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. Although much progress has been made in modeling students' learning from data and predicting performance, these models have not been applied in real classrooms. 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 schools where it was compared to a baseline sequencing algorithm that was designed by pedagogical experts. In both schools, students using the BKT sequencing approach solved more difficult questions, and with better performance than did students who used the expert-based approach. Students were also more engaged using the BKT approach, as determined by their log-ins in the system and a questionnaire. 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.
: Yossi Ben-David, Avi Segal and Kobi Gal (2016). "Sequencing Educational Content in Classrooms using Bayesian Knowledge Tracing". In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York.