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Purpose of the present study was to test a conceptual model of relations among achievement goal orientation, self-efficacy, cognitive processing, and achievement of students working within a particular collaborative task context. The task involved a collaborative computer-based modeling task. In order to test the model, group measures of mastery-approach goal orientation, performance-avoidance goal orientation, self-efficacy, and achievement were employed. Students' cognitive processing was assessed using an online log-file measure. As predicted, mastery-approach goal orientation had a significant positive effect on achievement, which was mediated through students' use of deep processes. No significant relationships could be found between performance-avoidance goal orientation and surface processing and between surface processing and achievement. Results are discussed with respect to general theoretical implications and lead to suggestions for the design of appropriate scaffolds.

Although computer modelling is widely advocated as a way to offer students a deeper understanding of complex phenomena, the process of modelling is rather complex itself and needs scaffolding. In order to offer adequate support, we need a thorough understanding of the reasoning processes students employ and of difficulties they encounter during a modelling task. Therefore, in this study 26 students, working in dyads, were observed while working on a modelling task in the domain of physics. A coding scheme was developed in order to capture the types of reasoning processes used by students. Results indicate that most students had a strong focus on adjusting model parameters to fit the empirical data with little reference to prior knowledge. The successful students differed from the less successful students in using more prior knowledge and in showing more inductive reasoning. These observations lead to suggestions for the design of appropriate scaffolds.

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.