Incorporating scaffolding and tutor context into Bayesian knowledge tracing to predict inquiry skill acquisition
Michael São Pedro, Ryan S. Baker, Janice D. Gobert · 2013
In this paper, we incorporate scaffolding and change of tutor context within the Bayesian Knowledge Tracing (BKT) framework to track students ’ developing inquiry skills. These skills are demonstrated as students experiment within interactive simulations for two science topics. Our aim is twofold. First, we desire to improve the models ’ predictive performance by adding these factors. Second, we aim to interpret these extended models to reveal if our scaffolding approach is effective, and if inquiry skills transfer across the topics. We found that incorporating scaffolding yielded better predictions of individual students’ performance over the classic BKT model. By interpreting our models, we found that scaffolding appears to be effective at helping students acquire these skills, and that the skills transfer across topics.