ASSISTments Longitudinal Data Mining Competition Special Issue: A Preface

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

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

PDF
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
Abstract 1204 | PDF Downloads 644

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

Keywords

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

References
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.

BECK, JE, AND GONG, Y. 2013. Wheel-spinning: Students who fail to master a skill. Proceedings of the International Conference on Artificial Intelligence in Education,431-440.

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.

DUCKWORTH, A. 2016. Grit: The Power of Passion and Perseverance. New York, NY: Scribner.

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

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
  2. Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
  3. The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons 4.0 License (Attribution-Noncommercial-No Derivatives 4.0 International), or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
    1. Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
    2. Noncommercial—other users (including Publisher) may not use this Work for commercial purposes;
    3. No Derivative Works—other users (including Publisher) may not alter, transform, or build upon this Work,with the understanding that any of the above conditions can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
  4. The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
  5. Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
  6. Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
  7. The Author represents and warrants that:
    1. the Work is the Author’s original work;
    2. the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
    3. the Work is not pending review or under consideration by another publisher;
    4. the Work has not previously been published;
    5. the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
    6. the Work contains no libel, invasion of privacy, or other unlawful matter.
  8. The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.

Most read articles by the same author(s)

1 2 > >>