The paper, “CMKT: Concept Map Driven Knowledge Tracing”, from the AI Lab of Advanced Innovation Center for Future Education (AICFE) of Beijing Normal University has been accepted by IEEE Transactions on Learning Technologies (TLT). This program proposes a novel knowledge tracing model, called CMLT, that utilizes an educational concept map for learner modeling. The pairwise educational relations in the concept map are formulated as the ordering pairs and are used as mathematical constraints for model construction. The topology information in the concept map is extracted and used as the model input by employing the network embedding techniques. CMKT adopts the recurrent neural network to perform knowledge tracing task. Comprehensive evaluations conducted on five public educational datasets of four different subjects (more than 8,000 learners and their 300,000 records) demonstrate the promise and effectiveness of CMKT: the average area under ROC curve (AUC) and overall prediction accuracy (ACC) achieve 0.82 and 0.75 respectively, and CMKT outperforms all the baselines by at least 12.2% and 9.2% in terms of AUC and ACC.
Figure1: The Screenshot of the Abstract of the Paper Accepted by IEEE TLT
