Using learner modeling to determine effective conditions of learning for optimal transfer
Abstract
Semantic network theories of knowledge organization support the idea that recall of organized information depends on how well a learner encodes the connections between the items in the semantic network. However, there is need for more research into what this implies for configuring instruction so that strong semantic network learning is supported with the goal of creating an integrated mental model in the student's mind. We investigate this question in the context of map learning, where country names are encoded relative to geographic border, internal features, or external features. The main hypothesis was that external features as cues would encourage transfer, since students would practice a network of relationships. The results primarily supported a theory of cue reinstatement, where transfer occurred when cues present at learning were present at testing. These effects were analyzed with a mixed effects logistic regression learner model of trial-by-trial learning. © 2013 Springer-Verlag Berlin Heidelberg.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Maass, J., & Pavlik, P. (2013). Using learner modeling to determine effective conditions of learning for optimal transfer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7926 LNAI, 189-198. https://doi.org/10.1007/978-3-642-39112-5_20
