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e-Infrastructure and e-Services for Developing Countries. 13th EAI International Conference, AFRICOMM 2021, Zanzibar, Tanzania, December 1-3, 2021, Proceedings

Research Article

Towards Provision of Online Peer Assisted Learning: Understanding the Contemporary Participation Trends

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  • @INPROCEEDINGS{10.1007/978-3-031-06374-9_33,
        author={Henrick Mwasita and Joel S. Mtebe and Mercy Mbise},
        title={Towards Provision of Online Peer Assisted Learning: Understanding the Contemporary Participation Trends},
        proceedings={e-Infrastructure and e-Services for Developing Countries. 13th EAI International Conference, AFRICOMM 2021, Zanzibar, Tanzania, December 1-3, 2021, Proceedings},
        proceedings_a={AFRICOMM},
        year={2022},
        month={5},
        keywords={eLearning Machine learning Learning support Education Learning Management System (LMS) Educational data mining Autonomous learning support},
        doi={10.1007/978-3-031-06374-9_33}
    }
    
  • Henrick Mwasita
    Joel S. Mtebe
    Mercy Mbise
    Year: 2022
    Towards Provision of Online Peer Assisted Learning: Understanding the Contemporary Participation Trends
    AFRICOMM
    Springer
    DOI: 10.1007/978-3-031-06374-9_33
Henrick Mwasita1,*, Joel S. Mtebe1, Mercy Mbise1
  • 1: Department of Computer Science and Engineering
*Contact email: henrick.mwasita@gmail.com

Abstract

Although learning materials for eLearning platforms are keenly developed, learner support has remained unreliable. Mainly, the focus has been on managing and easily delivering learning resources to learners. The aim of this study was to thoroughly analyze the participation trends of both teachers and students from one of the deployed learning management systems (LMS). To accomplish this objective, activities logfile from Halostudy LMS implemented for secondary schools in Tanzania for the period of 11 months were extracted and analyzed. The study found that learning support is not always evident as it is entirely reliant on subject teachers who were found to be not actively using the system. Drawing on reflection of this finding, this study provides analytical commentary on the consequences of relying on subject teachers for the provision of learning support. With this understanding, future work will look at the use of machine learning techniques to facilitate automatic recommendation and pairing of potential peers to students facing challenges in their learning.

Keywords
eLearning Machine learning Learning support Education Learning Management System (LMS) Educational data mining Autonomous learning support
Published
2022-05-26
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-06374-9_33
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