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

Knowledge convergence in computer-supported collaborative learning

This study investigates how two types of graphical representation tools influence the way in which learners use knowledge resources in two different collaboration conditions. In addition, the study explores the extent to which learners share knowledge with respect to individual outcomes under these different conditions. The study also analyzes the relationship between the use of knowledge resources and different types of knowledge. The type of external representation (content-specific vs. content-independent) and the collaboration condition (videoconferencing vs. face-to-face) were varied. Sixty-four (64) university students participated in the study. Results showed that learning partners converged strongly with respect to their use of resources during the collaboration process. Convergence with respect to outcomes was rather low, but relatively higher for application-oriented knowledge than for factual knowledge. With content-specific external representation, learners used more appropriate knowledge resources without sharing more knowledge after collaboration. Learners in the computer-mediated collaboration used a wider range of resources. Moreover, in exploratory qualitative and quantitative analyses, the study found evidence for a relation between aspects of the collaborative process and knowledge convergence

Investigation of the use of navigation tools in web-based learning: A data mining approach

Web-based learning is widespread in educational settings. The popularity of Web-based learning is in great measure because of its flexibility. Multiple navigation tools provided some of this flexibility. Different navigation tools offer different functions. Therefore, it is important to understand how the navigation tools are used by learners with different backgrounds, knowledge, and skills. This article presents two empirical studies in which data-mining approaches were used to analyze learners' navigation behavior. The results indicate that prior knowledge and subject content are two potential factors influencing the use of navigation tools. In addition, the lack of appropriate use of navigation tools may adversely influence learning performance. The results have been integrated into a model that can help designers develop Web-based learning programs and other Web-based applications that can be tailored to learners' needs

Knowledge convergence in collaborative learning

In collaborative learning the question has been raised as to how learners in small groups influence one another and converge or diverge with respect to knowledge. Knowledge convergence can be conceptualised as knowledge equivalence and as shared knowledge prior to, during, and subsequent to collaborative learning. Knowledge equivalence refers to learners becoming more similar to their learning partners with regard to the extent of their individual knowledge. Shared knowledge means that learners have knowledge on the very same concepts as their learning partners. In this article, we provide measures for assessing both, knowledge equivalence and shared knowledge