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RETRACTED ARTICLE: Real-time personalization and recommendation in Adaptive Learning Management System

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This article was retracted on 30 May 2022

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Abstract

Various e-learning environments have been developed to provide sufficient materials for learners and thereby guide them to gain knowledge in any domain. Even though there were several factors that determine the motivation of the student, the skill set possessed by them is definitely an important factor. Hence in our proposed work, the behavioural and educational skill of the learners is tested by a skill test and learning content is provided only based on their skill test evaluation reports. The entire process is done by real time personalization based Adaptive Learning Management System and Personalized Page Rank algorithm. Finally, the Navies’ Bayes classifier was employed to classify the learners based on the performance over the skill test. Learners on their own can express both the optimistic and adverse skills that considerably impact the adaptive learning in e-learning environment. High skilled categorized learners are offered with advanced, intermediate learners with moderate and beginner or slow learners are provided with basic level of study content. The results are analysed based on their skills, individual's method of learning and time constraints.

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Authors and Affiliations

  1. Adhiparasakthi Engineering College, Melmaruvathur, India

    Sowmya Jagadeesan

  2. Department of Electronics and Communication, Adhiparasakthi Engineering College, Melmaruvathur, India

    Jayashri Subbiah

Authors
  1. Sowmya Jagadeesan
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  2. Jayashri Subbiah
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Correspondence to Sowmya Jagadeesan.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-03996-6

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Jagadeesan, S., Subbiah, J. RETRACTED ARTICLE: Real-time personalization and recommendation in Adaptive Learning Management System. J Ambient Intell Human Comput 11, 4731–4741 (2020). https://doi.org/10.1007/s12652-020-01729-1

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  • DOI: https://doi.org/10.1007/s12652-020-01729-1

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