Book contents
- The Cambridge Handbook of Motivation and Learning
- The Cambridge Handbook of Motivation and Learning
- Copyright page
- Epigraph
- Contents
- Figures
- Tables
- Contributors
- Foreword
- Acknowledgments
- Introduction Motivation and Its Relation to Learning
- Part I The Self and Its Impact
- Part II Rewards, Incentives, and Choice
- Part III Interest and Internal Motivation
- Part IV Curiosity and Boredom
- Part V Goals and Values
- Part VI Methods, Measures, and Perspective
- Index
- References
Part VI - Methods, Measures, and Perspective
Published online by Cambridge University Press: 15 February 2019
Book contents
- The Cambridge Handbook of Motivation and Learning
- The Cambridge Handbook of Motivation and Learning
- Copyright page
- Epigraph
- Contents
- Figures
- Tables
- Contributors
- Foreword
- Acknowledgments
- Introduction Motivation and Its Relation to Learning
- Part I The Self and Its Impact
- Part II Rewards, Incentives, and Choice
- Part III Interest and Internal Motivation
- Part IV Curiosity and Boredom
- Part V Goals and Values
- Part VI Methods, Measures, and Perspective
- Index
- References
Summary
In this chapter we examine measures and methods that have come to prominence over the last two decades exploring how they build on, and are shaped by, relevant theory. In addition, we identify how contemporary measures and methods have expanded as researchers investigate interactive influences of person and context. First, the importance of distinguishing levels of generality and specificity in definitions of motivation constructs is explored. Second, we examine attempts to define the type of relation between motivation constructs and learning, for example, mediation relations and reciprocal relations. As specific research is considered we direct attention to the types of analytic procedures that have been used to test hypotheses and assess models of the relations between motivation and learning. In particular we highlight the development of research methods that go beyond the range of insights into motivation and learning that can be achieved using only self-report questionnaires.
- Type
- Chapter
- Information
- The Cambridge Handbook of Motivation and Learning , pp. 663 - 782Publisher: Cambridge University PressPrint publication year: 2019
References
References
Ainley, M. & Ainley, J. (2011). Student engagement with science in early adolescence: The contribution of enjoyment to students’ continuing interest in learning about science. Contemporary Educational Psychology, 36(1), 4–12. doi: 10.1016/j.cedpsych.2010.08.001.Google Scholar
Ainley, M., Corrigan, M., & Richardson, N. (2005). Students, tasks and emotions: Identifying the contribution of emotions to students' reading of popular culture and popular science texts. Learning and Instruction, 15(5), 433–47.Google Scholar
Ainley, M., Enger, L., & Kennedy, G. (2008). The elusive experience of ‘flow’: Qualitative and quantitative indicators. International Journal of Educational Research, 47, 109–21. doi: 10.1016/j.ijer.2007.11.011.CrossRefGoogle Scholar
Ainley, M. & Hidi, S. (2002). Dynamic measures for studying interest and learning. In Pintrich, P. R. & Maehr, M. L. (Eds.), New directions in measures and methods (Vol. 12, pp. 43–76). Amsterdam: JAI, Elsevier Science.Google Scholar
Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, learning, and the psychological processes that mediate their relationship. Journal of Educational Psychology, 94(3), 545–61. doi: 10.1037//0022-0663.94.3545.Google Scholar
Anderman, E. M., Gimbert, B., O'Connell, A. A., & Riegel, L. (2015). Approaches to academic growth assessment. British Journal of Educational Psychology, 85, 138–53.Google Scholar
Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50(1), 84–94. doi: 10.1080/00461520.2015.1004069.Google Scholar
Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–82.Google Scholar
Bertling, J., Marksteiner, M., & Kyllonen, P. C. (2016). General noncognitive outcomes. In Kuger, S., Klieme, E., Jude, N., & Kaplan, D. (Eds.), Assessing contexts of learning: An international perspective. (pp. 255–81). Cham: Springer.Google Scholar
Betebenner, D. W. & Linn, R. L. (2010). Growth in student achievement: Issues of measurement, longitudinal data analysis, and accountability. Princeton, NJ: Educational Testing Service.Google Scholar
Boekaerts, M. (1999). Motivated learning: Studying student * situation transactional units. European Journal of Psychology of Education, 14(1), 41–55.Google Scholar
Boekaerts, M. (2002). The on-line motivation questionnaire: A self-report instrument to assess students' context sensitivity. In Pintrich, P. R. & Maehr, M. L. (Eds.), New directions in measures and methods (Vol. 12, pp. 77–120). Oxford: Elsevier Science.Google Scholar
Boekaerts, M. (2007). Understanding students' affective processes in the classroom. In Schutz, P. & Pekrun, R. (Eds.), Emotion in education (pp. 37–56). Amsterdam: Elsevier.Google Scholar
Bond, T. G. & Fox, C. M. (2015). Applying the Rasch model: Fundamental measurement in the human sciences. (3rd ed.). New York, NY: Routledge.Google Scholar
Brown, A. L. & Maydeu-Olivares, A. (2013). How IRT can solve problems of ipsative data in forced-choice questionnaires. Psychological Methods, 18(1), 36–52. doi: 10.1037/a0030641.Google Scholar
Brown, T. A. (2014). Confirmatory factor analysis for applied research (2nd ed.). New York: Guilford Publications.Google Scholar
Crombach, M. J., Boekaerts, M., & Voeten, M. J. M. (2003). Online measurement of appraisals of students faced with curricular tasks. Educational and Psychological Measurement, 63(1), 96–111. doi: 10.1177/0013164402239319.Google Scholar
Drechsel, B., Carstensen, C., & Prenzel, M. (2011). The role of content and context in PISA interest scales: A study of the embedded interest items in the PISA 2006 science assessment. International Journal of Science Education, 33(1), 73–95. doi: 10.1080/09500693.2010.518646.CrossRefGoogle Scholar
Eccles, J. S., Wigfield, A., & Schiefele, U. (1998). Motivation to succeed. In Eisenberg, N. (Ed.), Handbook of child psychology: Social, emotional, and personality development. (Vol. 3, pp. 1017–96). New York, NY: Wiley.Google Scholar
Elliot, A. J. & McGregor, H. A. (2001). A 2 X 2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501–19. doi: 10.1037//0022-3514.80.3.501.CrossRefGoogle ScholarPubMed
Frenzel, A. C., Pekrun, R., Dicke, A.-L., & Goetz, T. (2012). Beyond quantitative decline: Conceptual shifts in adolescents’ development of interest in mathematics. Developmental Psychology, 48(4), 1069–82. doi: 10.1037/a0026895.Google Scholar
Fulmer, S. M. & Frijters, J. C. (2009). A review of self-report and alternative approaches in the measurement of student motivation. Educational Psychology Review, 21, 219–46. doi: 10.1007/s10648-009-9107-x.Google Scholar
Gogol, K., Brunnera, M., Martin, R., Preckel, F., & Goetz, T. (2017). Affect and motivation within and between school subjects: Development and validation of an integrative structural model of academic self-concept, interest, and anxiety. Contemporary Educational Psychology, 49, 46–65. doi: 10.1016/j.cedpsych.2016.11.003.Google Scholar
Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14–30. doi: 10.1080/00461520.2014.989230.Google Scholar
Harackiewicz, J. M., Durik, A. M., Barron, K. E., Linnenbrink-Garcia, L., & Tauer, J. M. (2008). The role of achievement goals in the development of interest: Reciprocal relations between achievement goals, interest, and performance. Journal of Educational Psychology, 100(1), 105–22. doi: 10.1037/ 0022-0663.100.1.105.Google Scholar
Hershberger, S. L. (2003). The growth of structural equation modeling 1994–2001. Structural Equation Modeling: A Multidisciplinary Journal, 10, 35–46.CrossRefGoogle Scholar
Hidi, S. & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–27.Google Scholar
Holstermann, N., Ainley, M., Grube, D., Roick, T., & Bögeholz, S. (2012). The specific relationship between disgust and interest: Relevance during biology class dissections and gender differences. Learning and Instruction, 22, 185–92. doi: 10.1016/j.learninstruc.2011.10.005.Google Scholar
Hunter, J. P. & Csikszentmihalyi, M. (2003). The positive psychology of interested adolescents. Journal of Youth and Adolescence, 32, 27–35.Google Scholar
Järvelä, S., Salonen, P., & Lepola, J. (2002). Dynamic assessment as a key to understanding student motivation in a classroom context. In Pintrich, P. R. & Maehr, M. L. (Eds.), New directions in measures and methods (Vol. 12, pp. 207–40). Oxford: Elsevier Science.Google Scholar
Järvenoja, H., Järvelä, S., & Malmberg, J. (2015). Understanding regulated learning in situative and contextual frameworks. Educational Psychologist, 50(3), 204–19. doi: 10.1080/00461520.2015.1075400.Google Scholar
Judd, C. M. & Kenny, D. A. (2010). Data analysis in social psychology: Recent and recurring issues. In Fiske, S., Gilbert, D. T., & Lindzey, G. (Eds.), Handbook of social psychology (pp. 115–39). Hoboken, NJ: John Wiley & Sons.Google Scholar
Kaplan, D. (2009). Structural equation modeling: Foundations and extensions. Los Angeles, CA: Sage.Google Scholar
Karabenick, S. A., Woolley, M. E., Friedel, J. M., Ammon, B. V., Blazevski, J., Bonney, C. R., & Kelly, K. L. (2007). Cognitive processing of self-report items in educational research: Do they think what we mean? Educational Psychologist, 42(3), 139–51. doi: 10.1080/00461520701416231.Google Scholar
Klassen, R. M. & Usher, E. L. (2010). Self-efficacy in educational settings: Recent research and emerging directions. In Urdan, T. C. & Karabenick, S. A. (Eds.), Advances in motivation and achievement. The decade ahead: Theoretical perspectives on motivation and achievement (Vol. 16A, pp. 1–33). Bingley: Emerald.Google Scholar
Knogler, M., Harackiewicz, J. M., Gegenfurtner, A., & Lewalter, D. (2015). How situational is situational interest? Investigating the longitudinal structure of situational interest. Contemporary Educational Psychology, 43, 39–50. doi: 10.1016/j.cedpsych.2015.08.004.Google Scholar
Kyllonen, P. C. & Bertling, J. (2014). Innovative questionnaire assessment methods to increase cross-country comparability. In Rutkowski, L., von Davier, M., & Rutkowski, D. (Eds.), Handbook of international large-scale assessment: background, technical issues, and methods of data analysis. Boca Raton, FL: CRC Press.Google Scholar
Larson, R. W. & Csikszentmihalyi, M. (1983). The experience sampling method. New Directions for Methodology of Science & Behavioral Science, 15, 41–56.Google Scholar
Lewalter, D. & Krapp, A. (2008). The role of contextual conditions of vocational education for motivational orientations and emotional experiences. European Psychologist, 9(4), 210–21. doi: 10.1027/1016-9040.9.4.210.Google Scholar
Lüdtke, O., Robitzsch, A., Trautwein, U., & Kunter, M. (2009). Assessing the impact of learning environments: How to use student ratings of classroom or school characteristics in multilevel modelling. Contemporary Educational Psychology, 34(2), 120–31. doi: 10.1016/j.cedpsych.2008.12.001.Google Scholar
Marsh, H. W. & Hau, K. (2007). Applications of latent-variable models in educational psychology: The need for methodological-substantive synergies. Contemporary Educational Psychology, 32(1), 151–70. doi: 10.1016/j.cedpsych.2006.10.008.Google Scholar
Marsh, H. W., Koller, O., Trautwein, U., Ludtke, O., & Baumert, J. (2005). Academic self-concept, interest, grades, and standardized test scores: Reciprocal effects models of causal ordering. Child Development, 76(2), 397–416.CrossRefGoogle ScholarPubMed
Marsh, H. W. & Martin, A. J. (2011). Academic self-concept and academic achievement: Relations and causal ordering. British Journal of Educational Psychology, 81, 59–77. doi: 10.1348/000709910X503501.Google Scholar
Martin, A. J. (2015). Growth approaches to academic development: Research into academic trajectories and growth assessment, goals, and mindsets. British Journal of Educational Psychology, 85, 133–7. doi: 10.1111/bjep.12071.Google Scholar
McClelland, D. C., Atkinson, J. W., Clark, R. A., & Lowell, E. L. (1953). The achievement motive. New York, NY: Appleton-Century.CrossRefGoogle Scholar
Mega, C., Ronconi, L., & De Beni, R. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology, 106(1), 121–31. doi: 10.1037/a0033546.Google Scholar
Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman, L., Freeman, K. E., ... Urdan, T. (2000). Manual for the patterns of adaptive learning scale. Ann Arbor, MI: University of Michigan.Google Scholar
Moos, D. C. & Azevedo, R. (2008). Exploring the fluctuation of motivation and use of self-regulatory processes during learning with hypermedia. Instructional Science, 36, 203–31. doi: 10.1007/s11251-007-9028-3.Google Scholar
Murphy, P. K. & Alexander, P. A. (2000). A motivated exploration of motivation terminology. Contemporary Educational Psychology, 25, 3–53. doi: 10.1006/ceps.1999.1019.Google Scholar
OECD (Organisation for Economic Co-operation and Development). (2006). Assessing scientific, reading, and mathematical literacy: A framework for PISA 2006. Paris: OECD.Google Scholar
OECD (Organisation for Economic Co-operation and Development). (2009). PISA 2006 Technical report. Paris: OECD.Google Scholar
Pekrun, R., Elliot, A. J., & Maier, M. A. (2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of Educational Psychology, 101, 115–35. doi: 10.1037/a0013383.Google Scholar
Pekrun, R., Frenzel, A. C., Goetz, T., & Perry, R. P. (2007). The control-value theory of achievement emotions: An integrative approach to emotions in education. In Schutz, P. A. & Pekrun, R. (Eds.), Emotion in education (pp. 13–36). San Diego, CA: Academic.Google Scholar
Pintrich, P. R. (2000). An achievement goal theory perspective on issues in motivation terminology, theory, and research. Contemporary Educational Psychology, 25, 92–104. doi: 10.1006/ceps.1999.1017.CrossRefGoogle ScholarPubMed
Pintrich, P. R. & Maehr, M. L. (Eds.). (2002). New directions in measures and methods: Advances in motivation and achievement (Vol. 12). Oxford: Elsevier Science.Google Scholar
Pintrich, P. R., Smith, D., Garcia, T., & McKeachie, W. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor, MI: The University of Michigan.Google Scholar
Renninger, K. A. & Hidi, S. (2011). Revisiting the conceptualization, measurement, and generation of interest. Educational Psychologist, 46(3), 168–84. doi: 10.1080/00461520.2011.587723.CrossRefGoogle Scholar
Rotgans, J. I. & Schmidt, H. G. (2011). Situational interest and academic achievement in the active-learning classroom. Learning and Instruction, 21, 58–67. doi: 10.1016/j.learninstruc.2009.11.001.Google Scholar
Ryan, R. M. & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54–67. doi: 10.1006/ceps.1999.1020.Google Scholar
Scherer, R., Nilsen, T., & Jansen, M. (2016). Evaluating individual students' perceptions of instructional quality: An investigation of their factor structure, measurement invariance, and relations to educational outcomes. Frontiers in Psychology, 7, 110. doi: 10.3389/fpsyg.2016.00110.Google Scholar
Schulz, W. (2009). Questionnaire construct validation in the international civic and citizenship education study. IERI Monograph Series, 2, 85–107.Google Scholar
Schwinger, M., Steinmayr, R., & Spinath, B. (2016). Achievement goal profiles in elementary school: Antecedents, consequences, and longitudinal trajectories. Contemporary Educational Psychology, 46(1), 164–79. doi: 10.1016/j.cedpsych.2016.05.006.Google Scholar
Shanley, L. (2016). Evaluating longitudinal mathematics achievement growth: Modeling and measurement considerations for assessing academic progress. Educational Researcher, 45(6), 347–57. doi: 10.3102/0013189X16662461.Google Scholar
Student Learning, Student Achievement Task Force. (2011). Student learning, student achievement: How do teachers measure up? Retrieved from https://eric.ed.gov/?id=ED517573.Google Scholar
Tempelaar, D. T., Gijselaers, W. H., van der Loeff, S. S., & Nijhuis, J. F. (2007). A structural equation model analyzing the relationship of student achievement motivations and personality factors in a range of academic subject-matter areas. Contemporary Educational Psychology, 32(1), 105–31. doi: 10.1016/j.cedpsych.2006.10.004.Google Scholar
Tulis, M. & Ainley, M. (2011). Interest, enjoyment and pride after failure experiences? Predictors of students’ state-emotions after success and failure during learning in mathematics. Educational Psychology: An International Journal of Experimental Educational Psychology, 31(7), 779–807. doi: org/10.1080/01443410.2011.608524.Google Scholar
Tulis, M. & Fulmer, S. M. (2013). Students' motivational and emotional experiences and their relationship to persistence during academic challenge in mathematics and reading. Learning and Individual Differences, 27, 35–46. doi: 10.1016/j.lindif.2013.06.003.CrossRefGoogle Scholar
Tuominen-Soini, H., Salmela-Aro, K., & Niemivirta, M. (2011). Stability and change in achievement goal orientations: A person-centered approach. Contemporary Educational Psychology, 36, 82–100. doi: 10.1016/j.cedpsych.2010.08.002.Google Scholar
Urdan, T. C. & Mestas, M. (2006). The goals behind performance goals. Journal of Educational Psychology, 97(2), 354–65. doi: 10.1037/0022-0663.98.2.354.Google Scholar
Van de Vijver, F. J. R. & He, J. (2016). Bias assessment and prevention in noncognitive outcome measures in context assessments. In Kuger, S., Klieme, E., Jude, N., & Kaplan, D. (Eds.), Assessing contexts of learning: An international perspective. (pp. 229–53). Cham: Springer.Google Scholar
Vygotsky, L. S. (1978). Mind in society: The development of higher mental processes. Cambridge, MA: Harvard University Press.Google Scholar
Wigfield, A. & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30, 1–35. doi: 10.1016/j.dr.2009.12.001.Google Scholar
References
Antonenko, P., Paas, F., Grabner, R. & van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22, 425–38. doi: 10.1007/s10648-010-9130-y.Google Scholar
Appleton, J. J. (2012). Systems consultation: Developing the assessment-to-intervention link with the student engagement instrument. In Christenson, S., Reschly, A., & Wylie, C. (Eds.), Handbook of research on student engagement (pp. 725–42). New York, NY: Springer.Google Scholar
Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45, 369–86. doi: 10.1002/pits.20303.Google Scholar
Arroyo, I., Cooper, D. G., Burleson, W., Woolf, B. P., Muldner, K., & Christopherson, R. (2009). Emotion sensors go to school. Conference on Artificial Intelligence in Education, 200, 17–24.Google Scholar
Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50, 84–94. doi: 10.1080/00461520.2015.1004069.Google Scholar
Azevedo, R., Moos, D., Johnson, A., & Chauncey, A. (2010). Measuring cognitive and metacognitive regulatory processes used during hypermedia learning: Issues and challenges. Educational Psychologist, 45, 210–23. doi: 10.1080/00461520.2010.515934.Google Scholar
Balfanz, R., Herzog, L., & MacIver, P. J. (2007). Preventing student disengagement and keeping students on graduation path in urban middle grade schools: Early identification and effective interventions. Educational Psychologist, 42, 223–35. doi: 10.1080/00461520701621079.Google Scholar
Blumenfeld, P., Modell, J., Bartko, W. T., Secada, W., Fredricks, J., Friedel, J., & Paris, A. (2005). School engagement of inner city students during middle childhood. In Cooper, C. R., Garcia Coll, C., Bartko, W. T., Davis, H. M. & Chatman, C. (Eds.), Developmental pathways through middle childhood: Rethinking diversity and contexts as resources (pp. 145–70). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Boucheix, J. M., Lowe, R. K., Putri, D. K., & Groff, J. (2013). Cueing animations: Dynamic signaling aids information extraction and comprehension. Learning and Instruction, 25, 71–84. doi: 10.1016/j.learninstruc.2012.11.005.Google Scholar
Briesch, A. M., Hemphill, E. M., Volpe, R. J., & Daniels, B. (2015). An evaluation of observational methods for measuring response to class-wide intervention. School Psychology Quarterly, 30, 37–49. doi: 10.1037/spq0000065.Google Scholar
Christenson, S., Reschly, A., & Wylie, C. (Eds.). (2012). Handbook of research on student engagement. New York, NY: Springer.Google Scholar
Conchas, G. Q. (2001). Structuring failure and success: Understanding the variability in Latino school engagement. Harvard Educational Review, 71, 475–504. doi: 10.17763/haer.71.3.280w814v1603473k.Google Scholar
Connell, J. P., Klem, A., Lacher, T., Leiderman, S., & Moore, W. (2009). First things first: Theory, research, and practice. Howell, NJ: Institute for Research and Reform in Education.Google Scholar
D'Mello, S. & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22, 145–57. doi: 10.1016/j.learninstruc.2011.10.001.Google Scholar
Duchowski, A. (2007). Eye tracking methodology: Theory and practice (2nd ed.). New York, NY: Springer.Google Scholar
Eccles, J. S. & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–32. doi: 10.1146/annurev.psych.53.100901.135153.Google Scholar
Engle, R. A. & Conant, F. R. (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners’ classroom. Cognition and Instruction, 20, 399–483. doi: 10.1207/S1532690XCI2004_1.Google Scholar
Ferguson, R. F. & Danielson, C. (2014). How framework for teaching and tripod 7Cs evidence distinguish key components of effective teaching. Designing Teacher Evaluation Systems, 98–143.Google Scholar
Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59, 117–42. doi: 10.3102/00346543059002117.Google Scholar
Fredricks, J. A., Blumenfeld, P. C. & Paris, A. (2004). School engagement: Potential of the concept: State of the evidence. Review of Educational Research, 74, 59–119. doi: 10.3102/00346543074001059.Google Scholar
Fredricks, J. A. & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In Christenson, S., Reschy, A. L., & Wylie, C. (Eds.), Handbook of research on student engagement (pp. 763–83). New York, NY: Springer.Google Scholar
Fredricks, J., McColskey, W., Meli, J., Mordica, J., Montrosse, B., & Mooney, K. (2011). Measuring student engagement in upper elementary through high school: A description of 21 instruments. (Issues & Answers Report, REL 2010–No. 098). Washington, DC: US Department of Education, Institute of Education Sciences, National Center for Education. Available at http://ies.ed.gov/ncee/edlabs/projects/project.asp?projectID=268.Google Scholar
Fredricks, J. A., Wang, M. T., Schall, J., Hofkens, T., Snug, H., Parr, A., & Allerton, J. (2016). Using qualitative methods to develop a survey of math and science engagement. Learning and Instruction, 43, 5–15. doi: 10.1016/j.learninstruc.2016.01.009.Google Scholar
Gelhbach, H. & Brinkworth, M. E. (2011). Measure twice: cut down error: A process for enhancing the validity of survey scales. Review of General Psychology, 15, 380–7. doi: 10.1037/a0025704.Google Scholar
Gobert, J. D., Baker, R. S., & Wixon, M. B. (2015). Operationalizing and detecting disengagement within online science microworlds. Educational Psychologist, 50, 43–57. doi: 10.1080/00461520.2014.999919.Google Scholar
Greene, B. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50, 13–40. doi: 10.1080/00461520.2014.989230.Google Scholar
Greene, J. A. & Azevedo, R. (2010). The measurement of learners’ self-regulated cognitive and metacognitive processes while using computer-based learning environments. Educational Psychologist, 45, 203–9. doi: 10.1080/ 00461520.2014.989230.Google Scholar
Gresalfi, M. S. (2009). Taking up opportunities to learn: Constructing dispositions in mathematics classrooms. Journal of the Learning Sciences, 18, 327–69. doi: 10.1080/10508400903013470.Google Scholar
Grossman, P., Loeb, S., Cohen, J. & Wyckoff, J. (2013). Measure for measure: The relationship between measures of instructional practice in middle school English language arts and teachers' value-added scores. American Journal of Education, 50, 4–36. doi: 10.1086/669901.Google Scholar
Hektner, J. M., Schmidt, J. A., & Csikzentmihalyi, M. (2007). Experience sampling method: Measuring the quality of everyday life. Thousand Oaks, CA: Sage Publications.Google Scholar
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers and Education, 90, 36–53. doi: 10.1016/j.compedu.2015.09.005.Google Scholar
Johnson, R. W. & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33, 14–26. doi: 10.3102/0013189X033007014.Google Scholar
Kane, T., Kerr, K., & Pianta, R. (2014). Designing teacher evaluation systems: New guidance from the measures of effective teaching project. New York, NY: John Wiley & Sons.Google Scholar
Kapoor, A., Burleson, W., & Picard, R. W. (2007). Automatic prediction of failure. International Journal of Human Computer Studies, 65, 724–6. doi: 10.1016/j.ijhcs.2007.02.003.Google Scholar
Lam, S., Wong, B. P. H., Yang, H. & Liu, Y. (2012). Understanding student engagement with a contextual model. In Christenson, S., Reschy, A. L., & Wylie, C. (Eds.), Handbook of research on student engagement (pp. 403–19). New York, NY: Springer.Google Scholar
Lane, K. L., Menzies, H. M, Oakes, W. P., & Kalberg, J. R. (2012). Systematic screenings of behavior to support instruction: From preschool to high school. New York, NY: Guilford Press.Google Scholar
Larson, R. W. (2000). Toward a psychology of positive youth development. American Psychologist, 55, 170–83. doi: 10.1037/0003-066X.55.1.170.Google Scholar
Larson, R. W. & Kleiber, D. (1993). Daily experiences of adolescents. In Tolan, P. H. & Cohler, B. J. (Eds.), Handbook of clinical research and practice with adolescents (pp. 125–45). Oxford: John Wiley.Google Scholar
Mandernach, J. (2015). Assessment of student engagement in higher education: A synthesis of literature and assessment tools. International Journal of Learning, Teaching, and Educational Research, 12, 1–14.Google Scholar
Mason, B., Gunersel, A. B., & Ney, E. (2014). Cultural and ethnic bias in teacher ratings of behavior: A criterion-focused review. Psychology in the Schools, 51, 1017–30. doi: 10.1002/pits.21800.Google Scholar
McNeal, K. S., Spry, J. M., Mitra, R., & Tipton, J. L. (2014). Measuring student engagement, knowledge, and perceptions of climate change in an introductory environment geology course. Journal of Geoscience Education, 62, 655–67. doi: 10.5408/13-111.1.CrossRefGoogle Scholar
Meece, J., Blumenfeld, P. C., & Hoyle, R. H. (1988). Students’ goal orientation and cognitive engagement in classroom activities. Journal of Educational Psychology, 80, 514–23. doi: 10.1037/0022-0663.80.4.514.Google Scholar
Miller, B. W. (2015). Using reading times and eye-movements to measure cognitive engagement. Educational Psychologist, 50, 31–42. doi: 10.1080/00461 520.2015.1004068.Google Scholar
Mostow, J., Chang, K. M., & Nelson, J. (2011, June). Toward exploiting EEG input in a reading tutor. In International conference on artificial intelligence in education (pp. 230–7). Berlin and Heidelberg: Springer.Google Scholar
Muthén, L. K. & Muthén, B. O. (1998–2014). Mplus user's guide. Seventh edition. Los Angeles, CA: Muthén & Muthén.Google Scholar
Nystrand, M. & Gamoran, A. (1991). Instructional discourse, student engagement, and literature achievement. Research in the Teaching of English, 25, 261–90.Google Scholar
Pianta, R. C., Hamre, B. K., Haynes, N. J., Mintz, S. L., & La Paro, K. M. (2007). Classroom Assessment Scoring System Manual, Middle/Secondary Version. Charlottesville, NC: University of Virginia Press.Google Scholar
Pianta, R. C., Hamre, B. K., & Mintz, S. L. (2012). Classroom Assessment Scoring System (CLASS): Secondary class manual. Charlottesville, VA: Teachstone.Google Scholar
Poh, M., Swenson, N. C., & Picard, R. W. (2010). A wearable sensor for unobstrusive, long-term assessment of electrodermal activity. IEE Transactions on Biomedical Engineering, 57, 1243–57.Google Scholar
Ponitz, C. C., Rimm-Kaufman, S. E., Grimm, K. J., & Curby, T. W. (2009). Kindergarten classroom quality, behavioral engagement, and reading achievement. School Psychology Review, 38, 102–20.Google Scholar
Reeve, J. M. & Tseng, C. (2011). Agency as a fourth aspect of students’ engagement with learning activities. Contemporary Educational Psychology, 36, 357–67. doi: 10.1016/j.cedpsych.2011.05.002.Google Scholar
Renninger, K. A. & Bachrach, J. E. (2015). Studying triggers for interest and engagement using observational methods. Educational Psychologist, 50, 58–69. doi: 10.1080/00461520.2014.999920.Google Scholar
Renninger, K. A. & Hidi, S. (2016). The power of interest for motivation and learning. New York, NY: Routledge.Google Scholar
Rimm-Kaufman, S. E., Baroody, A. E., Larsen, R. A., Curby, T. W., & Abruy, T. (2015). To what extent do teacher-student interaction quality and student gender contribution to fifth graders’ engagement in mathematics learning? Journal of Educational Psychology, 107, 17–185. doi: 10.1037/a0037252.Google Scholar
Rimm-Kaufman, S. E., Curby, T. W., Grimm, K. J., Nathanson, L., & Brock, L. (2009). The contribution of children's self-regulation and classroom quality to children's adaptive behaviors in the kindergarten classroom. Developmental Psychology, 45, 958–72. doi: 10.1037/a0015861.Google Scholar
Ryu, S. & Lombardi, D. (2015). Coding classroom interactions for collective and individual engagement. Educational Psychologist, 50, 70–83. doi: 10.1080/00461520.2014.1001891.Google Scholar
Shen, L., Wang, M., & Shen, R. (2009). Affective e-Learning: Using “emotional” data to improve learning in pervasive learning environment. Educational Technology & Society, 12(2), 176–89.Google Scholar
Shernoff, D. J. & Csikszentmihalyi, M. (2009). Flow in schools: Cultivating engaged learners and optimal learning environments. In Gilman, R., Huebner, E. S., & Furlong, M. (Eds.), Handbook of positive psychology in schools (pp. 131–45). New York, NY: Routledge.Google Scholar
Shernoff, D. J., Csikzentmihalyi, M., Schneider, B., & Shernoff, E. S. (2003). Student engagement in high schools from the perspective of flow theory. School Psychology Quarterly, 18, 158–76. doi: 10.1521/scpq.18.2.158.21860.Google Scholar
Sinatra, G., Heddy, B. C., & Lombard, D. (2015). The challenge of defining and measuring student engagement in science. Educational Psychologist, 1, 1–13. doi: 10.1080/00461520.2014.1002924.Google Scholar
Skiba, R. J., Michael, R. S., Nardo, A. C., & Peterson, R. L. (2002). The color of discipline: Sources of racial and gender disproportionality in school punishment. The Urban Review, 34, 317–42. doi: 10.1023/A:1021320817372.Google Scholar
Skinner, E. A., Kindermann, T. A., & Furrer, C. J. (2009). A motivational perspective on engagement and disaffection. Conceptualization and assessment of children's behavioral and emotional participation in academic activities in the classroom. Educational and Psychological Measurement, 69, 493–525. doi: 10.1177/0013164408323233.Google Scholar
Skinner, E. A. & Pitzer, J. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience. In Christenson, S., Reschy, A. L., & Wylie, C. (Eds.), Handbook of research on student engagement (pp. 21–45). New York, NY: Springer.Google Scholar
Stevens, R. H., Galloway, T. L., Berka, C., Johnson, R., & Sprang, M. (2008). Assessing student's mental representations of complex problem spaces with EEG technologies. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 52, no. 3, pp. 167–71). Thousand Oaks, CA: SAGE Publications.Google Scholar
Tolan, P. H. & Deutsch, N. L. (2015). Mixed methods in developmental science. In Lerner, R. (Ed.), Handbook of child psychology and developmental science (Vol. 1, 7th ed., 1–45), Hoboken, NJ: Wiley.Google Scholar
Turner, J. C. & Meyer, D. K. (2000). Studying and understanding the instructional context of classroom: Using our past to forge our future. Educational Psychologist, 35, 69–85. doi: 10.1207/S15326985EP3502_2.Google Scholar
Uekawa, K., Borman, K., & Lee, R. (2007). Student engagement in the U.S. urban high school mathematics and science classrooms: Findings on social organization, race, and ethnicity. Urban Review, 39, 1–43. doi: 10.1007/s11256-006-0039-1.Google Scholar
Valentine, J. (2005). The instructional practices inventory: A process for profiling student engaged learning for school improvement. Columbia, MO: University of Missouri, Middle Level Leadership Center. Retrieved at http://mllc.missouri.edu/Upload%20Area-Docs/IPI%20Manuscript%2012-07.pdf.Google Scholar
Voelkl, K. E. (1997). Identification with school. American Journal of Education, 105, 204–319. doi: 10.1086/444158.Google Scholar
Volpe, R. J., DiPerna, J. C., Hintze, J. M., & Shapiro, E. S. (2005). Observing students in classroom settings: A review of seven coding schemes. School Psychology Review, 34(4), 454–74.Google Scholar
Walker, H. & Severson, H. (1992). Systematic Screening for Behavioral Disorders (SSBD). (2nd ed.). Technical Manual. Longmont, CA: Sopris West.Google Scholar
Wang, M. T., Chow, A., Hofkens, T., & Salmela-Aro, K. (2015). The trajectories of student emotional engagement and school burnout with academic and psychological development: Findings from Finnish adolescents. Learning and Instruction, 36, 57–65. doi: 10.1016/j.learninstruc.2014.11.004.Google Scholar
Wang, M. T. & Degol, J. (2014). Staying engaged: Knowledge and research needs in student engagement. Child Development Perspectives, 8, 137–43. doi: 10.1111/cdep.12073.Google Scholar
Wang, M. T. & Fredricks, J. A. (2014). The reciprocal links between school engagement and youth problem behavior during adolescence. Child Development, 85, 722–37. doi: 10.1111/cdev.12138.Google Scholar
Wang, M. T., Fredricks, J. A., Ye, F., Hofkens, T., & Schall, J. (2016). The math science engagement scale: Development, validation, and psychometric properties. Learning and Instruction, 43, 16–26. doi: 10.1016/j.learninstruc.2016.01.008.Google Scholar
Wang, M. T. & Peck, S. C. (2013). Adolescent educational success and mental health vary across school engagement profiles. Developmental Psychology, 49, 1266–76. doi: 10.1037/a0030028.Google Scholar
Waxman, H. C., Tharp, R. G., & Hilberg, R. S. (2004). Future directions for classroom observation research. In Waxman, H. C., Hilberg, R. S., & Tharp, R. G. (Eds.), Observational research in U.S. classrooms: New approaches for understanding cultural and linguistic diversity (pp. 266–77). Cambridge: Cambridge University Press.Google Scholar
Wigfield, A., Guthrie, J. T., Perencevich, K. C., Taboada, A., Klauda, S. L., McRae, A., & Barbosa, P. (2008). Role of reading engagement in mediating the effects of reading comprehension instruction on reading outcomes. Psychology in the Schools, 45, 432–45. doi: 10.1002/pits.20307.Google Scholar
Winne, P. H. & Perry, N. E. (2000). Measuring self-regulated learning. In Boekaerts, M., Pintrich, P., & Zeidner, M. (Eds.), Handbook of self-regulation (pp. 531–66). San Diego, CA: Academic Press.Google Scholar
Wood, B. K., Hojnoski, R. L., Laracy, S. D., & Olson, C. L. (2016). Comparison of observational methods and their relation to ratings of engagement in young children. Topics in Early Childhood Special Education, 35(4), 211. doi: 10.1177/0271121414565911.Google Scholar
Yair, G. (2000). Educational battlefields in America: The tug of war over students’ engagement with instruction. Sociology of Education, 73, 247–69. doi: 10.2307/2673233.Google Scholar
References
AERA, APA, & NCME. (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.Google Scholar
Atkinson, J. W. (1964). An introduction to motivation. Princeton, NJ: D. Van Nostrand Company.Google Scholar
Bandura, A. & Schunk, D. H. (1981). Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. Journal of Personality and Social Psychology, 41(3), 586–98. doi: 10.1037/0022-3514.41.3.586.Google Scholar
Barron, K. E., Grays, M., & Hulleman, C. S. (2014). Assessing motivation in general education. Poster presented at the Annual Meeting of the American Educational Research Association, Philadelphia, PA.Google Scholar
Barron, K. E. & Hulleman, C. S. (2015). Expectancy-value-cost model of motivation. In J. D. Wright (Ed.) International encyclopedia of the social & behavioral sciences (Vol. 8, pp. 503–9). Elsevier. doi: 10.1016/B978-0-08-097086-8.26099-6.Google Scholar
Bell, C. A., Gitomer, D. H., McCaffrey, D. F., Hamre, B. K., Pianta, R. C., & Qi, Y. (2012). An argument approach to observation protocol validity. Educational Assessment, 17(2–3), 62–87. doi: 10.1080/10627197.2012.715014.Google Scholar
Bill and Melinda Gates Foundation. (2013). Ensuring fair and reliable measures of effective teaching. Retrieved from www.edweek.org/media/17teach-met1.pdf.Google Scholar
Bowman, N. A. (2010). Can 1st-year college students accurately report their learning and development? American Educational Research Journal, 47(2), 466–96. doi: 10.3102/0002831209353595.Google Scholar
Byrne, B. M. (2012). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Multivariate applications series. New York, NY: Routledge.Google Scholar
Cronbach, L. J. & Meehl, P. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281–302.Google Scholar
Duckworth, A. L. & Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44(4), 237–51. doi: 10.3102/0013189X15584327.Google Scholar
Eccles, J. S. (1983). Expectancies, values, and academic behaviors. In Spence, J. T. (Ed.), Achievement-related motives and behaviors (pp. 119–46). San Francisco, CA: W. H. Freeman.Google Scholar
Eccles, J. S., Adler, T., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In Spence, J. T. (Ed.), Achievement and achievement motivation (pp. 75–146). San Francisco, CA: W. H. Freeman.Google Scholar
Embretson, S. E. & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Fredricks, J. A. & McColsky, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In Christenson, S. L., Reschly, A. L., & Wylie, C. (Eds.), Handbook of research on student engagement (pp. 763–82). New York, NY: Springer. doi: 10.1007/978-1-4614-2018-7_37.Google Scholar
Gaspard, H., Dicke, A., Flunger, B., Brisson, B. M., Häfner, I., Nagengast, B., & Trautwein, U. (2015). Fostering adolescents’ value beliefs for mathematics with a relevance intervention in the classroom. Developmental Psychology, 51(9), 1226–40. doi: 10.1037/dev0000028.Google Scholar
Goldstein, J. & Flake, J. K. (2016). Towards a framework for the validation of early childhood assessment systems. Educational Assessment, Evaluation and Accountability, 28(3), 273–93. doi: 10.1007/s11092-015-9231-8.Google Scholar
Harackiewicz, J. M., Canning, E. A., Tibbetts, Y., Giffen, C. J., Blair, S. S., Rouse, D. I., & Hyde, J. S. (2014). Closing the social class achievement gap for first-generation students in undergraduate biology. Journal of Educational Psychology, 106(2), 375–89. doi: 10.1037/a0034679.Google Scholar
Hektner, J. M. & Csikszentmihalyi, M. (1996). A longitudinal exploration of flow and intrinsic motivation in adolescents. In Annual Meeting of the American Educational Research Association, NYC, New York.Google Scholar
Hidi, S. & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–27. doi: 10.1207/s15326985ep4102_4.Google Scholar
Holstermann, N., Ainley, M., Grube, D., Roick, T., & Bögeholz, S. (2012). The specific relationship between disgust and interest: Relevance during biology class dissections and gender differences. Learning and Instruction, 22(3), 185–92. doi: 10.1016/j.learninstruc.2011.10.005.Google Scholar
Hulleman, C. S. (2007). The role of utility value in the development of interest and achievement. University of Wisconsin-Madison. Retrieved from http://files.eric.ed.gov/fulltext/ED498264.pdf.Google Scholar
Hulleman, C. S., Barron, K. E., Kosovich, J. J., & Lazowski, R. A. (2016). Expectancy-value models of achievement motivation in education. In Lipnevich, A. A., Preckel, F., & Robers, R. D. (Eds.), Psychosocial skills and school systems in the twenty-first century: Theory, research, and applications. (pp. 241–78). Basel, Switzerland: Springer. doi: 10.1007/978-3-319-28606-8.Google Scholar
Hulleman, C. S., Godes, O., Hendricks, B. L., & Harackiewicz, J. M. (2010). Enhancing interest and performance with a utility value intervention. Journal of Educational Psychology 102, 880–95. doi: 10.1037/a0019506.Google Scholar
Hulleman, C. S. & Harackiewicz, J. M. (2009). Promoting interest and performance in high school science classes. Science, 326(5958), 1410–12. doi: 10.1126/science.1177067.Google Scholar
Hulleman, C. S., Kosovich, J. J., Barron, K. E., & Daniel, D. B. (2016). Making connections: Replicating and extending the utility value intervention in the classroom. Journal of Educational Psychology. 109(3), 387–404. doi: 10.1037/edu0000146.Google Scholar
Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: Different labels for the same constructs or different constructs with similar labels? Psychological Bulletin, 136(3), 422–49. doi: 10.1037/a0018947.Google Scholar
Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in children's self-competence and values: Gender and domain differences across grades one through twelve. Child Development, 73(2), 509–27. doi: 10.1111/1467-8624.00421.Google Scholar
Kane, M. T. (1992). An argument-based approach to validity. Psychological Bulletin, 112(3), 527–35.Google Scholar
Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 1–73. doi: 10.1111/jedm.12000.Google Scholar
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press.Google Scholar
Kosovich, J. J. (2017). Pragmatic measurement for education science: A method-substance synergy of validation and motivation. Charolottesville, VA: University of Virginia.Google Scholar
Kosovich, J. J., Flake, J. K., & Hulleman, C. S. (2017). Short-term motivation trajectories: A parallel process model of expectancy-value. Contemporary Educational Psychology. 49(3), 130–139. doi: 10.1016/j.cedpsych.2017.01.004.Google Scholar
Kosovich, J. J., Hulleman, C. S., Barron, K. E., & Getty, S. (2015). A practical measure of student motivation: Establishing validity evidence for the expectancy-value-cost scale in middle school. The Journal of Early Adolescence, 35(5–6), 790–816. doi: 10.1177/0272431614556890.Google Scholar
Kosovich, J. J., Hulleman, C. S., & Flake, J. K. (2017). Practical measurement: An argument-based approach to exploring alternative psychometric validity evidence. Poster presented at the annual Society for Research on Educational Effectiveness, Washington, DC.Google Scholar
Krumm, A. E., Beattie, R., Takahashi, S., D'Angelo, C., Feng, M., & Cheng, B. (2016). Practical measurement and productive persistence: Strategies for using digital learning system data to drive improvement. Journal of Learning Analytics, 3(2), 116–38. doi: 10.18608/jla.2016.32.6.Google Scholar
Lazowski, R. A. & Hulleman, C. S. (2016). Motivation interventions in education: A meta-analytic review. Review of Educational Research, 86(2), 602–40. doi: 10.3102/0034654315617832.Google Scholar
Lewin, K., Dembo, T., Festinger, L., & Sears, P. S. (1944). Level of aspiration. In Hunt, J. M. (Ed.), Personality and the behavior disorders (pp. 333–78). New York, NY: Ronal Press. doi: 10.1037/10319-006.Google Scholar
Messick, S. (1989). Meaning and values in test validation: The science and ethics of assessment. Educational Researcher, 18(2), 5–11.Google Scholar
Paulhus, D. L. (1984). Two-component models of socially desirable responding. Journal of Personality and Social Psychology, 46(3), 598–609. doi: 10.1037//0022-3514.46.3.598.Google Scholar
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–86. doi: 10.1037/0022-0663.95.4.667.Google Scholar
Raynor, J. O. (1982). Future orientation, self-evaluation, and achievement motivation: Use of an expectancy × value theory of personality functioning and change. In Feather, N. T. (Ed.), Expectations and actions: Expectancy-value models in psychology (pp. 97–124). Hillsdale, NJ: Erlbaum.Google Scholar
Renninger, K. A. & Bachrach, J. E. (2015). Studying triggers for interest and engagement using observational methods. Educational Psychologist, 50(March), 58–69. doi: 10.1080/00461520.2014.999920.Google Scholar
Renninger, K. A. & Hidi, S. (2016). The power of interest for motivation and engagement. Abingdon: Routledge. Retrieved from http://works.swarthmore.edu/fac-education/91.Google Scholar
Rosenzweig, E. Q., Hulleman, C. H., Barron, K. E., et al. (in press). Promises and pit- falls of adapting utility value interventions for online mathematics courses. Journal of Experimental Education. doi: 10.1080/00220973.2018.1496059.Google Scholar
Rosenzweig, E. Q. & Wigfield, A. (2016). STEM motivation interventions for adolescents: A promising start, but further to go. Educational Psychologist, 51(2), 146–63. doi: 10.1080/00461520.2016.1154792.Google Scholar
Rotgans, J. I. & Schmidt, H. G. (2011). The role of teachers in facilitating situational interest in an active-learning classroom. Teaching and Teacher Education, 27(1), 37–42. doi: 10.1016/j.tate.2010.06.025.Google Scholar
Schmeiser, C. B. & Welch, C. J. (2006). Test development. In Brennan, R. L. (Ed.), Educational measurement (4th ed., pp. 307–53). Westport, CT: Praeger Publishers.Google Scholar
Schwarz, N. & Oyserman, D. (2001). Asking questions about behavior: Cognition, communication, and questionnaire construction. American Journal of Evaluation, 22(2), 127–60. doi: 10.1177/109821400102200202.Google Scholar
Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach's alpha. Psychometrika, 74(1), 107–20. doi: 10.1007/s11336-008-9101-0.Google Scholar
Tibbetts, Y., Harackiewicz, J. M., Canning, E. A., Boston, J. S., Priniski, S. J., & Hyde, J. S. (2016). Affirming independence: Exploring mechanisms underlying a values affirmation intervention for first-generation students. Journal of Personality and Social Psychology, 110, 635–59. doi: 10.1037/pspa0000049.Google Scholar
Traub, R. E. & Rowley, G. L. (1991). Understanding reliability. Educational Measurement: Issues and Practice, 10(1), 37–45. doi: 10.1111/j.1745-3992.1991.tb00183.x.Google Scholar
Vroom, V. H. (1964). Work and motivation: Classic readings in organizational behavior. New York, NY: John Wiley & Sons.Google Scholar
Wise, S. L. & DeMars, C. (2005). Low examinee effort in low-stakes assessment: Problems and potential solutions. Educational Assessment, 10(1), 1–17.Google Scholar
Yeager, D. S., Bryk, A., Muhich, J., Hausman, H., & Morales, L. (2013). Practical measurement. Palo Alto, CA: Carnegie Foundation for the Advancement of Teaching. www.carnegiefoundation.org/resources/publications/practical-measurement/.Google Scholar
Yeager, D. S. & Walton, G. M. (2011). Social-psychological interventions in education: They're not magic. Review of Educational Research, 81(2), 267–301. doi: org/10.3102/0034654311405999.Google Scholar
References
Anderman, E. M. & Wolters, C. A. (2006). Goals, values, and affect: Influences on student motivation. In Alexander, P. A. & Winne, P. H. (Eds.), Handbook of educational psychology (pp. 369–89). Mahwah, NJ: Lawrence Erlbaum Associates Publishers. doi: 10.4324/9780203874790.ch17.Google Scholar
Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64, 359–72. doi: 10.1037/h0043445.Google Scholar
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (1996). Multifaceted impact of self-efficacy beliefs on academic functioning. Child Development, 67, 1206–22. doi: 10.1111/j.1467-8624.1996.tb01791.x.Google Scholar
Barron, K. E. & Harackiewicz, J. M. (2001). Achievement goals and optimal motivation: Testing multiple goal models. Journal of Personality and Social Psychology, 80, 706–22. doi: 10.1037/0022-3514.80.5.706.Google Scholar
Bembenutty, H. (1999). Sustaining motivation and academic goals: The role of academic delay of gratification. Learning and Individual Differences, 11, 233–57. doi: 10.1016/S1041-6080(99)80002-8.Google Scholar
Bergman, L. R., Magnusson, D., & El Khouri, B. (2003). Studying individual development in an interindividual context: A person-oriented approach. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Bong, M. & Skaalvik, E. M. (2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15(1), 1–40. doi: 10.1023/A:1021302408382.Google Scholar
Bräten, I. & Olaussen, B. S. (2005). Profiling individual differences in student motivation: A longitudinal cluster-analytic study in different academic contexts. Contemporary Educational Psychology, 30, 359–96. doi: 10.1016/j.cedpsych.2005.01.003.Google Scholar
Cano, F. & Berbén, A. B. G. (2009). University students’ achievement goals and approaches to learning in mathematics. British Journal of Educational Psychology, 79, 131–53. doi: 10.1348/000709908X314928.Google Scholar
Conley, A. M. (2012). Patterns of motivation beliefs: Combining achievement goal and expectancy-value perspectives. Journal of Educational Psychology, 104, 32–47. doi: 10.1037/a0026042.Google Scholar
Corpus, J. H. & Wormington, S. V. (2014). Profiles of intrinsic and extrinsic motivations in elementary school: A longitudinal analysis. Journal of Experimental Education, 82, 480–501. doi: 10.1080/00220973.2013.876225.Google Scholar
Deci, E. L. & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.Google Scholar
Dina, F. & Efklides, A. (2009). Student profiles of achievement goals, goal instructions and external feedback: Their effect on mathematical task performance and affect. European Journal of Education and Psychology, 2, 235–62.Google Scholar
Eccles, J. S. (1983). Expectancies, values, and academic behaviors. In Spence, J. T. (Ed.), Achievement and achievement motives (pp. 75–146). San Francisco, CA: Freeman.Google Scholar
Elliot, A. J. (1999). Approach and avoidance motivation and achievement goals. Educational Psychologist, 34, 169–89. doi: 10.1207/s15326985ep3403_3.Google Scholar
Elliot, A. J. & McGregor, H. A. (1999). Test anxiety and the hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 80, 501–19. doi: 10.1037/0022-3514.76.4.628.Google Scholar
Harackiewicz, J. M., Barron, K. E., Pintrich, P. R., Elliot, A. J., & Thrash, T. M. (2002). Revision of achievement goal theory: Necessary and illuminating. Journal of Educational Psychology, 94, 638–45. doi: 10.1037/0022-0663.94.3.638.Google Scholar
Harackiewicz, J. M., Durik, A. M., Barron, K. E., Linnenbrink-Garcia, L., & Tauer, J. M. (2008). The role of achievement goals in the development of interest: Reciprocal relations between achievement goals, interest, and performance. Journal of Educational Psychology, 100, 105–22. doi: 10.1037/0022-0663.100.1.105.Google Scholar
Hayenga, A. O. & Corpus, J. H. (2010). Profiles of intrinsic and extrinsic motivation: A person-centered approach to motivation and achievement in middle school. Motivation and Emotion, 34, 371–83. doi: 10.1007/s11031-010-9181-x.Google Scholar
Hidi, S. & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41, 111–27. doi: 10.1207/s15326985ep4102_4.Google Scholar
Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: Different labels for the same constructs or different constructs with similar labels? Psychological Bulletin, 136, 422–49. doi: 10.1037/a0018947.Google Scholar
Lau, S. & Roeser, R. W. (2008). Cognitive abilities and motivational processes in science achievement and engagement: A person-centered analysis. Learning and Individual Differences, 18, 497–504. doi: 10.1016/j.lindif.2007.11.002.Google Scholar
Laursen, B. & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly, 52, 377–89. doi: 10.1353/mpq.2006.0029.Google Scholar
Law, W., Elliot, A. J., & Murayama, K. (2012). Perceived competence moderates the relation between performance-approach and performance-avoidance goals. Journal of Educational Psychology, 104, 806–19. doi: 10.1037/a0027179.Google Scholar
Lee, Y.-K., Wormington, S. V., Linnenbrink-Garcia, L., & Roseth, C. (2017). A short-term longitudinal study of stability and change in achievement goal profiles. Learning and Individual Differences, 55, 49–60. doi: 10.1016/j.lindif.2017.02.002.Google Scholar
Linnenbrink, E. A. & Pintrich, P. R. (2000). Multiple pathways to learning and achievement: The role of goal orientation in fostering adaptive motivation, affect, and cognition. In Sansone, C. & Harackiewicz, J. M. (Eds.), Intrinsic and extrinsic motivation: The search for optimal motivation and performance (pp. 195–227). New York, NY: Academic Press.Google Scholar
Linnenbrink-Garcia, L. & Barger, M. M. (2014). Achievement goals and emotions. In Pekrun, R. & Linnenbrink-Garcia, L. (Eds.), International handbook of emotions in education (pp. 142–61). New York, NY: Routledge.Google Scholar
Linnenbrink-Garcia, L., Middleton, M. J., Ciani, K. D., Easter, M. A., O'Keefe, P. A., & Zusho, A. (2012a). The strength of the relation between performance-approach and performance-avoidance goal orientations: Theoretical, methodological, and instructional implications. Educational Psychologist, 47, 281–301. doi: 10.1080/00461520.2012.722515.Google Scholar
Linnenbrink-Garcia, L. & Patall, E. A. (2016). Motivation. In Anderman, E. & Corno, L. (Eds.), Handbook of educational psychology (3rd ed., pp. 91–103). New York, NY: Taylor & Francis.Google Scholar
Linnenbrink-Garcia, L., Patall, E. A., & Pekrun, R. (2016). Adaptive motivation and emotion in education: Research and principles for instructional design. Policy Insights from Behavioral and Brain Sciences, 3, 228–36.Google Scholar
Linnenbrink-Garcia, L., Perez, T., & Wormington, S. V. (2014). Development of undergraduates’ motivation in science: A person-centered approach. Poster presented at the International Conference on Motivation, Helsinki.Google Scholar
Linnenbrink-Garcia, L., Riggsbee, J., Hill, N. E., Snyder, K. E., & Ben-Eliyahu, A. (2012b). Motivational profiles of upper elementary school students: Stability and change in relation to academic engagement. Paper presented at the annual meeting of the American Educational Research Association, Vancouver.Google Scholar
Linnenbrink-Garcia, L. & Wormington, S. V. (2017). Key challenges and potential solutions for studying the complexity of motivation in schooling: An integrative, dynamic person-oriented perspective. British Journal of Educational Psychology Monograph Series, 12, 89–108.Google Scholar
Linnenbrink-Garcia, L., Wormington, S. V., Snyder, K. E., Riggsbee, J., Perez, T., Ben-Eliyahu, A., & Hill, N. E. (2018). Multiple pathways to success: An examination of integrative motivational profiles among upper elementary and college students. Journal of Educational Psychology, 110, 1026–48. doi: 10.1037/edu0000245.Google Scholar
Luo, W., Paris, S. G., Hogan, D., & Luo, Z. (2011). Do performance goals promote learning? A pattern analysis of Singapore students’ achievement goals. Contemporary Educational Psychology, 36, 165–76. doi: 10.1016/j.cedpsych.2011.02.003.Google Scholar
Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoidance goals in high school: Distinguishing between intrapersonal and task-based standards of competence. Contemporary Educational Psychology, 36, 268–79. doi: 10.1016/j.cedpsych.2011.03.003.Google Scholar
Marsh, H. W. & Hao, K. T. (2007). Applications of latent-variable models in educational psychology: The need for methodological-substantive synergies. Contemporary Educational Psychology, 32, 151–71. doi: 10.1016/j.cedpsych.2006.10.008.Google Scholar
Maehr, M. L. & Zusho, A. (2009). Achievement goal theory: The past, present, and future. In Wentzel, K. (Ed.), Handbook of motivation at school (pp. 77–104). New York, NY: Routledge.Google Scholar
Middleton, M. J. & Midgley, C. (1997). Avoiding the demonstration of lack of ability: An underexplored aspect of goal theory. Journal of Educational Psychology, 89, 710–18. doi: 10.1037/0022-0663.89.4.710.Google Scholar
Midgley, C., Kaplan, A., & Middleton, M. (2001). Performance-approach goals: Good for what, for whom, under what circumstances, and at what cost? Journal of Educational Psychology, 93, 77–86. doi: 10.1037/0022-0663.93.1.77.Google Scholar
Nagengast, B., Marsh, H. W., Scalas, L. F., Xu, M. K., Hau, K. T., & Trautwein, U. (2011). Who took the “×” out of expectancy-value theory? A psychological mystery, a substantive-methodological synergy, and a cross-national generalization. Psychological Science, 22, 1058–66. doi: 10.1177/0956797611415540.Google Scholar
Nelson, K. G., Shell, D. F., Husman, J., Fishman, E. J., & Soh, L. K. (2015). Motivational and self-regulated learning profiles of students taking a foundational engineering course. Journal of Engineering Education, 104, 74–100. doi: 10.1002/jee.20066.Google Scholar
Ng, C. (2006). The role of achievement goals in completing a course assignment: Examining the effects of performance-approach and multiple goals. Open Learning, 21, 33–48. doi: 10.1080/02680510500472189.Google Scholar
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66, 543–78. doi: 10.2307/1170653.Google Scholar
Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92, 544–55. doi: 10.1037/0022-0663.92.3.544.Google Scholar
Pulkka, A. T. & Niemivirta, M. (2013). Adult students' achievement goal orientations and evaluations of the learning environment: A person-centered longitudinal analysis. Educational Research and Evaluation, 19, 297–322. doi: 10.1080/13803611.2013.767741.Google Scholar
Renninger, K. A. & Hidi, S. E. (2016). The power of interest for motivation and engagement. New York, NY: Routledge/Taylor & Francis.Google Scholar
Rosenthal, R. & Jacobson, L. (1968). Pygmalion in the classroom. New York, NY: Holt, Rinehart & Winston.Google Scholar
Schiefele, U. (2001). The role of interest in motivation and learning. InCollis, J. & Messick, S. (Eds.), Intelligence and personality: Bridging the gap in theory and measurement (pp. 163–94). Mahwah, NJ: Erlbaum.Google Scholar
Schiefele, U. (2009). Situational and individual interest. In Wentzel, K. R. & Wigfield, A. (Eds.), Handbook of motivation at school (pp. 197–222). New York, NY: Routledge.Google Scholar
Schunk, D. H., Meece, J. L., & Pintrich, P. R. (2014). Motivation in education: Theory, research, and applications (4th ed.). Upper Saddle River, NJ: Merrill Prentice Hall.Google Scholar
Schunk, D. H. & Pajares, F. (2005). Competence perceptions and academic functioning. In Elliot, A. J. & Dweck, C. S. (Eds.), Handbook of competence and motivation (pp. 85–104). New York, NY: Guilford Press.Google Scholar
Seifert, T. L. & O'Keefe, B. A. (2001). The relationship of work avoidance and learning goals to perceived competence, externality, and meaning. British Journal of Educational Psychology, 71, 81–92. doi: 10.1348/000709901158406.Google Scholar
Senko, C., Hulleman, C. S., & Harackiewicz, J. M. (2011). Achievement goal theory at the crossroads: Old controversies, current challenges, and new directions. Educational Psychologist, 46, 26–47. doi: 10.1080/00461520.2011.538646.Google Scholar
Shell, D. F. & Husman, J. (2008). Control, motivation, affect, and strategic self-regulation in the college classroom: A multidimensional phenomenon. Journal of Educational Psychology, 100, 443–59. doi: 10.1037/0022-0663.100.2.443.Google Scholar
Shell, D. F. & Soh, L. (2013). Profiles of motivated self-regulation in college computer science courses: Differences in major versus required non-major courses. Journal of Science Education and Technology, 22, 899–913. doi: 10.1007/s10956-013-9437-9.Google Scholar
Trautwein, U., Marsh, H. W., Nagengast, B., Lüdtke, O., Nagy, G., & Jonkmann, K. (2012). Probing for the multiplicative term in modern expectancy–value theory: A latent interaction modeling study. Journal of Educational Psychology, 104, 763–77. doi: 10.1037/a0027470.Google Scholar
Tuominen-Soini, H., Salmela-Aro, K., & Niemivirta, M. (2011). Stability and change in achievement goal orientations: A person-centered approach. Contemporary Educational Psychology, 36, 82–100. doi: 10.1016/j.cedpsych.2010.08.002.Google Scholar
Tuominen-Soini, H., Salmela-Aro, K., & Niemivirta, M. (2012). Achievement goal orientations and academic well-being across the transition to upper secondary education. Learning and Individual Differences, 22, 290–305. doi: 10.1016/j.lindif.2012.01.002.Google Scholar
Turner, J. C., Thorpe, P. K., & Meyer, D. K. (1998). Students’ reports of motivation and negative affect: A theoretical and empirical analysis. Journal of Educational Psychology, 90, 758–71. doi: 10.1037/0022-0663.90.4.758.Google Scholar
Usher, E. L. (2016). Personal capability beliefs. In Anderman, E. & Corno, L. (Eds.), Handbook of educational psychology (3rd ed., pp. 146–59). New York, NY: Taylor & Francis.Google Scholar
Vansteenkiste, M., Soenens, B., Sierens, E., Luyckx, K., & Lens, W. (2009). Motivational profiles from a self-determination perspective: The quality of motivation matters. Journal of Educational Psychology, 101, 671–88. doi: 10.1037/a0015083.Google Scholar
Wigfield, A. & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30, 1–35. doi: 10.1016/j.dr.2009.12.001.Google Scholar
Wigfield, A. & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. doi: 10.1006/ceps.1999.1015.Google Scholar
Wigfield, A., Eccles, J. S., Fredricks, J. A., Simpkins, S., Roeser, R. W., & Schiefele, U. (2015). Development of achievement motivation and engagement. In Lerner, R. (series Ed.) and Lamb, M. & Coll, C. Garcia (vol. Eds.), Handbook of child psychology and developmental science (7th ed., Vol. 3, pp. 657–700). New York, NY: Wiley.Google Scholar
Wigfield, A., Eccles, J. S., Schiefele, U., Roeser, R. W., & Davis-Kean, P. (2006). Development of achievement motivation. In Eisenberg, N., Damon, W., & Lerner, R. M. (Eds.), Handbook of child psychology, Vol. 3: Social, emotional, and personality development (6th ed., pp. 933–1002). Hoboken, NJ: John Wiley & Sons.Google Scholar
Wolters, C. A. (2004). Advancing achievement goal theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology, 96, 236–50. doi: 10.1037/0022-0663.96.2.236.Google Scholar
Wormington, S. V. (2016). Smooth sailing or choppy waters? Patterns and predictors of motivation in on-line mathematics courses (Doctoral dissertation). Retrieved from Proquest. https://mvlri.org/blog/is-there-more-than-one-path-to-success-in-math-patterns-and-predictors-of-students-motivation-and-achievement-in-online-math-courses/Google Scholar
Wormington, S. V., Barger, M. M., & Linnenbrink-Garcia, L. (2014). One size fits all? longitudinal, profile-centered examinations of adolescents’ motivation in mathematics and social studies. Poster presented at the annual meeting of the American Educational Research Association, Philadelphia, PA.Google Scholar
Wormington, S. V., Corpus, J. H., & Anderson, K. G. (2012). A person-centered investigation of academic motivation and its correlates in high school. Learning and Individual Differences, 22, 429–38. doi: 10.1016/j.lindif.2012.03.004.Google Scholar
Wormington, S. V. & Linnenbrink-Garcia, L. (2016). A new look at multiple goal pursuit: The promise of a person-centered approach. Educational Psychology Review, 1–39. doi: 10.1007/s10648-016-9358-2.Google Scholar
References
Anderson, B. A. (2016). The attention habit: How reward learning shapes attentional selection. Annals of the New York Academy of Science, 1369, 24–39. doi: 10.1111/nyas.12957.Google Scholar
Anselme, P. (2007). Some conceptual problems with the classical theory of behavior. Behavioural Processes, 75, 259–75.Google Scholar
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman/Times Books/ Henry Holt & Co.Google Scholar
Bandura, A. (2016). The power of observational learning through social modeling. In Sternberg, R. J., Fisk, S. T., & Foss, D. J. (Eds.), Scientists making a difference: One hundred eminent behavioral and brain scientists talk about their most important contributions (pp. 235–9). New York, NY: Cambridge University Press.Google Scholar
Bereiter, C. & Scardamalia, M. (2014). Knowledge building and knowledge creation: One concept, two hills to climb. In Tan, S. C., So, H. Y., & Yeo, J. (Eds.), Knowledge creation in education (Education Innovation Series). Singapore: Springer. doi: 10.1007/978-981-287-047-6_3.Google Scholar
Berridge, K. C. & Kringelbach, M. L. (2008). Affective neuroscience of pleasure: Reward in humans and animals. Psychopharmacology (Berl), 199(3): 457–80. doi: 10.1007/s00213-008-1099-6.Google Scholar
Blumenfeld-Katzir, T., Pasternak, O., Dagan, M., & Assaf, Y. (2011). Diffusion MRI of structural brain plasticity induced by a learning and memory task. PLoS ONE 6(6): e20678. doi: 10.1371/journal.pone.0020678.Google Scholar
Boekaerts, M. & Cascallar, E. (2006). How far have we moved toward the integration of theory and practice in self-regulation? Educational Psychology Review, 18(3), 199–210.Google Scholar
Bourgeois, A., Chelazzi, L., & Vuilleumier, P. (2016). How motivation and reward learning modulate selective attention. Progress in Brain Research, 229, 325–42. doi: 10.1016/bs.pbr.2016.06.004.Google Scholar
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control: Rewarding, aversive, and alerting. Neuron, 68(5), 815–34. doi: 10.1016/j.neuron.2010.11.022.Google Scholar
Brooks, D. W. & Shell, D. F. (2006). Working memory, motivation, and teacher-initiated learning. Journal of Science Education and Technology, 15(1), 17–30.Google Scholar
Brown, A. L., Bransford, J. D., Ferrara, R. A., & Campione, J. C. (1983). Learning, remembering, and understanding. In Flavell, J. H. & Markman, E. M. (Eds.), Handbook of child psychology (4th ed., Vol. 3). New York, NY: Wiley.Google Scholar
Cajete, G. A. (2015). Indigenous community: Rekindling the teaching of the seventh fire. St. Paul, MN: Living Justice Press.Google Scholar
Caporale, N. & Dan, Y. (2009). Spike timing-dependent plasticity: A Hebbian learning rule. Annual Review of Neuroscience, 31, 25–46.Google Scholar
Chemero, A. (2003). An outline of a theory of affordances. Ecological Psychology, 15(2), 181–95.Google Scholar
Corbetta, M. (2012). Functional connectivity and neurological recovery. Developmental Psychobiology, 54, 239–53.Google Scholar
Cowan, N. (2010). The magical mystery four: How is working memory capacity limited, and Why? Current Directions in Psychological Science, 19(1): 51–7. doi: 10.1177/0963721409359277.Google Scholar
Deco, G., Rolls, E. T., Albantakis, L., & Romo, R. (2013). Brain mechanisms for perceptual and reward-related decision making. Progress in Neurobiology, 103, 194–213.Google Scholar
Deloria, V. Jr. & Wildcat, D. (2001). Power and place: Indian education in America. Golden, CO: Fulcrum Resources.Google Scholar
Driemeyer, J., Boyke, J., Gaser, C., Buchel, C., & May, A. (2008) Changes in gray matter induced by learning – Revisited. PLoS ONE 3(7): e2669. doi: 10.1371/journal.pone.0002669.Google Scholar
Eccles, J. S. & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–32.Google Scholar
Eck, A., Soh, L.-K., & Shell, D. F. (2016). Investigating differences in wiki-based collaborative activities between student engagement profiles in CS1. Proceedings of the 47th ACM Technical Symposium on Computer Science Education (SIGCSE'2016) (pp. 36–41). New York, NY: ACM. doi: 10.1145/2839509.2844615.Google Scholar
Erickson, L. C. & Thiessen, E. D. (2015). Statistical learning of language: Theory, validity, and predictions of a statistical learning account of language acquisition. Developmental Review, 37, 66–108.Google Scholar
Feldman, R., Monakhov, M., Pratt, M., & Ebstein, R. P. (2016). Oxytocin pathway genes: Evolutionary ancient system impacting on human affiliation, sociality, and psychopathology. Biological Psychiatry, 79(3), 174–84. doi: 10.1016/j.biopsych.2015.08.008.Google Scholar
Flanigan, A. E., Peteranetz, M. S., Shell, D. F., & Soh, L.-K. (2017). Implicit intelligence beliefs of computer science students: exploring change across the semester. Contemporary Educational Psychology, 48, 179–96. doi: 10.1016/j.cedpsych.2016.10.003.Google Scholar
Flowerday, T. (2016). Using motivation to teach motivation. In Smith, M. C. & DeFrates-Densch, N. (Eds.), Challenges and innovations in educational psychology teaching and learning (pp. 109–22). Charlotte, NC: Information Age Publishing.Google Scholar
Flowerday, T. & Shell, D. F. (2015). Disentangling the effects of interest and choice on learning, engagement, and attitude. Learning and Individual Differences, 40, 134–40. doi: 10.1016/j.lindif.2015.05.003.Google Scholar
Flowerday, T., Shell, D. F., & Moreno, R., (2018). Using profiles of motivated strategic self-regulation to understand mathematics achievement of ethnically diverse elementary school students. Manuscript submitted for publication.Google Scholar
Gibson, J. J. (1979). The ecological approach to visual perception. Boston, MA: Houghton Mifflin.Google Scholar
Harmon-Jones, E. & Inzlicht, M. (Eds.) (2016). Social neuroscience: Biological approaches to social psychology. New York, NY: Routledge, Psychology Press.Google Scholar
Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York, NY: Wiley & Sons.Google Scholar
Hidi, S. (2016). Revisiting the role of rewards in motivation and learning: Implications of neuroscientific research. Educational Psychology Review, 28, 61–93. doi: 10.1007/s10648-015-9307-5.Google Scholar
Hidi, S. & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41, 111–27.Google Scholar
Horvitz, J. C. (2000). Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events. Neuroscience, 96(4), 651–6.Google Scholar
Husman, J., Brem, S. K., Banegas, S., Duchrow, D. W., & Haque, S. (2015). Learning and future time perspective: The promise of the future–rewarding in the present. In Stolarski, M., Fieulaine, N., & van Beek, W. (Eds.), Time perspective theory; Review, research and application: Essays in Honor of Philip G. Zimbardo (pp. 131–41). Springer International Publishing. doi: 10.1007/978-3-319-07368-2_8.Google Scholar
Husman, J. & Lens, W. (1999). The role of the future in student motivation. Educational Psychologist, 34(2), 113–25.Google Scholar
Husman, J. & Shell, D. F. (2008). Beliefs and perceptions about the future: A measurement of future time perspective. Learning and Individual Differences, 18, 166–75.Google Scholar
Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., & Caldara, R. (2009). Cultural confusions show that facial expressions are not universal. Current Biology, 19(18), 1543–8. doi: 10.1016/j.cub.2009.07.051.Google Scholar
Jairam, D., Kiewra, K. A., Kauffman, D. F., & Zhao, R. (2012). How to study a matrix. Contemporary Educational Psychology, 37, 128–35.Google Scholar
Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., & Hudspeth, A. J. (2012). Principles of neural science (5th ed.). New York, NY: McGraw-Hill.Google Scholar
Kiani, R. & Shadlen, M. N. (2009). Representation of confidence associated with a decision by neurons in the parietal cortex. Science, 324, 759–64.Google Scholar
Kirkham, N. Z., Slemmer, J. A., & Johnson, S. P. (2002). Visual statistical learning in infancy: Evidence for domain-general learning mechanism. Cognition, 83, B35–B42. doi: 10.1016/S0010-0277(02)00004-5.Google Scholar
Kóbor, A., Janacsek, K., Takács, A., & Nemeth, D. (2017). Statistical learning leads to persistent memory: Evidence for one-year consolidation. Scientific Reports, 7, Article number: 760. doi: 10.1038/s41598-017-00807-3.Google Scholar
Kolmogorov, A. N. (1950). Foundations of the theory of probability. New York, NY: Chelsea Publishing.Google Scholar
Kuhbandner, C., Lichtenfeld, S., & Pekrun, R. (2011). Always look on the broad side of life: Happiness increases the breadth of sensory memory. Emotion, 11(4), 958–64. doi: 10.1037/a0024075.Google Scholar
Knudsen, E. I. (2007). Fundamental components of attention. Annual Review of Neuroscience, 30, 57–78. doi: 10.1146/annurev.neuro.30.051606.094256.Google Scholar
Matzel, L. D., Hel, F. P., & Miller, R. R. (1988). Information and expression of simultaneous and backward associations: Implications for contiguity theory. Learning and Motivation, 19, 317–44.Google Scholar
Mayer, R. E. & Fiorella, L. (2014). Principles for reducing extraneous processing in multimedia learning: Coherence, signaling, redundancy, special contiguity, and temporal contiguity principles. In Mayer, R. E. (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 279–315). New York, NY: Cambridge University Press.Google Scholar
McInerney, D. M. & Flowerday, T. (2016). Indigenous issues in education and research: Looking forward. Contemporary Educational Psychology, 47, 1–3.Google Scholar
Miendlarzewska, E. A., Bavelier, D., & Schwartz, S. (2016). Influence of reward motivation on human declarative memory. Neuroscience and Biobehavioral Reviews, 61, 156–76.Google Scholar
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information, Psychological Review, 63, 81–97.Google Scholar
Mitchel, A. D. & Weiss, D. J. (2011). Learning across senses: Cross-modal effects in multisensory statistical learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 37, 1081–91.Google Scholar
Mooney, J., Seaton, M., Kaur, G., Marsh, H. W., & Yeung, A. S. (2016). Cultural perspectives on Indigenous and non-indigenous Australian students’ school motivation and engagement. Contemporary Educational Psychology, 47, 11–23.Google Scholar
Na, J., Grossmann, I., Varnum, M. E. W., Kitayama, S., Gonzalez, R., & Nisbett, R. E. (2010). Cultural differences are not always reducible to individual differences. Proceedings of the National Academy of Sciences of the United States of America, 107(14), 6192–7. doi: 10.1073/pnas.1001911107.Google Scholar
Nelson, K. G., Shell, D. F., Husman, J., Fishman, E. J., & Soh, L. K. (2015). Motivational and self-regulated learning profiles of students taking a foundational engineering course. Journal of Engineering Education, 104(1), 74–100. doi: 10.1002/jee.20066.Google Scholar
Numan, M. (2014). Neurobiology of social behavior: Toward an understanding of the prosocial and antisocial brain. Oxford: Academic Press.Google Scholar
Park, D. C. & Huang, C. M. (2010). Culture wires the brain: A cognitive neuroscience perspective. Perspectives on Psychological Science, 5(4), 391–400.Google Scholar
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review 18(4), 315–41.Google Scholar
Peteranetz, M. S., Flanigan, A. E., Shell, D. F., & Soh, L.-K. (2017). Computational creativity exercises: An avenue for promoting learning in computer science. IEEE Transactions on Education, 60(4), 305–13. doi: 10.1109/TE.2017.2705152.Google Scholar
Peteranetz, M. S., Flanigan, A. E., Shell, D. F., & Soh, L.-K. (2018). Career aspirations, perceived instrumentality, and achievement in undergraduate computer science courses. Contemporary Educational Psychology, 53, 27–44. doi: 10.1016/j.cedpsych.2018.01.006.Google Scholar
Renninger, K. A. & Hidi, S. (2016). The power of interest for motivation and engagement. New York, NY: Routledge.Google Scholar
Rotter, J. B. (1966), Generalized expectancies for internal versus external control of reinforcement. Psychology Monographs, 80, 1–28.Google Scholar
Rubie-Davis, C. M. & Peterson, E. R. (2016). Relations between teachers’ achievement over- and underestimation and students’ beliefs for Maori and Pakeha students. Contemporary Educational Psychology, 47, 72–83.Google Scholar
Sarter, M., Givens, B., & Bruno, J. P. (2001). The cognitive neuroscience of sustained attention: Where top-down meets bottom-up. Brain Research Reviews, 35, 146–60.Google Scholar
Schapiro, A. C., Turk-Browne, N. B., Norman, K. A., & Botvinick, M. M. (2016). Statistical learning of temporal community structure in the hippocampus. Hippocampus, 26(1), 3–8. doi: 10.1002/hipo.22523.Google Scholar
Schultz, W. (2015). Neuronal reward and decision signals: From theories to data. Physiological Review, 95, 853–951. doi: 10.1152/physrev.00023.Google Scholar
Schunk, D. H. & Zimmerman, B. J. (2013). Self-regulation and learning. In Reynolds, W. M., Miller, G. E., & Weiner, I. B. (Eds.), Handbook of psychology (Vol. 7, pp. 45–68). Hoboken, NJ: John Wiley & Sons.Google Scholar
Seyfartha, R. M. & Cheney, D. L. (2013). Affiliation, empathy, and the origins of theory of mind. Proceedings of the National Academy of Sciences of the United States of America, 110 (Supplement 2), 10349–56. doi: 10.1073/pnas.1301223110.Google Scholar
Shell, D. F., Brooks, D. W., Trainin, G., Wilson, K., Kauffman, D. F., & Herr, L. (2010). The unified learning model: How motivational, cognitive, and neurobiological sciences inform best teaching practices. Dordrecht, Netherlands: Springer.Google Scholar
Shell, D. F., Colvin, C., & Bruning, R. H. (1995). Self-efficacy, attribution, and outcome expectancy mechanisms in reading and writing achievement: Grade level and achievement level differences. Journal of Educational Psychology, 87, 386–98. doi: 10.1037/0022-0663.87.3.386.Google Scholar
Shell, D. F. & Husman, J. (2008). Control, motivation, affect, and strategic self-regulation in the college classroom: A multidimensional phenomenon. Journal of Educational Psychology, 100(2) 443–59.Google Scholar
Shell, D. F., Murphy, C. C., & Bruning, R. H. (1989). Self efficacy and outcome expectancy mechanisms in reading and writing achievement. Journal of Educational Psychology, 81, 91, 100. doi: 10.1037/0022-0663.81.1.91.Google Scholar
Shell, D. F. & Soh, L.-K. (2013). Profiles of motivated self-regulation in college computer science courses: Differences in major versus required non-major courses. Journal of Science Education and Technology, 22(6), 899–913.Google Scholar
Shell, D. F., Soh, L.-K., & Chiriacescu, V. (2015). Modeling self-efficacy as a dynamic cognitive process with the Computational-Unified Learning Model (C-ULM): Implications for cognitive informatics and cognitive computing. International Journal of Cognitive Informatics and Natural Intelligence, 9(3), 1–24. doi: 10.4018/IJCINI.2015070101.Google Scholar
Shors, T. J. (2014). The adult brain makes new neurons, and effortful learning keeps them alive. Current Directions in Psychological Science, 23(5) 311–18.Google Scholar
Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50, 1–13, doi: 10.1080/00461520.2014.1002924.Google Scholar
Skinner, E. A. (1996). A guide to constructs of control. Journal of Personality and Social Psychology, 71, 549–70.Google Scholar
Stoffregen, T. A. (2003). Affordances as properties of the animal–environment system. Ecological Psychology, 15(2), 115–34.Google Scholar
Striepens, N., Matusch, A., Kendrick, K. M., Mihov, Y., Elmenhorst, D., Becker, B., ... Bauer, A. (2014). Oxytocin enhances attractiveness of unfamiliar female faces independent of the dopamine reward system. Psychoneuroendocrinology, 39, 74–87.Google Scholar
Stuchlik, A. (2014). Dynamic learning and memory, synaptic plasticity and neurogenesis: An update. Frontiers in Behavioral Neuroscience, 8(Article 106), 1–6. doi: 10.3389/fnbeh.2014.00106.Google Scholar
Sweller, J., Ayres, P. L., & Kalyuga, S. (2011), Cognitive load theory. New York, NY: Springer. doi: 10.1007/978-1-4419-8126-4.Google Scholar
Tolman, E. C. (1932). Purposive behavior in animal and men. New York, NY: The Century Company.Google Scholar
Turk-Browne, N. B., Scholl, B. J., Chun, M. M., & Johnson, M. K. (2008). Neural evidence of statistical learning: Efficient detection of visual regularities without awareness. Journal of Cognitive Neuroscience, 21, 1934–45.Google Scholar
Vuilleumier, P. (2005). How brains beware: Neural mechanisms of emotional attention. Trends in Cognitive Science, 9, 585–94.Google Scholar
Vuilleumier, P. (2015). Affective and motivational control of vision. Current Opinion in Neurology, 28, 29–35.Google Scholar
Vygotsky, L. S. (1980). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.Google Scholar
Wang, Y., Fariello, G., Gavrilova, M. L., Kinsner, W., Mizoguchi, F., Patel, S., ... Tsumoto, S. (2013). Perspectives on cognitive computers and knowledge processors. International Journal of Cognitive Informatics and Natural Intelligence, 7(3), 1–24. doi: 10.4018/ijcini.2013070101.Google Scholar
Warner, L. S. (2006). Native ways of knowing: Let me count the ways. Canadian Journal of Native Education, 29(2), 149.Google Scholar
Weinstein, C. E., Husman, J., & Dierking, D. R. (2000). Interventions with a focus on learning strategies. In Boekaerts, M., Pintrich, P. R., & Zeidner, M. (Eds.), Handbook of self-regulation (pp. 727–47). San Diego, CA: Academic Press.Google Scholar
Weisz, J. R. & Stipek, D. J. (1982). Competence, contingency, and the development of perceived control. Human Development, 25, 250–81.Google Scholar
Yin, H. H., Ostlund, S. B., & Balleine, B. W. (2008). Reward-guided learning beyond dopamine in the nucleus accumbens: The integrative functions of cortico-basal ganglia networks. European Journal of Neuroscience, 28(8), 1437–48. doi: 10.1111/j.1460-9568.2008.06422.x.Google Scholar