Modelling Competence in Teacher Education: Comparing Meta-modelling Knowledge, Modelling Practices and Modelling Products Between Pre-service and In-service Teachers

IF 2.2 3区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Research in Science Education Pub Date : 2024-07-05 DOI:10.1007/s11165-024-10183-4

Song Xue, Keith Topping, Elizabeth Lakin, Moritz Krell

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Abstract

There has been increased attention recently on models and modelling within the global science education field. Research has begun to skew towards a competence-based perspective of models and modelling, as teachers are experiencing challenges and do not have the required competence in modelling from either theoretical or practical perspectives. This study was designed to comparatively investigate pre-service science teachers’ (PSTs) and in-service science teachers’ (ISTs) modelling competence A rating scale questionnaire was developed to assess meta-modelling knowledge. Additionally, a Black Box modelling task was designed to evaluate modelling practices and products by using two techniques: think-aloud and drawings. The resulting data was then coded and scored with validated rubrics. Quantitative analysis revealed that ISTs outperformed the PSTs in meta-modelling knowledge but they had an almost equal level in modelling practices and products, which were not at a satisfactory level. Furthermore, modelling practices and products were positively related, but no significant relationships were found between meta-modelling knowledge, modelling practices and products. Results of qualitative analyses further indicated higher-level practices were reflected in the analysis of correct model products, which was accompanied by sophisticated scientific knowledge and other advanced scientific skills. Implications of this study for science education research and teacher professional development are discussed.

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师范教育中的建模能力：比较职前和在职教师的元建模知识、建模实践和建模产品

最近，全球科学教育领域越来越关注模型和建模问题。由于教师在建模方面面临挑战，而且无论从理论还是实践角度来看，都不具备建模所需的能力，因此研究开始偏向于从能力的角度来看待模型和建模。本研究旨在比较调查职前科学教师（PSTs）和在职科学教师（ISTs）的建模能力。此外，还设计了一个 "黑盒建模任务"，通过两种技术：思考-朗读和绘图来评估建模实践和产品。然后，使用经过验证的评分标准对所得数据进行编码和评分。定量分析结果表明，在元建模知识方面，ISTs 的表现优于 PSTs，但在建模实践和建模产品方面，ISTs 和 PSTs 的水平几乎相当，但并不令人满意。此外，建模实践和产品呈正相关，但元建模知识、建模实践和产品之间没有发现显著的关系。定性分析的结果进一步表明，较高水平的建模实践体现在对正确模型产品的分析上，而正确模型产品则伴随着复杂的科学知识和其他高级科学技能。讨论了本研究对科学教育研究和教师专业发展的影响。

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来源期刊

Research in Science Education EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

6.40

自引率

8.70%

发文量

期刊介绍： 2020 Five-Year Impact Factor: 4.021 2020 Impact Factor: 5.439 Ranking: 107/1319 (Education) – Scopus 2020 CiteScore 34.7 – Scopus Research in Science Education (RISE ) is highly regarded and widely recognised as a leading international journal for the promotion of scholarly science education research that is of interest to a wide readership. RISE publishes scholarly work that promotes science education research in all contexts and at all levels of education. This intention is aligned with the goals of Australasian Science Education Research Association (ASERA), the association connected with the journal. You should consider submitting your manscript to RISE if your research: Examines contexts such as early childhood, primary, secondary, tertiary, workplace, and informal learning as they relate to science education; and Advances our knowledge in science education research rather than reproducing what we already know. RISE will consider scholarly works that explore areas such as STEM, health, environment, cognitive science, neuroscience, psychology and higher education where science education is forefronted. The scholarly works of interest published within RISE reflect and speak to a diversity of opinions, approaches and contexts. Additionally, the journal’s editorial team welcomes a diversity of form in relation to science education-focused submissions. With this in mind, RISE seeks to publish empirical research papers. Empircal contributions are: Theoretically or conceptually grounded; Relevant to science education theory and practice; Highlight limitations of the study; and Identify possible future research opportunities. From time to time, we commission independent reviewers to undertake book reviews of recent monographs, edited collections and/or textbooks. Before you submit your manuscript to RISE, please consider the following checklist. Your paper is: No longer than 6000 words, including references. Sufficiently proof read to ensure strong grammar, syntax, coherence and good readability; Explicitly stating the significant and/or innovative contribution to the body of knowledge in your field in science education; Internationalised in the sense that your work has relevance beyond your context to a broader audience; and Making a contribution to the ongoing conversation by engaging substantively with prior research published in RISE. While we encourage authors to submit papers to a maximum length of 6000 words, in rare cases where the authors make a persuasive case that a work makes a highly significant original contribution to knowledge in science education, the editors may choose to publish longer works.