Abstract:
In this article, we advocate for and propose a novel concept map driven knowledge tracing (CMKT) model, which utilizes educational concept map for learner modeling. This ...Show MoreMetadata
Abstract:
In this article, we advocate for and propose a novel concept map driven knowledge tracing (CMKT) model, which utilizes educational concept map for learner modeling. This article particularly addresses the issue of learner data sparseness caused by the unwillingness to practice and irregular learning behaviors on the learner side. CMKT considers the concept map as a new information source and explicitly exploits its inherent information to help the estimation of the learner's knowledge state. Specifically, 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. Integrating both educational relation information and topology information in the concept map, CMKT adopts the recurrent neural network to perform knowledge tracing tasks. Comprehensive evaluations conducted on five public educational datasets of four different subjects (more than 8000 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.
Published in: IEEE Transactions on Learning Technologies ( Volume: 15, Issue: 4, 01 August 2022)
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- IEEE Keywords
- Index Terms
- Concept Mapping ,
- Knowledge Tracing ,
- Neural Network ,
- Receiver Operating Characteristic Curve ,
- Model Input ,
- Public Datasets ,
- State Of Knowledge ,
- Recurrent Neural Network ,
- Sparse Data ,
- Cognitive Learning ,
- Educational Information ,
- Pairwise Relationships ,
- Topological Information ,
- Network Embedding ,
- Inherent Information ,
- Cognitive Processes ,
- Latent Variables ,
- Types Of Information ,
- Deep Learning Models ,
- Long Short-term Memory ,
- Gated Recurrent Unit ,
- Massive Open Online Courses ,
- Hard Constraints ,
- Learning Performance ,
- Directed Acyclic Graph ,
- Graph Neural Networks ,
- Correctly Answered ,
- Form Of Constraints ,
- Intelligent Tutoring Systems ,
- Boolean Function
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Concept Mapping ,
- Knowledge Tracing ,
- Neural Network ,
- Receiver Operating Characteristic Curve ,
- Model Input ,
- Public Datasets ,
- State Of Knowledge ,
- Recurrent Neural Network ,
- Sparse Data ,
- Cognitive Learning ,
- Educational Information ,
- Pairwise Relationships ,
- Topological Information ,
- Network Embedding ,
- Inherent Information ,
- Cognitive Processes ,
- Latent Variables ,
- Types Of Information ,
- Deep Learning Models ,
- Long Short-term Memory ,
- Gated Recurrent Unit ,
- Massive Open Online Courses ,
- Hard Constraints ,
- Learning Performance ,
- Directed Acyclic Graph ,
- Graph Neural Networks ,
- Correctly Answered ,
- Form Of Constraints ,
- Intelligent Tutoring Systems ,
- Boolean Function
- Author Keywords