Student Modeling with Automatic Knowledge Component Extraction for Adaptive Textbooks

Abstract

Online textbooks have become a significant component of online and blended learning environments. Taking this medium one step further, Adaptive online Textbooks (AoT) recommend the most relevant pages and practice activities based on students current knowledge state. AoT use student interaction data to infer the current state of student knowledge through student modeling (SM). The knowledge is inferred on knowledge components (KCs) associated with textbook material (sections/pages, practice activities, and quizzes). However, most of these techniques rely on expert annotated knowledge components. A challenge of student modeling in the context of adaptive textbooks is that traditional student models are constructed based on performance data (question answers or problem solving) Student interaction with online textbooks, however, produces large volume of student reading data, but a very limited amount of question-answering data. This leads to the requirement of annotating reading materials (textbook sections and paragraphs) with related Kcs. However, given large number of textbook sections it becomes impractical and time consuming to annotate these large components with Kcs in practice. To bridge this gap between practical and theoretical SM models in AoTs, we have proposed the use of automatic KC extraction to annotate textbook sections with KCs. This can help us to utilize current student models for AoT.