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Science Teachers' Attitudes towards Computational Modeling in the Context of an Inquiry-Based Learning Module.

ABSTRACT: This study focuses on science teachers' first encounter with computational modeling in professional development workshops. It examines the factors shaping the teachers' self-efficacy and attitudes towards integrating computational modeling within inquiry-based learning modules for 9th grade physics. The learning modules introduce phenomena, the analysis of measurement data, and offer a method for coordinating the experimental findings with a theory-based computational model. Teachers' attitudes and self-efficacy were studied using survey questions and workshop activity transcripts. As expected, prior experience in physics teaching was related to teachers' self-efficacy in teaching physics in 9th grade. Also, teachers' prior experience with programming was strongly related to their self-efficacy regarding the programming component of model construction. Surprisingly, the short interaction with computational modeling increased the group's self-efficacy, and the average rating of understanding and enjoyment was similar among teachers with and without prior programming experience. Qualitative data provides additional insights into teachers' predispositions towards the integration of computational modeling into the physics teaching.

SUBMITTER: Langbeheim E

PROVIDER: S-EPMC7447203 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Science Teachers' Attitudes towards Computational Modeling in the Context of an Inquiry-Based Learning Module.

Langbeheim Elon E Perl David D Yerushalmi Edit E

Journal of science education and technology 20200825 6

This study focuses on science teachers' first encounter with computational modeling in professional development workshops. It examines the factors shaping the teachers' self-efficacy and attitudes towards integrating computational modeling within inquiry-based learning modules for 9th grade physics. The learning modules introduce phenomena, the analysis of measurement data, and offer a method for coordinating the experimental findings with a theory-based computational model. Teachers' attitudes ...[more]

PMID: 32863694

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Project description:This article concerns the synergy between science learning, understanding complexity, and computational thinking (CT), and their impact on near and far learning transfer. The potential relationship between computer-based model construction and knowledge transfer has yet to be explored. We studied middle school students who modeled systemic phenomena using the Much.Matter.in.Motion (MMM) platform. A distinct innovation of this work is the complexity-based visual epistemic structure underpinning the Much.Matter.in.Motion (MMM) platform, which guided students' modeling of complex systems. This epistemic structure suggests that a complex system can be described and modeled by defining entities and assigning them (1) properties, (2) actions, and (3) interactions with each other and with their environment. In this study, we investigated students’ conceptual understanding of science, systems understanding, and CT. We also explored whether the complexity-based structure is transferable across different domains. The study employs a quasi-experimental, pretest-intervention-posttest-control comparison-group design, with 26 seventh-grade students in an experimental group, and 24 in a comparison group. Findings reveal that students who constructed computational models significantly improved their science conceptual knowledge, systems understanding, and CT. They also showed relatively high degrees of transfer—both near and far—with a medium effect size for the far transfer of learning. For the far-transfer items, their explanations included entities’ properties and interactions at the micro level. Finally, we found that learning CT and learning how to think complexly contribute independently to learning transfer, and that conceptual understanding in science impacts transfer only through the micro-level behaviors of entities in the system. A central theoretical contribution of this work is to offer a method for promoting far transfer. This method suggests using visual epistemic scaffolds of the general thinking processes we would like to support, as shown in the complexity-based structure on the MMM interface, and incorporating these visual structures into the core problem-solving activities. Supplementary Information The online version contains supplementary material available at 10.1007/s11251-023-09624-w.

Project description:Self-generation of knowledge can activate deeper cognitive processing and improve long-term retention compared to the passive reception of information. It plays a distinctive role within the concept of inquiry-based learning, which is an activity-oriented, student-centered collaborative learning approach in which students become actively involved in knowledge construction by following an idealized hypothetico-deductive method. This approach allows students to not only acquire content knowledge, but also an understanding of investigative procedures/inquiry skills - in particular the control-of-variables strategy (CVS). From the perspective of cognitive load theory, generating answers and solutions during inquiry-based learning is inefficient as it imposes an intrinsic and extraneous load on learners. Previous research on self-generation of content knowledge in inquiry-based learning has demonstrated that (1) a high cognitive load impairs retention of the generated information, (2) feedback is a fundamental requirement for self-generation of complex content knowledge, (3) self-generation success is key to long-term retention, and (4) generating and rereading place different demands on learners. However, there is still no research on the self-generation of scientific reasoning skills (procedural knowledge) and no knowledge of interaction between the (long-term) retention of these skills with prior knowledge, feedback and self-generation success. That is why this experiment was conducted. The focus of this research is to analyze the distinctive role of self-generation of scientific reasoning skills within the concept of inquiry-based learning and to identify the influence of prior knowledge and self-generation success on short-term and long-term retention. For this purpose, an experiment involving 133 6th and 7th graders was conducted. An inquiry activity that included the self-generation of scientific reasoning skills was compared to an inquiry task that had students simply read information about the experimental design. We used both an immediate and a delayed test to examine which treatment better developed a deeper understanding of CVS and an ability to apply this knowledge to novel problems (transfer). Direct instruction was clearly superior to self-generation in facilitating students' acquisition of CVS immediately after the inquiry task. However, after a period of 1 week had elapsed, both treatment conditions turned out to be equally effective. A generation effect was only found among students with high self-generation success after a 1-week delay.

Project description:Theory Digital technologies have become an integral part of everyday life that children are exposed to. Therefore, it is important for children to acquire an understanding of these technologies early on by teaching them computational thinking (CT) as a part of STEM. However, primary school teachers are often reluctant to teach CT. Expectancy-value theory suggests that motivational components play an important role in teaching and learning. Thus, one hindrance to teachers’ willingness to teach CT might be their low expectancies of success and high emotional costs, e.g., anxiety towards CT. Thus, introducing preservice teachers to CT during their university years might be a promising way to support their expectancies and values, while simultaneously alleviating their emotional costs. Prior CT competences might contribute to these outcomes. Aims We investigated whether a specifically designed seminar on CT affected preservice teachers’ expectancies and values towards programming.Method: A total of 311 German primary school and special education preservice teachers took part in the study. The primary school preservice teachers received a seminar on CT and programming with low-threshold programming tasks, while the special education teachers served as a baseline group. The seminar was specifically designed to enhance expectancies and values and decrease emotional costs, following implications of research on expectancy-value theory. Results The preservice teachers who visited the seminar gained higher expectancies and values towards CT and programming compared to the baseline group. Moreover, their emotional costs decreased. CT was positively related to change in expectancies and values and negatively related to emotional costs. Discussion Interventions with low-threshold programming tasks can support primary school preservice teachers in finding trust in their abilities and values towards CT. Moreover, their anxiety towards CT and programming can be alleviated. Thus, first steps in preparing preservice teachers to teach CT in their future classrooms can be taken in university.

Dataset Information

Science Teachers' Attitudes towards Computational Modeling in the Context of an Inquiry-Based Learning Module.

Publications

Science Teachers' Attitudes towards Computational Modeling in the Context of an Inquiry-Based Learning Module.

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