ASSISTments Longitudinal Data Mining Competition Special Issue: A Preface

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Published Aug 23, 2020
DOI https://doi.org/10.5281/zenodo.4008048
Thanaporn Patikorn
Worcester Polytechnic Institute
Ryan S. Baker
University of Pennsylvania
Neil T. Heffernan
Worcester Polytechnic Institute

Abstract

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. https://doi.org/10.5281/zenodo.4008048
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Keywords

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

References
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Issue
Vol. 12 No. 2 (2020): Scientific Findings from the ASSISTments Longitudinal Data Mining Competition
Section
Editorial Comment
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