Abstract
There are several challenges associated with on-line based teaching-learning systems. The most important challenge is to recognize the student, who fails to complete the assigned task in a stipulated time. The existing models try to find a solution to this problem, but most of the algorithm fails to classify the input documents correctly and to create linearly-separable clusters of learners. To overcome these issues, the proposed methodology tries to apply the topic models like Latent Dirichlet Allocation (LDA) to create clusters of linearly separable learners. Initially, the required features are extracted and transformed into words-sentences suitable LDA. The words are then fed to the topic-modeling algorithm, (LDA) to generate clusters similar documents or learners. Several experiments were conducted to evaluate the performance of different predictive models. The results show the topic modeling algorithm LDA attains significant clustering of documents over the other state-of-art.
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Deepak, N.A., Shobha, N.S. (2021). Analysis of Learner’s Behavior Using Latent Dirichlet Allocation in Online Learning Environment. In: Singh, V., Asari, V.K., Kumar, S., Patel, R.B. (eds) Computational Methods and Data Engineering. Advances in Intelligent Systems and Computing, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7907-3_18
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