Towards Automatically Detecting Whether Student Learning is Shallow

In IJAIED 23: "Best of ITS 2012"

Publication information

50-70

Abstract

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 enables the student 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 automated detectors which identify shallow learners, who are likely to need different intervention than students who have not yet learned at all. These detectors are developed using K* machine learned models, with data from college students learning introductory genetics from an intelligent tutoring system.