Abstract:
To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to s...Show MoreMetadata
Abstract:
To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or learning states are still underexplored, and the personalized early warning is unavailable for students at different levels. To accurately identify the possible learning risks faced by students at different levels, this article proposes a personalized early warning approach to learning performance for college students via cognitive ability and learning state modeling. In this approach, students' learning process data and historical performance data are analyzed to track students' cognitive abilities in the whole learning process, and model their learning states from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning state. Then, the Adaboost algorithm is used to predict students' learning performance, and an evaluation rule with five levels is designed to dynamically provide multilevel personalized early warning to students. Finally, the comparative experiments based on real-world datasets demonstrate that the proposed approach could effectively predict all students' learning performance, and provide accurate early warning services to them.
Published in: IEEE Transactions on Learning Technologies ( Volume: 17)
Funding Agency:
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- IEEE Keywords
- Index Terms
- Cognitive Function ,
- Cognitive Status ,
- Cognitive Model ,
- Learning Performance ,
- Early Warning ,
- Cognitive Learning ,
- Multilevel Approach ,
- Learning Process ,
- Student Learning ,
- Quality Of Learning ,
- Learning Engagement ,
- AdaBoost ,
- E-learning System ,
- Evaluation Rules ,
- Convolutional Neural Network ,
- Support Vector Machine ,
- Long Short-term Memory ,
- Recurrent Neural Network ,
- Conceptual Knowledge ,
- Radial Basis Function ,
- Performance In Phase ,
- Cognitive Diagnosis ,
- Skill Proficiency ,
- Gated Recurrent Unit ,
- Long Short-term Memory Structure ,
- Gradient Boosting Decision Tree ,
- Ability Of Students ,
- Deep Artificial Neural Networks ,
- Final Performance ,
- Early Warning System
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Cognitive Function ,
- Cognitive Status ,
- Cognitive Model ,
- Learning Performance ,
- Early Warning ,
- Cognitive Learning ,
- Multilevel Approach ,
- Learning Process ,
- Student Learning ,
- Quality Of Learning ,
- Learning Engagement ,
- AdaBoost ,
- E-learning System ,
- Evaluation Rules ,
- Convolutional Neural Network ,
- Support Vector Machine ,
- Long Short-term Memory ,
- Recurrent Neural Network ,
- Conceptual Knowledge ,
- Radial Basis Function ,
- Performance In Phase ,
- Cognitive Diagnosis ,
- Skill Proficiency ,
- Gated Recurrent Unit ,
- Long Short-term Memory Structure ,
- Gradient Boosting Decision Tree ,
- Ability Of Students ,
- Deep Artificial Neural Networks ,
- Final Performance ,
- Early Warning System
- Author Keywords