Abstract
AbstractContext-based learning (CBL) environments are widely used in science education to create authentic learning opportunities. Contexts can be authentic through their relation to everyday life, to uncommon scientific phenomena, or to the chemical laboratory. Previous research revealed that students choose contexts that are authentic in different ways depending on their individual characteristics. Self-determination theory and psychological research indicate that it is not the choice itself that is beneficial for learning, but rather the congruence between the characteristics of the participants and the task. The extent to which these results are transferable to CBL in chemistry education and the effects on cognitive load have not yet been analyzed. The focus of the present study was to investigate whether the choice of a contextualized task or the congruence between context and student are causal for beneficial effects in situational interest, cognitive load, and task-related satisfaction. We conducted an experimental study with 217 third-year chemistry students comparing three treatments while learning in a CBL environment. In the first group, students could choose a contextual task that was varied in terms of authenticity. Students in the second group were assigned a contextual task by an artificial neural network that matched their individual characteristics. Students in the third group were assigned a contextualized task by the neural network that did not match their individual characteristics. Multilevel analyses show that whether the context is chosen or not is irrelevant for situational interest and task-related satisfaction if the context fits the individual characteristics of the students.
Funder
Universität Duisburg-Essen
Publisher
Springer Science and Business Media LLC
Reference90 articles.
1. Aikenhead, G. S. (1994). What is STS science teaching? In J. Solomon & G. S. Aikenhead (Eds.), STS Education: International Perspectives on Reform. Teacher’s College Press.
2. Barr, D. J. (2013). Random effects structure for testing interactions in linear mixed-effects models. Frontiers in Psychology, 4, 328. https://doi.org/10.3389/fpsyg.2013.00328
3. Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3). https://doi.org/10.1016/j.jml.2012.11.001
4. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01
5. Bennett, J., & Lubben, F. (2006). Context-based chemistry: The Salters approach. International Journal of Science Education, 28(9), 999–1015. https://doi.org/10.1080/09500690600702496
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献