Date of this Version
Purpose –The motive of this study is to examine the aptness and approachability of existing recommender system and search engines of Massive Open Online Courses (MOOCs). Furthermore, the paper presents a pilot study to expose the appropriateness of faceto-analytical approach to search and retrieve.
Design/methodology/approach – Features and model of recommender systems and search engines of MOOCs is explored by review. Concurrently, relevance of faceted classification model for information retrieval is also analysed. After due consideration, a tentative faceted classification model of MOOCs is developed. Data are collected from various sources like- MOOCs platforms, handbooks and glossary of online learning terms. The final enumeration of facets is based on analysis, synthesis and several guiding principles for selecting facets.
Findings – There is a strong need to incorporate more features of MOOCs in existing recommender system and search engine. Better personalization for learners is also required. At preliminary stage, the facet analysis paradigm has been considered the most pertinent remedy to resolve the problem identified in this study. In the provisional faceted classification model of MOOCs, 23 facets are enumerated with examples.
Originality/value – The present study will pave the way for the development of integrated search interface for MOOCs. After exhaustive study a conceptual model may be formulated to develop a practical tool and facilitate learners with faceted search and smooth navigation.