Validation of the self-regulated online learning questionnaire

Abstract The number of students engaged in Massive Open Online Courses (MOOCs) is increasing rapidly. Due to the autonomy of students in this type of education, students in MOOCs are required to regulate their learning to a greater extent than students in traditional, face-to-face education. However, there is no questionnaire available suited for this online context that measures all aspects of self-regulated learning (SRL). In this study, such a questionnaire is developed based on existing SRL questionnaires. This is the self-regulated online learning ques- tionnaire. Exploratory factor analysis (EFA) on the first dataset led to a set of scales differing from those theoretically defined beforehand. Confirmatory factor analysis (CFA) was conducted on a second dataset to compare the fit of the theoretical model and the exploratively obtained model. The exploratively obtained model provided much better fit to the data than the theoretical model. All models under investigation provided better fit when excluding the task strategies scale and when merging the scales measuring metacognitive activities. From the results of the EFA and the CFA it can be concluded that further development of the questionnaire is necessary.

Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown

People recommenders are a widespread feature of social networking sites and educational social learning platforms alike. However, when these systems are used to extend learners’ Personal Learning Networks, they often fall short of providing recommendations of learning value to their users. This paper proposes a design of a people recommender based on content-based user profiles, and a matching method based on dissimilarity therein. It presents the results of an experiment conducted with curators of the content curation site Scoop.it!, where curators rated personalized recommendations for contacts. The study showed that matching dissimilarity of interpretations of shared interests is more successful in providing positive experiences of breakdown for the curator than is matching on similarity. The main conclusion of this paper is that people recommenders should aim to trigger constructive experiences of breakdown for their users, as the prospect and potential of such experiences encourage learners to connect to their recommended peers.