Development and validation of an observation protocol for measuring science teachers' modeling‐based teaching performance

Abstract

To achieve the goal of cultivating students' scientific modeling competence, it is crucial to improve teachers' epistemic knowledge and skills with respect to modeling‐based teaching. Taking into consideration existing studies on enhancing teaching competence and teachers' professional development on modeling, and the importance of classroom teaching quality for effective and meaningful teaching and learning, we proposed a framework for the assessment of teachers' modeling‐based teaching performance (MBTP) in science classrooms. Based on the framework, we developed hypotheses on 18 MBTP indicators and five levels of MBTP, and created a modeling‐based teaching observation protocol (MBTOP) to test these hypotheses, to see how well they represent teachers' levels of MBTP, and how well they differentiate teachers with different MBTPs. We found that most of the lessons in our research sample were at a low level, and MBTP indicators proposed in this study could distinguish individual lessons in terms of their levels of MBTP. The validated performance indicators and levels can provide insights for the assessment and development of teachers' modeling‐based teaching competencies in science teaching.

Investigating the dimensions of modeling competence among preservice science teachers: Meta‐modeling knowledge, modeling practice, and modeling product

“…However, these statements leave unclear whether meta‐modeling knowledge guides the practice or engagement in practices promotes understanding (Gobert & Pallant, 2004; Schwarz et al, 2009). Moreover, our findings suggest that helping secondary preservice biology teachers improve their meta‐modeling knowledge, for example in university courses, may not necessarily improve their modeling practice (Shi et al, 2021).…”

“…Most studies indicate that classroom practice typically gives students few opportunities to meaningfully engage with models (Campbell et al, 2015;Khan, 2011). Studies on the instructional practice of teachers in science classrooms suggest that teachers encounter a multitude of challenges and mainly focus on knowledge aspects represented in models, disregarding the predictive nature of models (Harlow et al, 2013;Nielsen & Nielsen, 2021b;Shi et al, 2021;Vo et al, 2015Vo et al, , 2019. However, it is commonly assumed, that teachers' own ideas about models and modeling shape their instructional classroom practice (Harlow et al, 2013;Vo et al, 2015Vo et al, , 2019.…”

The research trends and contributions of science education during the COVID‐19 pandemic: A narrative systematic literature review of publications in selected journals

“…Studies in the branch of chemistry and chemistry education at the high school level include: studying atomic structure through a fine‐grained analysis under various conditions (Zarkadis & Papageorgiou, 2020); chemistry learning approaches in soft skills development and research practice (Stammes et al., 2020); games which are positively correlated to the emotions of students in the medium and low categories (Chen, Jamiatul Husnaini, & Chen, 2020); based on the concept map it is known that high school students do not understand the quantum structure of atoms (Ekinci & Şen, 2020); chemical dramas that support the exploration of electronegativity and chemical bonds (Danckwardt‐Lillieström et al., 2020); the principles of green chemistry through pro socio‐scientific context‐based projects that contribute to social and civic responsibility (Koulougliotis et al., 2021); student attitudes toward chemistry, environmental protection and information technology in Hungary (Szalkay et al., 2021); and protocols for assessing competence based on chemical modelling (Shi et al., 2021).…”

Exploring Chinese high school students' performance and perceptions of scientific argumentation by understanding it as a three‐component progression of competencies

“…To assign students to corresponding progression levels based on their Rasch score, we used the average of the item/threshold estimates in each level as the cutoff point between SAC levels as shown in Table 6 (Shi et al, 2021). To illustrate the characteristics of each level, a Rasch measure below −1.73 indicates Level 0 of SAC, where students have not reached the average measure of items in Level 1.…”

“…Any disagreements between raters were further discussed and resolved, and the remaining test papers were scored by the first author. To assess how well individual items contribute to an overall score of SAC statistical models are required and the Rasch model has been widely used in science education research (Romine et al, 2020; Shi et al, 2021). Some items in the instrument were scored polytomously, so an extension of the Rasch model called the partial credit Rasch model (PCM) was used in this study (Wright & Masters, 1982).…”