Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX

Open Online Courses (MOOCs) are an increasingly pervasive newcomer to the virtual landscape of higher-education, delivering a wide variety of topics in science, engineering, and the humanities. However, while technological innovation is enabling unprecedented open access to high quality educational material, these systems generally inherit similar homework, exams, and instructional resources to that of their classroom counterparts and currently lack an underlying model with which to talk about learning. In this paper we will show how existing learner modeling techniques based on Bayesian Knowledge Tracing can be adapted to the inaugural course, 6.002x: circuit design, on the edX MOOC platform. We identify three distinct challenges to modeling MOOC data and provide predictive evaluations of the respective modeling approach to each challenge. The challenges identified are; lack of an explicit knowledge component model, allowance for unpenalized multiple problem attempts, and multiple pathways through the system that allow for learning influences outside of the current assessment.

[1]  Antonija Mitrovic,et al.  Evaluating and improving adaptive educational systems with learning curves , 2011, User Modeling and User-Adapted Interaction.

[2]  Zachary A. Pardos,et al.  KT-IDEM: introducing item difficulty to the knowledge tracing model , 2011, UMAP'11.

[3]  Jim Reye,et al.  Student Modelling Based on Belief Networks , 2004, Int. J. Artif. Intell. Educ..

[4]  K. Tatsuoka RULE SPACE: AN APPROACH FOR DEALING WITH MISCONCEPTIONS BASED ON ITEM RESPONSE THEORY , 1983 .

[5]  Gerd Kortemeyer,et al.  Gender differences in the use of an online homework system in an introductory physics course , 2009 .

[6]  Marsha C. Lovett,et al.  Cognotive Task Analysis in Service of Intelligent Tutoring System Design: A Case Study in Statistics , 1998, Intelligent Tutoring Systems.

[7]  John R. Anderson,et al.  Skill Acquisition and the LISP Tutor , 1989, Cogn. Sci..

[8]  Kenneth R. Koedinger,et al.  Automated Student Model Improvement , 2012, EDM.

[9]  John R. Anderson,et al.  Rules of the Mind , 1993 .

[10]  Hans Spada,et al.  6 – The Assessment of Learning Effects with Linear Logistic Test Models , 1985 .

[11]  John R. Anderson,et al.  Knowledge tracing: Modeling the acquisition of procedural knowledge , 2005, User Modeling and User-Adapted Interaction.

[12]  Zachary A. Pardos,et al.  The sum is greater than the parts: ensembling models of student knowledge in educational software , 2012, SKDD.

[13]  Vincent Aleven,et al.  Improving Contextual Models of Guessing and Slipping with a Trucated Training Set , 2008, EDM.

[14]  Füsun F. Gönül,et al.  Innovative Teaching: An Empirical Study of Computer-Aided Instruction in Quantitative Business Courses , 2013 .

[15]  Neil T. Heffernan,et al.  Comparing Knowledge Tracing and Performance Factor Analysis by Using Multiple Model Fitting Procedures , 2010, Intelligent Tutoring Systems.

[16]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[17]  Zachary A. Pardos,et al.  Learning What Works in its from Non-Traditional Randomized Controlled Trial Data , 2010, Int. J. Artif. Intell. Educ..

[18]  Albert T. Corbett,et al.  Does Help Help? Introducing the Bayesian Evaluation and Assessment Methodology , 2008, Intelligent Tutoring Systems.

[19]  Zachary A. Pardos,et al.  Interleaved Practice with Multiple Representations: Analyses with Knowledge Tracing Based Techniques. , 2012, EDM 2012.

[20]  Yigal Attali,et al.  Immediate Feedback and Opportunity to Revise Answers , 2011 .