinghe, A., & Pope, C. (2016). A study of code design skills in novice programmers using the SOLO taxonomy. In Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 251–259). New York: ACM.Google Scholar

Jadud, M. C. (2006). Methods and tools for exploring novice compilation behaviour. In Proceedings of the Second International Workshop on Computing Education Research (pp. 73–84). New York: ACM.Google Scholar

Jimoyiannis, A. (2013). Using SOLO taxonomy to explore students’ mental models of the programming variable and the assignment statement. Themes in Science and Technology Education, 4(2), 53–74.Google Scholar

Johnson-Laird, P. N. (1983). Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge, MA: Harvard University Press.Google Scholar

Johnson, C. G., & Fuller, U. (2006). Is Bloom’s taxonomy appropriate for computer science? In Proceedings of the 6th Baltic Sea Conference on Computing Education Research, Koli Calling (pp. 120–123). New York: ACM.Google Scholar

Kay, J., Barg, M., Fekete, A., Greening, T., Hollands, O., Kingston, J., & Crawford, K. (2000). Problem-based learning for foundation computer science courses. Computer Science Education, 10, 109–128.Google Scholar

Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys (CSUR), 37(2), 83–137.Google Scholar

Kim, J., & Lerch, F. J. (1997). Why is programming (sometimes) so difficult? Programming as scientific discovery in multiple problem spaces. Information Systems Research, 8(1), 25–50.Google Scholar

Kinnunen, P., & Malmi, L. (2006). Why students drop out CS1 course? In Proceedings of the Second International Workshop on Computing Education Research (pp. 97–108). New York: ACM.Google Scholar

Kölling, M. (2009). Quality-oriented teaching of programming. Retrieved from: https://blogs.kcl.ac.uk/proged/2009/09/04/quality-oriented-teaching-of-programming Google Scholar

Kölling, M. (2010). The Greenfoot programming environment. ACM Transactions on Computing Education (TOCE), 10(4), 14.Google Scholar

Kölling, M., Quig, , Patterson, B., , A., & Rosenberg, J. (2003). The BlueJ system and its pedagogy. Computer Science Education, 13(4), 249–268.Google Scholar

Koulouri, T., Lauria, S., & Macredie, R. D. (2014). Teaching introductory programming: A quantitative evaluation of different approaches. ACM Transactions on Computing Education (TOCE), 14(4), 26.Google Scholar

Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41(4), 212–218.Google Scholar

Kunkle, W. M., & Allen, R. B. (2016). The impact of different teaching approaches and languages on student learning of introductory programming concepts. ACM Transactions on Computing Education (TOCE), 16(1), 3.Google Scholar

Kurland, D. M., Pea, R. D., Clement, C., & Mawby, R. (1989). A study of the development of programming ability and thinking skills in high school students. In Soloway, E. & Spohrer, J. C. (Eds.), Studying the Novice Programmer (pp. 83–112). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Kurland, D. M., & Pea, R. D. (1985). Children’s mental models of recursive LOGO programs. Journal of Educational Computing Research, 1(2), 235–243.Google Scholar

Lahtinen, E., Ala-Mutka, K. & Järvinen, H. M. (2005). A study of the difficulties of novice programmers. ACM SIGCSE Bulletin, 37(3), 14–18.Google Scholar

Lau, W. W., & Yuen, A. H. (2011). Modelling programming performance: Beyond the influence of learner characteristics. Computers & Education, 57(1), 1202–1213.Google Scholar

Lawson, C. (1962). A survey of computer facility management. Datamation, 8(7), 29–32.Google Scholar

Lewis, T. L., Rosson, M. B., & Pérez-Quiñones, M. A. (2004). What do the experts say?: Teaching introductory design from an expert’s perspective. ACM SIGCSE Bulletin, 36(1), 296–300.Google Scholar

Linn, M. C., & Dalbey, J. (1989). Cognitive consequences of programming instruction. In Soloway, E. & Spohrer, J. C. (Eds.), Studying the Novice Programmer (pp. 57–81). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Linn, M. C., & Clancy, M. J. (1992). The case for case studies of programming problems. Communications of the ACM, 35(3), 121–132.Google Scholar

Lister, R. (2000). On blooming first year programming, and its blooming assessment. In Proceedings of the Australasian Conference on Computing Education (ACSE ‘00) (pp. 158–162). Darlinghurst, Australia: Australian Computer Society.CrossRef Google Scholar

Lister, R. (2010). Geek genes and bimodal grades. ACM Inroads, 1(3), 16–17.Google Scholar

Lister, R. (2011). Concrete and other neo-Piagetian forms of reasoning in the novice programmer. In Proceedings of the Thirteenth Australasian Computing Education Conference Volume 114 (pp. 9–18). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Lister, R., & Leaney, J. (2003a). Introductory programming, criterion-referencing, and Bloom. ACM SIGCSE Bulletin, 35(1), 143–147.Google Scholar

Lister, R., & Leaney, J. (2003b). First year programming: Let all the flowers bloom. In Proceedings of the Fifth Australasian Computing Education Conference (ACE 2003) Volume 20 (pp. 221–230). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Moström, J. E., Sanders, K., Seppälä, O., & Simon, B. (2004). A multi-national study of reading and tracing skills in novice programmers. ACM SIGCSE Bulletin, 36(4), 119–150.Google Scholar

Lister, R., Clear, T., Bouvier, D. J., Carter, P., Eckerdal, A., Jacková, J., Lopez, , McCartney, M., Robbins, R., Seppälä, P., O., & Thompson, E. (2010). Naturally occurring data as research instrument: Analyzing examination responses to study the novice programmer. ACM SIGCSE Bulletin, 41(4), 156–173.Google Scholar

Lister, R., Simon, B., Thompson, E., Whalley, J. L., & Prasad, C. (2006). Not seeing the forest for the trees: Novice programmers and the SOLO taxonomy. ACM SIGCSE Bulletin, 38(3), 118–122.Google Scholar

Lopez, M., Whalley, J., Robbins, P., & Lister, R. (2008). Relationships between reading, tracing and writing skills in introductory programming. In Proceedings of the Fourth International Workshop on Computing Education Research (pp. 101–112). New York: ACM.Google Scholar

Luxton-Reilly, A. (2016). Learning to program is easy. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (pp. 284–289). New York: ACM.Google Scholar

Ma, L., Ferguson, J., Roper, M., & Wood, M. (2007). Investigating the viability of mental models held by novice programmers. ACM SIGCSE Bulletin, 39(1), 499–503.Google Scholar

Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education (TOCE), 10(4), 16.Google Scholar

Mannila, L., Peltomäki, M., & Salakoski, T. (2006). What about a simple language? Analyzing the difficulties in learning to program. Computer Science Education, 16(3), 211–227.Google Scholar

Margulieux, L. E., Catrambone, R., & Schaeffer, L. M. (2018). Varying effects of subgoal labeled expository text in programming, chemistry, and statistics. Instructional Science, 46(5), 707–722.Google Scholar

Mason, R., & Cooper, G. (2013). Distractions in programming environments. In Proceedings of the Fifteenth Australasian Computing Education Conference Volume 136 (pp. 23–30). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Mayer, R. E. (1989). The psychology of how novices learn computer programming. In Soloway, E. & Spohrer, J. C. (Eds.), Studying the Novice Programmer (pp. 129–159). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Mayer, D. B., & Stalnaker, A. W. (1968). Selection and evaluation of computer personnel – The research history of SIG/CPR. In The Proceedings of the 1968 ACM National Conference (23rd ACM National Conference) (pp. 657–670). New York: ACM.Google Scholar

Mayer, R. E. (1985). Learning in complex domains: A cognitive analysis of computer programming. Psychology of Learning and Motivation, 19, 89–130.Google Scholar

Mayer, R. E. (1992). Teaching for transfer of problem-solving skills to computer programming. In De Corte, E., Linn, M. C., Mandl, H., & Verschaffel, L. (Eds.), Computer-Based Learning Environments and Problem Solving. NATO ASI Series (Series F: Computer and Systems Sciences), Vol. 84 (pp. 193–206). Berlin, Germany: Springer.Google Scholar

McCall, D., & Kölling, M. (2014). Meaningful categorisation of novice programmer errors. In Frontiers in Education Conference (FIE) (pp. 1–8). New York: IEEE.Google Scholar

McCane, B., Ott, C., Meek, N., & Robins, A. (2017). Mastery learning in introductory programming. In Proceedings of the Nineteenth Australasian Computing Education Conference (pp. 1–10). New York: ACM.Google Scholar

McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B., Laxer, C., Thomas, L., Utting, I., & Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. ACM SIGCSE Bulletin, 33, 125–180.Google Scholar

McNamara, W. J. (1967). The selection of computer personnel: Past, present, future. In Proceedings of the Fifth SIGCPR Conference on Computer Personnel Research (SIGCPR ‘67) (pp. 52–56). New York: ACM Press.Google Scholar

Mead, J., Gray, S., Hamer, J., James, R., Sorva, J., Clair, C. S., & Thomas, L. (2006). A cognitive approach to identifying measurable milestones for programming skill acquisition. ACM SIGCSE Bulletin, 38(4), 182–194.Google Scholar

Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2010). Learning computer science concepts with Scratch. In Proceedings of the Sixth International Workshop on Computing Education Research (ICER ‘10) (pp. 69–76). New York: ACM.CrossRef Google Scholar

Mendes, A. J., Paquete, L., Cardoso, A., & Gomes, A. (2012). Increasing student commitment in introductory programming learning. In Frontiers in Education Conference (FIE) (pp. 1–6). New York: IEEE.Google Scholar

Meyer, J. H., & Land, R. (Eds.) (2006). Overcoming Barriers to Student Understanding: Threshold Concepts and Troublesome Knowledge. London, UK: Routledge.Google Scholar

Meyer, J. H. F., & Land, R. (2003). Threshold Concepts and Troublesome Knowledge: Linkages to Ways of Thinking and Practising within the Disciplines (ETL Project: Occasional Report No. 4). Edinburgh, UK: University of Edinburgh Press.Google Scholar

Mitchell, M. L., & Jolley, J. M. (2012). Research Design Explained, 8th edn. Wadsworth, CA: Cengage Learning.Google Scholar

Morra, S., Gobbo, C., Marini, Z., & Sheese, R. (2007). Cognitive Development: Neo-Piagetian Perspectives. New York: Psychology Press.Google Scholar

Morrison, B. B., Dorn, B., & Guzdial, M. (2014). Measuring cognitive load in introductory CS: Adaptation of an instrument. In Proceedings of the Tenth Annual Conference on International Computing Education Research (pp. 131–138). New York: ACM.Google Scholar

Newman, R., Gatward, R., & Poppleton, M. (1970). Paradigms for teaching computer programming in higher education. WIT Transactions on Information and Communication Technologies, 7, 299–305.Google Scholar

Newstead, P. R. (1975). Grade and ability predictions in an introductory programming course. ACM SIGCSE Bulletin, 7, 87–91.Google Scholar

O’Donnell, R. (2009). Threshold concepts and their relevance to economics. In 14th Annual Australasian Teaching Economics Conference (ATEC 2009) (pp. 190–200). Brisbane, Australia: School of Economics and Finance, Queensland University of Technology.Google Scholar

Oliver, D., Dobele, T., Greber, M., & Roberts, T. (2004). This course has a Bloom rating of 3.9. In Proceedings of the Sixth Australasian Conference on Computing Education (ACE ‘04) (pp. 227–231). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Ormerod, T. (1990). Human cognition and programming. In Hoc, J. M., Green, T. R. G., Samurçay, R., & Gillmore, D. J. (Eds.), Psychology of Programming (pp. 63–82). London, UK: Academic Press.CrossRef Google Scholar

Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4.Google Scholar

Palumbo, D. (1990). Programming language/problem-solving research: A review of relevant issues. Review of Educational Research, 60(1), 65–89.Google Scholar

Parsons, D., & Haden, P. (2006). Parson’s programming puzzles: A fun and effective learning tool for first programming courses. In Proceedings of the 8th Australasian Conference on Computing Education (ACE ‘06) Volume 52 (pp. 157–163). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Patitsas, E., Berlin, J., Craig, M., & Easterbrook, S. (2016). Evidence that computer science grades are not bimodal. In Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 113–121). New York: ACM.Google Scholar

Pea, R. D. (1986). Language-independent conceptual “bugs” in novice programming. Journal of Educational Computing Research, 2(1), 25–36.Google Scholar

Pea, R. D., & Kurland, D. M. (1984). On the Cognitive Prerequisites of Learning Computer Programming. Technical Report No. 18. New York: Bank Street College of Education.Google Scholar

Pears, A., Seidman, S., Malmi, L., Mannila, L., Adams, E., Bennedsen, J., Devlin, M., & Paterson, J. (2007). A survey of literature on the teaching of introductory programming. ACM SIGCSE Bulletin, 39(4), 204–223.Google Scholar

Perkins, D. N., & Martin, F (1986). Fragile knowledge and neglected strategies in novice programmers. In Soloway, E. & Iyengar, S. (Eds.), Empirical Studies of Programmers, First Workshop (pp. 213–229). Norwood, NJ: Ablex.Google Scholar

Perkins, D. N., Hancock, C., Hobbs, R., Martin, F., & Simmons, R. (1989). Conditions of learning in novice programmers. In Soloway, E. & Spohrer, J. C. (Eds.), Studying the Novice Programmer (pp. 261–279). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Piaget, J. (1964). Part I: Cognitive development in children: Piaget development and learning. Journal of Research in Science Teaching, 2(3), 176–186.Google Scholar

Piaget, J. (1971a). The theory of stages in cognitive development. In Green, D. R., Ford, M. P., & Flamer, G. B. (Eds.), Measurement and Piaget (pp. 1–11). New York: McGraw-Hill.Google Scholar

Piaget, J. (1971b). Developmental stages and developmental processes. In Green, D. R., Ford, M. P., & Flamer, G. B. (Eds.), Measurement and Piaget (pp. 172–188). New York: McGraw-Hill.Google Scholar

Plass, J. L., Moreno, R., & Brünken, R. (Eds.) (2010). Cognitive Load Theory. Cambridge, UK: Cambridge University Press.Google Scholar

Porter, L., & Zingaro, D. (2014). Importance of early performance in CS1: Two conflicting assessment stories. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (pp. 295–300). New York: ACM.Google Scholar

Porter, L., Zingaro, D., & Lister, R. (2014). Predicting student success using fine grain clicker data. In Proceedings of the Tenth Annual Conference on International Computing Education Research (pp. 51–58). New York: ACM.Google Scholar

Price, T. W., & Barnes, T. (2015). Comparing textual and block interfaces in a novice programming environment. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research (pp. 91–99). New York: ACM.Google Scholar

Rist, R. S. (1995). Program structure and design. Cognitive Science, 19, 507–562.Google Scholar

Rist, R. S. (1986). Plans in programming: Definition, demonstration, and development. In Soloway, E. & Iyengar, S. (Eds.), Empirical Studies of Programmers (pp. 28–47). Norwood, NJ: Ablex Publishing.Google Scholar

Rist, R. S. (1989). Schema creation in programming. Cognitive Science, 13, 389–414.Google Scholar

Rist, R. S. (2004). Learning to program: Schema creation, application, and evaluation. In Fincher, S. & Petre, M. (Eds.), Computer Science Education Research (pp. 175–195). London, UK: Taylor & Francis.Google Scholar

Robins, A. V. (2010). Learning edge momentum: A new account of outcomes in CS1. Computer Science Education, 20, 37–71.Google Scholar

Robins, A. V. (2018). Outcomes in introductory programming. Computer Science Technical Report, OUCS-2018-02, The University of Otago. Retrieved from www.otago.ac.nz/computer-science/otago685184.pdf Google Scholar

Robins, A. V., Haden, P., & Garner, S. (2006). Problem distributions in a CS1 course. In Proceedings of the Eighth Australasian Computing Education Conference (ACE2006), CRPIT, 52 (pp. 165–173). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Robins, A. V., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137–172.Google Scholar

Rogalski, J., & Samurçay, R. (1990). Acquisition of programming knowledge and skills. In Hoc, J. M., Green, T. R. G., Samurçay, R., & Gillmore, D. J. (Eds.), Psychology of Programming (pp. 157–174). London, UK: Academic Press.Google Scholar

Rountree, J., Robins, A., & Rountree, N. (2013). Elaborating on threshold concepts, Computer Science Education, 23(3), 265–289.Google Scholar

Rountree, N., Rountree, J., Robins, A., & Hannah, R. (2004). Interacting factors that predict success and failure in a CS1 course. ACM SIGCSE Bulletin, 36(4), 101–104.Google Scholar

Rowbottom, D. P. (2007). Demystifying threshold concepts. Journal of Philosophy of Education, 41, 263–270.Google Scholar

Sackman, H., Erickson, W. J., & Grant, E. E. (1968). Exploratory experimental studies comparing online and offline programming performance. Communications of the ACM, 11(1), 3–11.Google Scholar

Sarawagi, N. (2014). A flipped CS0 classroom: Applying Bloom’s taxonomy to algorithmic thinking. Journal of Computing Sciences in Colleges, 29(6), 21–28.Google Scholar

Schneider, G. M. (1978). The introductory programming course in computer science: Ten principles. ACM SIGCSE Bulletin, 10(1), 107–114.Google Scholar

Schulte, C., & Bennedsen, J. (2006). What do teachers teach in introductory programming? In Proceedings of the Second International Workshop on Computing Education Research (pp. 17–28). New York: ACM.Google Scholar

Schwill, A. (1997). Computer science education based on fundamental ideas. In Passey, D. & Samways, B. (Eds.), Information Technology. IFIP Advances in Information and Communication Technology (pp. 285–291). Boston, MA: Springer.Google Scholar

Schwill, A. (1994). Fundamental ideas of computer science. EATCS-Bulletin, 53, 274–295.Google Scholar

Scott, T. (2003). Bloom’s Taxonomy applied to testing in computer science classes. Journal of Computing in Small Colleges, 19(1), 267–274.Google Scholar

Sheard, J., & Hagan, D. (1998). Our failing students: A study of a repeat group. ACM SIGCSE Bulletin, 30(3), 223–227.CrossRef Google Scholar

Sheard, J., Carbone, A., Lister, R., Simon, B., Thompson, E., & Whalley, J. L. (2008). Going SOLO to assess novice programmers. ACM SIGCSE Bulletin, 40(3), 209–213.Google Scholar

Sheil, B. A. (1981). The psychological study of programming. Computing Surveys, 13, 101–120.Google Scholar

Shih, Y. F., & Alessi, S. M. (1993). Mental models and transfer of learning in computer programming. Journal of Research on Computing in Education, 26(2), 154–175.Google Scholar

Shinners-Kennedy, D., & Fincher, S. A. (2013). Identifying threshold concepts: From dead end to a new direction. In Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research (pp. 9–18). New York: ACM.Google Scholar

Simon, , Fincher, S., Robins, A., Baker, B., Box, I., Cutts, Q., de Raadt, M., Haden, P., Hamer, J., Hamilton, M., Lister, R., Petre, M., Sutton, K., Tolhurst, D., & Tutty, J. (2006). Predictors of success in a first programming course. In Proceedings of the 8th Australasian Conference on Computing Education Volume 52 (pp. 189–196). Darlinghurst, Australia: Australian Computer Society,Google Scholar

Soloway, E., Ehrlich, K., Bonar, J., & Greenspan, J. (1983). What do novices know about programming? In Shneiderman, B. & Badre, A. (Eds.), Directions in Human–Computer Interactions (pp. 27–54). Norwood, NJ: Ablex.Google Scholar

Soloway, E. (1986). Learning to program = learning to construct mechanisms and explanations. Communications of the ACM, 29(9), 850–858.Google Scholar

Soloway, E., & Ehrlich, K. (1984). Empirical studies of programming knowledge. IEEE Transactions on Software Engineering, 5, 595–609.Google Scholar

Soloway, E., & Spohrer, J. C. (Eds.) (1989). Studying the Novice Programmer. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Sorva, J. (2010). Reflections on threshold concepts in computer programming and beyond. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, Koli Calling ‘10 (pp. 21–30). New York: ACM.Google Scholar

Sorva, J. (2012). Visual Program Simulation in Introductory Programming Education (doctoral dissertation 61/2012). Aalto University.Google Scholar

Sorva, J. (2013). Notional machines and introductory programming education. ACM Transactions on Computing Education (TOCE), 13(2), 8.Google Scholar

Spohrer, J. C., & Soloway, E. (1989). Novice mistakes: Are the folk wisdoms correct? In Soloway, E. & Spohrer, J. C. (Eds.), Studying the Novice Programmer (pp. 401–416). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Spohrer, J. C., Soloway, E., & Pope, E. (1989). A goal/plan analysis of buggy Pascal programs. In Soloway, E. & Spohrer, J. C. (Eds.), Studying the Novice Programmer (pp. 355–399). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar

Stachel, J., Marghitu, D., Brahim, T. B., Sims, R., Reynolds, L., & Czelusniak, V. (2013). Managing cognitive load in introductory programming courses: A cognitive aware scaffolding tool. Journal of Integrated Design and Process Science, 17(1), 37–54.Google Scholar

Starr, C. W., Manaris, B., & Stalvey, R. H. (2008). Bloom’s taxonomy revisited: Specifying assessable learning objectives in computer science. ACM SIGCSE Bulletin, 40(1), 261–265.Google Scholar

Storey, M. A., Fracchia, F. D., & Müller, H. A. (1999). Cognitive design elements to support the construction of a mental model during software exploration. Journal of Systems and Software, 44(3), 171–185.Google Scholar

Subramanian, A., & Joshi, K. (1996). Computer aptitude tests as predictors of novice computer programmer performance. Journal of Information Technology Management, 7, 31–41.Google Scholar

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.Google Scholar

Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.Google Scholar

Teague, D. (2015) Neo-Piagetian Theory and the Novice Programmer (doctoral thesis). Queensland University of Technology.Google Scholar

Teague, M. M. (2011). Pedagogy of Introductory Computer Programming: A People-First Approach (master’s thesis). Queensland University of Technology.Google Scholar

Thompson, E., Luxton-Reilly, A., Whalley, J. L., Hu, M., & Robbins, P. (2008). Bloom’s taxonomy for CS assessment. In Proceedings of the Tenth Conference on Australasian Computing Education (ACE ‘08) (pp. 155–161). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Utting, I., Tew, A. E., McCracken, M., Thomas, L., Bouvier, D., Frye, R., Paterson, J., Caspersen, M., Kolikant, Y., Sorva, J., & Wilusz, T. (2013). A fresh look at novice programmers’ performance and their teachers’ expectations. In Proceedings of the ITICSE Working Group Reports Conference on Innovation and Technology in Computer Science Education (pp. 15–32). New York: ACM.Google Scholar

van Merriënboer, J. J. G. (1990). Strategies for programming instruction in high school: Program completion vs. program generation. Journal of Educational Computing Research, 6(3), 265–285.Google Scholar

van Merriënboer, J. J. G., & de Croock, M. B. M. (1992). Strategies for computer–based programming instruction: Program completion vs. program generation. Journal of Educational Computing Research, 8(3), 365–394.Google Scholar

van Merriënboer, J. J., & Paas, F. G. (1990). Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice. Computers in Human Behavior, 6(3), 273–289.Google Scholar

Venables, A., Tan, G., & Lister, R. (2009). A closer look at tracing, explaining and code writing skills in the novice programmer. In Proceedings of the Fifth International Workshop on Computing Education Research (pp. 117–128). New York: ACM.Google Scholar

Ventura, P. (2005). Identifying predictors of success for an objects-first CS1. Computer Science Education, 15(3), 223–243.Google Scholar

Vihavainen, A., Airaksinen, J., & Watson, C. (2014). A systematic review of approaches for teaching introductory programming and their influence on success. In Proceedings of the Tenth Annual Conference on International Computing Education Research (pp. 19–26). New York: ACM.Google Scholar

Watson, C., & Li, F. W. (2014). Failure rates in introductory programming revisited. In Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education (pp. 39–44). New York: ACM.Google Scholar

Webster, B. F. (1996). The real software crisis: The shortage of top-notch programmers threatens to become the limiting factor in software development. Byte Magazine, 21, 218.Google Scholar

Weinberg, G. M. (1971). The Psychology of Computer Programming. New York: Van Nostrand Reinhold.Google Scholar

Weintrop, D., & Wilensky, U. (2015). To block or not to block, that is the question: Students’ perceptions of blocks-based programming. In Proceedings of the 14th International Conference on Interaction Design and Children (pp. 199–208). New York: ACM.Google Scholar

Weintrop, D., Killen, H., & Franke, B. (2018). Blocks or Text? How Programming Language Modality Makes a Difference in Assessing Underrepresented Populations. In Proceedings of the 13th International Conference of the Learning Sciences (ICLS2018) (pp. 328–335). London, UK: International Society of the Learning Sciences.Google Scholar

Werth, L. H. (1986). Predicting student performance in a beginning computer science class. ACM SIGCSE Bulletin, 18(1), 138–143.Google Scholar

Whalley, J. L., Lister, R., Thompson, E., Clear, T., Robbins, P., Ajith Kumar, P. K., & Prasad, C. (2006). An Austalasian study of reading and comprehension skills in novice programmers, using the Bloom and SOLO taxonomies. In Proceedings of the 8th Australian Conference on Computing Education (ACE ‘06) (pp. 243–252). Darlinghurst, Australia: Australian Computer Society.Google Scholar

White, G., & Sivitanides, M. (2002). A theory of the relationships between cognitive requirements of computer programming languages and programmers’ cognitive characteristics. Journal of Information Systems Education, 13(1), 59–66.Google Scholar

Wick, M. R. (2005). Teaching design patterns in CS1: A closed laboratory sequence based on the game of life. ACM SIGCSE Bulletin, 37(1), 487–491.Google Scholar

Widowski, D., & Eyferth, K. (1986). Comprehending and recalling computer programs of different structural and semantic complexity by experts and novices. In Willumeit, H. P. (Ed.), Human Decision Making and Manual Control (pp. 267–275). Amsterdam, The Netherlands: North–Holland, Elsevier.Google Scholar

Wiedenbeck, S., Fix, V., & Scholtz, J. (1993). Characteristics of the mental representations of novice and expert programmers: An empirical study. International Journal of Man–Machine Studies, 25, 697–709.Google Scholar

Wiedenbeck, S., & Ramalingam, V. (1999). Novice comprehension of small programs written in the procedural and object-oriented styles. International Journal of Human–Computer Studies, 51(1), 71–87.Google Scholar

Wiedenbeck, S., Ramalingam, V., Sarasamma, S., & Corritore, C. L. (1999). A comparison of the comprehension of object-oriented and procedural programs by novice programmers. Interacting with Computers, 11(3), 255–282.Google Scholar

Wileman, S. A., Konvalina, J., & Stephens, L. J. (1981). Factors influencing success in beginning computer science courses. Journal of Educational Research, 74, 223–226.Google Scholar

Wilson, B. C., & Shrock, S. (2001). Contributing to success in an introductory computer science course: A study of twelve factors. ACM SIGCSE Bulletin, 33(1), 184–188.Google Scholar

Winslow, L. E. (1996) Programming pedagogy – A psychological overview. ACM SIGCSE Bulletin, 28(3), 17–22.Google Scholar

Woszczynski, A., Haddad, H., & Zgambo, A. (2005). Towards a model of student success in programming courses. In Proceedings of the 43rd Annual Southeast Regional Conference – Volume 1 (ACM-SE 43) (pp. 301–302). New York: ACM.Google Scholar

Yadin, A. (2011). Reducing the dropout rate in an introductory programming course. ACM Inroads, 2(4), 71–76.Google Scholar

Yadin, A. (2013). Using unique assignments for reducing the bimodal grade distribution. ACM Inroads, 4(1), 38–42.Google Scholar

References

Abelson, H., & Sussman, G. J. (1985). Structure and Interpretation of Computer Programs. Cambridge, MA: MIT Press.Google Scholar

Altadmri, A., & Brown, N. C. (2015). 37 million compilations: Investigating novice programming mistakes in large-scale student data. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 522–527). New York: ACM.Google Scholar

Armoni, M., Meerbaum-Salant, O., & Ben-Ari, M. (2015). From Scratch to “real” programming. Transactions on Computing Education (TOCE), 14(4), 25:1–25:15.Google Scholar

Bailie, F., Courtney, M., Murray, K., Schiaffino, R., & Tuohy, S. (2003). Objects first – Does it work? Journal of Computing Sciences in Small Colleges, 19(2), 303–305.Google Scholar

Bainomugisha, E., Carreton, A. L., van Cutsem, T., Mostinckx, S., & de Meuter, W. (2013). A survey on reactive programming. ACM Computing Surveys, 45(4), 52:1–52:34.Google Scholar

Baker, C. M., Milne, L. R., & Ladner, R. E. (2015). StructJumper: A Tool to Help Blind Programmers Navigate and Understand the Structure of Code. In Proceedings of the ACM Conference on Human Factors in Computing Systems (pp. 3043–3052). New York: ACM.Google Scholar

Barik, T., Witschey, J., Johnson, B., & Murphy-Hill, E. (2014). Compiler error notifications revisited: An interaction-first approach for helping developers more effectively comprehend and resolve error notifications. In Companion Proceedings of the 36th International Conference on Software Engineering (ICSE Companion) (pp. 536–539). New York: ACM.Google Scholar

Barik, T., Smith, J., Lubick, K., Holmes, E., Feng, J., Murphy-Hill, E., & Parnin, C. (2017). Do developers read compiler error messages? In International Conference on Software Engineering (ICSE) (pp. 575–585). New York: IEEE Press.Google Scholar

Bau, D., Bau, D. A., Dawson, M., & Pickens, C. S. (2015). Pencil code: Block code for a text world. In Proceedings of the International Conference on Interaction Design and Children (pp. 445–448). New York, NY: ACM.Google Scholar

Begel, A., & Graham, S. L. (2005). Spoken programs. In IEEE Symposium on Visual Languages and Human-Centric Computing (VLHCC) (pp. 99–106). New York: IEEE Press.Google Scholar

Bennedsen, J., & Schulte, C. (2007). What does “objects-first” mean?: An international study of teachers’ perceptions of objects-first. In Proceedings of the Koli Calling Conference on Computing Education (pp. 21–29). Sydney, Australia: Australian Computer Society, Inc.Google Scholar

Bootstrap (2017). The Bootstrap Blog–Accessibility (Part 2): Images. Retrieved from www.bootstrapworld.org/blog/accessibility/Describing-Images-Screenreaders.shtml Google Scholar

Bootstrap (2018). Data Science Curriculum (Spring 2018 edition). Retrieved from www.bootstrapworld.org/materials/spring2018/courses/data-science/english/Google Scholar

Brilliant, S. S., & Wiseman, T. R. (1996). The first programming paradigm and language dilemma. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 338–342). New York: ACM.Google Scholar

Bruce, K. B. (2005). Controversy on how to teach CS 1: A discussion on the SIGCSE-members mailing list. SIGCSE Bulletin, 37(2), 111–117.Google Scholar

Cartwright, M. (1998). An empirical view of inheritance. Information and Software Technology, 40(14), 795–799.Google Scholar

Code.org (n.d.). Computer Science in Algebra. Retrieved from https://code.org/curriculum/algebra Google Scholar

Cooper, G. H., Guha, A., Krishnamurthi, S., McCarthy, J., & Findler, R. B. (2013). Teaching garbage collection without implementing compilers or interpreters. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 385–390). New York: ACM.Google Scholar

Cooper, K. D., Hall, M. W., Hood, R. T., Kennedy, K., McKinley, K. S., Mellor-Crummey, J. M., Torczon, L., & Warren, S. K. (1993). The ParaScope parallel programming environment. Proceedings of the IEEE, 81(2), 244–263.Google Scholar

Cooper, S., Dann, W., & Pausch, R. (2003). Teaching objects-first in introductory computer science. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 191–195). New York: ACM.Google Scholar

Dann, W., Cosgrove, D., Slater, D. Culyba, D., & Cooper, S. (2012). Mediated transfer: Alice 3 to Java. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 141–146). New York: ACM.Google Scholar

de Raadt, M., Watson, R., & Toleman, M. (2002). Language trends in introductory programming courses. In Proceedings of Informing Science (pp. 329–337). Santa Rosa, CA: Informing Science Institute.Google Scholar

de Raadt, M., Watson, R., & Toleman, M. (2009). Teaching and assessing programming strategies explicitly. In Proceedings of the Australasian Computing Education Conference (ACE) (pp. 45–54). Sydney, Australia: Australian Computer Society, Inc.Google Scholar

Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. In Communications of the ACM, 51(1), 107–113.Google Scholar

Decker, R., & Hirshfield, S. (1994). The top 10 reasons why object-oriented programming can’t be taught in CS 1. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 51–55). New York: ACM.Google Scholar

Denny, P., Luxton-Reilly, A., & Carpenter, D. (2014). Enhancing syntax error messages appears ineffectual. In Proceedings of the SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE) (pp. 273–278). New York: ACM.Google Scholar

DiSalvo, B. (2012). Glitch game testers: the design and study of a learning environment for computational production with young African American males (PhD thesis). Georgia Institute of Technology.Google Scholar

diSessa, A. A., & Abelson, H. (1986). Boxer: A reconstructible computational medium. Communications of the ACM, 29(9), 859–868.Google Scholar

Dix, A. J. (2013). Formal methods. In The Encyclopedia of Human–Computer Interaction, 2nd edn. Aarhus, Denmark: The Interaction Design Foundation, Article 29.Google Scholar

du Boulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57–73.Google Scholar

du Boulay, B., O’Shea, T., & Monk, J. (1999). The black box inside the glass box. International Journal of Human–Computer Studies, 51(2), 265–277.Google Scholar

Ebrahimi, A. (1994). Novice programmer errors: language constructs and plan composition. International Journal of Human–Computer Studies, 41, 457–480.Google Scholar

Eckerdal, A., & Thune, M. (2005). Novice Java programmers conceptions of object and class, and variation theory. SIGCSE Bulletin, 37(3), 89–93.Google Scholar

Ehlert, A., & Schulte, C. (2009). Empirical comparison of objects-first and objects-later. In Proceedings of the Conference on International Computing Education Research (ICER) (pp. 15–26). New York: ACM.Google Scholar

Felleisen, M., & Hieb, R. (1992). The revised report on the syntactic theories of sequential control and state. Theoretical Computer Science, 102, 235–271.Google Scholar

Felleisen, M., Findler, R. B., Flatt, M., & Krishnamurthi, S. (2001). How to Design Programs. Cambridge, MA: MIT Press.Google Scholar

Felleisen, M., Findler, R. B., Flatt, M. & Krishnamurthi, S. (2009). A functional I/O system or, fun for freshman kids. In Proceedings of the 14th ACM SIGPLAN International Conference on Functional Programming (ICFP ‘09) (pp. 47–58). New York, NY: ACM.Google Scholar

Felleisen, M., Findler, R. B., Flatt, M., & Krishnamurthi, S. (2014). How to Design Programs, 2nd edn. Cambridge, MA: MIT Press.Google Scholar

Fisler, K. (2014). The recurring Rainfall Problem. In Proceedings of the Conference on International Computing Education Research (ICER) (pp. 35–42). New York: ACM.Google Scholar

Fisler, K., Krishnamurthi, S., & Siegmund, J. (2016). Modernizing plan-composition studies. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 211–216). New York: ACM.Google Scholar

Fisler, K., Krishnamurthi, S., & Tunnell Wilson, P. (2017). Assessing and teaching scope, mutation, and aliasing in upper-level undergraduates. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 213–218). New York: ACM.Google Scholar

Fitter, M. (1979). Towards more natural interactive systems. International Journal of Man-Machine Studies, 11(3), 339–350.Google Scholar

Fitzgerald, S., Lewandowski, G., McCauley, R., Murphy, L., Simon, B., Thomas, L., & Zander, C. (2008). Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers. Computer Science Education, 18(2), 93–116.Google Scholar

Flatt, M., Krishnamurthi, S., & Felleisen, M. (1998). Classes and mixins. In ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (pp. 171–183). New York: ACM.Google Scholar

Fleury, A. E. (1991). Parameter passing: The rules the students construct. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 283–286). New York: ACM.Google Scholar

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderotha, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences of the United States of America, 111(23), 8410–8415.Google Scholar

Goldman, K., Gross, P., Heeren, C., Herman, G. L., Kaczmarczyk, L., Loui, M. C., & Zilles, C. (2010). Setting the scope of concept inventories for introductory computing subjects. Transactions on Computing Education (TOCE), 10(2), 5:1–5:29.Google Scholar

Gosling, J., Joy, B., Steele, Jr., G. L., Bracha, G., & Buckley, A. (2015). The Java Language Specification, Java SE 8 Edition. Redwood Shores, CA: Oracle America, Inc.Google Scholar

Green, C. C. (1969). Application of theorem proving to problem solving. In International Joint Conference on Artificial Intelligence (pp. 219–239). San Francisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar

Greening, T. (1999). Emerging constructivist forces in computer science education: Shaping a new future. In Greening, T. (Ed.), Computer Science Education in the 21st Century (pp. 47–80). Berlin, Germany: Springer.Google Scholar

Grover, S., & Basu, S. (2017). Measuring student learning in introductory block-based programming: Examining misconceptions of loops, variables, and Boolean logic. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 267–272). New York: ACM.Google Scholar

Guha, A., Saftoiu, C., & Krishnamurthi, S. (2010). The essence of JavaScript. In European Conference on Object-Oriented Programming (ECOOP) (pp. 126–150). Berlin, Germany: Springer-Verlag.Google Scholar

Gulwani, S., Polozov, O., & Singh, R. (2017). Program synthesis. Foundations and Trends in Programming Languages, 4(1–2), 1–119.Google Scholar

Gupta, A., Hammer, D., & Redish, E. F. (2010). The case for dynamic models of learners’ ontologies in physics. Journal of the Learning Sciences, 19, 285–321.Google Scholar

Guzdial, M. (2015). Learner-Centered Design of Computing Education: Research on Computing for Everyone. San Rafael, CA: Morgan and Claypool.Google Scholar

Haarslev, V. (1995). Formal semantics of visual languages using spatial reasoning. In Proceedings of the IEEE Symposium on Visual Languages (pp. 156–163). New York: IEEE Press.Google Scholar

Harvey, B. (1993. Discussion forum comment on comp. lang. scheme. Retrieved from https://groups.google.com/forum/#!msg/comp.lang.scheme/zZRSDcZdV2M/MZJLiU6gE64J Google Scholar

Harvey, B., & Wright, M. (1999). Simply Scheme: Introducing Computer Science, 2nd edn. Cambridge, MA: MIT Press.Google Scholar

Holt, R. C., & Wortman, D. B. (1974). A sequence of structured subsets of PL/I. SIGCSE Bulletin, 6(1), 129–132.Google Scholar

Homer, M., & Noble, J. (2014). Combining tiled and textual views of code. In IEEE Working Conference on Software Visualization (VISSOFT) (pp. 1–10). New York: IEEE Press.Google Scholar

Hristova, M., Misra, A., Rutter, M., & Mercuri, R. (2003). Identifying and correcting Java programming errors for introductory computer science students. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE). New York: ACM.Google Scholar

Hu, C. (2004). Rethinking of teaching objects-first. Education and Information Technologies, 9(3), 209–218.Google Scholar

Hundhausen, C. D., Douglas, S. A., & Stasko, J. T. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages and Computing, 13(3), 259–290.Google Scholar

Jackson, J., Cobb, M., & Carver, C. (2005). Identifying top Java errors for novice programmers. In Frontiers in Education (FIE ‘05) (pp. T4C:24-T4C:27). New York, NY: IEEE.Google Scholar

Jadud, M. C. (2006). Methods and tools for exploring novice compilation behaviour. In Proceedings of the Conference on International Computing Education Research (ICER) (pp. 73–84). New York: ACM.Google Scholar

Johnson, L. F. (1995). C in the first course considered harmful. Communications of the ACM, 38(5), 99–101.Google Scholar

Kahn, K. M., & Saraswat, V. A. (1990). Complete Visualizations of Concurrent Programs and Their Executions. Tech. Rept. SSL-90-38 [P90-00099]. Palo Alto, CA: Xerox Palo Alto Research Center.Google Scholar

Kay, A. C. (1993). The early history of Smalltalk. In ACM SIGPLAN Conference on History of Programming Languages (pp. 69–95). New York: ACM.Google Scholar

Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, 37(2), 83–137.Google Scholar

Kessel, C. J., & Wickens, C. D. (1982). The transfer of failure-detection skills between monitoring and controlling dynamic systems. Human Factors, 24(1), 49–60.Google Scholar

Ko, A. J., Myers, B. A., & Aung, H. (2004). Six learning barriers in end-user programming systems. In IEEE Symposium on Visual Languages and Human-Centric Computing (VLHCC) (pp. 199–206). New York: IEEE Press.Google Scholar

Kölling, M., Brown, , , N. C. C., & Altadmri, A. (2015). Frame-based editing: Easing the transition from blocks to text-based programming. In Workshop in Primary and Secondary Computing Education (pp. 29–38). New York, NY: ACM.Google Scholar

Krishnamurthi, S. (2006). Programming Languages: Application and Interpretation. Retrieved from www.cs.brown.edu/~sk/Publications/Books/ProgLangs/Google Scholar

Krishnamurthi, S. (2008). Teaching programming languages in a post-Linnaean age. In SIGPLAN Notices, 43(11), 81–83.Google Scholar

Lee, M. J., & Ko, A. J. (2011). Personifying programming tool feedback improves novice programmers’ learning. In Proceedings of the Conference on International Computing Education Research (ICER) (pp. 109–116). New York: ACM.Google Scholar

Lewis, C., Esper, S., Bhattacharyya, V., Fa-Kaji, N., Dominguez, N., & Schlesinger, A. (2014). Children’s perceptions of what counts as a programming language. Journal of Computing Sciences in Colleges, 29(4), 123–133.Google Scholar

Lewis, C. M. (2010). How programming environment shapes perception, learning and goals: Logo vs. Scratch. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 346–350). New York: ACM.Google Scholar

Lister, R., Berglund, A., Clear, T., Bergin, J., Garvin-Doxas, K., Hanks, B., Hitchner, L., Luxton-Reilly, A., Sanders, K., Schulte, C., & Whalley, J. L. (2006). Research perspectives on the objects-early debate. In Working Group Reports on ITiCSE on Innovation and Technology in Computer Science Education (pp. 146–165). New York: ACM.Google Scholar

Ma, L. (2007). Investigating and Improving Novice Programmers Mental Models of Programming Concepts (PhD thesis). University of Strathclyde, Department of Computer & Information Sciences.Google Scholar

Mace, R. L., Hardie, G. J., & Place, J. P. (1991). Accessible environments: Toward universal design. In Preiser, W. E., Vischer, J. C., & White, E. T. (Eds.), Design Intervention: Toward a More Humane Architecture (pp. 155–176). Reinhold, NY: Van Nostrand.Google Scholar

Malan, D. J., & Leitner, H. H. (2007). Scratch for budding computer scientists. SIGCSE Bulletin, 39(1), 223–227.Google Scholar

Marceau, G., Fisler, K., & Krishnamurthi, S. (2011). Measuring the effectiveness of error messages designed for novice programmers. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 499–504). New York: ACM.Google Scholar

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.Google Scholar

McCauley, R., Hanks, B., Fitzgerald, S., & Murphy, L. (2015). Recursion vs. iteration: An empirical study of comprehension revisited. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 350–355). New York: ACM.Google Scholar

Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2011). Habits of programming in Scratch. In Proceedings of the SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE) (pp. 168–172). New York: ACM.Google Scholar

Miller, L. A. (1974). Programming by non-programmers. International Journal of Man Machine Studies, 6, 237–260.Google Scholar

Miller, L. A. (1975). Naive programmer problems with specification of transfer-of-control. In Proceedings of the National Computer Conference and Exposition (pp. 657–663). New York: ACM.Google Scholar

Miller, L. A. (1981). Natural language programming: Styles, strategies, and contrasts. IBM Systems Journal, 20, 184–215.Google Scholar

Miller, M. S., Von Dincklage, D., Ercegovac, V., & Chin, B. (2017). Uncanny valleys in declarative language design. In LIPIcs-Leibniz International Proceedings in Informatics, Vol. 71 (pp. 9:1–9:12). Wadern, Germany: Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.Google Scholar

Mönig, J., Ohshima, , , Y., & Maloney, J. (2015). Blocks at your fingertips: Blurring the line between blocks and text in GP. In Proceedings of the Blocks and Beyond Workshop (pp. 51–53). New York, NY: IEEE.Google Scholar

Muller, O., Ginat, D., & Haberman, B. (2007). Pattern-oriented instruction and its inuence on problem decomposition and solution construction. In Proceedings of the SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE) (pp. 151–155). New York: ACM.Google Scholar

Myers, B. A. (1991). Separating application code from toolkits: Eliminating the spaghetti of call-backs. In ACM Symposium on User Interface Software and Technology (pp. 211–220). New York: ACM.Google Scholar

Naps, T. L., Rössling, G., Almstrum, , Dann, V., Fleischer, W., Hundhausen, R., Korhonen, C., Malmi, A., McNally, L., Rodger, M., S., & Velázquez-Iturbide, J. A. (2003). Exploring the role of visualization and engagement in computer science education. SIGCSE Bulletin, 35(2), 131–152.Google Scholar

Nelson, G. L., Xie, B., & Ko, A. J. (2017). Comprehension first: Evaluating a novel pedagogy and tutoring system for program tracing in CS1. In Proceedings of the Conference on International Computing Education Research (ICER) (pp. 2–11). New York: ACM.Google Scholar

Nordström, M., & Börstler, J. (2011). Improving OO Example Programs. IEEE Transactions on Education, 54(10), 1–5.Google Scholar

Pane, J. F. (2002). A Programming System for Children That Is Designed for Usability (PhD thesis). Carnegie Mellon University, Computer Science Department.Google Scholar

Pane, J. F., Ratanamahatana, C., & Myers, B. A. (2001). Studying the language and structure in non-programmers’ solutions to programming problems. International Journal of Human-Computer Studies, 54(2), 237–264.Google Scholar

Parker, M. C., Guzdial, M., & Engleman, S. (2016). Replication, Validation, and Use of a Language Independent CS1 Knowledge Assessment. In Proceedings of the 2016 ACM Conference on International Computing Education Research (ICER) (pp. 93–101). New York: ACM.Google Scholar

Pea, R. D. (1986). Language-independent conceptual “bugs” in novice programming. Journal of Educational Computing Research, 2(1), 25–36.Google Scholar

Pettit, R. S., Homer, J., & Gee, R. (2017). Do enhanced compiler error messages help students?: Results inconclusive. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 465–470). New York: ACM.Google Scholar

Pirolli, P. L., & Anderson, J. R. (1985). The role of learning from examples in the acquisition of recursive programming skills. Canadian Journal of Psychology/Revue Canadienne de Psychologie, 39(2), 240–272.Google Scholar

Plotkin, G. D. (1975). Call-by-name, call-by-value, and the λ-calculus. Theoretical Computer Science, 1(2), 125–159.Google Scholar

Plum, T. (1977). Fooling the user of a programming language. Software Practice and Experience, 7, 215–221.Google Scholar

Powers, K., Ecott, S., & Hirshfield, L. M. (2007). Through the looking glass: Teaching CS0 with Alice. SIGCSE Bulletin, 39(1), 213–217.Google Scholar

Price, T. W., Brown, N. C. C., Lipovac, D., Barnes, T., & Kölling, M. (2016). Evaluation of a frame-based programming editor. In Proceedings of the Conference on International Computing Education Research (ICER) (pp. 32–42). New York: ACM.Google Scholar

Reges, S. (2006). Back to basics in CS1 and CS2. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 293–297). New York: ACM.Google Scholar

Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B., & Kafai, Y. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 80–87.Google Scholar

Rist, R. S. (1991). Knowledge creation and retrieval in program design: A comparison of novice and intermediate student programmers. Human–Computer Interaction, 6(1), 1–46.Google Scholar

Rivers, K., & Koedinger, K. R. (2015). Data-driven hint generation in vast solution spaces: A self-improving Python programming tutor. International Journal of Artificial Intelligence in Education, 27(1), 1–28.Google Scholar

Sajaniemi, J., & Kuittinen, M. (2008). From procedures to objects: A research agenda for the psychology of object-oriented programming education. Human Technology, 4(1), 75–91.Google Scholar

Savery, J. R., & Duffy, T. M. (2001). Problem Based Learning: An Instructional Model and Its Constructivist Framework. Tech. Rept. 16-01. Bloomington, IN: Indiana University Center for Research on Learning and Technology.Google Scholar

Schanzer, E., Krishnamurthi, S., & Fisler, K. (2015). Blocks versus text: Ongoing lessons from Bootstrap. In Proceedings of the Blocks and Beyond Workshop (pp. 125–126). New York, NY: IEEE.Google Scholar

Scholtz, J., & Wiedenbeck, S. (1990). Learning second and subsequent programming languages: A problem of transfer. International Journal of Human-Computer Interaction, 2(1), 51–72.Google Scholar

Schumacher, R. M., & Czerwinski, M. P. (1992). Mental models and the acquisition of expert knowledge. In Hoffman, R. R. (Ed.), The Psychology of Expertise: Cognitive Research and Empirical AI (pp. 61–79). Berlin, Germany: Springer.Google Scholar

Schwill, A. (1994). Fundamental ideas of computer science. Bulletin of the European Association for Theoretical Computer Science, 53, 274–274.Google Scholar

Seppälä, O., Ihantola, , Isohanni, P., Sorva, E., , J., & Vihavainen, A. (2015). Do we know how difficult the rainfall problem is? In Proceedings of the Koli Calling Conference on Computing Education (pp. 87–96). New York: ACM.Google Scholar

Shinners-Kennedy, D. (2008). The everydayness of threshold concepts: State as an example from computer science. In Land, R. & Meyer, J. H. F. (Eds.), Threshold Concepts within the Disciplines (pp. 119–128). Rotterdam, The Netherlands: Sense Publishers.Google Scholar

Simon, (2013). Soloway’s Rainfall Problem has become harder. In LATICE '13 Proceedings of the 2013 Learning and Teaching in Computing and Engineering (pp. 130–135). Washington, DC: IEEE Computer Society.Google Scholar

Sirkiä, T., & Sorva, J. (2012). Exploring programming misconceptions: An analysis of student mistakes in visual program simulation exercises. In Proceedings of the Koli Calling Conference on Computing Education (pp. 19–28). New York: ACM.Google Scholar

Slotta, J. D., & Chi, M. T. H. (2006). The impact of ontology training on conceptual change: Helping students understand the challenging topics in science. Cognition and Instruction, 24, 261–289.Google Scholar

Smaragdakis, Y., & Balatsouras, G. (2015). Pointer analysis. Foundations and Trends in Programming Languages, 2(1), 1–69.Google Scholar

Soloway, E. (1986). Learning to program = learning to construct mechanisms and explanations. Communications of the ACM, 29(9), 850–858.Google Scholar

Soloway, E., Bonar, J., & Ehrlich, K. (1983). Cognitive strategies and looping constructs: An empirical study. Communications of the ACM, 26(11), 853–860.Google Scholar

Somers, J. (2017). The coming software apocalypse. The Atlantic, Sept. Retrieved from www.theatlantic.com/technology/archive/2017/09/saving-the-world-from-code/540393/Google Scholar

Sorva, J. (2012). Visual Program Simulation in Introductory Programming Education (PhD thesis). Aalto University, Department of Computer Science and Engineering.Google Scholar

Sorva, J. (2013). Notional machines and introductory programming education. Transactions on Computing Education (TOCE), 13(2), 8:1–8:31.Google Scholar

Spohrer, J. C., & Soloway, E. (1989). Simulating Student Programmers (pp. 543–549). Morgan Kaufmann Publishers, Inc. San Francisco, CA, USA.Google Scholar

Stanton, J., Goldsmith, L., Adrion, W. R., Dunton, S., Hendrickson, K. A., Peterfreund, A., Youngpradit, P., Zarch, R., & Zinth, J. D. (2017). State of the States Landscape Report: State-Level Policies Supporting Equitable K12 Computer Science Education. Retrieved from www.edc.org/sites/default/files/uploads/State-States-Landscape-Report.pdf Google Scholar

Stefik, A., & Gellenbeck, E. (2009). Using spoken text to aid debugging: An empirical study. In IEEE International Conference on Program Comprehension (pp. 110–119). New York: IEEE Press.Google Scholar

Stefik, A., & Gellenbeck, E. (2011). Empirical studies on programming language stimuli. Software Quality Journal, 19(1), 65–99.Google Scholar

Stefik, A., & Siebert, S. (2013). An empirical investigation into programming language syntax. Transactions on Computing Education (TOCE), 13(4), 19:1–19:40.Google Scholar

Stefik, A., Hundhausen, C., & Patterson, R. (2011). An empirical investigation into the design of auditory cues to enhance computer program comprehension. International Journal of Human–Computer Studies, 69(12), 820–838.Google Scholar

Stefik, A. M. (2008). On the Design of Program Execution Environments for Non-Sighted Computer Programmers (PhD thesis). Washington State University.Google Scholar

Sudol, L. A. (2011). Deepening Students Understanding of Algorithms: Effects of Problem Context and Feedback Regarding Algorithmic Abstraction (PhD thesis). Carnegie Mellon University.Google Scholar

Tessler, J., Beth, B., & Lin, C. (2013). Using Cargo-bot to provide contextualized learning of recursion. In Proceedings of the Conference on International Computing Education Research (ICER) (pp. 161–168). New York: ACM.Google Scholar

Tew, A. E., McCracken, W. M., & Guzdial, M. (2005). Impact of alternative introductory courses on programming concept understanding. In Proceedings of the Conference on International Computing Education Research (ICER) (pp. 25–35). New York: ACM.Google Scholar

The College Board (n.d.). AP Computer Science Principles: Course Overview. Retrieved from https://apstudent.collegeboard.org/apcourse/ap-computer-science-principles Google Scholar

Tichy, W. F., & Prechelt, L. (1998). A controlled experiment to assess the benefits of procedure argument type checking. IEEE Transactions on Software Engineering, 24(4), 302–312.Google Scholar

Tirronen, V., Uusi-Mäakelä, S., & Isomöttönen, V. (2015). Understanding beginners’ mistakes with Haskell. Journal of Functional Programming, 25, e11.Google Scholar

Traver, V. J. (2010). On compiler error messages: What they say and what they mean. Advances in Human–Computer Interaction, 2010, 602570.Google Scholar

Tunnell Wilson, P., Pombrio, J., & Krishnamurthi, S. (2017). Can we crowdsource language design? In Proceedings of SPLASH Onward (Onward! ‘17) (17 pages). Vancouver, Canada: Onward.Google Scholar

Tunnell Wilson, P., Fisler, K., & Krishnamurthi, S. (2017). Student understanding of aliasing and procedure calls. In SPLASH Education Symposium (SPLASH-E ‘17) (6 Pages). New York, NY: ACM.Google Scholar

Tunnell Wilson, P., Krishnamurthi, S., & Fisler, K. (2018). Evaluating the tracing of recursion in the substitution notional machine. In Proceedings of the ACM Symposium on Computer Science Education (SIGCSE) (pp. 1023–1028). New York: ACM.Google Scholar

Vasek, M. (2012). Representing Expressive Types in Blocks Programming Languages. Retrieved from https://repository.wellesley.edu/thesiscollection/24/Google Scholar

Vickers, P., & Alty, J. L. (2002). When bugs sing. Interacting with Computers, 14(6), 793–819.Google Scholar

Victor, B. (2012). Learnable Programming: Designing a Programming System for Understanding Programs. Retrieved from http://worrydream.com/#!/LearnableProgramming Google Scholar

Vilner, T., Zur, E., & Gal-Ezer, J. (2007). Fundamental concepts of CS1: Procedural vs. object oriented paradigm – A case study. SIGCSE Bulletin, 39(3), 171–175.Google Scholar

Wand, M. (1986). Finding the source of type errors. In ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (pp. 38–43). New York: ACM.Google Scholar

Weintrop, D., & Wilensky, U. (2015a). To block or not to block, that is the question: Students perceptions of blocks-based programming. In Proceedings of the International Conference on Interaction Design and Children (pp. 199–208). New York, NY: ACM.Google Scholar

Weintrop, D., & Wilensky, U. (2015b). Using commutative assessments to compare conceptual understanding in blocks-based and text-based programs. In Proceedings of the Conference on International Computing Education Research (ICER). New York: ACM.Google Scholar

Weintrop, D., & Wilensky, U. (2017a). Comparing blocks-based and text-based programming in high school computer science classrooms. Transactions on Computing Education (TOCE), 18(1), 3:1–3:25.Google Scholar

Weintrop, D., & Wilensky, U. (2017b). Between a block and a typeface: Designing and evaluating hybrid programming environments. In Proceedings of the International Conference on Interaction Design and Children (pp. 183–192). New York, NY: ACM.Google Scholar

References

Ala-Mutka, K. M. (2005). A survey of automated assessment approaches for programming assignments. Computer Science Education, 15(2), 83–102.Google Scholar

Alam, L. (2004). Is plagiarism more prevalent in some forms of assessment than others. In Beyond the Comfort Zone: Proceedings of the 21st ASCILITE Conference (pp. 48–57). Tugun, Australia: Australasian Society for Computers in Learning in Tertiary Education.Google Scholar

Amelung, M., Piotrowski, M., & Rösner, D. (2006). EduComponents: Experiences in e-assessment in computer science education. In Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITICSE ‘06) (pp. 88–92). New York: ACM.Google Scholar

Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., Raths, J. & Wittrock, M. C. (Eds.) (2001). A Taxonomy for Learning and Teaching and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. New York: Addison Wesley Longman.Google Scholar

Assessment Reform Group (1999). Assessment for Learning: Beyond the Black Box. Cambridge, UK: Cambridge University Press.Google Scholar

Astrachan, O., Walker, H., Stephenson, C., Diaz, L., & Cuny, J. (2009). Advanced placement computer science: the future of tracking the first year of instruction. ACM SIGCSE Bulletin, 41(1), 397–398.Google Scholar

Attwood, R. (2008). Institutions limit access to anti-cheat software. Times Higher Education, June 26, 2008. Retrieved from www.timeshighereducation.co.uk/story.asp?sectioncode=26&storycode=402540&c=2 Google Scholar

Austin, M., & Brown, L. (1999). Internet plagiarism: Developing strategies to curb student academic dishonesty. The Internet and Higher Education, 2(1), 21–33.Google Scholar

Baugh, J., Kovacs, P., & Davis, G. (2012). Does the computer programming student understand what constitutes plagiarism. Issues in Information Systems, 13(2), 138–145.Google Scholar

Biggs, J., & Tang, C. (2007). Teaching for Quality Learning at University, 3rd edn. Maidenhead, UK: Society for Research into Higher Education and Open University Press.Google Scholar

Biggs, J. B., & Collis, K. F. (1982). Evaluating the Quality of Learning: The SOLO Taxonomy (Structure of the Observed Learning Outcome). New York: Academic Press.Google Scholar

Birenbaum, M., DeLuca, C., Earl, L., Heritage, M., Klenowski, V., Looney, A., Smith, K., Timperley, H., Volant, L., & Wyatt-Smith, C. (2015). International trends in the implementation of assessment for learning: Implications for policy and practice. Policy Futures in Education, 13(1), 117–140.Google Scholar

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74.Google Scholar

Bloom, B. S., Englehart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. (1956). Taxonomy of Educational Objectives: Handbook I: Cognitive Domain. New York: Longmans.Google Scholar

Bloom, B. S., Hasting, T., & Madaus, G. (1971). Handbook of Formative and Summative Evaluation of Student Learning. New York: McGraw-Hill.Google Scholar

Boywer, K., & Hall, L. (1999). Experience using “MOSS” to detect cheating on programming assignments. In 29th ASEE/IEEE Frontiers in Education Conference (pp. 18–22). New York: IEEE.Google Scholar

Brookhart, S., & Durkin, D. (2003). Classroom assessment, student motivation, and achievement in high school social studies classes. Applied Measurement in Education, 16(1), 27–54.Google Scholar

Brown, N., & Janssen, R. (2017). Preventing plagiarism and fostering academic integrity: A practical approach. Journal of Perspectives in Applied Academic Practice, 5(3), 102–109.Google Scholar

Brown, S., & Knight, P. (2012). Assessing Learners in Higher Education. New York, NY: Routledge.Google Scholar

Buffum, P. S., Lobene, E. V., Frankosky, M. H., Boyer, K. E., Wiebe, E. N., & Lester, J. C. (2015). A practical guide to developing and validating computer science knowledge assessments with application to middle school. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE ‘15) (pp. 622–627). New York: ACM.Google Scholar

Caceffo, R., Wolfman, S., Booth, K. S., & Azevedo, R. (2016). Developing a computer science concept inventory for introductory programming. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE ‘16) (pp. 364–369). New York: ACM.Google Scholar

Cadmus (2017). What is Cadmus? Retrieved from http://cadmus.io Google Scholar

Cardell-Oliver, R. (2011). How can software metrics help novice programmers? In Proceedings of the Thirteenth Australasian Computing Education Conference Volume 114 (pp. 55–62). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Carter, J., Ala-Mutka, K., Fuller, U., Dick, M., English, J., Fone, W., & Sheard, J. (2003). How shall we assess this? ACM SIGCSE Bulletin, 35(4), 107–123.Google Scholar

Clare, J., Walker, S., & Hobson, J. (2017). Can we detect contract cheating using existing assessment data? Applying crime prevention theory to an academic integrity issue. International Journal for Educational Integrity, 13, 4.Google Scholar

Clarke, R., & Lancaster, T. (2006). Eliminating the successor to plagiarism? Identifying the usage of contract cheating sites. In 2nd Plagiarism: Prevention, Practice and Policy Conference 2006. Newcastle, UK. Retrieved from www.plagiarism.org/paper/eliminating-the-successor-to-plagiarism Google Scholar

Clear, T., Whalley, J., Lister, R. F., Carbone, A., Hu, M., Sheard, J., Simon, B., & Thompson, E. (2008). Reliably classifying novice programmer exam responses using the SOLO taxonomy. In 21st Annual conference of the National Advisory Committee on Computing Qualifications (NACCQ 2008) (pp. 23–30). Auckland, New Zealand: National Advisory Committee on Computing Qualifications.Google Scholar

College Board (2018). AP Computer Science A. Retrieved from https://apcentral.collegeboard.org/courses/ap-computer-science-a/course Google Scholar

Crocker, L., & Algina, J. (1986). Introduction to Classical and Modern Test Theory. Orlando, FL: Holt, Rinehart and Winston.Google Scholar

Culwin, F. (2009). The efficacy of Turnitin and Google. In Proceedings of the Tenth Annual Higher Education Academy Conference in Information and Computer Sciences (pp. 65–69). Newtownabbey, UK: HE Academy Subject Centre for ICS.Google Scholar

Culwin, F., MacLeod, A., & Lancaster, T. (2001). Source code plagiarism in UK HE computing schools. In 2nd Annual Conference of the LTSN Centre for Information and Computer Sciences. London, UK: LTSN Centre for Information and Computer Sciences.Google Scholar

Curtis, G., & Clare, J. (2017). How prevalent is contract cheating and to what extent are students repeat offenders? Journal of Academic Ethics, 15(2), 115–124.Google Scholar

Cutts, Q., Connor, R., Michaelson, G., & Donaldson, P. (2014). Code or (not code). In Proceedings of the 9th Workshop in Primary and Secondary Computing Education (WiPSCE ‘14) (pp. 20–28). New York: ACM.Google Scholar

Daly, C., & Waldron, J. (2004). Assessing the assessment of programming ability. ACM SIGCSE Bulletin, 36(1), 210–213.Google Scholar

Davis, M., & Carroll, J. (2009). Formative feedback within plagiarism education: Is there a role for text-matching software? International Journal for Educational Integrity, 5(2), 58–70.Google Scholar

Denny, P., Hamer, J., Luxton-Reilly, A., & Purchase, H. (2008). PeerWise: Students sharing their multiple choice questions. In Proceedings of the Fourth International Workshop on Computing Education Research (pp. 51–58). New York: ACM.Google Scholar

Denny, P., Luxton-Reilly, A., & Simon, B. (2008). Evaluating a new exam question: Parsons problems. In Proceedings of the Fourth International Workshop on Computing Education Research (ICER ‘08) (pp. 113–124). New York: ACM.Google Scholar

Dick, M., Sheard, J., Bareiss, C., Carter, J., Joyce, D., Harding, T., & Laxer, C. (2003). Addressing student cheating: Definitions and solutions. ACM SIGCSE Bulletin, 35(2), 172–184.Google Scholar

Dikli, S. (2006). An overview of automated scoring of essays. The Journal of Technology, Learning and Assessment, 5(1), 36 pages.Google Scholar

Doctupus (2017). Contract Cheating Detection. Retrieved from https://angel.co/doctupus Google Scholar

Dunlosky, J. (2013). Strengthening the student toolbox: Study strategies to boost learning. American Educator, 37(3), 12–21.Google Scholar

Ďuračík, M., Kršák, E., & Hrkút, P. (2017). Current trends in source code analysis, plagiarism detection and issues of analysis big datasets. Procedia Engineering, 192, 136–141.Google Scholar

Earl, L. M. (2012). Assessment as Learning: Using Classroom Assessment to Maximize Student Learning. Thousand Oaks, CA: Corwin Press.Google Scholar

Ebel, R., & Frisbie, D. (1986). Essentials of Educational Measurement. Englewood Cliffs, NJ: Prentice Hall.Google Scholar

Engels, S., Lakshmanan, V., & Craig, M. (2007). Plagiarism detection using feature-based neural networks. ACM SIGCSE Bulletin, 39(1), 34–38.Google Scholar

Evans, C. (2013). Making sense of assessment feedback in higher education. Review of Educational Research, 83(1), 70–120.Google Scholar

Evans, C. (2016). Enhancing assessment feedback practice in higher education: The EAT framework. Retrieved from https://eatframework.org.uk/Google Scholar

Ferrero, J., Agnès, F., Besacier, , , L., & Schwab, D. (2017). Using word embedding for cross-language plagiarism detection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers (pp. 415–421). Stroudsburg, PA: Association for Computational Linguistics.Google Scholar

Fincher, S., & Finlay, J. (2016). Computing graduate employability: Sharing practice. Project report. Council of Professors and Heads of Computing, Kent, UK. Retrieved from https://kar.kent.ac.uk/53848/Google Scholar

Fincher, S., & Knox, D. (2013). The porous classroom: Professional practices in the computing curriculum. Computer, 9, 44–51.Google Scholar

Fincher, S., Kölling, M., Utting, , Brown, I., , N., & Stevens, P. (2010). Repositories of teaching material and communities of use: nifty assignments and the greenroom. In Proceedings of the Sixth International Workshop on Computing Education Research (pp. 107–114). New York: ACM.Google Scholar

Fincher, S., Petre, M., & Clark, M. (Eds.) (2001). Computer Science Project Work: Principles and Pragmatics. London, UK: Springer.Google Scholar

Fitzgerald, S., Hanks, B., McCauley, R., Lister, R., & Murphy, L. (2013). What are we thinking when we grade programs? In Proceedings of the 44th ACM Technical Symposium on Computer Science Education (SIGCSE '13) (pp. 471–476). New York: ACM.Google Scholar

Fraser, R. (2014). Collaboration, collusion and plagiarism in computer science coursework. Informatics in Education – An International Journal, 13(2), 179–195.Google Scholar

Fuller, U., Johnson, C. G., Ahoniemi, T., Cukierman, D., Hernán–Losada, I., Jackova, , Lahtinen, J., Lewis, E., Thompson, T. L., Riedesel, D. M., , C., & Thompson, E. (2007). Developing a computer science-specific learning taxonomy. ACM SIGCSE Bulletin, 39(4), 152–170.Google Scholar

Gibson, J. (2009). Software reuse and plagiarism: a code of practice. ACM SIGCSE Bulletin, 41(3), 55–59.Google Scholar

Gillam, L., Marinuzzi, J., & Ioannou, P. (2010). Turnitoff – Defeating plagiarism detection systems. In 11th Annual Conference of the Subject Centre for Information and Computer Sciences (pp. 84–88). Heslington, UK: Higher Education Academy.Google Scholar

Gilliver-Brown, K., & Ballinger, D. (2017). The integrity games: Using interactive comics to teach academic integrity concepts. ATLAANZ Journal, 2(1), 68–81.Google Scholar

Giordano, D., Maiorana, F., Csizmadia, A. P., Marsden, S., Riedesel, C., Mishra, S., & Vinikienė, L. (2015). New horizons in the assessment of computer science at school and beyond: Leveraging on the viva platform. In Proceedings of the 2015 ITiCSE on Working Group Reports (pp. 117–147). New York: ACM.Google Scholar

Goldman, K., Gross, P., Heeren, C., Herman, G. L., Kaczmarczyk, L., Loui, M. C., & Zilles, C. (2010). Setting the scope of concept inventories for introductory computing subjects. ACM Transactions on Computing Education (TOCE), 10(2), 5.Google Scholar

Gouws, L. A., Bradshaw, K., & Wentworth, P. (2013). Computational thinking in educational activities. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘13) (pp. 10–15). New York: ACM.Google Scholar

Grieve, A., & Ross, B. (2016). Plagiarism by contract writing: Insights from forensic linguistics. In Learning and Teaching Conference. Melbourne, Australia. Retrieved from www.academia.edu/26115664/Plagiarism_by_contract_writing_Insights_from_forensic_linguistics Google Scholar

Guzdial, M. (2010). How computing and physics learning differ. Retrieved from https://computinged.wordpress.com/2010/04/01/how-computing-and-physics-learning-differ/Google Scholar

Hahn, J. H., Mentz, E., & Meyer, L. (2009). Assessment strategies for pair programming. Journal of Information Technology Education: Research, 8, 273–284.Google Scholar

Hakulinen, L., Auvinen, T., & Korhonen, A. (2015). The effect of achievement badges on students’ behavior: An empirical study in a university-level computer science course. International Journal of Emerging Technologies in Learning (iJET), 10(1), 18–29.Google Scholar

Halgamuge, M. (2017). The use and analysis of anti-plagiarism software: Turnitin tool for formative assessment and feedback. Computer Applications in Engineering Education, 25(6), 895–909.Google Scholar

Hendrick, C., & MacPherson, R. (2017) What Does This Look Like in the Classroom?: Bridging the Gap between Research and Practice. Woodbridge, UK: John Catt Educational.Google Scholar

Ibáñez, M. B., Di-Serio, A., & Delgado-Kloos, C. (2014). Gamification for engaging computer science students in learning activities: A case study. IEEE Transactions on Learning Technologies, 7(3), 291–301.Google Scholar

ICAI (2014). Fundamental values of academic integrity. Retrieved from https://academicintegrity.org/fundamental-values Google Scholar

Ihantola, P., Ahoniemi, T., Karavirta, V., & Seppälä, O. (2010). Review of recent systems for automatic assessment of programming assignments. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research (pp. 86–93). New York: ACM.Google Scholar

Jenkins, T., & Helmore, S. (2006). Coursework for cash: The threat from online plagiarism. In Proceedings of 7th Annual Higher Education Academy Conference in Information & Computer Sciences (pp. 121–126). Newtownabbey, UK: HE Academy Subject Centre for ICS.Google Scholar

Jones, M., & Sheridan, L. (2015). Back translation: An emerging sophisticated cyber strategy to subvert advances in “digital age” plagiarism detection and prevention. Assessment & Evaluation in Higher Education, 40(5), 712–724.Google Scholar

Joy, M., & Luck, M. (1999). Plagiarism in programming assignments. IEEE Transactions on Education, 42(2), 129–133.Google Scholar

Joy, M., Cosma, G., Yau, J., & Sinclair, J. (2011). Source code plagiarism – A student perspective. IEEE Transactions on Education, 54(1), 125–132.Google Scholar

Juola, P. (2017). Detecting contract cheating via stylometric methods. In Plagiarism Across Europe and Beyond 2017 (pp. 187–198). Brno, Czech Republic: European Network for Academic Integrity.Google Scholar

Kapp, K. M. (2012). The Gamification of Learning and Instruction: Game-Based Methods and Strategies for Training and Education. San Francisco, CA: John Wiley & Sons.Google Scholar

Koh, K. H., Basawapatna, A., Nickerson, H., & Repenning, A. (2014). Real time assessment of computational thinking. In Visual Languages and Human-Centric Computing (VL/HCC) (pp. 49–52). New York: IEEE.Google Scholar

Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41(4), 212–218.Google Scholar

Lancaster, T. (2013a). The application of intelligent context-aware systems to the detection of student cheating. In Complex, Intelligent, and Software Intensive Systems (CISIS) (pp. 517–522). New York: IEEE.Google Scholar

Lancaster, T. (2013b). The use of text matching tools for the prevention and detection of student plagiarism. In Plagiarism Phenomenon In Europe: Research Contributes To Prevention (pp. 37–49). Braga, Portugal: Aletheia – Associação Científica e Cultural da Faculdade de Filosofia da Universidade Católica Portuguesa.Google Scholar

Lancaster, T. (2017). Plagiarism and assessment. Retrieved from http://thomaslancaster.co.uk/blog/plagiarism-and-assessment Google Scholar

Lancaster, T., & Clarke, R. (2007). Assessing contract cheating through auction sites – A computing perspective. In 8th Annual Higher Education Academy Conference in Information and Computer Sciences (pp. 91–95). Newtownabbey, UK: HE Academy Subject Centre for ICS.Google Scholar

Lancaster, T., & Clarke, R. (2014). An initial analysis of the contextual information available within auction posts on contract cheating agency websites. In Advanced Information Networking and Applications Workshops (WAINA) (pp. 548–553). New York: IEEE.Google Scholar

Lancaster, T., & Clarke, R. (2015). The implications of plagiarism and contract cheating for the assessment of database modules. In 13th International Workshop on Teaching, Learning and Assessment of Databases (TLAD 2015) (pp. 55–66). York, UK: Higher Education Academy.Google Scholar

Lancaster, T., & Clarke, R. (2016). Contract cheating – The outsourcing of assessed student work. In Bretag, T. (Ed.), Handbook of Academic Integrity (pp. 639–654). Berlin, Germany: Springer.Google Scholar

Lancaster, T., & Culwin, F. (2004). A comparison of source code plagiarism detection engines. Journal of Computer Science Education, 14(2), 101–112.Google Scholar

Linn, R., & Gronlund, N. (1995). Measurement and Assessment in Teaching. Upper Saddle River, NJ: Prentice Hall.Google Scholar

Lister, R. (2000). On blooming first year programming, and its blooming assessment. In Proceedings of the Australasian Conference on Computing Education (pp. 158–162). New York: ACM.Google Scholar

Lister, R., Clear, T., Bouvier, D. J., Carter, P., Eckerdal, A., Jacková, J., Lopez, , McCartney, M., Robbins, R., P., Seppälä, O., & Thompson, E. (2010). Naturally occurring data as research instrument: Analyzing examination responses to study the novice programmer. ACM SIGCSE Bulletin, 41(4), 156–173.Google Scholar

Lukashenko, R., Graudina, V., & Grundspenkis, J. (2007). Computer-based plagiarism detection methods and tools: an overview. In Proceedings of the 2007 International Conference on Computer Systems and Technologies (p. 40). New York: ACM.Google Scholar

Luxton-Reilly, A., & Petersen, A. (2017). The compound nature of novice programming assessments. In Proceedings of the Nineteenth Australasian Computing Education Conference (pp. 26–35). New York: ACM.Google Scholar

McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, H., Kolikant, Y. B., Laxer, C., Thomas, L., Utting, I., & Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. SIGCSE Bulletin, 33(4), 125–180.Google Scholar

McNamara, D. S., Crossley, S. A., Roscoe, R. D., Allen, L. K., & Dai, J. (2015). A hierarchical classification approach to automated essay scoring. Assessing Writing, 23, 35–59.Google Scholar

Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2010). Learning computer science concepts with scratch. In Proceedings of the Sixth International Workshop on Computing Education Research (ICER ‘10) (pp. 69–76). New York: ACM.Google Scholar

Milligan, S., & Kennedy, G. (2017). To what degree? Alternative micro-credentialing in a digital age. In James, R., French, S. & Kelly, P. (Eds.), Visions for Australian Tertiary Education (pp. 41–54). Melbourne, Australia: Melbourne Centre for the Study of Higher Education.Google Scholar

O’Malley, M., & Roberts, T. (2012). Plagiarism on the rise? Combating contract cheating in science courses. International Journal of Innovation in Science & Mathematics Education, 20(4), 16–24.Google Scholar

Ottenstein, K. (1976). An algorithmic approach to the detection and prevention of plagiarism. ACM SIGCSE Bulletin, 8(4), 30–41.Google Scholar

Palomba, C. A., & Banta, T. W. (1999). Assessment Essentials: Planning, Implementing, and Improving Assessment in Higher Education. San Francisco, CA: Jossey-Bass.Google Scholar

Parker, A., & Hamblen, J. (1989). Computer algorithms for plagiarism detection. IEEE Transactions on Education, 32(2), 94–99.Google Scholar

Parker, M. C., Guzdial, M., & Engleman, S. (2016). Replication, validation, and use of a language independent CS1 knowledge assessment. In Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 93–101). New York: ACM.Google Scholar

Parlante, N. (2018) Nifty Assignments. Retrieved from http://nifty.stanford.edu/Google Scholar

Parsons, D., & Haden, P. (2006). Parson’s programming puzzles: a fun and effective learning tool for first programming courses. In Proceedings of the 8th Australasian Conference on Computing Education (pp. 157–163). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Parsons, D., Wood, K., & Haden, P. (2015). What are we doing when we assess programming? In Proceedings of the 17th Australasian Computing Education Conference, CPIT 160 (pp. 119–127). Sydney, Australia: ACS.Google Scholar

Patterson, B. F., & Ewing, M. (2013). Validating the use of AP exam scores for college course placement. College Board Research Report 2013-2. Retrieved from https://files.eric.ed.gov/fulltext/ED558108.pdf Google Scholar

Petersen, A., Craig, M., & Zingaro, D. (2011). Reviewing CS1 exam question content. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (SIGCSE ‘11) (pp. 631–636). New York: ACM.Google Scholar

Prechelt, L., Malpohl, G., & Philippsen, M. (2002). Finding plagiarisms among a set of programs with JPlag. Journal of Universal Computer Science, 8(11), 1016–1038.Google Scholar

Project Quantum (2016). Project Quantum: Tests worth teaching. Retrieved from http://community.computingatschool.org.uk/resources/4382/single Google Scholar

Ramsden, P. (2003). Learning to Teach in Higher Education, 2nd edn. Abingdon, UK: RoutledgeFalmer.Google Scholar

Riedesel, C., Clear, A., Cross, G., Hughes, J., Simon, , & Walker, H. (2012). Academic integrity policies in a computing education context. In Proceedings of the Final Reports on Innovation and Technology in Computer Science Education 2012 Working Groups (pp. 1–15). New York: ACM.Google Scholar

Roberts, E. (2002). Strategies for promoting academic integrity in CS courses. In Frontiers in Education (FIE 2002) (pp. F3G-14–F3G-19). New York: IEEE.Google Scholar

Rogerson, A., & McCarthy, G. (2017). Using Internet based paraphrasing tools: Original work, patchwriting or facilitated plagiarism? International Journal for Educational Integrity, 13, 2.Google Scholar

Rowntree, D. (1977). Assessing Students: How Shall We Know Them. London, UK: Harper & Row.Google Scholar

Sanders, K., Ahmadzadeh, M., Clear, T., Edwards, S. H., Goldweber, M., Johnson, C., Lister, L., McCartney, R., Patitsas, E., & Spacco, J. (2013). The Canterbury questionbank: Building a repository of multiplechoice CS1 and CS2 questions. In Proceedings of the 18th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE) – Working Group Reports (pp. 33–52). New York: ACM.Google Scholar

Schleimer, S., Wilkerson, D. S., & Aiken, A. (2003). Winnowing: Local algorithms for document fingerprinting. In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (pp. 76–85). New York: ACM.Google Scholar

Scriven, M. (1967). The methodology of evaluation. In Stake, R. E. (Ed.), Perspectives of Curriculum Evaluation Vol. 1 (pp. 39–55). Chicago, IL: Rand McNally.Google Scholar

Sheard, J. (2012). Exams in computer programming: What do they examine and how complex are they? In Proceedings of the 23rd Annual Conference of the Australasian Association for Engineering Education (pp. 283–291). Barton, Australia: Engineers Australia.Google Scholar

Sheard, J., & Dick, M. (2011). Computing student practices of cheating and plagiarism: A decade of change. In Proceedings of the 2011 Conference on Innovation & Technology in Computer Science Education (pp. 233–237). New York: ACM.Google Scholar

Sheard, J., & Dick, M. (2012). Directions and dimensions in managing cheating and plagiarism of IT students. In Proceedings of the 14th Australasian Computing Education Conference (pp. 177–185). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Sheard, J., Carbone, A., D’Souza, D. & Hamilton, M. (2013). Assessment of programming: pedagogical foundations of exams. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education (pp. 141–146). New York: ACM.Google Scholar

Sheard, J., Carbone, A., Lister, R., Simon, B., Thompson, E., & Whalley, J. L. (2008). Going SOLO to assess novice programmers. ACM SIGCSE Bulletin, 40(3), 209–213.Google Scholar

Sheard, J., Markham, S., & Dick, M. (2003). Investigating differences in cheating behaviours of IT undergraduate and graduate students: The maturity and motivation factors. Journal of Higher Education Research and Development, 22(1), 91–108.Google Scholar

Sheard, J., Simon, , Butler, M., Falkner, K., Morgan, M., & Weerasinghe, A. (2017). Strategies for maintaining academic integrity in first-year computing courses. In Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education (pp. 244–249). New York: ACM.Google Scholar

Sheard, J., Simon, , Carbone, A., Chinn, D., Laakso, M. J., Clear, T., De Raadt, M., D’Souza, D., Harland, J., Lister, R., Philpott, A., & Warburton, G. (2011). Exploring programming assessment instruments: A classification scheme for examination questions. In Proceedings of the Seventh International Workshop on Computing Education Research (pp. 33–38). New York: ACM.Google Scholar

Si, A., Leong, H., & Lau, R. (1997). Check: a document plagiarism detection system. In Proceedings of the 1997 ACM Symposium on Applied Computing (pp. 70–77). New York: ACM.Google Scholar

Simon, , Cook, B., Sheard, J., Carbone, A., & Johnson, C. (2014). Student perceptions of the acceptability of various code-writing practices. In Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education (pp. 105–110). New York: ACM.Google Scholar

Simon, , Sheard, J. Carbone, , A., Chinn, D., Laakso, M., Clear, T., de Raadt, M., D’Souza, D., Lister, R., Philpott, A., Skene, A., & Warburton, G. (2012). Introductory programming: Examining the exams. In Proceedings of the Fourteenth Australasian Computing Education Conference (pp. 61–70). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Simon, , Sheard, J., Morgan, M., Petersen, A., Settle, A., Sinclair, J., Cross, G., & Riedesel, C. (2016). Negotiating the maze of academic integrity in computing education. In Proceedings of the 2016 ITiCSE Working Group Reports (pp. 57–80). New York: ACM.Google Scholar

Singh, A. (2013). Detecting plagiarism in MS Access assignments. Journal of Information Systems Education, 24(3), 177–180.Google Scholar

Sorva, J. (2012). Visual Program Simulation in Introductory Programming Education (doctoral dissertation). Aalto University.Google Scholar

Šprajc, P., Urh, , Jerebic, M., Trivan, J., , D., & Jereb, E. (2017). Reasons for plagiarism in higher education. Organizacija, 50(1), 33–45.Google Scholar

Staubitz, T., Klement, H., Renz, J., Teusner, R., & Meinel, C. (2015). Towards practical programming exercises and automated assessment in Massive Open Online Courses. In Teaching, Assessment, and Learning for Engineering (TALE 2015) (pp. 23–30). New York: IEEE.Google Scholar

Taylor, C., Zingaro, D., Porter, L., Webb, K. C., Lee, C. B., & Clancy, M. (2014). Computer science concept inventories: past and future. Computer Science Education, 24(4), 253–276.Google Scholar

Tew, A. E., & Dorn, B. (2013). The case for validated tools in computer science education research. Computer, 46(9), 60–66.Google Scholar

Tew, A. E., & Guzdial, M. (2010). Developing a validated assessment of fundamental CS1 concepts. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (pp. 97–101). New York: ACM.Google Scholar

Tew, A. E., & Guzdial, M. (2011). The FCS1: A language independent assessment of CS1 knowledge. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (pp. 111–116). New York: ACM.Google Scholar

Thompson, E., Luxton–Reilly, A., Whalley, J. L., Hu, M., & Robbins, P. (2008). Bloom’s taxonomy for CS assessment. In Proceedings of the Tenth Conference on Australasian Computing Education (ACE ‘08) (pp. 155–161). Darlinghurst, Australia: Australian Computer Society.Google Scholar

Vasilevskaya, M., Broman, D., & Sandahl, K. (2014). An assessment model for large project courses. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (SIGCSE ‘14) (pp. 253–258). New York: ACM.Google Scholar

Wallace, M., & Newton, P. (2014). Turnaround time and market capacity in contract cheating. Educational Studies, 40(2), 233–236.Google Scholar

Wiggins, G. (1990). The case for authentic assessment. Practical Assessment Research and Evaluation, 2(2), 6.Google Scholar

Wittie, L., Kurdia, A., & Huggard, M. (2017). Developing a concept inventory for computer science 2. In Frontiers in Education Conference (FIE) (pp. 1–4). New York: IEEE.Google Scholar

Yorke, M. (2003). Formative assessment in higher education: Moves towards theory and the enhancement of pedagogic practice. Higher Education, 45(4), 477–501.Google Scholar

Zhang, Y., Lo, D., Kochhar, P., Xia, X., Li, Q., & Sun, J. (2017). Detecting similar repositories on GitHub. In IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER) (pp. 13–23). New York: IEEE.Google Scholar

References

Abad, C. L. (2008). Learning through creating learning objects: Experiences with a class project in a distributed systems course. In 13th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘08) (pp. 255–259). New York: ACM.Google Scholar

Armellini, A., & Padilla Rodriguez, B. C. (2016). Are Massive Open Online Courses (MOOCs) pedagogically innovative? In Journal of Interactive Online Learning, 14(1), 17–28.Google Scholar

Aronson, E., Blaney, N., Stephen, C., Sikes, J., & Snapp, M. (1978). The Jigsaw Classroom. Beverly Hills, CA: Sage.Google Scholar

Atkins, M. J. (1993). Theories of learning and multimedia: An overview. Research Papers in Education, 8(2), 251–271.Google Scholar

Atapattu, T., & Falkner, K. (2016). A framework for topic generation and labeling from MOOC discussions. In 3rd ACM Conference on Learning@Scale (L@S’2016) (pp. 201–204). New York, NY: ACM.Google Scholar

Atapattu, T., Falkner, K., & Falkner, N. (2017). A comprehensive text analysis of lecture slides to generate concept maps. Computers & Education, 115, 96–113.Google Scholar

Barrows, H. S. (1986). A taxonomy of problem-based learning methods. Medical Education, 20, 481–486.Google Scholar

Batista, A. L. F., Connolly, T., & Angotti, J. A. P. (2016). A framework for games-based construction learning: A text-based programming languages approach. In European Conference on Games Based Learning (pp. 815–823). Reading, UK: Academic Conferences International Ltd.Google Scholar

Battistella, P., & Gresse von Wangenheim, C. (2016). Games for teaching computing in higher education – A systematic review. IEEE Technology and Engineering Education, 1(3), 8–30.Google Scholar

Bayne, S., & Ross, J. (2014). The Pedagogy of the Massive Open Online Course: The UK view. York, UK: The Higher Education Academy.Google Scholar

Beck, K. (2002). Test-Driven Development: By Example. Boston, MA: Addison-Wesley.Google Scholar

Beck, L., & Chizhik, A. (2013). Cooperative learning instructional methods for CS1: Design, implementation, and evaluation. Transactions on Computing Education (TOCE), 13(3), 10.Google Scholar

Ben-Ari, M. (1998). Constructivism in computer science education. In Joyce, D., , D. & Impagliazzo, J. (Eds.), 29th SIGCSE Technical Symposium on Computer Science Education (SIGCSE ‘98) (pp. 257–261). New York: ACM.Google Scholar

Bishop, J. L., & Verleger, M. A. (2013). The flipped classroom: A survey of the research. ASEE National Conference, 30(9), 1–18.Google Scholar

Blanc, R., DeBuhr, L., & Martin, D. (1983). Breaking the attrition cycle: The effects of supplemental instruction on undergraduate performance and attrition. The Journal of Higher Education, 54(1), 80–90.Google Scholar

Blank, D. (2006). Robots make computer science personal. Communications of the ACM, 9(12), 5–27.Google Scholar

Brady, C., Orton, K., Weintrop, D., Anton, G., Rodriguez, S., & Wilensky, U. (2017). All roads lead to computing: making, participatory simulations, and social computing as pathways to computer science. IEEE Transactions on Education, 60(1), 59–66.Google Scholar

Buckley, M., Nordlinger, J., & Subramanian, D. (2008). Socially relevant computing. In 39th SIGCSE Technical Symposium on Computer Science Education (SIGCSE ‘08) (pp. 347–351). New York: ACM.Google Scholar

Buffardi, K., & Edwards, S. H. (2012). Impacts of teaching test-driven development to novice programmers. International Journal of Information and Computer Science, 1(6), 135–143.Google Scholar

Carbone, A., & Sheard, J. (2002). A studio-based teaching and learning model in IT: What do first year students think? In 7th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘02) (pp. 213–217) New York: ACM.Google Scholar

Ching, E., Chen, C. T., Chou, C. Y., Deng, Y. C., & Chan, T. W. (2005). A pilot study of computer supported learning by constructing instruction notes and peer expository instruction. In Conference on Computer Support for Collaborative Learning: Learning 2005: The Next 10 Years! (CSCL) (pp. 63–67). Taipei, Taiwan: International Society of the Learning Sciences.Google Scholar

Clarke, D., Clear, T., Fisler, K., Hauswirth, M., Krishnamurthi, S., Politz, J. G., Tirronen, V., & Wrigstad, T. (2014). In-flow peer review. In Clear, A. & Lister, R. (Eds.), Working Group Reports of the 2014 Innovation & Technology in Computer Science Education Conference (ITiCSE-WGR ‘14) (pp. 59–79). New York: ACM.Google Scholar

Collis, B., & Moonen, J. (2005). An On-Going Journey: Technology as a Learning Workbench. Enschede, The Netherlands: University of Twente.Google Scholar

Computing Research Association (2017). Generation CS: Computer Science Undergraduate Enrollments Surge Since 2006. Retrieved from https://cra.org/data/Generation-CS/Google Scholar

Cooper, S., Dann, W., & Pausch, R. (2003). Teaching objects-first in introductory computer science. In 34th SIGCSE Technical Symposium on Computer Science Education (SIGCSE ‘03) (pp. 191–195). New York: ACM.Google Scholar

Corral, J. M. R., Balcells, A. C., Estévez, A. M., Moreno, G. J., & Ramos, M. J. F. (2014). A game-based approach to the teaching of object-oriented programming languages. Computers & Education, 73, 83–92.Google Scholar

Creese, A., & Blackledge, A. (2010). Translanguaging in the bilingual classroom: A pedagogy for learning and teaching? The Modern Language Journal, 94(1), 103–115.Google Scholar

Crescenzi, P., & Nocentini, C. (2007). Fully integrating algorithm visualization into a CS2 course: A two-year experience. In 12th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘07) (pp. 296–300). New York: ACM.Google Scholar

CSER (2017). A look at IT and Engineering Enrolments in Australia – Updated. Retrieved from: https://blogs.adelaide.edu.au/cser/2017/02/15/a-look-at-it-and-engineering-enrolments-in-australia-updated/Google Scholar

Cunningham, K., Blanchard, S., Ericson, B., & Guzdial, G. (2017). Using tracing and sketching to solve programming problems: Replicating and extending an analysis of what students draw. In 2017 ACM Conference on International Computing Education Research (ICER ‘17) (pp. 164–172). New York: ACM.Google Scholar

Day, J. A., & Foley, J. D. (2006). Evaluating a web lecture intervention in a human–computer interaction course. IEEE Transactions on Education, 49(4), 420–431.Google Scholar

Denny, P., Luxton-Reilly, A., & Hamer, J. (2008). The PeerWise system of student contributed assessment questions. In Simon, & Hamilton, M. (Eds.), 10th conference on Australasian computing education – Volume 78 (ACE ‘08), Vol. 78 (pp. 69–74). Darlinghurst, Australia: Australian Computer Society, Inc.Google Scholar

DesPortes, K., Anupam, A., Pathak, N., & DiSalvo, B. (2016). BitBlox: A redesign of the breadboard. In 15th International Conference on Interaction Design and Children (IDC ‘16) (pp. 255–261). New York: ACM.Google Scholar

Dragon, T., & Dickson, P. E. (2016). Memory diagrams: A consistent approach across concepts and languages. In 47th ACM Technical Symposium on Computing Science Education (SIGCSE ‘16) (pp. 546–551). New York: ACM.Google Scholar

du Boulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57–73.Google Scholar

du Boulay, B., O’Shea, T., & Monk, J. (1981). The black box inside the glass box: Presenting computing concepts to novices. International Journal of Man-Machine Studies, 14(3), 237–249.Google Scholar

Duffy, T. M., & Cunningham, D. J. (1996). Constructivism: Implications for the design and delivery of instruction. In Jonassen, D. H. (Ed.), Handbook of Research for Educational Communications and Technology (pp. 170–198). New York: Macmillan.Google Scholar

Earl, S. E. (1986). Staff and peer assessment – Measuring an individual’s contribution to group performance. Assessment & Evaluation in Higher Education, 11(1), 60–69.Google Scholar

Ebert, M., & Ring, M. (2016). A presentation framework for programming in programing lectures. In Global Engineering Education Conference (EDUCON ‘16) (pp. 369–374). New York, NY: IEEE.Google Scholar

Edwards, S. H. (2004). Using software testing to move students from trial-and-error to reflection-in-action. In 35th SIGCSE Technical Symposium on Computer Science Education (SIGCSE ‘04) (pp. 26–30). New York: ACM.Google Scholar

Elgort, I., Smith, A., & Toland, J. (2008). Is Wiki an effective platform for group course work? Australasian Journal of Educational Technology, 24(2), 195–210.Google Scholar

Ernest, E. (1995). The one and the many. In Steffe, L. & Gale, J. (Eds.), Constructivism in Education (pp. 459–486). New York: Lawrence Erlbaum.Google Scholar

Ewing, J. M., Dowling, J. D., & Coutts, N. (1998). Learning using the World Wide Web: A collaborative learning event. Journal of Educational Multimedia and Hypermedia, 8(1), 3–22.Google Scholar

Falkner, N. J. G., & Falkner, K. (2014). “Whither, badges?” or “wither, badges!”: A metastudy of badges in computer science education to clarify effects, significance and influence. In 14th Koli Calling International Conference on Computing Education Research (Koli Calling ‘14) (pp. 127–135). New York: ACM.Google Scholar

Falkner, K., Falkner, N., Szabo, C., & Vivian, R. (2016). Applying validated pedagogy to MOOCs: An introductory programming course with media computation. In 2016 ACM Conference on Innovation and Technology in Computer Science Education (pp. 326–331). New York, NY: ACM.Google Scholar

Falkner, K., & Palmer, E. (2009). Developing authentic problem solving skills in introductory computing classes. In 40th ACM Technical Symposium on Computer Science Education (SIGCSE ‘09) (pp. 4–8). New York: ACM.Google Scholar

Falkner, K., Vivian, R., Falkner, N., & Williams, S. (2017). Reflecting on three offerings of a community-centric MOOC for k-6 computer science teachers. In 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ‘17) (pp. 195–200). New York: ACM.Google Scholar

Findler, R. B., Clements, J., Flanagan, C., Flatt, M., Krishnamurthi, S., Steckler, P., & Felleisen, M. (2002). DrScheme: A programming environment for Scheme. Journal of. Functional Programming, 12(2), 159–182.Google Scholar

Firmin, S., Sheard, J., Carbone, A., & Hurst, J. (2012). An exploration of factors influencing tertiary IT educators’ pedagogies. In de Raadt, M. & Carbone, A. (Eds.), 14th Australasian Computing Education Conference – Volume 123 (ACE ‘12), Vol. 123 (pp. 157–166). Darlinghurst, Australia: Australian Computer Society, Inc.Google Scholar

Fisher, A., & Margolis, J. (2002). Unlocking the clubhouse: The Carnegie Mellon experience. SIGCSE Bulletin, 34(2), 79–83.Google Scholar

Geer, U., & Rudge, D. (2002). A review of research on constructivist-based strategies for large lecture science classes. Electronic Journal of Science Education, 7(2). Retrieved from www.scholarlyexchange.org/ojs/index.php/EJSE/article/view/7701 Google Scholar

Gehringer, E. F., & Miller, C. S. (2009). Student-generated active-learning exercises. In 40th ACM Technical Symposium on Computer Science Education (SIGCSE ‘09) (pp. 81–85). New York: ACM.Google Scholar

Giannakos, M. N., Krogstie, J., & Chrisochoides, N. (2014). Reviewing the flipped classroom research: Reflections for computer science education. In Barendsen, E. & Dagiené, V. (Eds.), Computer Science Education Research Conference (CSERC ‘14) (pp. 23–29). New York: ACM.Google Scholar

Glance, D., Forsey, M., & Riley, M. (2013). The pedagogical foundations of massive open online courses. First Monday, 18(5). Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/4350/3673 Google Scholar

Grover, S., Pea, R., & Cooper, S. (2014). Promoting active learning & leveraging dashboards for curriculum assessment in an OpenEdX introductory CS course for middle school. In 1st ACM Conference on Learning @ Scale Conference (L@S ‘14) (pp. 205–206). New York: ACM.Google Scholar

Guo, P. J. (2013). Online python tutor: Embeddable web-based program visualization for cs education. In 44th ACM Technical Symposium on Computer Science Education (SIGCSE ‘13) (pp. 579–584). New York: ACM.Google Scholar

Guzdial, M. (2013). Exploring hypotheses about media computation. In 9th Annual International ACM Conference on International Computing Education Research (ICER ‘13) (pp. 19–26). New York: ACM.Google Scholar

Hamer, J., Cutts, Q., Jackova, J., Luxton-Reilly, A., McCartney, R., Purchase, H., Riedesel, C., Saeli, M., Sanders, K., & Sheard, J. (2008). Contributing student pedagogy. SIGCSE Bulletin, 40(4), 194–212.Google Scholar

Hamer, J., Luxton-Reilly, A., Purchase, H. C., & Sheard, J. (2011). Tools for “contributing student learning”. ACM Inroads, 2(2), 78–91.Google Scholar

Hativa, N. (2000). Active learning during lectures. In Teaching for Effective Learning in Higher Education (pp. 87–110). Dordrecht, The Netherlands: Springer.Google Scholar

Hertz, M., & Jump, M. (2013). Trace-based teaching in early programming courses. In 44th ACM Technical Symposium on Computer Science Education (SIGCSE ‘13) (pp. 561–566). New York: ACM.Google Scholar

Higher Education Commission (2016). From Bricks to Clicks: The Potential of Data and Analytics in Higher Education. London, UK: Higher Education Commission.Google Scholar

Horton, D., & Craig, M. (2015). Drop, fail, pass, continue: Persistence in CS1 and beyond in traditional and inverted delivery. In 46th ACM Technical Symposium on Computer Science Education (SIGCSE ‘15) (pp. 235–240). New York: ACM.Google Scholar

Hundhausen, C. D., Agrawal, A., & Agarwal, P. (2013). Talking about code: Integrating pedagogical code reviews into early computing courses. ACM Transactions on Computing Education (TOCE). 13(3), 14.Google Scholar

Hundhausen, C. D., Douglas, S. A., & Stasko, J. T. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages & Computing, 13(3), 259–290.Google Scholar

Hundhausen, C. D., Narayanan, N. H., & Crosby, M. E. (2008). Exploring studio-based instructional models for computing education. In 39th SIGCSE Technical Symposium on Computer Science Education (SIGCSE ‘08) (pp. 392–396). New York: ACM.Google Scholar

Jonas, M. (2013). Group note taking in Mediawiki, a collaborative approach. In 14th Annual ACM SIGITE Conference on Information Technology Education (SIGITE ‘13) (pp. 131–132). New York: ACM.Google Scholar

Jonassen, D. H. (1991). Objectivism versus constructivism: Do we need a new philosophical paradigm? Educational Technology Research & Development, 39(3), 5–14.Google Scholar

Kaczmarczyk, L., Boutell, M., & Last, M. (2007). Challenging the advanced first-year student’s learning process through student presentations. In 3rd International Workshop on Computing Education Research (ICER ‘07) (pp. 17–26). New York: ACM.Google Scholar

Kandiko, C. B., & Mawer, M. (2013). Student Expectations and Perceptions of Higher Education. London, UK: King’s Learning Institute.Google Scholar

Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.Google Scholar

Khan, N. Z., & Luxton-Reilly, A. (2016). Is computing for social good the solution to closing the gender gap in computer science? In Australasian Computer Science Week Multiconference (ACSW ‘16) (p. 17). New York: ACM.Google Scholar

Koh, J. H. L., Chai, C. S., Wong, B., & Hong, H.-Y. (2015). Design Thinking for Education. Singapore: Springer.Google Scholar

Kölling, M., & Barnes, D. J. (2004). Enhancing apprentice-based learning of Java. In 35th SIGCSE Technical Symposium on Computer Science Education (SIGCSE ‘04) (pp. 286–290). New York: ACM.Google Scholar

Kurtz, B. L., Fenwick, J. B., Tashakkori, R., Esmail, A., & Tate, S. R. (2014). Active learning during lecture using tablets. In 45th ACM Technical Symposium on Computer Science Education (SIGCSE ‘14) (pp. 121–126). New York: ACM.Google Scholar

Lacher, L. L., & Lewis, M. C. (2015). The effectiveness of video quizzes in a flipped class. In 46th ACM Technical Symposium on Computer Science Education (SIGCSE ‘15) (pp. 224–228). New York: ACM.Google Scholar

Lawson, B. (2005). How Designers Think: The Design Process Demystified, 4th edn. London, UK: The Architectural Press.Google Scholar

Lee, E., Shan, V., Beth, B., & Lin, C. (2014). A structured approach to teaching recursion using cargo-bot. In 10th Annual Conference on International Computing Education Research (ICER ‘14) (pp. 59–66). New York: ACM.Google Scholar

Leutenegger, S., & Edgington, J. (2007). A games first approach to teaching introductory programming. In 38th SIGCSE Technical Symposium on Computer Science Education (SIGCSE ‘07) (pp. 115–118). New York: ACM.Google Scholar

Lui, D., Anderson, E., Kafai, Y. B., & Jayathirtha, G. (2017). Learning by fixing and designing problems: A reconstruction kit for debugging e-textiles. In 7th Annual Conference on Creativity and Fabrication in Education (FabLearn ‘17) (p. 6). New York: ACM.Google Scholar

Lui, A. K., Ng, S. C., Cheung, Y. H. Y., & Gurung, P. (2010). Facilitating independent learning with Lego Mindstorms robots. ACM Inroads, 1(4), 49–53.Google Scholar

Lockwood, K., & Esselstein, R. (2013). The inverted classroom and the CS curriculum. In 44th ACM Technical Symposium on Computer Science Education (SIGCSE ‘13) (pp. 113–118). New York: ACM.Google Scholar

Lowyck, J., & Elen, J. (1993). Transitions in the theoretical foundation of instructional design. In Duffy, T. M., Lowyck, J., & Jonassen, D. H. (Eds.), Designing Environments for Constructive Learning (pp. 213–229). Berlin, Germany: Springer-Verlag.Google Scholar

Lutteroth, C., & Luxton-Reilly, A. (2008). Flexible learning in CS2: A case study. In 21st Annual Conference of the National Advisory Committee on Computing Qualifications (pp. 77–83). New Zealand: Computing and Information Technology Research and Education New Zealand (CITRENZ).Google Scholar

Luxton-Reilly, A., Bertinshaw, D., Denny, P., Plimmer, B., & Sheehan, R. (2012). The impact of question generation activities on performance. In 43rd ACM Technical Symposium on Computer Science Education (SIGCSE ‘12) (pp. 391–396). New York: ACM.Google Scholar

Luxton-Reilly, A., Plimmer, B., & Sheehan, R. (2010). StudySieve: A tool that supports constructive evaluation for free-response questions. In 11th International Conference of the NZ Chapter of the ACM Special Interest Group on Human-Computer Interaction (CHINZ ‘10) (pp. 65–68). New York: ACM.Google Scholar

Maher, M. L., Latulipe, C., Lipford, H., & Rorrer, A. (2015). Flipped classroom strategies for CS education. In 46th ACM Technical Symposium on Computer Science Education (SIGCSE ‘15) (pp. 218–223). New York: ACM.Google Scholar

Mayer, R. E. (1975). Different problem-solving competencies established in learning computer programming with and without meaningful models. Journal of Educational Psychology, 67(6), 725–734.Google Scholar

Mayer, R. E. (1976). Some conditions of meaningful learning for computer programming: Advance organizers and subject control of frame order. Journal of Educational Psychology, 68(2), 143–150.Google Scholar

Medel, P., & Pournaghshband, V. (2017). Eliminating gender bias in computer science education materials. In 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ‘17) (pp. 411–416). New York: ACM.Google Scholar

Milligan, S. (2015). Crowd-sourced learning in MOOCs: learning analytics meets measurement theory. In 5th International Conference on Learning Analytics & Knowledge (LAK ‘15) (pp. 151–155). New York: ACM.Google Scholar

Milligan, S., He, J., Bailey, J., Zhang, R., & Rubinstein, B. I. P. (2016). Validity: A framework for cross-disciplinary collaboration in mining indicators of learning from MOOC forums. In 6th International Conference on Learning Analytics & Knowledge (LAK ‘16) (pp. 546–547). New York: ACM.Google Scholar

Moog, R. S. & Spencer, J. N. (Eds.) (2008). Process-Oriented Guided-Inquiry Learning (POGIL). Washington, DC: American Chemical Society.Google Scholar

Morgan, M., Butler, M., Sinclair, J., Cross, G., Fraser, J., Jackova, J., & Thota, N. (2017). Understanding international benchmarks on student engagement: Awareness, research alignment and response from a computer science perspective. In 2017 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘17) (pp. 383–384). New York: ACM.Google Scholar

Moritz, S. H., & Blank, G. D. (2005). A design-first curriculum for teaching Java in a CS1 course. SIGCSE Bulletin, 37(2), 89–93.Google Scholar

Nelson, G. L., Xie, B., & Ko, A. J. (2017). Comprehension first: Evaluating a novel pedagogy and tutoring system for program tracing in CS1. In 2017 ACM Conference on International Computing Education Research (ICER ‘17) (pp. 2–11). New York: ACM.Google Scholar

Nortcliffe, A. (2005). Student-driven module: promoting independent learning. International Journal of Electrical Engineering Education, 42(3), 247–512.Google Scholar

Papert, S. (1993). Mindstorms: Children, Computers, and Powerful Ideas. New York: Basic Books.Google Scholar

Parsons, D., & Haden, P. (2006). Parson’s programming puzzles: a fun and effective learning tool for first programming courses. In Tolhurst, D. & Mann, S. (Eds.), 8th Australasian Conference on Computing Education – Volume 52 (ACE ‘06) (pp. 157–163). Darlinghurst, Australia: Australian Computer Society, Inc.Google Scholar

Pask, G. (1976). Conversation Theory, Applications in Education and Epistemology. Amsterdam, The Netherlands: Elsevier.Google Scholar

Patitsas, E., Craig, M., & Easterbrook, S. (2016). How CS departments are managing the enrolment boom: Troubling implications for diversity. In Research on Equity and Sustained Participation in Engineering, Computing and Technology (RESPECT) (pp. 1–2). New York, NY: IEEE.Google Scholar

Paul, J. (2016). Test-driven approach in programming pedagogy. Journal of Computing Sciences in Colleges, 32(2), 53–60.Google Scholar

Perkins, D. N., Schwartz, S., & Simmons, R. (1990). Instructional strategies for the problems of novice programmers. In Meyer, R. E. (Ed.), Teaching and Learning Computer Programming: Multiple Research Perspectives (pp. 153–178). Mahwah, NJ: Lawrence Erlbaum.Google Scholar

Phillips, D., & Soltis, J. F. (2015). Perspectives on Learning, 5th edn. New York: Teachers College Press.Google Scholar

Piaget, J. (1972). Psychology and Epistemology: Towards a Theory of Knowledge (Vol. 105). London, UK: Penguin Books Ltd.Google Scholar

Piccioni, M., Estler, C., & Meyer, B. (2014). SPOC-supported introduction to programming. In 19th Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘14) (pp. 3–8). New York: ACM.Google Scholar

Pirker, J., Riffnaller-Schiefer, M., & Gütl, C. (2014). Motivational active learning: engaging university students in computer science education. In 2014 Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘14) (pp. 297–302). New York: ACM.Google Scholar

Politz, J. G., Krishnamurthi, S., & Fisler, K. (2014). In-flow peer-review of tests in test-first programming. In 10th Annual Conference on International Computing Education Research (ICER ‘14) (pp. 11–18). New York: ACM.Google Scholar

Pollock, L., & Harvey, T. (2011). Combining multiple pedagogies to boost learning and enthusiasm. In 16th Annual Joint Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘11) (pp. 258–262). New York: ACM.Google Scholar

Porter, L., & Simon, B. (2013). Retaining nearly one-third more majors with a trio of instructional best practices in CS1. In 44th ACM Technical Symposium on Computer Science Education (SIGCSE ‘13) (pp. 165–170). New York: ACM.Google Scholar

Portillo, J. A. P., & Campos, P. G. (2009). The jigsaw technique: Experiences teaching analysis class diagrams. In Mexican International Conference on Computer Science (pp. 289–293). New York: IEEE Press.Google Scholar

Pulimood, S. M., & Wolz, U. (2008). Problem solving in community: A necessary shift in cs pedagogy. ACM SIGCSE Bulletin, 40(1), 210–214.Google Scholar

Purchase, H. (2000). Learning about interface design through peer assessment. Assessment & Evaluation in Higher Education, 24(4), 341–352.Google Scholar

Preston, G., Phillips, R., Gosper, M., McNeill, M., Woo, K., & Green, D. (2010). Web-based lecture technologies: Highlighting the changing nature of teaching and learning. Australasian Journal of Educational Technology, 26(6), 717–728.Google Scholar

Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231.Google Scholar

Ragonis, N., & Ben-Ari, M. (2005). A long-term investigation of the comprehension of OOP concepts by novices. Computational Science Education, 15(3), 203–221.Google Scholar

Reeves, T. C., & Reeves, P. M. (1997). Effective dimensions of interactive learning on the World Wide Web. In Khan, B. H. (Ed.), Web-Based Instruction (pp. 59–66). Englewood Cliffs, NJ: Educational Technology Publications.Google Scholar

Reilly, M., Shen, H., Calder, P., & Duh, H. (2014). Towards a collaborative classroom through shared workspaces on mobile devices. In 28th International BCS Human Computer Interaction Conference on HCI 2014 – Sand, Sea and Sky – Holiday HCI (BCS-HCI ‘14) (pp. 335–340). London, UK: BCS.Google Scholar

Reza, S., & Baig, M. (2015). A study of inverted classroom pedagogy in computer science teaching. International Journal of Research Studies in Educational Technology, 4(2). Retrieved from www.learntechlib.org/p/151048/Google Scholar

Rich, L., Perry, H., & Guzdial, M. (2004). A CS1 course designed to address interests of women. SIGCSE Bulletin, 36(1), 190–194.Google Scholar

Rizzardini, R. H., & Amado-Salvatierra, H. R. (2017). Full engagement educational framework: A practical experience for a MicroMaster. In 4th ACM Conference on Learning @ Scale (L@S ‘17) (pp. 145–146). New York: ACM.Google Scholar

Rubin, M. J. (2013). The effectiveness of live-coding to teach introductory programming. In 44th ACM Technical Symposium on Computer Science Education (SIGCSE ‘13) (pp. 651–656). New York: ACM.Google Scholar

Rubio, M. A., Romero-Zaliz, R., Mañoso, C., & De Madrid, A. P. (2015). Closing the gender gap in an introductory programming course. Computers & Education, 82, 409–420.Google Scholar

Rudestam, K., & Schoenholtz-Read, J. (2010). The flourishing of adult online education: an overview. In Rudestam, K. & Schoenholtz-Read, J. (Eds.), Handbook of Online Learning, 2nd edn (pp. 1–28). Thousand Oaks, CA: SAGE.Google Scholar

Rudman, P. (2002). Investigating Domain Information as Dynamic Support for the Learner During Spoken Conversations (PhD thesis). University of Birmingham.Google Scholar

Salmon, G. (2004). E-Tivities: The Key to Active Online Learning, 2nd edn. Abingdon, UK: Taylor & Francis e-Library.Google Scholar

Sanders, K., Boustedt, J., Eckerdal, A., McCartney, R., & Zander, C. (2017). Folk pedagogy: Nobody doesn’t like active learning. In 2017 ACM Conference on International Computing Education Research (ICER ‘17) (pp. 145–154). New York: ACM.Google Scholar

Schneider, B., & Blikstein, P. (2016). Flipping the flipped classroom: A study of the effectiveness of video lectures versus constructivist exploration using tangible user interfaces. IEEE Transactions on Learning Technologies, 9(1), 5–17.Google Scholar

Sharples, M., de Roock, R., Ferguson, R., Gaved, M., Herodotou, C., Koh, E., Kukulska-Hulme, A., Looi, C-K., McAndrew, P., Rienties, B., Weller, M. & Wong, L. H. (2016). Innovating Pedagogy 2016: Open University Innovation Report 5. The Open University.Google Scholar

Shulman, L. (2005). Pedagogies. Liberal Education, 91(2), 18–25.Google Scholar

Siemens, G. (2005). Connectivism: A learning theory for the digital age. In International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.Google Scholar

Siemens, G. (2012). MOOCs are really a platform. ELearnspace. Retrieved from www.elearnspace.org/blog/2012/07/25/moocs-are-really-a-platform/Google Scholar

Simon, B., & Cutts, Q. (2012). Peer instruction: A teaching method to foster deep understanding. Communications of the ACM, 55(2), 27–29.Google Scholar

Sirkiä, T. (2016). Combining parson’s problems with program visualization in CS1 context. In 16th Koli Calling International Conference on Computing Education Research (Koli Calling ‘16) (pp. 155–159). New York: ACM.Google Scholar

Sirkiä, T., & Sorva, J. (2015). Tailoring animations of example programs. In 15th Koli Calling Conference on Computing Education Research (Koli Calling ‘15) (pp. 147–151). New York: ACM.Google Scholar

Slavin, R. E. (1991). Synthesis of research on cooperative learning. Educational Leadership, 48(5), 71–82.Google Scholar

Slavin, R. E., & Cooper, R. (1999). Improving intergroup relations: Lessons learned from cooperative learning programs. Journal of Social Issues, 55, 647–663.Google Scholar

Smith, P. A., & Webb, G. I. (1995). Reinforcing a generic computer model for novice programmers. In 7th Australian Society for Computer in Learning in Tertiary Education Conference (ASCILITE ’95). Retrieved from www.ascilite.org/conferences/melbourne95/smtu_bak/papers/smith.pdf Google Scholar

Smith, J., Tessler, J., Kramer, E., & Lin, C. (2012). Using peer review to teach software testing. In 9th Annual International Conference on International Computing Education Research (ICER ‘12) (pp. 93–98). New York: ACM.Google Scholar

Sonnentag, S. (1998). Expertise in professional software design: A process study. Journal of Applied Psychology, 83, 703–715.Google Scholar

Sorva, J. (2013). Notional machines and introductory programming education. Transactions on Computing Education (TOCE), 13(2), 8.Google Scholar

Sorva, J., Karavirta, V., & Malmi, L. (2013). A review of generic program visualization systems for introductory programming education. Transactions on Computing Education (TOCE), 13(4), 15.Google Scholar

Sorva, J., & Sirkiä, T. (2010). UUhistle: A software tool for visual program simulation. In 10th Koli Calling International Conference on Computing Education Research (Koli Calling ‘10) (pp. 49–54). New York: ACM.Google Scholar

Srinivasan, V., Butler-Purry, K. & Pedersen, S. (2008). Using video games to enhance learning in digital systems. In 2008 Conference on Future Play: Research, Play, Share (Future Play ‘08) (pp. 196–199). New York: ACM.Google Scholar

Summet, J., Kumar, D., O’Hara, K., Walker, D., Ni, L., Blank, D., & Balch, T. (2009). Personalizing CS1 with robots. ACM SIGCSE Bulletin, 41(1), 433–437.Google Scholar

Swan, K., Day, S., & Bogle, L. (2016). Metaphors for learning and MOOC pedagogies. In 3rd ACM Conference on Learning @ Scale (pp. 125–128). New York: ACM.Google Scholar

Tarimo, W. T., Deeb, F. A., & Hickey, T. J. (2015). A flipped classroom with and without computers. In International Conference on Computer Supported Education (pp. 333–347). New York: Springer International Publishing.Google Scholar

Titterton, N., Lewis, C. M., & Clancy, M. J. (2010). Experiences with lab-centric instruction. Computer Science Education, 20(2), 79–102.Google Scholar

Toven-Lindsey, B., Rhoads, R. A., & Lozano, J. B. (2015). Virtually unlimited classrooms: Pedagogical practices in massive open online courses. The Internet and Higher Education, 24, 1–12.Google Scholar

Umapathy, K., & Ritzhaupt, A. D. (2017). A meta-analysis of pair-programming in computer programming courses: Implications for educational practice. ACM Transactions on Computing Education (TOCE), 17(4), 16.Google Scholar

Utting, I., Cooper, S., Kölling, M., Maloney, , , J., & Resnick, M. (2010). Alice, Greenfoot, and Scratch – A discussion. ACM Transactions on Computing Education (TOCE), 10(4), 17.Google Scholar

Vassiliadis, B., Kameas, A., & Sgouropoulou, C. (2016). A closer look at MOOC’s adoption from a qualitative perspective. In 20th Pan-Hellenic Conference on Informatics (PCI ‘16) (p. 17). New York: ACM.Google Scholar

Vickers, R., Cooper, G., Field, J., Thayne, M., Adams, R., & Lochrie, M. (2014). Social media and collaborative learning: Hello Scholr. In 18th International Academic MindTrek Conference: Media Business, Management, Content & Services (AcademicMindTrek ‘14) (pp. 103–109). New York: ACM.Google Scholar

Vozniuk, A., Holzer, A., & Gillet, D. (2014). Peer assessment based on ratings in a social media course. In 4th International Conference on Learning Analytics And Knowledge (LAK ‘14) (pp. 133–137). New York: ACM.Google Scholar

Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA: Harvard University Press.Google Scholar

Wakeling, D. (2008). A robot in every classroom: Robots and functional programming across the curriculum. In 2008 International Workshop on Functional and Declarative Programming in Education (FDPE ‘08) (pp. 51–60). New York: ACM.Google Scholar

Walker, J. D., Brooks, D. C., & Baepler, P. (2011). Pedagogy and space: Empirical research on new learning environments. EDUCAUSE Quarterly, 34(4). Retrieved from https://eric.ed.gov/?id=EJ958727 Google Scholar

Watson, C., & Li, F. W. B. (2014). Failure rates in introductory programming revisited. In 2014 Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘14) (pp. 39–44). New York: ACM.Google Scholar

Wenger, E. (1998). Communities of practice. Learning as a social system. Systems Thinker. 9(5). Retrieved from https://thesystemsthinker.com/communities-of-practice-learning-as-a-social-system/Google Scholar

Williams, L. (2000). The Collaborative Software Process (PhD dissertation). University of Utah.Google Scholar

Williams, L., & Kessler, R. (2002). Pair Programming Illuminated. Boston, MA: Addison-Wesley Longman Publishing Co., Inc.Google Scholar

Williams, L., McCrickard, D. S., Layman, L., & Hussein, K. (2008). Eleven guidelines for implementing pair programming in the classroom. In Melnik, G. & Poppendieck, M. (Eds.), Agile Conference (pp. 445–452). New York: IEEE Press.Google Scholar

References

Alvarado, C., Dodds, Z., & Libeskind-Hadas, R. (2012). Increasing women’s participation in computing at Harvey Mudd College. ACM Inroads, 3(4), 55–64.Google Scholar

Amodio, D. M. (2014). The neuroscience of prejudice and stereotyping. Nature Reviews Neuroscience, 15(10), 670–682.Google Scholar

Andrews, D. J. (2012). Black achievers’ experiences with racial spotlighting and ignoring in a predominantly White high school. In Teachers College Record, 114(10), 1–46.Google Scholar

Apple, M. W. (1992). Do the standards go far enough? Power, policy, and practice in mathematics education. Journal for Research in Mathematics Education, 23(5), 412–431.Google Scholar

Barker, L. (2010). Avoiding Unintended Gender Bias in Letters of Recommendation (Case Study 1). Reducing Unconscious Bias to Increase Women’s Success in IT. Promising Practices, National Center for Women and Information Technology. Retrived from www.ncwit.org/sites/default/files/resources/avoidingunintendedgenderbiaslettersrecommendation.pdf Google Scholar

Barker, L. J. Garvin-Doxas, K., & Jackson, M. (2002). Defensive climate in the computer science classroom. ACM SIGCSE Bulletin, 34(1), 43–47.Google Scholar

Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review, 94(4), 991–1013.Google Scholar

Bobb, K. (2016). Why teaching computer science to students of color is vital to the future of our nation. The Root. Retrieved from www.theroot.com/articles/culture/2016/03/why_teaching_computer_science_to_students_of_color_is_vital_to_the_future/Google Scholar

Bobo, L. D., Charles, C. Z., Krysan, M., Simmons, A. D., & Fredrickson, G. M. (2012). The real record on racial attitudes. In Social Trends in American Life: Findings from the General Social Survey since 1972 (pp. 38–83). Princeton, NJ: Princeton University Press.Google Scholar

Bonilla-Silva, E. (2003). “New racism,” color-blind racism, and the future of whiteness in America. In Doane, A. W. & Bonlilla-Silva, E. (Eds.), White Out: The Continuing Significance of Racism (pp. 271–284). London, UK: Routledge.Google Scholar

Brady, C., Orton, K., Weintrop, D., Anton, G., Rodriguez, S., & Wilensky, U. (2017). All roads lead to computing: Making, participatory simulations, and social computing as pathways to computer science. IEEE Transactions on Education, 60(1), 59–66.Google Scholar

Cheryan, S., Plaut, V. C., Davies, P. G., & Steele, C. M. (2009). Ambient belonging: How stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology, 97(6), 1045–1060.Google Scholar

Chinn, D., Martin, K., & Spencer, C. (2007). Treisman workshops and student performance in CS. ACM SIGCSE Bulletin, 39(1), 203–207.Google Scholar

Clance, P. R., & Imes, S. A. (1978). The imposter phenomenon in high achieving women: Dynamics and therapeutic intervention. Psychotherapy: Theory, Research & Practice, 15(3), 241–247.Google Scholar

Cohen, G. L., Steele, C. M., & Ross, L. D. (1999). The mentor’s dilemma: Providing critical feedback across the racial divide. Personality and Social Psychology Bulletin, 25(10), 1302–1318.Google Scholar

Cohoon, J. M., Wu, Z., & Chao, J. (2009). Sexism: Toxic to women’s persistence in CSE doctoral programs. ACM SIGCSE Bulletin, 41(1), 158–162.Google Scholar

College Board (2017). AP program participation and performance data 2017 [Data file]. Retrieved from https://research.collegeboard.org/programs/ap/data/archived/ap-2017 Google Scholar

Cvencek, D., Nasir, N. I. S., O’Connor, K., Wischnia, S., & Meltzoff, A. N. (2015). The development of math–race stereotypes: “They say Chinese people are the best at math”. Journal of Research on Adolescence, 25(4), 630–637.Google Scholar

Dweck, C. S. (2008). Mindset: The New Psychology of Success. New York: Random House.Google Scholar

Eglash, R. (2002). Race, sex, and nerds: From black geeks to Asian American hipsters. Social Text, 20(2), 49–64.Google Scholar

Eglash, R., Gilbert, J. E., Taylor, V., & Geier, S. R. (2013). Culturally responsive computing in urban, after-school contexts: Two approaches. Urban Education, 48(5), 629–656.Google Scholar

Ensmenger, N. (2010). Making programming masculine. In Misa, T. J. (Ed.), Gender Codes: Why Women Are Leaving Computing (pp. 115–141). Hoboken, NJ: Wiley.Google Scholar

Ensmenger, N. L. (2012). The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise. Cambridge, MA: MIT Press.Google Scholar

Essed, P. (2002). Cloning cultural homogeneity while talking diversity: Old wine in new bottles in Dutch organizations. Transforming Anthropology, 11(1), 2–12.Google Scholar

Fairchild, H. H. (1991). Scientific racism: The cloak of objectivity. Journal of Social Issues, 47(3), 101–115.Google Scholar

Falkner, K., Falkner, N., Szabo, C., & Vivian, R. (2016). Applying validated pedagogy to MOOCs: An introductory programming course with media computation. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE ‘16) (pp. 326–331). New York: ACM.Google Scholar

Fiarman, S. E. (2016). Unconscious bias: When good intentions aren’t enough. Educational Leadership, 74(3), 10–15.Google Scholar

Fields, D. A., Kafai, Y. B., Nakajima, T., & Goode, J. (2017). Teaching practices for making e-textiles in high school computing classrooms. In Proceedings of the 7th Annual Conference on Creativity and Fabrication in Education (FabLearn ‘17) (p. 5). New York: ACM.Google Scholar

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415.Google Scholar

Goode, J. (2008). Increasing diversity in K–12 computer science: Strategies from the field. ACM SIGCSE Bulletin, 40(1), 362–366.Google Scholar

Goode, J., Chapman, G., & Margolis, J. (2012). Beyond curriculum: The exploring computer science program. ACM Inroads, 3(2), 47–53.Google Scholar

Google Inc. (2014). Women who choose computer science – What really matters: The critical role of exposure and encouragement. Retrieved from https://docs.google.com/file/d/0B-E2rcvhnlQ_a1Q4VUxWQ2dtTHM/edit Google Scholar

Google Inc., & Gallup Inc. (2015). Searching for computer science: Access and barriers in U.S. K–12 education. Retrieved from http://g.co/cseduresearch Google Scholar

Google Inc., & Gallup Inc. (2016). Diversity Gaps in Computer Science: Exploring the Underrepresentation of Girls, Blacks and Hispanics. Retrieved from http://goo.gl/PG34aH Google Scholar

Google Inc., & Gallup Inc. (2017). Encouraging Students Toward Computer Science Learning. Results From the 2015–2016 Google–Gallup Study of Computer Science in U.S. K–12 Schools (Issue Brief No. 5). Retrieved from https://goo.gl/iM5g3A Google Scholar

Gould, S. J. (1996). The Mismeasure of Man. New York: WW Norton & Company.Google Scholar

Greenberg, J. B. (1989). Funds of knowledge: Historical constitution, social distribution, and transmission. Presented at Annual Meeting of the Society for Applied Anthropology, Santa Fe, NM.Google Scholar

Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102(1), 4.Google Scholar

Gutiérrez, R. (2008). A “gap-gazing” fetish in mathematics education? Problematizing research on the achievement gap. Journal for Research in Mathematics Education, 39(4), 357–364.Google Scholar

Guzdial, M. (2013). Exploring hypotheses about media computation. In Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research (pp. 19–26). New York: ACM.Google Scholar

Harper, S. R., & Hurtado, S. (2007). Nine themes in campus racial climates and implications for institutional transformation. New Directions for Student Services, 2007(120), 7–24.Google Scholar

Harrell, D. F. (2013). Phantasmal Media: An Approach to Imagination, Computation, and Expression. Cambridge, MA: MIT Press.Google Scholar

Hunt, V., Yee, L., Prince, S., & Dixon-Fyle, S. (2018). Delivering through Diversity. McKinsey & Company Report. Retrieved from www.mckinsey.com/business-functions/organization/our-insights/delivering-through-diversity Google Scholar

Hyde, J. S., & Jaffee, S. (1998). Perspectives from social and feminist psychology. Educational Researcher, 27(5), 14–16.Google Scholar

Information is Beautiful (2016). Diversity in tech: Employee breakdown of key technology companies. Retrieved from www.informationisbeautiful.net/visualizations/diversity-in-tech/Google Scholar

Kafai, Y. B., Lee, E., Searle, K., Fields, D., Kaplan, E., & Lui, D. (2014). A crafts-oriented approach to computing in high school: Introducing computational concepts, practices, and perspectives with electronic textiles. ACM Transactions on Computing Education (TOCE), 14(1), 1–20.Google Scholar

Kafai, Y., Searle, K., Martinez, C., & Brayboy, B. (2014). Ethnocomputing with electronic textiles: Culturally responsive open design to broaden participation in computing in American indian youth and communities. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (pp. 241–246). New York: ACM.Google Scholar

Keller, C. (2001). Effect of teachers’ stereotyping on students’ stereotyping of mathematics as a male domain. The Journal of Social Psychology, 141(2), 165–173.Google Scholar

Kendi, I. X. (2016). Stamped from the Beginning: The Definitive History of Racist Ideas in America. New York: Nation Books.Google Scholar

Kumar, A. N. (2012). A study of stereotype threat in computer science. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (pp. 273–278). New York: ACM.Google Scholar

Ladner, R., & Israel, M. (2016). “For all” in “computer science for all.” Communications of the ACM, 59(9), 26–28.Google Scholar

Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge, UK: Cambridge University Press.Google Scholar

Leonard, J., & Martin, D. B. (Eds.) (2013). The Brilliance of Black Children in Mathematics. Charlotte, NC: IAP.Google Scholar

Lewis, C. M., Anderson, R. E., & Yasuhara, K. (2016). I don’t code all day: Fitting in computer science when the stereotypes don’t fit. In Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 23–32). New York: ACM.Google Scholar

Lewis, C. M., Yasuhara, K., & Anderson, R. E. (2011a). Deciding to major in computer science: A grounded theory of students’ self-assessment of ability. In Proceedings of the Seventh International Workshop on Computing Education Research (pp. 3–10). New York: ACM.Google Scholar

Lewis, C. M., Titterton, N., & Clancy, M. (2012). Using collaboration to overcome disparities in Java experience. In Proceedings of the Ninth Annual International Conference on International Computing Education Research (pp. 79–86). New York: ACM.Google Scholar

Lewis, C. M. (2017). Twelve tips for creating a culture that supports all students in computing. ACM Inroads, 8(4), 17–20.Google Scholar

Lewis, C. M., & Shah, N. (2015). How equity and inequity can emerge in pair programming. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research (pp. 41–50). New York: ACM.Google Scholar

Lindberg, S. M., Hyde, J. S., Petersen, J. L., & Linn, M. C. (2010). New trends in gender and mathematics performance: A meta-analysis. Psychological Bulletin, 136(6), 1123–1135.Google Scholar

Madera, J. M., Hebl, M. R., & Martin, R. C. (2009). Gender and letters of recommendation for academia: Agentic and communal differences. Journal of Applied Psychology, 94(6), 1591–1599.Google Scholar

Malcom, S. M., Hall, P. Q., & Brown, J. W. (1976). The Double Bind: The Price of Being a Minority Woman in Science. Washington, DC: American Association for the Advancement of Science.Google Scholar

Malouff, J. M., & Thorsteinsson, E. B. (2016). Bias in grading: A meta-analysis of experimental research findings. Australian Journal of Education, 60(3), 245–256.Google Scholar

Margolis, J., Estrella, R., Goode, J., Jellison-Holme, J., & Nao, K. (2008). Stuck in the Shallow End: Education, Race, and Computing. Cambridge, MA: MIT Press.Google Scholar

Margolis, J., & Fisher, A. (2003). Unlocking the Clubhouse: Women in Computing. Boston, MA: MIT Press.Google Scholar

Margolis, J., Goode, J., & Chapman, G. (2015). An equity lens for scaling: A critical juncture for exploring computer science. ACM Inroads, 6(3), 58–66.Google Scholar

Margolis, J., Goode, J., Chapman, G., & Ryoo, J. J. (2014). That classroom “magic”. Communications of the ACM, 57(7), 31–33.Google Scholar

Margolis, J., Ryoo, J., Sandoval, C., Lee, C., Goode, J., & Chapman, G. (2012). Beyond access: Broadening participation in high school computer science. ACM Inroads, 3(4), 72–78.Google Scholar

Martell, R. F., Lane, D. M., & Emrich, C. (1996). Male–female differences: A computer simulation. Retrieved from www.ruf.rice.edu/~lane/papers/male_female.pdf Google Scholar

Martin, D. B. (2003). Hidden assumptions and unaddressed questions in mathematics for all rhetoric. The Mathematics Educator, 13(2), 7–21.Google Scholar

McAfee, M. (2014). The kinesiology of race. Harvard Educational Review, 84(4), 468–491.Google Scholar

McGee, E. O., & Martin, D. B. (2011). “You would not believe what I have to go through to prove my intellectual value!” Stereotype management among academically successful Black mathematics and engineering students. American Educational Research Journal, 48(6), 1347–1389.Google Scholar

Medel, P., & Pournaghshband, V. (2017). Eliminating gender bias in computer science education materials. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (pp. 411–416). New York: ACM.Google Scholar

Miller, D. I., Nolla, K. M., Eagly, A. H., & Uttal, D. H. (2018). The development of children’s gender-science stereotypes: A meta-analysis of 5 decades of US draw-a-scientist studies. Child Development, doi:10.1111/cdev.13039.Google Scholar

Milner IV, H. R. (2012). Beyond a test score: Explaining opportunity gaps in educational practice. Journal of Black Studies, 43(6), 693–718.Google Scholar

Moll, L., Amanti, C., Neff, D., & González, N. (2006). Funds of knowledge for teaching: Using a qualitative approach to connect homes and classrooms. In González, N., Moll, L., & Amanti, C. (Eds.), Funds of Knowledge: Theorizing Practices in Households, Communities, and Classrooms (pp. 71–87). New York: Routledge.Google Scholar

Moreno, S. E., & Muller, C. (1999). Success and diversity: The transition through first-year calculus in the university. American Journal of Education, 108(1), 30–57.Google Scholar

Morewedge, C. K., Yoon, H., Scopelliti, I., Symborski, C. W., Korris, J. H., & Kassam, K. S. (2015). Debiasing decisions: Improved decision making with a single training intervention. Policy Insights from the Behavioral and Brain Sciences, 2(1), 129–140.Google Scholar

Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences, 109(41), 16474–16479.Google Scholar

Mulkey, L. M., Catsambis, S., Steelman, L. C., & Crain, R. L. (2005). The long-term effects of ability grouping in mathematics: A national investigation. Social Psychology of Education, 8(2), 137–177.Google Scholar

Nasir, N. I. S., & Cooks, J. (2009). Becoming a hurdler: How learning settings afford identities. Anthropology & Education Quarterly, 40(1), 41–61.Google Scholar

Nasir, N. S., Atukpawu, G., O’Connor, K., Davis, M., Wischnia, S., & Tsang, J. (2009). Wrestling with the legacy of stereotypes: Being African American in math class. In Martin, D. B. (Ed.), Mathematics Teaching, Learning, and Liberation in the Lives of Black Children (pp. 231–248). New York: Routledge.Google Scholar

Nasir, N. S., & Shah, N. (2011). On defense: African American males making sense of racialized narratives in mathematics education. Journal of African American Males in Education, 2(1), 24–45.Google Scholar

National Science Foundation, National Center for Science and Engineering Statistics (2017). Women, Minorities, and Persons with Disabilities in Science and Engineering: 2017. Special Report NSF 17-310. Arlington, VA. Retrieved from www.nsf.gov/statistics/wmpd/Google Scholar

National Center for Women and Information Technology (2017). By the Numbers. Retrieved from www.ncwit.org/bythenumbers Google Scholar

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown.Google Scholar

Ong, M. (2011). The status of women of color in computer science. Communications of the ACM, 54(7), 32–34.Google Scholar

Ong, M., Wright, C., Espinosa, L., & Orfield, G. (2011). Inside the double bind: A synthesis of empirical research on undergraduate and graduate women of color in science, technology, engineering, and mathematics. Harvard Educational Review, 81(2), 172–209.Google Scholar

Omi, M., & Winant, H. (2015). Racial Formation in the United States. New York: Routledge.Google Scholar

Orfield, G., & Eaton, S. E. (1996). Dismantling Desegregation. The Quiet Reversal of Brown v. Board of Education. New York: The New Press.Google Scholar

Paek, H. J., & Shah, H. (2003). Racial ideology, model minorities, and the “not-so-silent partner”: Stereotyping of Asian Americans in US magazine advertising. Howard Journal of Communication, 14(4), 225–243.Google Scholar

Pager, D. (2003). The mark of a criminal record. American Journal of Sociology, 108(1), 937–975.Google Scholar

Pollock, M. (2004). Race wrestling: Struggling strategically with race in educational practice and research. American Journal of Education, 111(1), 25–67.Google Scholar

Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93, 223–231.Google Scholar

Reich, D. (2018). Who We Are and How We Got Here: Ancient DNA and the New Science of the Human Past. Oxford, UK: Oxford University Press.Google Scholar

Reinholz, D., & Shah, N. (2018). Equity analytics: A methodological approach for quantifying participation patterns in mathematics classroom discourse. Journal for Research in Mathematics Education, 49(2), 140–177.Google Scholar

Robins, A. (2010). Learning edge momentum: A new account of outcomes in CS1. Computer Science Education, 20(1), 37–71.Google Scholar

Rose, A. (2010). Are face-detection cameras racist? TIME. Retrieved from http://content.time.com/time/business/article/0,8599,1954643,00.html Google Scholar

Rubio, M. A., Romero-Zaliz, R., Mañoso, C., & Angel, P. (2015). Closing the gender gap in an introductory programming course. Computers & Education, 82(1), 409–420.Google Scholar

Sadker, D., Sadker, M., & Zittleman, K. (2009). Still Failing at Fairness: How Gender Bias Cheats Girls and Boys in School and What We Can Do about It. New York: Scribner.Google Scholar

Schaffer, R., & Skinner, D. G. (2009). Performing race in four culturally diverse fourth grade classrooms: Silence, race talk, and the negotiation of social boundaries. Anthropology & Education Quarterly, 40(3), 277–296.Google Scholar

Schmader, T., Johns, M., & Forbes, C. (2008). An integrated process model of stereotype threat effects on performance. Psychological Review, 115(2), 336–356.Google Scholar

Schoenfeld, A. H. (2004). The math wars. Educational Policy, 18(1), 253–286.Google Scholar

Scott, K. A., & White, M. A. (2013). COMPUGIRLS’ standpoint: Culturally responsive computing and its effect on girls of color. Urban Education, 48(5), 657–681.Google Scholar

Scott, A., & Martin, A. (2014). Perceived barriers to higher education in science, technology, engineering, and mathematics. Journal of Women and Minorities in Science and Engineering, 20(3), 235–256.Google Scholar

Shah, N., & Lewis, C.M. (in press). Amplifying and attenuating inequity in collaborative learning: Toward an analytical framework. Cognition and Instruction.Google Scholar

Shah, N. (2013). Racial Discourse in Mathematics and its Impact on Student Learning, Identity, and Participation (PhD thesis). University of California, Berkeley.Google Scholar

Shah, N., Lewis, C. M., Caires, R., Khan, N., Qureshi, A., Ehsanipour, D., & Gupta, N. (2013). Building equitable computer science classrooms: Elements of a teaching approach. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (pp. 263–268). New York: ACM.Google Scholar

Shah, N. (2017). Race, ideology, and academic ability: A relational analysis of racial narratives in mathematics. Teachers College Record, 119(7), 1–42.Google Scholar

Shih, M., Pittinsky, T. L., & Ambady, N. (1999). Stereotype susceptibility: Identity salience and shifts in quantitative performance. Psychological Science, 10(1), 80–83.Google Scholar

Smith, D. G. (2015). Diversity’s Promise for Higher Education: Making it Work. Baltimore, MD: JHU Press.Google Scholar

Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69(5), 797–811.Google Scholar

Steele, C. M. (2010). Whistling Vivaldi: How Stereotypes Affect Us and What We Can Do. New York: WW Norton & Co.Google Scholar

Steinpreis, R. E., Anders, K. A., & Ritzke, D. (1999). The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: A national empirical study. Sex Roles, 41(7–8), 509–528.Google Scholar

Stinson, D. W. (2008). Negotiating sociocultural discourses: The counter-storytelling of academically (and mathematically) successful African American male students. American Educational Research Journal, 45(4), 975–1010.Google Scholar

Sue, D. W., Capodilupo, C. M., Torino, G. C., Bucceri, J. M., Holder, A., Nadal, K. L., & Esquilin, M. (2007). Racial microaggressions in everyday life: Implications for clinical practice. American Psychologist, 62(4), 271.Google Scholar

Tabuchi, H., & Ewing, J. (2016). Volkswagen to pay $14.7 billion to settle diesel claims in U.S. The New York Times. Retrieved from www.nytimes.com/2016/06/28/business/volkswagen-settlement-diesel-scandal.html Google Scholar

Tapia, J. (1991). Cultural Reproduction: Funds of Knowledge as Survival Strategies in the Mexican American Community (doctoral dissertation). University of Arizona.Google Scholar

Tatman, R. (2016). Google’s speech recognition has a gender bias. Making noise and hearing things. Retrieved from https://makingnoiseandhearingthings.com/2016/07/12/googles-speech-recognition-has-a-gender-bias/Google Scholar

Telles, E. E. (2004). Race in Another America: The Significance of Skin Color in Brazil. Princeton, NJ: Princeton University Press.Google Scholar

Tiedemann, J. (2000). Parents’ gender stereotypes and teachers’ beliefs as predictors of children’s concept of their mathematical ability in elementary school. Journal of Educational Psychology, 92(1), 144–151.Google Scholar

Toyama, K. (2015). Geek Heresy: Rescuing Social Change from the Cult of Technology. Philadelphia, PA: PublicAffairs.Google Scholar

Treisman, U. (1992). Studying students studying calculus: A look at the lives of minority mathematics students in college. The College Mathematics Journal, 23(5), 362–372.Google Scholar

Trix, F., & Psenka, C. (2003). Exploring the color of glass: Letters of recommendation for female and male medical faculty. Discourse & Society, 14(2), 191–220.Google Scholar

Trytten, D. A., Lowe, A. W., & Walden, S. E. (2012). “Asians are good at math. What an awful stereotype.” The model minority stereotype’s impact on asian american engineering students. Journal of Engineering Education, 101(3), 439–468.Google Scholar

Uhlmann, E. L., & Cohen, G. L. (2005). Constructed criteria: Redefining merit to justify discrimination. Psychological Science, 16(6), 474–480.Google Scholar

Vakil, S. (2014). A critical pedagogy approach for engaging urban youth in mobile app development in an after-school program. Equity & Excellence in Education, 47(1), 31–45.Google Scholar

Vélez-Ibáñez, C. G. (1988). Networks of exchange among Mexicans in the U.S. and Mexico: Local level mediating responses to national and international transformations. Urban Anthropology, 17, 27–51.Google Scholar

Vossoughi, S., Hooper, P. K., & Escudé, M. (2016). Making through the lens of culture and power: Toward transformative visions for educational equity. Harvard Educational Review, 86(2), 206–232.Google Scholar

Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447–1451.Google Scholar

Wang, J. Hejazi Moghadam, S., & Tiffany-Morales, J. (2017). Social perceptions in computer science and implications for diverse students. In Proceedings of the ACM Conference on International Computing Education Research (pp. 47–55). New York: ACM.Google Scholar

Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge, UK: Cambridge University Press.Google Scholar

Williams, J. C., Phillips, K. W., & Hall, E. V. (2014). Double Jeopardy: Gender Bias Against Women of Color in Science. Retrieved from https://repository.uchastings.edu/faculty_scholarship/1278 Google Scholar

Winkelmes, M. A., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., & Weavil, K. H. (2016). A teaching intervention that increases underserved college students’ success. Peer Review, 18(1/2), 31.Google Scholar

Wortham, S. (2006). Learning Identity: The Joint Emergence of Social Identification and Academic Learning. Cambridge, UK: Cambridge University Press.Google Scholar

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