The Electronic Journal of e-Learning provides perspectives on topics relevant to the study, implementation and management of e-Learning initiatives
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Journal Article

Highlighting E‑learning Adoption Challenges using data Analysis Techniques: University of Kufa as a Case Study  pp136-149

Ammar J. M. Karkar, Hayder K. Fatlawi, Ahmed A. Al-Jobouri

© Feb 2020 Volume 18 Issue 2, Editor: Heinrich Söbke and Marija Cubric, pp114 - 206

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Abstract

Electronic learning (e‑learning) plays a significant role in improving the efficiency of the education process. However, in many cases in developing countries, technology transfer without consideration of technology acceptance factors has limited the impact of e‑learning and the expected outcome of the education process. Therefore, this shift in learning method has been met with low enthusiasm from academic staff and students owing to its low perceived usefulness and perceived ease‑of‑use. The University of Kufa (UoK) in Iraq is considered a good case study because it has implemented the e‑learning platform since 2013. The UoK platform is based on open‑source Moodle owing to the latter’s advantages, such as low implementation cost, open community for support and continuous update and development. To identify and evaluate the challenges, this study uses a questionnaire survey that targets the level of adoption, implementation, familiarity and technology acceptance of staff and students. A total of 242 educators participate in the survey, and the data are subsequently analysed. Important information is extracted using data mining techniques, namely clustering and decision trees. One of the main crucial factors extracted from the analysis results is the perception that social media is easier to use compared with a dedicated e‑learning platform such as Moodle. This factor may also discourage educators/learners from adopting an offered e‑learning platform, regardless of actual usefulness, motivation and training programs. Therefore, this paper offers practical information regarding the main issues and a guideline to fully utilise e‑learning for policy makers and e‑learning developers, particularly in newly established institutions or developing countries.

 

Keywords: e-learning, technology acceptance model, Educational data mining, Moodle, social media, Facebook, clustering, decision trees

 

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Journal Article

Using Data Mining for e‑Learning Decision Making  pp65-81

David Monk

© Jan 2005 Volume 3 Issue 1, Editor: Shirley Williams, pp1 - 81

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Abstract

The initial investigation aimed to examine the paths learners followed when offered the course in a custom virtual learning environment (VLE) which is structured by tasks, course materials and learning resources. However, it quickly became clear that students were spending little time with the course materials online and the time spent with each page was usually less than 20 seconds. Consequently a better understanding of how learners accessed the electronic course materials was needed to evaluate the effectiveness of developing and delivering courses in this way. By combining data on the activity with content with user profiles it was possible to examine alternate information perspectives and reveal patterns in large volume data sets. Mining data in this way provides ways to learn about learners in order to make effective decisions regarding teaching methods, delivery models and infrastructure investment.

 

Keywords: data mining, decision making, elearning, VLE

 

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Journal Issue

Volume 18 Issue 2 / Feb 2020  pp114‑206

Editor: Heinrich Söbke, Marija Cubric

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Keywords: Blended learning; constructivism, behaviourism, objectivism, learning theory, context, feedback, peer feedback, peer review, discussion boards, learner-learner interaction, formative assessment, MOOC, e-learning, technology acceptance model, Educational data mining, Moodle, social media, Facebook, clustering, decision trees

 

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