User Preference Assessment Framework for Virtual Learning Platforms
Part-time postgraduate students usually interact with their peers and supervisors through virtual platforms hence this presents them with a myriad of challenges. Key amongst such challenges is the lack of connection with peers and supervisors. This has been proven to have some dire consequences for many part-time postgraduate students. To alleviate these challenges, there are various virtual platforms adopted by higher education institutions to enhance the learning experience for part-time postgraduate students. However, the quality and preference level of these virtual platforms in supporting the needs of such students vary widely. There have been several attempts to assess their quality and effectiveness. Yet, only a few such studies concern themselves with devising specific metrics for assessing the qualities of such platforms. This leaves a wide research gap as there are not many objective approaches for comparing users’ preferences of one Virtual Learning Platform (VLP) to another. This study proposes a simple computational framework for assessing the preference level of selected VLPs based on four attributes of online learning platforms: namely attractiveness, interactivity, transparency, and user-friendliness. Application of the proposed framework on a sample dataset obtained from part-time postgraduate students in Ghana indicates that social media platforms are the most preferred for supervisor-supervisee communication in the given context. The outcome of this study would guide educational institutions in their planning of part-time education that enhances the learning experience and minimises attrition.
Authors
- George Essah Yaw Okai
- Millicent Agangiba (ORCID: https://orcid.org/0000-0002-6183-4127)
- Muhammad Auwal Yakubu
- Abigail Mesrenyame Dogbe (ORCID: https://orcid.org/0009-0003-2908-4156)
Institutions
- University of Mines and Technology (GH)
- University of Cincinnati (US)
- University of Professional Studies (GH)
Publication Details
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1109/icast61769.2024.10856483
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00