Balance of Accuracy and Diversity in Adaptive Learning Material Recommendation

• In Event: Measurement Methodology Innovation

Abstract

Adaptive learning provides personalized learning support for e-learners and can solve the problems of cognitive disorientation. However, the current adaptive learning recommendation focuses on accuracy without considering diversity in recommended material sequence, resulting in low motivation of learners and uneven exposure of materials that wasting resources. Firstly, the particle swarm algorithm is improved to implement an adaptive learning recommendation strategy that balances accuracy and diversity. Then, simulated studies are conducted under different conditions to confirm the effect of the improved particle swarm and compare it with the randomized selection and basic particle swarm algorithm. Finally, the paper also gives suggestions for further enriching and refining the diversity criterion in future research and extending it to the practice of adaptive learning.

Authors

• Tongxin Zhang, Beijing Normal University
  Presenting Author

• Suzhou Chen
  Non-Presenting Author

• Tao Xin, Beijing Normal University
  Non-Presenting Author