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

From Expectations to Experiences: Using a Structural Typology to Understand First-Year Student Outcomes in Academically Based Living-Learning Communities

This longitudinal study investigated to what extent noncognitive variables (e.g., expectations for college) and the college environment (i.e., academically based living-learning communities) influence students\u27 college experience. This research goes beyond grouping all living-learning students into one category, which has dominated much of the literature, by using an empirically derived structural typology for living-learning communities (Inkelas, Longerbeam, Leonard, & Soldner, 2005). Results suggest that being a student in a collaborative living-learning community is more likely to predict greater peer academic interactions and an enriching educational environment. Implications for practice and future research are discussed

Application of a virtual scientific experiment model in different educational contexts

E-learning practice is continuously using experimentation in order to enhance the basic information transfer model where knowledge is passed from the system/ tutors to the students. Boosting student productivity through on-line experimentation is not simple since many organizational, educational and technological issues need to be dealt with. This work describes the application of a Learning Model for Virtual Scientific Experiments (VSEs) in two different scenarios: Information and Communication Technologies and Physics. As part of the first, a VSE for Wireless Sensor Networks was specified and deployed while the second involved the specification and design of a collaborative VSE for physics experiments. Preliminary implementation and deployment results are also discussed

Using Shared Workspaces in Higher Education

We evaluate the use of BSCW shared workspaces in higher education by means of a comparison of seven courses in which this environment was used. We identify a number of different functions for which the BSCW environment has been used and discuss the relative success of these functions across the cases. In addition, we evaluate the cases with the 4E model of Collis et al. (2000) which predicts the chances of acceptance of ICT in an educational setting. Effectiveness for the given task appears to be a prime success factor for using ICT. But an effective tool may fail due to other factors like ease of use and organisational, socialcultural or technological obstacles. The particular strength of a shared workspace, for which BSCW is most effective and efficient, is providing a repository for objects of collaborative work. Other types of usage showed mixed results. In the future we expect that learning takes place in an integrated, open ICT environment in which different kinds of tools are available for different purposes and users can switch between tools as appropriate. We could observe this in several of the case studies, where non-use of BSCW did not mean that a particular task was not performed, but, on the contrary, a more efficient solution for the same function was available. Shared workspaces have proven to be highly useful, but it seems advisable that their purpose be limited to what they were originally designed for

The engagement of mature distance students

This is an Accepted Manuscript of an article published by Taylor & Francis in Higher Education Research and Development in 2013, available online: http://www.tandfonline.com/10.1080/07294360.2013.777036.Publishe