@article {1179, title = {COMPARING THE EFFECTS OF MODELLING AND ANALOGY ON HIGH SCHOOL STUDENTS{\textquoteright} CONTENT UNDERSTANDING AND TRANSFERABILITY: THE CASE OF ATOMIC STRUCTURE}, journal = {Journal of Baltic Science Education}, volume = {21}, year = {2022}, month = {April/2022}, pages = {Continuous}, type = {Original article}, chapter = {325-341}, abstract = {Analogies and modelling have been developed and applied in learning and teaching science to facilitate students{\textquoteright} understanding of abstract concepts, such as atomic structure. Considering few studies focus on comparing the effects of two teaching strategies{\textemdash}analogy-based teaching (ABT) and modelling-based teaching (MBT){\textemdash}this study aims to compare the effects of ABT and MBT on high school students{\textquoteright} content understanding and transferability of atomic concepts in science. Implementing a quasi-experimental design with pre-post-delayed tests, the study compared learning outcomes achieved by the MBT group (N = 68) and the ABT group (N = 69). The results showed both MBT and ABT could improve students{\textquoteright} content understanding and promote transferability. However, the MBT group significantly outperformed the ABT group in terms of generating initial models and overall transferability. Although there was no difference in content understanding, or near or far transferability, at post-test between the two groups, the MBT group maintained more extended memory of atomic structure on the delayed post-test. Moreover, qualitative analysis of students{\textquoteright} drawings of atomic models revealed that both groups were able to develop and transfer their models, but inadequate scientific knowledge affected the quality of the transfer product. These findings have implications for designing and implementing instructional approaches that leverage analogy and modelling in the science class.}, keywords = {analogy-based teaching, atomic concepts, modelling-based teaching, science education}, issn = {1648-3898}, doi = {https://doi.org/10.33225/jbse/22.21.325}, url = {https://oaji.net/articles/2022/987-1652123108.pdf}, author = {Song Xue and Daner Sun and Liying Zhu and Hui-Wen Huang and Keith Topping} } @article {842, title = {EVALUATING SCIENTIFIC REASONING ABILITY: THE DESIGN AND VALIDATION OF AN ASSESSMENT WITH A FOCUS ON REASONING AND THE USE OF EVIDENCE}, journal = {Journal of Baltic Science Education}, volume = {19}, year = {2020}, month = {April/2020}, pages = {Continuous}, type = {Original article}, chapter = {261-275}, abstract = {Scientific reasoning ability (SRA) is widely recognized as an essential goal for science education. There is much discussion on the design and development of assessment frameworks as viable tools to foster SRA. However, established assessments mostly focus on the level of students reasoning attainment. Student ability to use evidence to support reasoning is not adequately addressed and evaluated. In this study, the 6-level SRA assessment framework was conceptualized and validated iteratively via synthesizing literature and a Delphi study. Guided by the framework, an SRA assessment tool adopting and adapting PISA test items and self-created items was developed and administered to 593 secondary students (including 318 8th Graders and 275 9th Graders) in mainland China. Pearson correlation analysis of SRA assessment score and their scores in scientific reasoning provided criterion-related validation for the former (Pearson correlation = .527). Rasch analysis conducted further confirmed the validity and reliability of the SRA test and the assessment framework. Combing quantitative and qualitative methods, the study provides a valid and reliable analytical framework of SRA. It can inform the design of SRA assessments in various science education contexts for diversified audiences.}, keywords = {Complexity of Scientific Reasoning, Evidence in Reasoning, Rasch Modeling, Scientific Reasoning Ability (SRA)}, issn = {1648-3898}, doi = {https://doi.org/10.33225/jbse/20.19.261}, url = {http://oaji.net/articles/2020/987-1586941634.pdf}, author = {Ma Luo and Zuhao Wang and Daner Sun and Zhi Hong Wan and Liying Zhu} }