The relationship among motivation, self-monitoring, self-management, and learning strategies of MOOC learners

In massive open online learning courses (MOOCs) with a low instructor-student ratio, students are expected to have self-directed learning abilities. This study investigated the relationship among motivation, self-monitoring, self-management, and MOOC learners’ use of learning strategies. An online survey was embedded at the end of three MOOCs with large enrollments asking for learners’ voluntary participation in the study. The survey results from 470 participants indicated that motivation positively influenced self-monitoring, self-management, and learning strategies. In addition, self-monitoring and self-management did not affect the utilization of learning strategies. This underscores learners’ motivation and the need to encourage them to adopt appropriate learning strategies for successful learning. The results also revealed that self-monitoring positively affected self-management. The findings highlight the critical need to enhance self-monitoring skills to further promote self-management skills in MOOCs. In addition, self-monitoring and self-management did not encourage learners to use related learning strategies in this study. This study should be extended to investigate practical ways to encourage MOOC learners to adopt learning strategies.

Instructors’ conceptualization and implementation of scaffolding in online higher education courses

This multiple case study explores how instructors conceptualize and employ scaffolding in online courses. Participants included full time faculty (n = 4) who have designed and taught at least one online course within the past 12 months. Data sources included pre-interview surveys, semi-structured interviews, and online course observations. Data were analyzed by employing a general analytical strategy for developing a case description (Yin, 2018). The results showed that (a) instructors define scaffolding as a support to help students achieve course outcomes and (b) instructors implement different types of scaffolding (i.e., conceptual, metacognitive, procedural, strategic, and motivational) in hard and soft scaffolding forms. The results also showed that instructors' conceptualization and implementation of scaffolding differed based on their discipline and teaching philosophy, and the unique features of online courses. Implications for practice and research are discussed.

Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

The aim of this study was to predict university students’ learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on posttests of domain knowledge. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.