QORECT – A Case-Based Framework for Quality-Based Recommending Open Courseware and Open Educational Resources

Abstract

Abstract. More than a decade has passed since the start of the MIT OCW initiative, which, along with other similar projects, has been expected to change dramatically the educational paradigms worldwide. However, better findability is still expected for open educational resources and open courseware, so online guidance and services that support users to locate the appropriate such resources are most welcome. Recommender systems have a very valuable role in this direction. We propose here a hybrid architecture that combines enhanced casebased recommending (driven by a quality model tenet) with (collaborative) feedback from users to recommend open courseware and educational resources. IntroductionMore than a decade has passed since the start of the MIT OCW initiative, which, along with other similar projects, has been expected to change dramatically the educational paradigms worldwide. However, despite the huge opportunities offered by open education, traditional textbooks and readings, and intranet educational resources are still here, dominating the majority of teaching and learning venues of Higher Education institutions even though all students are effectively online. Greater adoption of OERs both within formal and informal education seems to be impeded by four issues: discoverability, quality assurance, bridging the last mile, and acquisition [1]. Modern search engines generally do an ill job when searching for educational content because they are not tailored with this purpose, focusing mainly on content and metadata, and, moreover, they lack what it takes to locate the proper educational resource that is suited for a specific user's goal, that builds up on her prerequisites (for example, learner's previous knowledge), and that provide for making the next step towards her goal (e. g. mastering of a certain concept). For the time being, there is no quality assurance mechanism that could provide support for (1) learners and instructors in their quest for reaching the most appropriate educational resources for their specific educational needs in any particular context, neither for (2) faculty or institutions that are or want to become involved in this movement, and they may be concerned about the challenges or interested in the gains of this process, nor for (3) developers who need guidelines for designing and building such educational resources, nor for (4) educational resources' evaluators [2,3,4]. In many OCW/OER repositories educational content exists only immersed in context and without a significant effort this content cannot be both sorted out from its initial environment (becoming truly reusable and remixable) and entangled within a new educational context, bridging the last mile. Acquisition is also difficult, taking into account all the fears of OCW/OERs providers (faculty, teachers, educational resources designers etc.): lack of credit, of copyright control over derivative works, and so on. Therefore, better findability is expected for open courseware and OERs, so online guidance and services that su...

Systematic Literature Review on Open Educational Resources Recommender Systems

“…And OER could be relative to learning pathways in MOOCs. General OER Recommender systems [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [30], [30], [30], [30], [42] , [44], [45] , [46], [49], [50], [51], [52], [53], [54], [56], [57], [58], [59] [100], [104], [111], [113], [114], [126], [127], [129], [131] OER in MOOCs [28],…”

Autonomous recommender system architecture for virtual learning environments

“…In general, few papers exploit the advantages of the different types of recommendation systems in their architecture, only [38,7] and our proposition. There are a lot of architectures of intelligent recommendation systems, which use artificial intelligence techniques for the processing of the information before the recommendation, among other things.…”

Deep Learning-Based Recommendation: Current Issues and Challenges

“…www.ijacsa.thesai.org E-learning recommendation: This type of recommendation is adopted for the personalization of educational content. Many systems are based on hybrid recommendation approach which takes advantage of the rating data or the users feedback and tags associated to the courses to recommend the suitable pedagogical resources to users [10], [11]. Some systems are based only on the collaborative filtering approach like the work of [12] adopted for the recommendation of learning materials by the consideration of the context, the students' profile and the learning materials properties.…”