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 inquirybased 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 selfefficacy 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.

Teachers’ Disciplinary Boundedness in the Implementation of Integrated Computational Modeling in Physics

“…While most of our teacher participants scored relatively high on a conceptual pre-assessment of introductory physics (Hestenes et al, 1992), no diagnostic assessment was available at the time to measure teachers' specific abilities with CM. In future studies, we hope to use measures of self-efficacy for CM, which have been used in at least one other study (Langbeheim et al, 2020) as a proxy for computational skills and knowledge that are otherwise challenging to measure in a programming language-agnostic way.…”

“…Despite this tendency, multiple U.S. initiatives are calling for science teachers to integrate transdisciplinary content more explicitly, including computing (NRC, 2011;NAE & NRC, 2014). Mueller et al (2008) and Langbeheim et al (2020) are among the few researchers studying science teachers' attitudes about the integration of computing into their disciplines. Mueller et al (2008) identified that K-12 teachers' levels of computer integration in non-computing subjects were dependent upon the teacher's previous "positive outcomes with computers; teacher's comfort with computers; specific beliefs related to the use of computers as an instructional tool; and the teacher's own use of computers at home and at school" (p. 1532).…”

“…Notable, however, is that Mueller et al's study focused more on teachers' attitudes toward the computer as an object, rather than the computer as a pedagogical tool. More specific to CM, Langbeheim et al (2020) reported that prior experience with CM and programming can lead to higher initial self-efficacy scores for teachers to integrate programming into their teaching of 9th grade physics, but that all teachers, regardless of prior experience, can see significant self-efficacy gains.…”

Using a Modular Approach to Introduce Python Coding to Support Existing Course Learning Outcomes in a Lower Division Analytical Chemistry Course

“…There is evidence that fairly short training courses or professional development, in the context of subject matter content that an instructor is quite familiar with, can be very effective training tools. 13 In fact, in our project, two of the three faculty who have taught the course with Python integration had no prior Python programming experience but used a short training course on DataCamp 14 the same one assigned to studentsto learn the basic syntax and structure of Python. We think that the combination of modular programming activities linked to common quantitative learning objectives and a low barrier to entry for instructors will enable broader adoption of computer programming in lower division chemistry courses.…”

“…However, because the programming content is presented in the context of very familiar analytical concepts, like pH and titrations, or calibration curves and linear regressions, instructors were able to quickly pick up the basics of Python for the purposes of this course. There is evidence that fairly short training courses or professional development, in the context of subject matter content that an instructor is quite familiar with, can be very effective training tools . In fact, in our project, two of the three faculty who have taught the course with Python integration had no prior Python programming experience but used a short training course on DataCampthe same one assigned to studentsto learn the basic syntax and structure of Python.…”