Personalizing access to learning networks

Abstract

In this article, we describe a Smart Space for Learning T M (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address in this framework, enabling personalized access to federated learning repositories with a vast number of learning offers. Our infrastructure includes personalization strategies both at the query and the query results level. Query rewriting is based on learning and language preferences, rule-based and ranking-based personalization improves these results further. Rule-based reasoning techniques are supported by formal ontologies we have developed based on standard information models for learning domains, ranking based recommendations are supported through ensuring minimal sets of predicates appearing in query results. Our evaluation studies show that the implemented solution enables learners to find relevant learning resources in a distributed environment and through goal-based personalization improves relevancy of results.

Learning Path Construction Based on Association Link Network

“…available study time. [3] also allows students to search LRs for learning. However, the method in addition performs a query rewriting based on student profiles, which describe student learning preferences and learning performance (which indicate student knowledge level), such that students only need to focus on what they want to learn and the system will take care of the suitability of every LR, which matches the student searching criteria.…”

“…Identifying relevant LRs is essential to learning path [11] generation. Existing work determine such a relevancy by matching student specific requirements, including topics to learn, learning preferences or constraints [4,3] against the characteristics of LRs, which can be maintained by a list of attributes, such as related topic and difficulty level, or additionally by a structure that defines how LRs are related among each other [9]. Learning path generation methods aim at arranging selected LRs into a proper sequence for delivering to students, such that they can learn effectively in terms of minimizing the cognitive workload.…”

An empirical analysis of ontology-based query expansion for learning resource searches using MERLOT and the Gene ontology

“…The categories used are; relevant (R = 1.0), partially relevant (PR = 0.5), and not relevant (N = 0.0) [13,18]. The expert evaluates the topical relevance according to the following question: ''Is the learning resource relevant or does it satisfy the query?…”

How Can Learners Learn from Experience? A Case Study in Blended Learning at Higher Education

“…Many of the eLearning system developed today were merely the automation of the process and management of teaching and delivering of courses with the advantages of eliminating the time and space barrier. The value towards better learning outcomes is still an area of study, although some researchers have recognized the issues and provided innovative solutions to solve some related problems [3], [4].…”

“…Many of the eLearning system developed today were merely the automation of the process and management of teaching and delivering of courses with the advantages of eliminating the time and space barrier. The value towards better learning outcomes is still an area of study, although some researchers have recognized the issues and provided innovative solutions to solve some related problems [3], [4].Blended learning environment is the combination of traditional face-to-face learning and computer mediated or online instruction [5], [6]. In the academic field especially in higher education, the adoption of eLearning is on the rise [7].…”