Abstract
In recent years, the rapid development of internet technologies in educational resources requires some adaptability and customization of the learning methods. Still, there is some of the requirements are ignored in the traditional classroom while assessing the individual learners. Thus, the problem is to focus on the individual’s performance and provide a report regarding their learning pattern and behavior during the learning process. The main aim of this work is to employ personalization in e-learning systems to overcome the lack of monitoring the learner’s activities. Based on the learner’s learning history, the work retrieving the learning log of the user and identifying user profile information for the topic is used for profiling. The profiling of the user is based on the learner’s learning features obtained from the assessment. It is constructed as a profile to analyze the pattern in which the user behaves to determine their profile information. Based on the learning patterns of students, (KNC, RFC, and CNN classifiers) are used to label learners as good and Poor Learners.
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Christy Eunaicy, J.I., Sundaravadivelu, V., Suguna, S. (2022). Personalization and Prediction System Based on Learner Assessment Attributes Using CNN in E-learning Environment. In: Unhelker, B., Pandey, H.M., Raj, G. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 925. Springer, Singapore. https://doi.org/10.1007/978-981-19-4831-2_28
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DOI: https://doi.org/10.1007/978-981-19-4831-2_28
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