Assessment Study For E-Learning Using Bayesian Network
Rohit B. Kaliwal, Santosh L. Deshpande · 2021
E-Learning for educational institutions has created a challenging situation due to the COVID-19 pandemic. The universities and institutions can impart knowledge. However, evaluation of the learner’s learning and outcomes remained a challenge for them. The article aims to add its dimension towards the evaluation of outcomes especially for the learners of E-learning platforms. E-Learning delivers the training using the online mode of knowledge dissemination. E-learning has a wide range of resources that may improve the learning assessment. Stil,l assessment in education remains a challenge. E-Learning for learner’s knowledge is extremely significant because the beginner does not realize learning assessment properly. To improve the learner performance of an evaluation system for classified learners, the Bayesian Network (BN) for a random process is used. A BN is a graphical representation of the probabilistic relationships of a complex system. BN is the most challenging task in the e-learning framework. The goal of this work is achieved by constructing a BN to make causal analysis and then provide personalized interventions for different learners to improve learning. This network was effectively implemented with the accuracy of 45% of a slow learner, 15% of the average learner, and 40% of excellent learners from the required quantitative data set. Further the results of the experiments are promising.