Assessing Learning Behavior and Cognitive Bias from Web Logs

This research to practice, work in progress paper presents the analysis strategy used to assess the learning behavior using logs on an e-learning platform. Students who can link algebraic functions to their corresponding graphs perform well in STEM courses. Early algebra curricula teaches these concepts in tandem. However, it is challenging to assess whether students are linking the concepts. Video analyses, interviews and other traditional methods that aim to quantify how students link the concepts taught in school require precious classroom and teacher time. We use web logs to infer learning. Web logs are widely available and amenable to data science. Our approach partitions the web interface into components related to data and graph concepts. We collect click and mouse movement data as users interact with these components. We used statistical and data mining techniques to model their learning behavior. We built our models to assess learning behavior for a workshop presented in Summer 2016. Students in the workshop were middle-school math teachers planning to use this curriculum in their own classrooms. We used our models to assess participation levels, a prerequisite indicator for learning. Our models aligned with ground-truth traditional methods for 17 of 18 students. The results of the models with respect to the two types of components of the web portal have been used to infer possible data or graph oriented cognitive bias.