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Autor/inn/enPrabhakar, Sankalp; Zaiane, Osmar R.
TitelLearning Group Formation for Massive Open Online Courses (MOOCs)
[Konferenzbericht] Paper presented at the International Association for Development of the Information Society (IADIS) International Conference on Educational Technologies (5th, Sydney, Australia, Dec 11-13, 2017).
Quelle(2017), (8 Seiten)
PDF als Volltext kostenfreie Datei Verfügbarkeit 
Spracheenglisch
Dokumenttypgedruckt; online; Monographie
SchlagwörterLarge Group Instruction; Online Courses; Educational Technology; Technology Uses in Education; Program Design; Records (Forms); Age Differences; Geographic Location; Gender Differences; Qualifications; Student Interests; Grades (Scholastic); Cooperative Learning; Equations (Mathematics); Massachusetts
AbstractMassive open online courses (MOOCs) describe platforms where users with completely different backgrounds subscribe to various courses on offer. MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes. However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.). In collaborative learning contexts, the problem of automatic formation of effective groups becomes increasingly difficult due to very large base of users with different backgrounds. To address this concern, we propose an approach for group formation of users registered on MOOCs using a modified Particle Swarm Optimization (PSO) technique which automatically generates dynamic learning groups. The algorithm uses the profile attributes of users in terms of their age, gender, location, qualification, interests and grade as the grouping criteria. To form effective groups, we consider two important aspects: a) intra-group heterogeneity and b) inter-group homogeneity. While the former advocates the idea of diversity inside a particular group of users, the latter emphasizes that each group should be similar to one another. We test our algorithm on synthesized data sampled using the publicly available MITx-Harvardx dataset. Evaluation of the system is based on the fitness measures of groups generated using our algorithm which is compared against groups obtained using some of the standard clustering techniques like k-means. We see that our system performs better in terms of forming effective learning groups in the context of MOOCs. [For the complete proceedings, see ED579282.] (As Provided).
AnmerkungenInternational Association for the Development of the Information Society. e-mail: secretariat@iadis.org; Web site: http://www.iadisportal.org
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2020/1/01
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