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

Weblogs in Higher Education — why do Students (not) Blog?  pp203-214

Monika Andergassen, Reinhold Behringer, Janet Finlay, Andrea Gorra, David Moore

© Dec 2009 Volume 7 Issue 3, Special ICEL 2009 Issue, Editor: Florin Salajan and Avi Hyman, pp191 - 316

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Abstract

Positive impacts on learning through blogging, such as active knowledge construction and reflective writing, have been reported. However, not many students use weblogs in informal contexts, even when appropriate facilities are offered by their universities. While motivations for blogging have been subject to empirical studies, little research has addressed the issue of why students choose not to blog. This paper presents an empirical study undertaken to gain insights into the decision making process of students when deciding whether to keep a blog or not. A better understanding of students' motivations for (not) blogging may help decision makers at universities in the process of selecting, introducing, and maintaining similar services. As informal learning gains increased recognition, results of this study can help to advance appropriate designs of informal learning contexts in Higher Education. The method of ethnographic decision tree modelling was applied in an empirical study conducted at the Vienna University of Technology, Austria. Since 2004, the university has been offering free weblog accounts for all students and staff members upon entering school, not bound to any course or exam. Qualitative, open interviews were held with 3 active bloggers, 3 former bloggers, and 3 non‑ bloggers to elicit their decision criteria. Decision tree models were developed out of the interviews. It turned out that the modelling worked best when splitting the decision process into two parts: one model representing decisions on whether to start a weblog at all, and a second model representing criteria on whether to continue with a weblog once it was set up. The models were tested for their validity through questionnaires developed out of the decision tree models. 30 questionnaires have been distributed to bloggers, former bloggers and non‑ bloggers. Results show that the main reasons for students not to keep a weblog include a preference for direct (online) communication, and concerns about the loss of privacy through blogging. Furthermore, the results indicate that intrinsic motivation factors keep students blogging, whereas stopping a weblog is mostly attributable to external factors.

 

Keywords: weblog, blog, higher education, informal learning, ethnographic decision tree modelling, motivation research

 

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Journal Article

Knowledge Enriched Learning by Converging Knowledge Object & Learning Object  pp3-13

Sai Sabitha, Deepti Mehrotra, Abhay Bansal

© Jan 2015 Volume 13 Issue 1, Editor: Rikke Ørngreen and Karin Levinsen, pp1 - 56

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Abstract

Abstract: The most important dimension of learning is the content, and an LMS suffices this to a certain extent. The present day LMS are designed to primarily address issues like ease of use, search, content and performance. Many surveys had been conducte d to identify the essential features required for the improvement of LMS, which includes flexibility and a user centric approach. These features can suffice the need of all learners, when they have different learning requirements. For a true learning, kno wledge should also be delivered along with the domain information. Thus, there is a need to design an architecture for user centric Knowledge Driven Learning Management System. Thus for holistic learning, knowledge enriched teaching skills are required, w hich can enhance and increase the thinking skills of the learner to a higher level. The current LMS needs an improvement in the direction of knowledge discovery, exploration so that knowledge enriched learning can be provided to the learner.. It can be ba sed on knowledge engineering principles like ontology, semantic relationship between objects, cognitive approach and data mining techniques. In this paper, we are proposing an idea of an enhanced Learning Object (LO) called Knowledge Driven Learning Obj ect, which can be delivered to the user for better learning. We had used a data mining approach, classification to harness and exploit these objects and classify them according to their metadata, thereby strengthening the content of objects delivered thro ugh the LMS.

 

Keywords: Keywords: Learning object, knowledge objects, lms, kms, classification, decision tree, knowledge driven learning objects, knowledge driven learning management system, e-learning

 

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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 - 161

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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 Issue

Volume 18 Issue 2 / Feb 2020  pp114‑161

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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