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  • Cited by 73
  • Print publication year: 2014
  • Online publication date: November 2014

13 - Educational Data Mining and Learning Analytics

from Part II - Methodologies

References

Aleven, V., McLaren, B., Roll, I., & Koedinger, K. (2006). Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence and Education, 16, 101–128.
Allen, I. E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Sloan Consortium. Available from: http://sloanconsortium.org/publications/survey/changing_course_2012.
Amershi, S., & Conati, C. (2009). Combining unsupervised and supervised machine learning to build user models for exploratory learning environments. Journal of Educational Data Mining, 1(1), 71–81.
Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired.
Anderson, J. R., & Lebiere, C. (1998). Atomic components of thought. Mahwah, NJ: Lawrence Erlbaum Associates.
Arnold, K. E. (2010). Signals: Applying academic analytics. Educause Quarterly, 33, 1–10.
Arroyo, I., Ferguson, K., Johns, J., Dragon, T., Meheranian, H., Fisher, D., Barto, A., Mahadevan, S., & Woolf. B. P. (2007). Repairing disengagement with non-invasive interventions. Proceedings of the 13th International Conference on Artificial Intelligence in Education, 195–202.
Baker, R. S., Corbett, A. T., & Koedinger, K. R. (2004). Detecting student misuse of intelligent tutoring systems. Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 531–540.
Baker, R. S. J. d. (2007). Modeling and understanding students’ off-task behavior in intelligent tutoring systems. Proceedings of ACM CHI 2007: Computer-Human Interaction, 1059–1068.
Baker, R. S. J. d., Corbett, A. T., Gowda, S. M., Wagner, A. Z., MacLaren, B. M., Kauffman, L. R., Mitchell, A. P., & Giguere, S. (2010). Contextual slip and prediction of student performance after use of an intelligent tutor. Proceedings of the 18th Annual Conference on User Modeling, Adaptation, and Personalization, 52–63.
Baker, R. S. J. d., Corbett, A. T., Koedinger, K. R., Evenson, S. E., Roll, I., Wagner, A. Z., Naim, M., Raspat, J., Baker, D. J., & Beck, J. (2006). Adapting to when students game an intelligent tutoring system. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 392–401.
Baker, R. S. J. d., de Carvalho, A. M. J. A., Raspat, J., Aleven, V., Corbett, A. T., & Koedinger, K. R. (2009). Educational software features that encourage and discourage “gaming the system.” Proceedings of the 14th International Conference on Artificial Intelligence in Education, 475–482.
Baker, R. S. J. d., Gowda, S. M., & Corbett, A. T. (2011). Automatically detecting a student’s preparation for future learning: Help use is key. Proceedings of the 4th International Conference on Educational Data Mining, 179–188.
Baker, R. S. J. d., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
Barnes, T., Bitzer, D., & Vouk, M. (2005). Experimental analysis of the q-matrix method in knowledge discovery. Proceedings of the 15th International Symposium on Methodologies for Intelligent Systems, May 25–28, 2005, Saratoga Springs, NY.
Beal, C. R., Qu, L., & Lee, H. (2006). Classifying learner engagement through integration of multiple data sources. Paper presented at the 21st National Conference on Artificial Intelligence (AAAI-2006), Boston, MA.
Beal, C. R., Qu, L., & Lee, H. (2008). Mathematics motivation and achievement as predictors of high school students’ guessing and help-seeking with instructional software. Journal of Computer Assisted Learning, 24, 507–514.
Beck, J. E., Chang, K. -M., Mostow, J., & Corbett, A. T. (2008). Does help help? Introducing the Bayesian evaluation and assessment methodology. Proceedings of Intelligent Tutoring Systems, ITS 2008, 383–394.
Ben-Naim, D., Bain, M., & Marcus, N. (2009). User-driven and data-driven approach for supporting teachers in reflection and adaptation of adaptive tutorials. Proceedings of the 2nd International Conference on Educational Data Mining, 21–30.
Bowers, A. J. (2010). Analyzing the longitudinal K-12 grading histories of entire cohorts of students: Grades, data driven decision making, dropping out and hierarchical cluster analysis. Practical Assessment, Research & Evaluation (PARE), 15(7), 1–18.
Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Educational Technology and Society, 15(3), 3–26.
Cen, H., Koedinger, K., & Junker, B. (2006). Learning factors analysis – A general method for cognitive model evaluation and improvement. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 164–175.
Cetintas, S., Si, L., Xin, Y., & Hord, C. (2010). Automatic detection of off-task behaviors in intelligent tutoring systems with machine learning techniques. IEEE Transactions on Learning Technologies, 3(3), 228–236.
Chang, K.-M., Beck, J., Mosto, J., & Corbett, A. (2006). A Bayes net toolkit for student modeling in intelligent tutoring systems. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 104–113, Jhongli, Taiwan.
Choudhury, T., & Pentland, A. (2003). Sensing and modeling human networks using sociometer. Proceedings of the Seventh IEEE International Symposium on Wearable Computers (ISWC’03).
Cocea, M., Hershkovitz, A., & Baker, R. S. J. d. (2009). The impact of off-task and gaming behaviors on learning: Immediate or aggregate? Proceedings of the 14th International Conference on Artificial Intelligence in Education, 507–514.
Collins, F. S., Morgan, M., & Patrinos, A. (2004). The Human Genome Project: Lessons from large-scale biology. Science, 300(5617), 286–290.
Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253–278.
Corno, L., & Mandinach, E. B. (1983). The role of cognitive engagement in classroom learning and motivation. Educational Psychologist, 18(2), 88–108.
D’Mello, S. K., Craig, S. D., Witherspoon, A., McDaniel, B., & Graesser, A. (2008). Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction, 18, 45–80.
Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), 224–238.
Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1).
Dekker, G., Pechenizkiy, M., & Vleeshouwers, J. (2009). Predicting students drop out: A case study. Proceedings of the 2nd International Conference on Educational Data Mining, EDM’09, 41–50.
Desmarais, M. C. (2011). Conditions for effectively deriving a q-matrix from data with non-negative matrix factorization. In C. Conati, S. Ventura, T. Calders, & M. Pechenizkiy (Eds.), 4th International Conference on Educational Data Mining, EDM 2011 (pp. 41–50). Eindhoven, Netherlands.
Dyke, G., Adamson, D., Howley, I., & Rosé, C. P. (2012). Towards academically productive talk supported by conversational agents. Intelligent Tutoring Systems, 531–540.
Fancsali, S. (2012). Variable construction and causal discovery for cognitive tutor log data: Initial results. Proceedings of the 5th International Conference on Educational Data Mining, 238–239.
Feigenbaum, E. A., & McCorduck, P. (1983). The fifth generation: Artificial intelligence and Japan’s computer challenge to the world. Reading, MA: Addison-Wesley.
Feng, M., Heffernan, N., & Koedinger, K. (2009). Addressing the assessment challenge in an intelligent tutoring system that tutors as it assesses. User Modeling and User-Adapted Interaction, 19, 243–266.
Ferguson, R. (2012). The state of learning analytics in 2012: A review and future challenges. Technical Report KMI-12-01, Knowledge Media Institute, The Open University, UK. http://kmi.open.ac.uk/publications/techreport/kmi-12-01.
Greeno, J. G. (1998). The situativity of knowing, learning, and research. American Psychologist, 53(1), 5–26.
Halevy, A. Y., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Intelligent Systems, 24(2), 8–12.
Haythornthwaite, C. (2001). Exploring multiplexity: Social network structures in a computer-supported distance learning class. The Information Society: An International Journal, 17(3), 211–226.
Hernández, Y., Sucar, E., & Conati, C. (2009). Incorporating an affective behaviour model into an educational game. Proceedings of FLAIR 2009, 22nd International Conference of the Florida Artificial Intelligence Society, ACM Press.
Hershkovitz, A., Baker, R. S. J. d., Gobert, J., Wixon, M., & Sao Pedro, M. (in press). Discovery with models: A case study on carelessness in computer-based science inquiry. To appear in American Behavioral Scientist.
Hershkovitz, A., & Nachmias, R. (2008). Developing a log-based motivation measuring tool. Proceedings of the 1st International Conference on Educational Data Mining, 226–233.
Kay, J., Maisonneuve, N., Yacef, K., & Reimann, P. (2006). The big five and visualisations of team work activity. Proceedings of the International Conference on Intelligent Tutoring Systems, 197–206.
Kerr, D., & Chung, G. K. W. K. (2012). Identifying key features of student performance in educational video games and simulations through cluster analysis. Journal of Educational Data Mining, 4(1), 144–182.
Koedinger, K. R., Baker, R. S. J. d., Cunningham, K., Skogsholm, A., Leber, B., & Stamper, J. (2010). A data repository for the EDM community: The PSLC DataShop. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. d. Baker (Eds.), Handbook of educational data mining (pp. 43–56). Boca Raton, FL: CRC Press.
Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 61–78). New York: Cambridge University Press.
Koedinger, K. R., Corbett, A. T., & Perfetti, C. The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757–798.
Koedinger, K. R., McLaughlin, E. A., & Stamper, J. C. (2012). Automated student model improvement. Proceedings of the 5th International Conference on Educational Data Mining, 17–24.
Lin, L. (2012). edX platform integrates into classes. http://tech.mit.edu/V132/N48/801edx.html.
Lynch, C., Ashley, K., Pinkwart, N., & Aleven, V. (2008). Argument graph classification with genetic programming and C4.5. In R. S. J. d. Baker, T. Barnes, & J. E. Beck (Eds.), Educational data mining 2008: Proceedings of the 1st International Conference on Educational Data Mining (pp. 137–146). Montréal, Québec, Canada, June 20–21.
Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 588–599.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
Martinez, R., Yacef, K., Kay, J., Kharrufa, A., & AlQaraghuli, A. (2011) Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop. Proceedings of the 4th International Conference on Educational Data Mining, 111–120.
Martinez, R., Yacef, K., Kay, J., & Schwendimann, B. (2012). An interactive teacher’s dashboard for monitoring multiple groups in a multi-tabletop learning environment. Proceedings of Intelligent Tutoring Systems, 482–492. Springer.
Mayer, M. (2009). The physics of big data. http://www.parc.com/event/936/innovation-at-google.html.
McLaren, B. M., Scheuer, O., DeLaat, M., Hever, R., DeGroot, R., & Rosé, C. P. (2007). Using machine learning techniques to analyze and support mediation of student e-discussions. Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED 2007).
McLaren, B. M., Scheuer, O., & Mikšátko, J. (2010). Supporting collaborative learning and e-discussions using artificial intelligence techniques. International Journal of Artificial Intelligence in Education (IJAIED), 20(1), 1–46.
Ming, N. C., & Ming, V. L. (2012). Predicting student outcomes from unstructured data. Proceedings of the 2nd International Workshop on Personalization Approaches in Learning Environments, 11–16.
Muenchen, R. A. (2012). The popularity of data analysis software. http://r4stats.com/articles/popularity/.
Muldner, K., Burleson, W., Van de Sande, B., & VanLehn, K. (2011). An analysis of students’ gaming behaviors in an intelligent tutoring system: Predictors and impacts. User Modeling and User-Adapted Interaction, 21(1–2), 99–135.
Pardos, Z. A., Baker, R. S. J. d., Gowda, S. M., & Heffernan, N. T. (2011). The sum is greater than the parts: Ensembling models of student knowledge in educational software. SIGKDD Explorations, 13(2), 37–44.
Pavlik, P. I., Cen, H., & Koedinger, K. R. (2009). Performance factors analysis – A new alternative to knowledge tracing. Proceedings of AIED2009.
Perera, D., Kay, J., Koprinska, I., Yacef, K., & Zaiane, O. R. (2009). Clustering and sequential pattern mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering, 21(6), 759–772.
Prata, D., Letouze, P., Costa, E., Prata, M., & Brito, G. (2012). Dialogue analysis in collaborative learning. International Journal of e-Education, e-Business, e-Management, and e-Learning, 2(5), 365–372.
Reimann, P., Yacef, K., & Kay, J. (2011). Analyzing collaborative interactions with data mining methods for the benefit of learning. Computer-Supported Collaborative Learning Series, 12, 161–185.
Rodrigo, M. M. T., Baker, R. S. J. d., McLaren, B., Jayme, A., & Dy, T. (2012). Development of a workbench to address the educational data mining bottleneck. Proceedings of the 5th International Conference on Educational Data Mining, 152155.
Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state-of-the-art. IEEE Transaction on Systems, Man and Cybernetics, part C: Applications and Reviews, 40(6), 610–618.
Sabourin, J., Rowe, J., Mott, B., & Lester, J. (2011). When off-task is on-task: The affective role of off-task behavior in narrative-centered learning environments. Proceedings of the 15th International Conference on Artificial Intelligence in Education, 534–536.
San Pedro, M. O. C., Baker, R., & Rodrigo, M. M. (2011). Detecting carelessness through contextual estimation of slip probabilities among students using an intelligent tutor for mathematics. Proceedings of 15th International Conference on Artificial Intelligence in Education, 304–311.
San Pedro, M. O. C., Rodrigo, M. M., and Baker, R. S. J. D. (2011). The relationship between carelessness and affect in a cognitive tutor. Proceedings of the 4th bi-annual International Conference on Affective Computing and Intelligent Interaction.
Sao Pedro, M. A., Baker, R. S. J. d., Gobert, J., Montalvo, O., & Nakama, A. (2013). Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill. User Modeling and User-Adapted Interaction, 23(1), 1–39.
Shih, B., Koedinger, K., & Scheines, R. (2008). A response time model for bottom-out hints as worked examples. Proceedings of the 1st International Conference on Educational Data Mining, 117–126.
Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge.
Sil, A., Shelton, A., Ketelhut, D. J., & Yates, A. (2012). Automatic grading of scientific inquiry. The 7th Workshop on the Innovative Use of NLP for Building Educational Applications, 22–32.
Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data and Knowledge Engineering (DKE), 25 (1–2), 161–197.
Summers, D. J., et al. (1992). Charm physics at Fermilab E791. Proceedings of the XXVIIth Recontre de Moriond, Electroweak Interactions and Unified Theories, Les Arcs, France, 417–422.
Suthers, D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning. Proceedings of the 1st International Conference on Learning Analytics and Knowledge, 64–74.
Tatsuoka, K. K. (1995). Architecture of knowledge structures and cognitive diagnosis: A statistical pattern recognition and classification approach. In P. D. Nichols, S. F. Chipman, & R. L. Brennan (Eds.), Cognitively diagnostic assessment (pp. 327–359). Hillsdale, NJ: Lawrence Erlbaum Associates.
Vuong, A., Nixon, T., & Towle, B. (2011). A method for finding prerequisites within a curriculum. Proceedings of the 4th International Conference on Educational Data Mining, 211–216.
Walonoski, J. A., & Heffernan, N. T. (2006). Detection and analysis of off-task gaming behavior in intelligent tutoring systems. In M. Ikeda, K. Ashlay, & T.-W. Chan (Eds.), Proceedings of the 8th International Conference on Intelligent Tutoring Systems (pp. 382–391). Jhongli, Taiwan; Berlin: Springer-Verlag.
Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. San Francisco, CA: Morgan Kaufmann.
Wixon, M., Baker, R. S. J. d., Gobert, J., Ocumpaugh, J., & Bachmann, M. (2012). WTF? Detecting students who are conducting inquiry without thinking fastidiously. Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization (UMAP 2012), 286–298.
Xu, B., & Recker, M. (2011). Understanding teacher users of a digital library service: A clustering approach. Journal of Educational Data Mining, 3(1), 1–28.