A New Approach to Predicting Learner Performance with Reduced Forgetting

The work on predicting learner performance allows researchers through machine learning methods to participate in the improvement of e-learning. This improvement allows, little by little, e-learning to be promoted and adopted by several educational structures around the world. Neural networks, widely used in various performance prediction works, have made several exploits. However, factors that are highly influential in the field of learning have not been explored in machine learning models. For this reason, our study attempts to show the importance of the forgetting factor in the learning system. Thus, to contribute to the improvement of accuracy in performance predictions. The interest being to draw the attention of researchers in this field to very influential factors that are not exploited. Our model takes into account the study of the forgetting factor in neural networks. The objective is to show the importance of attenuation the forgetting, on the quality of performance predictions in e-learning. Our model is compared to those based on Random Forest and linear regression algorithms. The results of our study show first that neural networks (95.20%) are better than Random Forest (95.15%) and linear regression (93.80%). Then, with the attenuation of forgetting, these algorithms give 96.63%, 95.85% and 93.80% respectively. This work allowed us to show the great relevance of oblivion in neural networks. Thus, the exploration of other unexploited factors will make better performance prediction models.

@article{KOFFI2020,
  title     = {A New Approach to Predicting Learner Performance with Reduced Forgetting},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {5},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Dagou Dangui Augustin Sylvain Legrand KOFFI and Tchimou N’TAKPE and Assohoun ADJE and Souleymane OUMTANAGA},
  doi       = {10.14569/IJACSA.2020.0110532},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110532}
}