COMPUTATIONAL APPROACHES TO CONSTRUCT AND ASSESS KNOWLEDGE MAPS FOR STUDENT LEARNING

 

ABStract

Knowledge maps have been widely used in knowledge elicitation and representation to eval- uate and guide students’ learning. To improve upon current computational approaches to construct and assess knowledge maps, this thesis adopts a hybrid methodology that combines machine learning techniques and network science. By providing methods to extract features to evaluate knowledge maps and expand the assessment scope by accounting for group inter- action and multiple expert maps, this thesis addresses the overall gap of current approaches for map construction and assessment. Specifically, this thesis offers three major contribu- tions: 1) identifying necessary and sufficient graph features for knowledge maps evaluation,

2) assessing the role of group interaction during knowledge map construction and how group size affects the quality of map construction, and 3) providing an algorithmic framework to capture differences between student maps and multiple expert maps. Finally, this thesis examines the implications for the fields of network science and educational technology of applying knowledge maps in student learning.