Summary of Inductive Cognitive Diagnosis For Fast Student Learning in Web-based Online Intelligent Education Systems, by Shuo Liu et al.
Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems
by Shuo Liu, Junhao Shen, Hong Qian, Aimin Zhou
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed inductive cognitive diagnosis model (ICDM) addresses the limitation of traditional cognitive diagnosis methods by employing an novel student-centered graph (SCG). This allows for efficient inference of mastery levels for new students in web-based online intelligent education systems (WOIESs), which are essential for providing fast feedback and accelerating learning. ICDM consists of a construction-aggregation-generation-transformation process to learn the final representation of students, exercises, and concepts, achieving competitive performance while being much faster than existing transductive methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cognitive diagnosis is important in online intelligent education systems because it helps provide quick feedback and speed up learning. However, current methods are slow and costly when dealing with new students who were not part of the training data. This paper proposes a new approach called ICDM that uses a student-centered graph to quickly determine mastery levels for new students. Instead of updating student-specific embeddings, this method finds the best representations for different nodes in the graph. This makes it much faster and more efficient than existing methods. |
Keywords
» Artificial intelligence » Inference
