Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes
Ryan S. Baker
- 16 Jun 2019
Vol. 11, Iss: 1, pp 1-17
TL;DR: A vision for some directions the field should go is presented: towards greater interpretability, generalizability, transferability, applicability, and with clearer evidence for effectiveness: the Baker Learning Analytics Prizes (BLAP).
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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.
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References
Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge
TL;DR: An effort to model students' changing knowledge state during skill acquisition and a series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process.
2.1K
Course signals at Purdue: using learning analytics to increase student success
Kimberly E. Arnold,Matthew D. Pistilli +1 more
- 29 Apr 2012
TL;DR: An early intervention solution for collegiate faculty called Course Signals, developed to allow instructors the opportunity to employ the power of learner analytics to provide real-time feedback to a student, is discussed.
1K
Visual interpretability for deep learning: a survey
Quanshi Zhang,Song-Chun Zhu +1 more
TL;DR: In this paper, the authors review recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations, and discuss prospective trends in explainable artificial intelligence.
865
•Proceedings Article
Performance Factors Analysis --A New Alternative to Knowledge Tracing
Philip I. Pavlik,Hao Cen,Kenneth R. Koedinger +2 more
- 20 Jul 2009
TL;DR: This paper describes the work to modify an existing data mining model so that it can also be used to select practice adaptively, and compares this new adaptive datamining model (PFA, Performance Factors Analysis) with two versions of LFA and then compares PFA with standard KT.
Dynamic Key-Value Memory Networks for Knowledge Tracing
Jiani Zhang,Xingjian Shi,Irwin King,Dit-Yan Yeung +3 more
- 03 Apr 2017
TL;DR: Li et al. as discussed by the authors introduced a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept.
589