Sequencing educational content in classrooms using Bayesian knowledge tracing

Yossi Ben David, Avi Segal, Yakov Gal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Despite the prevalence of e-learning systems in schools, most of today's systems do not personalize educational data to the individual needs of each student. This paper proposes a new algorithm for sequencing questions to students that is empirically shown to lead to better performance and engagement in real schools when compared to a baseline approach. It is based on using knowledge tracing to model students' skill acquisition over time, and to select questions that advance the student's learning within the range of the student's capabilities, as determined by the model. The algorithm is based on a Bayesian Knowledge Tracing (BKT) model that incorporates partial credit scores, reasoning about multiple attempts to solve problems, and integrating item difficulty. This model is shown to outperform other BKT models that do not reason about (or reason about some but not all) of these features. The model was incorporated into a sequencing algorithm and deployed in two classes in different schools where it was compared to a baseline sequencing algorithm that was designed by pedagogical experts. In both classes, students using the BKT sequencing approach solved more difficult questions and attributed higher performance than did students who used the expert-based approach. Students were also more engaged using the BKT approach, as determined by their interaction time and number of log-ins to the system, as well as their reported opinion. We expect our approach to inform the design of better methods for sequencing and personalizing educational content to students that will meet their individual learning needs.
Original languageEnglish
Title of host publicationProceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK'16)
Place of PublicationEdinburgh, United Kingdom
PublisherACM
Pages354-363
Number of pages10
ISBN (Electronic)978-1-4503-4190-5
DOIs
Publication statusPublished - 25 Apr 2016
EventSixth International Conference on Learning Analytics & Knowledge - University of Edinburgh, Edinburgh, United Kingdom
Duration: 25 Apr 201629 Apr 2016
http://lak16.solaresearch.org/

Conference

ConferenceSixth International Conference on Learning Analytics & Knowledge
Abbreviated titleLAK'16
CountryUnited Kingdom
CityEdinburgh
Period25/04/1629/04/16
Internet address

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