Patrick Sins

The Difficult Process of Scientific Modelling: An Analysis of Novices' Reasoning During Computer-Based Modelling

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

While 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 = .04), and the negative correlation between level of epistemological understanding and surface processing (r = −0.51, p = .008). From these results, we emphasise the necessity of considering epistemological understanding in research as well as in educational practice.

Knowledge Practices Environment: Design and Application of Technology for Trialogical Learning

Current networked society present learners with challenges that cannot be sufficiently coped with in educational contexts that are characterized by transmission or participation epistemologies on learning. To address these challenges, the trialogical approach to learning is introduced which focuses on the ways in which people and their communities create knowledge through the tool-mediated development of shared objects. Supporting sustained collaborative knowledge creation requires learning technology that is modular and extensible rather than monolithic and fixed in nature which characterizes most existent learning technologies. The current paper presents the design considerations and the application of the KP-environment which aims to support these object-oriented practices.