MoodleREC: A recommendation system for creating courses using the moodle e-learning platform. (March 2020)

• Record Type: Journal Article
• Authors: De Medio, Carlo; Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco
• Is Part Of: Computers in human behavior. Volume 104(2020)
• Publication Date: 2020-03
• Subjects: Recommender systems -- E-learning -- Learning object -- Learning object repository; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2019.106168
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed. Highlights: Recommender system to create new courses using the Moodle platform. Similarity metrics among teachers using courses in the same Moodle platform. Learning Objects retrieval and import from web LO repositories. Learning Objects ranking and selecting through system recommendations.