Summary of Personalized Forgetting Mechanism with Concept-driven Knowledge Tracing, by Shanshan Wang et al.
Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing
by Shanshan Wang, Ying Hu, Xun Yang, Zhongzhou Zhang, Keyang Wang, Xingyi Zhang
First submitted to arxiv on: 18 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 A novel Knowledge Tracing (KT) model, called Concept-driven Personalized Forgetting (CPF), is proposed to overcome limitations of existing models by incorporating hierarchical relationships between knowledge concepts and personalized cognitive abilities. The CPF model integrates personalized capabilities into both learning and forgetting processes to distinguish individual learning gains and forgetting rates based on cognitive abilities. It also simulates the causal relationship in the forgetting process, considering potential impacts of forgetting prior knowledge points on subsequent ones. Experimental results on three public datasets show that CPF outperforms current methods in predicting student performance, demonstrating its ability to simulate changes in students’ knowledge status through personalized forgetting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Knowledge Tracing is a way to track how people learn and forget new information over time. Current models only consider the general idea that we tend to forget things over time, without taking into account individual differences or the relationship between different pieces of information. This new model, called Concept-driven Personalized Forgetting (CPF), tries to fix these problems by considering both personal abilities and relationships between different ideas. It’s like a map that shows how our minds change as we learn and forget things. The results show that this model is better at predicting how well people will do in the future. |
