Summary of Disentangling Heterogeneous Knowledge Concept Embedding For Cognitive Diagnosis on Untested Knowledge, by Miao Zhang et al.
Disentangling Heterogeneous Knowledge Concept Embedding for Cognitive Diagnosis on Untested Knowledge
by Miao Zhang, Ziming Wang, Runtian Xing, Kui Xiao, Zhifei Li, Yan Zhang, Chang Tang
First submitted to arxiv on: 25 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for Cognitive Diagnosis called Disentangling Heterogeneous Knowledge Cognitive Diagnosis (DisKCD), which focuses on untested knowledge concepts (UKCs). The authors leverage course grades, exercise questions, and learning resources to learn the potential representations of students, exercises, and knowledge concepts. They then construct a heterogeneous relation graph network via students, exercises, tested knowledge concepts (TKCs), and UKCs. A hierarchical heterogeneous message-passing mechanism is used to incorporate fine-grained relations into entity embeddings. The proposed model can be applied to multiple existing cognitive diagnosis models to infer students’ proficiency on UKCs. Experimental results on real-world datasets show that DisKCD effectively improves the performance of diagnosing students’ proficiency on UKCs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to figure out what students know and don’t know. Currently, we assume we can test all important knowledge concepts in a few exercises, but this isn’t always possible. This limits our ability to identify gaps in students’ understanding. The authors propose a new way to diagnose students’ knowledge deficits by combining information from course grades, exercise questions, and learning resources. They create a special graph that shows relationships between students, exercises, and knowledge concepts. This allows them to identify untested knowledge concepts (UKCs) and infer students’ proficiency on these topics. The results show that this new approach is more effective than previous methods. |
