Predicting and Improving Behavioural Factors that Boosts Learning Abilities in Post-Pandemic Times using AI Techniques

Quantifying student academic performance has always been challenging as it hinges on several factors including academic progress, personal characteristics and behaviours relating to learning activities. Several research studies are therefore being conducted to identify the factors so that appropriate measures can be conducted by academic institutions, family and the student to boost his/ her academic performance. The present study investigates personal characteristics, psychological factors, behavioural factors, social factors and learning capabilities, that directly or indirectly affect student’s academic performance, which was tapped by administering a self-designed questionnaire. The data was collected from 214 undergraduate students studying in various streams of the University of Delhi and post that semi-structured interview was conducted to get in- depth information. The result proved the correlation between the aforementioned factors and the learning capabilities of the students. Using the results of analysis a machine learning model based on k-nn algorithm was formed to predict student performance. A chatbot is also proposed to provide guidance to students in strenuous situations, motivate them and interact with them without having personal bias.

@article{Gera2023,
  title     = {Predicting and Improving Behavioural Factors that Boosts Learning Abilities in Post-Pandemic Times using AI Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jaya Gera and Ekta Bhambri Marwaha and Reema Thareja and Aruna Jain},
  doi       = {10.14569/IJACSA.2023.0141127},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141127}
}