Game-based learning can motivate learners and help them to acquire new knowledge in an active way. However, it is not always clear for learners how to learn effectively and efficiently within game-based learning environments. As metacognition comprises the knowledge and skills that learners employ to plan, monitor, regulate, and evaluate their learning, it plays a key role in improving their learning in general. Thus, if we want learners to become better at learning through game-based learning, we need to investigate how metacognition can be integrated into the design of game-based learning environments.In this paper we introduce a framework that aids designers and researchers to formally specify the design of game-based learning environments encouraging metacognition. With a more formal specification of the metacognitive objectives and the way the training design and game design aims to achieve these goals, we can learn more through analysing and comparing different approaches. The framework consists of design dimensions regarding metacognitive outcomes, metacognitive training, and metacognitive game design. Each design dimension represents two opposing directions for the design of a game-based learning environment that are likely to affect the encouragement of metacognitive awareness within learners. As such, we introduce a formalised method to design, evaluate and compare games addressing metacognition, thus enabling both researchers and designers to create more effective games for learning in the future.
The present study investigated whether text structure inference skill (i.e., the ability to infer overall text structure) has unique predictive value for expository text comprehension on top of the variance accounted for by sentence reading fluency, linguistic knowledge and metacognitive knowledge. Furthermore, it was examined whether the unique predictive value of text structure inference skill differs between monolingual and bilingual Dutch students or students who vary in reading proficiency, reading fluency or linguistic knowledge levels. One hundred fifty-one eighth graders took tests that tapped into their expository text comprehension, sentence reading fluency, linguistic knowledge, metacognitive knowledge, and text structure inference skill. Multilevel regression analyses revealed that text structure inference skill has no unique predictive value for eighth graders’ expository text comprehension controlling for reading fluency, linguistic knowledge and metacognitive knowledge. However, text structure inference skill has unique predictive value for expository text comprehension in models that do not include both knowledge of connectives and metacognitive knowledge as control variables, stressing the importance of these two cognitions for text structure inference skill. Moreover, the predictive value of text structure inference skill does not depend on readers’ language backgrounds or on their reading proficiency, reading fluency or vocabulary knowledge levels. We conclude our paper with the limitations of our study as well as the research and practical implications.
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.
