QuestionBuddy – A collaborative question search and play portal.

Generally itembanks are inaccessible to students. Current use of itembanks focus on the teacher as having responsibility to organise questions (place them in pools, associate them with course content) and make them available/deliver them to students. This limits students to the teachers perspective and to the questions that the teacher has made available. As the practice of itembanking increases it may be appropriate to encourage students to use questions from pools not directly prepared by their teacher. A mechanism for searching across itembanks and sharing recommendations with peers would be of help in facilitating this. We describe QuestionBuddy, a collaborative filter based question portal for students, built to study student usage of, and attitudes to, such a system

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin

An evaluation of e-learning standards

The aim of this investigation is to perform an independent study of the various emerging elearning standards. This paper presents a summary of these standards in order to make them more accessible and understandable, and provide preliminary evidence as to their utility and adoption by the various UK higher and further education institutions. Recently there have been efforts to define standards for the elearning contents and elearning components like the IEEELOM, UKLOM, IMS, SCORM and OKI. Since it was not possible to cover all the standards in detail within the time available, so our independent study focuses on eight standards Although the results of the preliminary study suggest that the eight standards considered in the study may help interoperability, accessibility and reusability of the elearning content and elearning components, but it is yet to be seen how many of these are actually followed at UK higher education institutions

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Mental tactility: the ascendance of writing in online management education

A qualitative study of online management education and the role of writing as an indicative measure of thinking and learning. Established educational models, such as Dale\u27s Cone of Experience, are expanded and redeveloped to illustrate the central role of writing as a critical thinking process which appears to be increasing, rather than decreasing, with the advent of online multimedia technology. In an environment of increasing reliance on audiovisual stimulus in online education, the authors contend that tertiary educators may witness an ascendance or re-emergence of writing as central to the academic experience. This may be both supply and demand driven. Drawing on a study of two undergraduate units in the Bachelor of Commerce and applying hermeneutics to develop challenging insights, the authors present a case for educators to remain conversant with the art of teaching writing, and to promote writing to improve educational outcomes. <br /