Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jun 16, 2019
Ryan S. Baker

Abstract

Learning analytics and educational data mining have come a long way in a short time. In this article, a lightly-edited transcript of a keynote talk at the Learning Analytics and Knowledge Conference in 2019, I present a vision for some directions I believe the field should go: towards greater interpretability, generalizability, transferability, applicability, and with clearer evidence for effectiveness. I pose these potential directions as a set of six contests, with concrete criteria for what would represent successful progress in each of these areas: the Baker Learning Analytics Prizes (BLAP). Solving these challenges will bring the field closer to achieving its full potential of using data to benefit learners and transform education for the better.

How to Cite

Baker, R. S. (2019). Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes. JEDM | Journal of Educational Data Mining, 11(1), 1-17. Retrieved from https://jedm.educationaldatamining.org/index.php/JEDM/article/view/432
Abstract 505 | PDF Downloads 499

##plugins.themes.bootstrap3.article.details##

Keywords

Baker Learning Analytics Prizes, BLAP, learning analytics, educational data mining, model generalizability

References
AlZoubi, O., Calvo, R. A., & Stevens, R. H. (2009, December). Classification of EEG for affect recognition: an adaptive approach. In Australasian Joint Conference on Artificial Intelligence, Springer, Berlin, Heidelberg, 52-61.

Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, ACM, 267-270

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. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 392-401.

Bergner, Y., Walker, E., & Ogan, A. (2017). Dynamic Bayesian network models for peer tutoring interactions. In Innovative Assessment of Collaboration, Springer, Cham, 249-268.

Bosch, N., Chen, H., Baker, R., Shute, V., D'Mello, S. (2015) Accuracy vs. availability heuristic in multimodal affect detection in the wild. In Proceedings of the 17th International Conference on Multimodal Interaction, 267-274.

Bull, S., & Kay, J. (2007). Student models that invite the learner in: The SMILI:() open learner modeling framework. International Journal of Artificial Intelligence in Education, 17, 2, 89-120.

Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 4, 253-278.

DeFalco, J.A., Rowe, J.P., Paquette, L., Georgoulas-Sherry, V., Brawner, K., Mott, B.W., Baker, R.S., Lester, J.C. (2018) Detecting and addressing frustration in a serious game for military training. International Journal of Artificial Intelligence and Education, 28, 2, 152-193.

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, 1-2, 45-80.

Eagle, M., Corbett, A., Stamper, J., McLaren, B., Baker, R.S. (2016) predicting individual differences for learner modeling in intelligent tutors from previous learner activities. In Proceedings of the 24th Conference on User Modeling, Adaptation, and Personalization, 55-63.

Floridi, L., Taddeo, M., & Turilli, M. (2009). Turing’s imitation game: still an impossible challenge for all machines and some judges––an evaluation of the 2008 Loebner contest. Minds and Machines, 19, 1, 145-150.

Hilbert, D. (1900). Mathematical problems. Presentation to the International Conference of Mathematicians. Paris, France. Text retrieved 3/12/2019 from https://mathcs.clarku.edu/~djoyce/hilbert/problems.html

Hollands, F., & Bakir, I. (2015). Efficiency of automated detectors of learner engagement and affect compared with traditional observation methods. New York, NY: Center for Benefit-Cost Studies of Education, Teachers College, Columbia University.

Kay, J. (2012). AI and education: grand challenges. IEEE Intelligent Systems, 27, 5, 66-69.
Khajah, M., Lindsey, R. V., & Mozer, M. C. (2016). How deep is knowledge tracing? In Proceedings of the 9th International Conference on Educational Data Mining, 94-101.

Liu, R., & Koedinger, K. R. (2015). Variations in learning rate: Student classification based on systematic residual error patterns across practice opportunities. In Proceedings of the 8th International Conference on Education Data Mining, 420–423.

Martinez-Maldonado, R., Kay, J., Yacef, K., & Schwendimann, B. (2012). An interactive teacher’s dashboard for monitoring groups in a multi-tabletop learning environment. In International Conference on Intelligent Tutoring Systems, Springer, Berlin, 482-492.

Milliron, M. D., Malcolm, L., & Kil, D. (2014). Insight and action analytics: Three case studies to consider. Research & Practice in Assessment, 9, 70-89.

Ocumpaugh, J., Baker, R.S., Rodrigo, M.M.T., Salvi, A. van Velsen, M., Aghababyan, A., Martin, T. (2015). HART: The human affect recording tool. In Proceedings of the ACM Special Interest Group on the Design of Communication (SIGDOC), 24:1-24:6.

Paquette, L., Baker, R.S., de Carvalho, A., Ocumpaugh, J. (2015) Cross-system transfer of machine learned and knowledge engineered models of gaming the system. In Proceedings of the 22nd International Conference on User Modeling, Adaptation, and Personalization, 183-194.

Pardos, Z.A., Baker, R.S., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M. (2014) Affective states and state tests: Investigating how affect and engagement during the school year predict end of year learning outcomes. Journal of Learning Analytics, 1, 1, 107-128.

Pavlik, P. I., Cen, H., & Koedinger, K. R. (2009). Performance factors analysis: A new alternative to knowledge tracing. In Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, 531-538.

Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2019). Sympathetic arousal commonalities and arousal contagion during collaborative learning: How attuned are triad members? Computers in Human Behavior, 92, 188-197.

Sao Pedro, M. A., Baker, R. S., Gobert, J. D., 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.

Sosnovsky, S., Dolog, P., Henze, N., Brusilovsky, P., & Nejdl, W. (2007) Translation of overlay models of student knowledge for relative domains based on domain ontology mapping. In R. Luckin, K. R. Koedinger and J. Greer (eds.) Proceedings of 13th International Conference on Artificial Intelligent in Education, IOS, 289-296.

Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009). Affect-aware tutors: recognizing and responding to student affect. International Journal of Learning Technology, 4. 3-4, 129-164.

Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI Magazine, 34, 4, 66-85.

Zhang, J., Shi, X., King, I., & Yeung, D. Y. (2017). Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th International Conference on the World Wide Web, 765-774.

Zhang, Q. S., & Zhu, S. C. (2018). Visual interpretability for deep learning: a survey. Frontiers of Information Technology & Electronic Engineering, 19, 1, 27-39.
Section
Editorial Comment

Most read articles by the same author(s)