Modelling Competence in Teacher Education: Comparing Meta-modelling Knowledge, Modelling Practices and Modelling Products Between Pre-service and In-service Teachers
Song Xue, Keith Topping, Elizabeth Lakin, Moritz Krell
{"title":"Modelling Competence in Teacher Education: Comparing Meta-modelling Knowledge, Modelling Practices and Modelling Products Between Pre-service and In-service Teachers","authors":"Song Xue, Keith Topping, Elizabeth Lakin, Moritz Krell","doi":"10.1007/s11165-024-10183-4","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":47988,"journal":{"name":"Research in Science Education","volume":"12 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in Science Education","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1007/s11165-024-10183-4","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
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.
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
