Abstract
Educational organizations aim to provide quality education to students by improving their performances. Analyzing the learners’ performance is one way of identifying the learners’ weak areas and hence guiding them accordingly for the future. Due to the current global pandemic situation of COVID-19, the educational system has converted mostly to online mode. This scenario has led to educational organizations’ inability to follow the students’ overall performance directly in offline mode. The online method of education has resulted in the evolution of the e-learning model in recent years. It is necessary to examine and conduct the attributes of students on regular basis in an online method to enhance the online education system including changing online training procedures and improving the nature of learning. In this paper, the authors have performed a detailed survey on some of the recent research work on students’ behavior in digital learning system techniques and also introduced the study of learners’ behavior on e-learning platforms using machine learning approaches. The proposed system allows us to better understand the learners’ behavior on online platforms. Various machine learning-based methods like Artificial Neural Networks, Decision Trees, Naïve Bayes, and Random Forest Classification have been used in the proposed system. The authors’ main aim is to create a Learner Behavioral Analysis System that can create profiles by clustering the learners with the same behavior.
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Dasmahapatra, J., Sil, R., Dasmahapatra, M. (2023). Machine Learning-Based Approach to Analyze Students’ Behaviour in Digital Learning Systems. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_49
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