Towards Automatically Detecting Whether Student Learning Is Shallow

Ryan S. J. D. Baker, Sujith M. Gowda, Albert T. Corbett, Jaclyn Ocumpaugh

Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning – learning that leads the student to be able to transfer their knowledge and prepares them for future learning (PFL). However, a student may fail to have robust learning in two fashions: they may have no learning, or they may have shallow learning (learning that applies only to the current skill, and does not support transfer or PFL). Within this paper, we present an automated detector which is able to identify shallow learners, who are likely to need different intervention than students who have not yet learned at all. This detector is developed using a step regression approach, with data from college students learning introductory genetics from an intelligent tutoring system.

The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_57.