This is the first issue of Metacognition and Learning, a new international journal dedicated to the study of metacognition and all its aspects within a broad context of learning processes. Flavell coined the term metacognition in the seventies of the last century (Flavell, 1979) and, since then, a huge amount of research has emanated from his initial efforts. Do we need metacognition as a concept in learning theory? Already in 1978, Brown posed the question whether metacognition was an epiphenomenon. Apparently, she was convinced otherwise as she has been working fruitfully for many years in the area of metacognition. Moreover, a review study by Wang, Haertel, and Walberg (1990) revealed metacognition to be a most powerful predictor of learning. Metacognition matters, but there are many unresolved issues that need further investigation. This introduction will present ten such issues, which are by no means exhaustive. They merely indicate what themes might be relevant to the journal. Definitions of MetacognitionMetacognition was originally referred to as the knowledge about and regulation of one_s cognitive activities in learning processes (

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We present a study on the effect of instruction on collaboration in a collaborative discovery learning environment. The instruction we used, called RIDE, is built upon four principles identified in the literature on collaborative processes: Respect, Intelligent collaboration, Deciding together, and Encouraging. In an experimental study, a group of learners (ages 15-17) receiving this instruction was compared to a control group. The learners worked in dyads on separate computers in a shared discovery learning environment in the physics domain of collisions, communicating through a chat channel. Qualitative and quantitative analyses of the logged actions in the learning environment and the chat protocols showed that the RIDE instruction can lead to more constructive communication, and improved discovery learning activities, as expected, although no direct effect on discovery learning results was found. This study shows the benefits of providing instruction on effective communication and the learning process in a collaborative discovery learning situation.

The relation between students' epistemological understanding of computer models and their cognitive processing on a modelling task AbstractWhile many researchers in science education have argued that students' epistemological understanding of models and of modelling processes would influence their cognitive processing on a modelling task, there has been little direct evidence for such an effect.Therefore, this study aimed to investigate the relation between students' epistemological understanding of models and modelling and their cognitive processing (i.e. deep versus surface processing) on a modelling task. Twenty-six students, working in dyads, were observed while working on a computer-based modelling task in the domain of physics.Students' epistemological understanding was assessed on four dimensions (i.e. nature of models, purposes of models, process of modelling, and evaluation of models). Students' cognitive processes were assessed based on their verbal protocols, using a coding scheme to classify their types of reasoning. The outcomes confirmed the expected positive correlation between students' level of epistemological understanding and their deep processing (r = 0.40, p = 0.04), and the negative correlation between level of epistemological understanding and surface processing (r = -0.51, p = 0.008). From these results, we emphasise the necessity of considering epistemological understanding in research as well as in educational practice.