Content Learning Analysis Using the Moment-by-Moment Learning Detector
Sujith M. Gowda, Zachary A. Pardos, Ryan S. J. D. Baker
In recent years, it has become clear that educational data mining methods can play a positive role in refining the content of intelligent tutoring systems. In particular, efforts to determine which content is more and less effective at promoting learning can help improve tutoring systems by identifying ineffective content and cycling it out of the system. Analysis of the learning value of content can also help teachers and system designers create better content by taking notice of what has and has not worked in the past. Past work has looked solely at student response data in doing this type of analysis; we extend this work by instead utilizing the moment-by-moment learning model, P(J). This model uses parameters learned from Bayesian Knowledge Tracing as well as other features extracted from log data to compute the probability that a student learned a skill at a specific problem step. By averaging P(J) values for a particular item across students, and comparing items using statistical testing with post-hoc controls, we can investigate which items typically produce more and less learning. We use this analysis to evaluate items within twenty problem sets completed by students using the ASSISTments Platform, and show how item learning results can be obtained and interpreted from this analysis.
The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_56.