Abstract
A virtual learning environment (VLE) is the type of environment that can attract more students because it allows them to study anywhere in the world, which means that the student's location is no longer a constraint. In addition, VLE facilitates access to teaching resources, which facilitate the monitoring of teacher activities and interaction between students and teachers. Therefore, the online environment can assess the factors that lead to an increase or decrease in the academic performance of students. Machine Learning approaches are used for the cognitive behavior and academic performance of students in Virtual Learning. There is still no decision on the parameters to be adopted for the evaluation of virtual teaching as each student may submit the same type of assignment and same Practical files, and can have the same attendance. In such a case, evaluation of a student’s academic performance became tough. So we need to adopt some LMS which records various actions of the learners and the teachers like Quiz Submitted On-time/Late, Number of Assignment Submitted On-time/Late, Number of Discussions attended, Number of CA attended, and Practical Submitted On-time/Late, Internet connectivity, etc. So, there is a need for a framework that accounts for all of these parameters’ consideration so that a Predictive model can be designed for Forecasting/estimation performance of students that are recommended system should be framed for enhancing the academic performance of the learner.
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Khajuria, R., Sharma, A., Sharma, A., Singh, P. (2023). A Survey on Various Approaches to Examine Cognitive Behavior and Academic Performance of Learner in Virtual Learning. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-19-2821-5_60
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