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. JEDM | Journal of Educational Data Mining, 12(2), i-xi.
Abstract 52 | PDF Downloads 60



data challenge, data competition, ASSISTments, longitudinal outcomes, career prediction

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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.

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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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