ASSISTments Longitudinal Data Mining Competition Special Issue: A Preface



Published Aug 23, 2020
Thanaporn Patikorn Ryan S. Baker Neil T. Heffernan


This special issue includes papers from some of the leading competitors in the ASSISTments Longitudinal Data Mining Competition 2017, as well as some research from non-competitors, using the same data set. In this competition, participants attempted to predict whether students would choose a career in a STEM field or not, making this prediction using a click-stream dataset from middle school students working on math assignments inside ASSISTments, an online tutoring platform. At the conclusion of the competition on December 3rd, 2017, there were 202 participants, 74 of whom submitted predictions at least once. In this special issue, some of the leading competitors present their results and what they have learned about the link between behavior in online learning and future STEM career development.

How to Cite

Patikorn, T., Baker, R. S., & Heffernan, N. T. (2020). ASSISTments Longitudinal Data Mining Competition Special Issue: A Preface. Journal of Educational Data Mining, 12(2), i-xi.
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data challenge, data competition, ASSISTments, longitudinal outcomes, career prediction

ALMEDA, M.V.Q., AND BAKER, RS 2020. Predicting student participation in STEM careers: The role of affect and engagement during middle school. Journal of Educational Data Mining, 33-47.

ARROYO, I., BURLESON, W., TAI, M., MULDNER, K, AND WOOLF, B.P. 2013. Gender differences in the use and benefit of advanced learning technologies for mathematics. Journal of Educational Psychology, 105 (4), 957-969.

BAKER, R.S., D'MELLO, S.K., RODRIGO, M.M.T., AND GRAESSER, A.C. 2010. Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68 (4), 223-241.

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BOWERS, A. J. 2010. Grades and graduation: A longitudinal risk perspective to identify student dropouts. The Journal of Educational Research, 103(3), 191-207.

CHIU, M-S. 2020. Predicting STEM choice by emotional traits and states of online mathematical problem-solving in middle school. Journal of Educational Data Mining, 48-77.

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DWECK, C.S. 2013. Self-theories: Their Role in Motivation, Personality, and Development. Hove, UK: Psychology Press.

HEFFERNAN, N.T., AND HEFFERNAN, C.L. 2014 The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence and Education, 24 (4), 470-497.

LIU, R., AND TAN, A. 2020. Towards interpretable automated machine learning for STEM career prediction. Journal of Educational Data Mining, 19-32.

KNOWLES, J. E. 2015. Of needles and haystacks: Building an accurate statewide dropout early warning system in Wisconsin. Journal of Educational Data Mining, 7(3), 18-67.

JIHED, M., AND MINE, T. 2020 Analysis of click-stream data to predict stem careers from student usage of an intelligent tutoring system. Journal of Educational Data Mining, 1-18.

OCUMPAUGH, J., BAKER, R., GOWDA, S., HEFFERNAN, N., AND HEFFERNAN, C. 2014 Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45 (3), 487-501.

OCUMPAUGH, J., SAN PEDRO, M.O., LAI, H-Y., BAKER, RS, AND BORGEN, F. 2016 Middle school engagement with mathematics software and later interest and self-efficacy for STEM careers. Journal of Science Education and Technology, 25 (6), 877-887.

PARDOS, Z.A., BAKER, R.S.J.D., SAN PEDRO, MOCZ, GOWDA, SM, AND GOWDA, SM 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.

REIDER, D., KNESTIS, K., AND MALYN-SMITH, J. 2016. Workforce education models for K-12 STEM education programs: Reflections on, and implications for, the NSF ITEST program. Journal of Science Education and Technology, 25(6), 847-858.

SAN PEDRO, M., BAKER, R., BOWERS, A. AND HEFFERNAN, N. 2013. Predicting college enrollment from student interaction with an intelligent tutoring system in middle school. In S. D'Mello, R. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining, 177–184.

SAN PEDRO, M.O., BAKER, R., HEFFERNAN, N., AND OCUMPAUGH, J. 2015. Exploring College major choice and middle school student behavior, affect and learning: What happens to students who game the system? Proceedings of the 5th International Learning Analytics and Knowledge Conference, 36-40.

SASS, T. R. 2015. Understanding the STEM pipeline. Working Paper 125. National Center for Analysis of Longitudinal Data in Education Research (CALDER).

YEUNG, C.K., AND YEUNG, D.Y. 2018 Incorporating features learned by an enhanced deep knowledge tracing model for STEM/non-STEM job prediction. International Journal of Artificial Intelligence and Education, 29 (3), 317-341.
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