@booklet {247,
	title = {Learning Introductory Physics with Computational Modelling and Interactive Environments},
	volume = {n/a},
	year = {2011},
	month = {Jan},
	abstract = {For the modern physics research community there is no doubt that the development of knowledge and cognition in physics involves modelling processes that balance different elements of theory, experimentation and scientific computation. However, the majority of the current introductory physics curricula and learning environments for STEM education do not always reflect this range of epistemological characteristics. Changing this situation requires introductory physics curricula and learning environments structured around pedagogical methodologies inspired in the modelling cycles of physics research, to help students create and explore balanced learning paths that go through the different cognitive stages associated with the modelling processes involved in the development of knowledge and cognition in physics. This expectation is supported by the results of many research efforts (see, e.g., [1, 2]), which have shown that the learning processes in diverse STEM areas can effectively be enhanced when students are embedded in atmospheres with activities that approximately recreate the cognitive involvement of scientists in modelling research activities. In this paper we present a strategy to approach this problem that is based on the development of interactive engagement learning activities built around exploratory and expressive computational modelling experiments implemented in the Modellus environment [3, 4]. The activities are conceived to help students going through the research inspired modelling cycle stages: meaningful qualitative contextual description, construction of mathematical physics models, model exploration, interpretation and validation, communication of results and enhancing generalizations. We describe a set of computational modelling activities implemented at FCT/UNL in the general physics and biophysics courses of the biomedical engineering and computer science majors. We report on the student{\textquoteright}s receptivity to our modelling approach and discuss its effect on the learning process.
References
[1] McDermott, L., \&amp; Redish, E. (1999). Resource Letter: PER-1: Physics Education Research. American Journal of Physics, 67, 755-767. [2] Slooten, O., van den Berg, E., \&amp; Ellermeijer, T. (Eds.) (2006). Proceedings of the International Group on Research on Physics Education (GIREP) 2006 conference: modelling in physics and physics education. Amsterdam, Holland: European Physical Society. [3] Neves, R., \&amp; Teodoro, V. (2010). Enhancing Science and Mathematics Education with Computational Modelling. Journal of Mathematical Modelling and Application, 1 (2), 2-15.[4] Teodoro, V., \&amp; Neves, R. (2011). Mathematical Modelling in Science and Mathematics Education. Computer Physics Communications, 182, 8-10.},
	author = {Neves R and Schwartz J and Silva J and Teodoro V and Vieira P}
}
