If we want game-based learning to make learning enjoyable as well as effective and efficient, we need to increase learner's awareness of and ability in learning itself. At the heart of learning is metacognition: a learner's understanding of how knowledge is constructed through learning, and the repertoire of strategies, tactics, and monitoring processes that enact learning. The goal of this PhD research is to inform designers and researchers who want to support and improve metacognition of learners within game-based learning environments, by identifying, implementing, and evaluating generic design principles for metacognitive interventions.
A promising contribution of Learning Analytics is the presentation of a learner's own learning behaviour and achievements via dashboards, often in comparison to peers, with the goal of improving self-regulated learning. However, there is a lack of empirical evidence on the impact of these dashboards and few designs are informed by theory. Many dashboard designs struggle to translate awareness of learning processes into actual self-regulated learning. In this study we investigate a Learning Analytics dashboard based on existing evidence on social comparison to support motivation, metacognition and academic achievement. Motivation plays a key role in whether learners will engage in self-regulated learning in the first place. Social comparison can be a significant driver in increasing motivation. We performed two randomised controlled interventions in different higher-education courses, one of which took place online due to the COVID-19 pandemic. Students were shown their current and predicted performance in a course alongside that of peers with similar goal grades. The sample of peers was selected in a way to elicit slight upward comparison. We found that the dashboard successfully promotes extrinsic motivation and leads to higher academic achievement, indicating an effect of dashboard exposure on learning behaviour, despite an absence of effects on metacognition. These results provide evidence that carefully designed social comparison, rooted in theory and empirical evidence, can be used to boost motivation and performance. Our dashboard is a successful example of how social comparison can be implemented in Learning Analytics Dashboards.
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Although self-regulation is an important feature related to students’ study success as reflected in higher grades and less academic course delay, little is known about the role of self- regulation in blended learning environments in higher education. For this review, we analysed 21 studies in which self-regulation strategies were taught in the context of blended learning. Based on an analysis of literature, we identified four types of strategies: cognitive, metacognitive, motivational and management. Results show that most studies focused on metacognitive strategies, followed by cognitive strategies, whereas little to no attention is paid to motivation and management strategies. To facilitate self-regulation strategies non-human student tool interactional methods were most commonly used, followed by a mix of human student-teacher and non-human student content and student environment methods. Results further show that the extent to which students actively apply self-regulation strategies also depends heavily on teacher's actions within the blended learning environment. Measurement of self-regulation strategies is mainly done with questionnaires such as the Motivation and Self-regulation of Learning Questionnaire.Implications for practice and policy:•More attention to self-regulation in online and blended learning is essential.•Lecturers and course designers of blended learning environments should be aware that four types of self-regulation strategies are important: cognitive, metacognitive, motivational and management.•Within blended learning environments, more attention should be paid to cognitive, motivation and management strategies to promote self-regulation.
How do learners understand, monitor, and regulate their own learning? A question of metacognition. Improving metacognitive knowledge and skills contributes too learning effectiveness and effiency. The goal of this PhD project is to study in what ways metacognitive training can be supported and facilitated trhough game-based learning.
