Session Information
16 SES 02 A, Teacher Education
Paper Session
Contribution
The objective of this study is to investigate the conceptual development of pre-service teachers regarding diffusion in both liquids and gases through a Modeling-Based Learning (MBL) approach. The focus was on examining whether teachers' involvement in modeling activities related to ink diffusion would facilitate the development of their ideas about how evaporated lavender oil diffuses in a classroom environment (gas diffusion). The research questions that the study aimed to address were: (1) What are pre-service teachers' ideas about diffusion in gas and liquid environments, and do these ideas differ based on the expressed state of matter? (2) How does their mechanistic reasoning about diffusion evolve as they transition between gas-liquid-gas phenomena?
Modeling, the process of constructing a conceptual representation of a phenomenon under study, is fundamental to scientific endeavors and plays a central role in science teaching and learning (Günther et al., 2019). To build an internal mental model of a scientific phenomenon, learners must create external representations or artifacts of the phenomenon. Understanding the underlying mechanism of a phenomenon is linked to mechanistic reasoning, defined as "reasoning systematically through the underlying factors and relationships that give rise to a phenomenon" (Krist et al., 2019, p. 161). This is particularly crucial for phenomena involving processes at the microscopic level, as mechanistic reasoning goes beyond observable patterns, revealing the regularities behind empirical observations. Consequently, engaging learners in modeling diffusion is proposed as a productive way to facilitate the development of their understanding of the underlying mechanism governing the process.
Nineteen participants were engaged in a specially crafted MBL unit where they constructed various models to explain diffusion in gases and liquids. Data sources encompassed pre- and post-test paper-and-pencil models for gas diffusion, as well as initial and revised models for liquid diffusion, along with subsequent computer-based models. Data analysis employed open coding methods and a mechanistic reasoning coding scheme derived from existing literature.
Three crucial findings emerged: Firstly, pre-service teachers expressed non-canonical ideas about fluid diffusion, with only a minority of these ideas not being specific to the state of matter. Secondly, there was an advancement in teachers' mechanistic reasoning from their initial to final models. Lastly, the computer-based modeling environment acted as a facilitator for their mechanistic reasoning, aiding in their explanations of how diffusion occurs in liquids. The implications of these findings are discussed in relation to MBL's potential to support pre-service teachers in understanding microscopic phenomena.
Method
The participants comprised nineteen pre-service teachers (3 males and 16 females), who were enrolled in a specialized science education course focusing on the integration of new technologies in science teaching and learning. The Modeling-Based Learning (MBL) unit within the course was divided into three phases, spanning eight 90-minute sessions each. In Phase 1, emphasis was placed on designing a drawing, including illustrations and an explanation of how the scent of evaporated lavender oil, released in the classroom, reached every student's nose. Phase 2 involved creating models, initially on paper and later in an online computer-based environment called MoDa, to demonstrate the diffusion of ink in cold and hot water. MoDa integrates building computational models using domain-specific code blocks and comparing models with real-world data (Fuhrmann et al., 2018). The initial ink model was developed after teachers observed the related phenomenon through an experiment conducted in pairs. The revised ink model was then created after each pair presented their model and received feedback from the instructor and other participants regarding the explanatory power of the presented model. Phase 3 replicated the activities of Phase 1. Data were collected from various sources, including the initial and final lavender diffusion paper-and-pencil models (pre- and post-test) created by the teachers. Additionally, the study involved the examination of the initial and revised ink diffusion paper-and-pencil models, as well as subsequent computer-based models. The analysis of these models was conducted using open coding techniques, and a mechanistic reasoning coding scheme was applied that derived from the works of Krist et al. (2019) and Russ et al. (2007). The mechanistic reasoning coding scheme consisted of four distinct levels: Level 0 (Providing a phenomenological description of the phenomenon), Level 1 (Identifying entities beyond what is directly observed), Level 2a (Identifying entities and their properties), Level 2b (Identifying entities and their interactions), Level 3 (Identifying entities, properties, and interactions among them), and Level 4 (Integrating all features into an explanatory scheme).
Expected Outcomes
The examination of teachers' models regarding diffusion in both gas and liquid contexts revealed a diverse array of advanced non-canonical ideas that shaped their initial, ongoing, and final conceptualizations of the diffusion process. Some of these ideas were specific to either liquids or gases, while a few were expressed in both states of matter. Teachers' mechanistic reasoning demonstrated a progression to more sophisticated levels from the pre-test to the post-test. The prevalent levels of teachers' initial mechanistic reasoning, focusing on phenomenological descriptions of diffusion and the identification of entities and/or properties, were notably absent in their post-test performance, where approximately one-fourth of them successfully linked all features in an explanatory manner. Notably, the computer-based environment (MoDa) played a significant role in facilitating the development of teachers' mechanistic reasoning, particularly at the highest levels. The outcomes of this study offer insights into how an MBL approach can aid learners in enhancing their mechanistic reasoning, a crucial aspect in explaining the functioning of microscopic-level phenomena. Notably, the teachers' involvement in modeling ink diffusion using the computer-based medium had a substantial impact on the evolution of their ideas regarding the diffusion of evaporated lavender oil. This is evident as their pre-test ideas predominantly focused on the phenomenological description of the diffusion phenomenon. Furthermore, the similarity between teachers' diffusion models and those expressed by younger students, as found in the literature (see Fuhrmann et al., 2022), suggests that curriculum developers should carefully consider both the instructional approach for teaching diffusion and the sequence of phenomena (e.g., transitioning from macro- to micro-level) to effectively scaffold learners' conceptual understanding. This consideration is crucial for ensuring a more productive learning experience for learners in the study of diffusion.
References
Fuhrmann, T., Wagh, A., Eloy, A., Wolf, J., Bumbacher, E., Wilkerson, M., & Blikstein, P. (2022). Infect, Attach or Bounce off?: Linking Real Data and Computational Models to Make Sense of the Mechanisms of Diffusion. Proceedings of International Conference of the Learning Sciences, ICLS, 1445–1448. Fuhrmann, T., Schneider, B., & Blikstein, P. (2018). Should students design or interact with models? Using the Bifocal Modelling Framework to investigate model construction in high school science. International Journal of Science Education, 40(8), 867–893. https://doi.org/10.1080/09500693.2018.1453175 Günther, S. L., Fleige, J., zu Belzen, A. U., & Krüger, D. (2019). Using the Case Method to Foster Preservice Biology Teachers’ Content Knowledge and Pedagogical Content Knowledge Related to Models and Modeling. Journal of Science Teacher Education, 30(4), 321–343. https://doi.org/10.1080/1046560X.2018.1560208 Krist, C., Schwarz, C. V., & Reiser, B. J. (2019). Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning. Journal of the Learning Sciences, 28(2), 160–205. Russ, R. S., Scherr, R. E., Hammer, D., & Mikeska, J. (2008). Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science. Science Education, 92(3), 499–525. https://doi.org/10.1002/sce.20264
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