سنجش درک فراگیران از مدل‌های‌ مفهومی و رابطه آن با مهارت استدلال علّی

Anderson, L. W., & Bloom, B. S. (2014). A taxonomy for learning, teaching, and assessing : a revision of Bloom’s. In TA - TT - (Pearson ne). Pearson. https://doi.org/ LK - https://worldcat.org/title/864384105

Blikstein, P., Fuhrmann, T., & Salehi, S. (2016). Using the Bifocal Modeling Framework to Resolve “Discrepant Events” Between Physical Experiments and Virtual Models in Biology. Journal of Science Education and Technology, 25(4), 513-526. https://doi.org/10.1007/s10956-016-9623-7

Bolger, M. S., Osness, J. B., Gouvea, J. S., & Cooper, A. C. (2021). Supporting Scientific Practice through Model-Based Inquiry: A Students’-Eye View of Grappling with Data, Uncertainty, and Community in a Laboratory Experience. CBE—Life Sciences Education, 20(4), ar59. https://doi.org/10.1187/cbe.21-05-0128

Burgin, S. R., Oramous, J., Kaminski, M., Stocker, L., & Moradi, M. (2018). High school biology students use of visual molecular dynamics as an authentic tool for learning about modeling as a professional scientific practice. Biochemistry and Molecular Biology Education, 46(3), 230-236. https://doi.org/10.1002/bmb.21113

Celebi, M. E., Kingravi, H. A., & Vela, P. A. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications, 40(1), 200-210. https://doi.org/10.1016/j.eswa.2012.07.021

Chiu, M.-H., & Lin, J.-W. (2019). Modeling competence in science education. Disciplinary and Interdisciplinary Science Education Research, 1(1), 12. https://doi.org/10.1186/s43031-019-0012-y

Crawford, B. A., & Cullin, M. J. (2004). Supporting prospective teachers’ conceptions of modelling in science. International Journal of Science Education, 26(11), 1379-1401. https://doi.org/10.1080/09500690410001673775

De Andrade, V., Shwartz, Y., Freire, S., & Baptista, M. (2022). Students’ mechanistic reasoning in practice: Enabling functions of drawing, gestures and talk. Science Education, 106(1), 199-225. https://doi.org/10.1002/sce.21685

Fortus, D., Shwartz, Y., & Rosenfeld, S. (2016). High School Students’ Meta-Modeling Knowledge. Research in Science Education, 46(6), 787-810. https://doi.org/10.1007/s11165-015-9480-z

Fuhrmann, T., Schneider, B., & Blikstein, P. (2018). Should students design or interact with models? Using the Bifocal Modelling Framework to investigate model construction in high school science. International Journal of Science Education, 40(8), 867-893. https://doi.org/10.1080/09500693.2018.1453175

Hofer, B. K. (2001). Personal Epistemology Research: Implications for Learning and Teaching. Educational Psychology Review, 13(4), 353-383. https://doi.org/10.1023/A:1011965830686

Hofer, S. I., Schumacher, R., & Rubin, H. (2017). The test of basic Mechanics Conceptual Understanding (bMCU): using Rasch analysis to develop and evaluate an efficient multiple choice test on Newton’s mechanics. International Journal of STEM Education, 4(1), 18. https://doi.org/10.1186/s40594-017-0080-5

Huang, S., Kang, Z., Xu, Z., & Liu, Q. (2021). Robust deep k-means: An effective and simple method for data clustering. Pattern Recognition, 117, 107996. https://doi.org/10.1016/j.patcog.2021.107996

Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210. https://doi.org/10.1016/j.ins.2022.11.139

Inkinen, J., Klager, C., Juuti, K., Schneider, B., Salmela-Aro, K., Krajcik, J., & Lavonen, J. (2020). High school students’ situational engagement associated with scientific practices in designed science learning situations. Science Education, 104(4), 667-692. https://doi.org/https://doi.org/10.1002/sce.21570

Iseki, H. (2020). Cohen’s kappa statistics as a convenient means to identify accurate SARS-CoV-2 rapid antibody tests. MedRxiv, 2020.06.13.20130070. https://doi.org/10.1101/2020.06.13.20130070

Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666. https://doi.org/10.1016/j.patrec.2009.09.011

Jansen, S., Knippels, M.-C. P. J., & van Joolingen, W. R. (2019). Assessing students’ understanding of models of biological processes: a revised framework. International Journal of Science Education, 41(8), 981-994. https://doi.org/10.1080/09500693.2019.1582821

Jarecki, J. B., Tan, J. H., & Jenny, M. A. (2020). A framework for building cognitive process models. Psychonomic Bulletin & Review, 27(6), 1218-1229. https://doi.org/10.3758/s13423-020-01747-2

Jimenez-Liso, M. R., Bellocchi, A., Martinez-Chico, M., & Lopez-Gay, R. (2022). A Model-Based Inquiry Sequence as a Heuristic to Evaluate Students’ Emotional, Behavioural, and Cognitive Engagement. Research in Science Education, 52(4), 1313-1334. https://doi.org/10.1007/s11165-021-10010-0

Jimenez-Liso, M. R., Martinez-Chico, M., Avraamidou, L., & López-Gay Lucio-Villegas, R. (2021). Scientific practices in teacher education: the interplay of sense, sensors, and emotions. Research in Science & Technological Education, 39(1), 44-67. https://doi.org/10.1080/02635143.2019.1647158

Jordan, R., Crall, A., Hmelo-Silver, C., Gray, S., Greg, N., & Sorensen, A. (2018). Developing Model-Building as a Scientific Practice in Collaborative Citizen Science. Natural Sciences Education, 47. https://doi.org/10.4195/nse2018.07.0013

Kang, H., Thompson, J., & Windschitl, M. (2014). Creating Opportunities for Students to Show What They Know: The Role of Scaffolding in Assessment Tasks. Science Education, 98. https://doi.org/10.1002/sce.21123

Kim, M. C., Hannafin, M. J., & Bryan, L. A. (2007). Technology-enhanced inquiry tools in science education: An emerging pedagogical framework for classroom practice. Science Education, 91(6), 1010-1030. https://doi.org/https://doi.org/10.1002/sce.20219

Kind, P. E. R., & Osborne, J. (2017). Styles of Scientific Reasoning: A Cultural Rationale for Science Education?. Science Education, 101(1), 8-31. https://doi.org/https://doi.org/10.1002/sce.21251

Krell, M., Reinisch, B., & Krüger, D. (2015). Analyzing Students’ Understanding of Models and Modeling Referring to the Disciplines Biology, Chemistry, and Physics. Research in Science Education, 45(3), 367-393. https://doi.org/10.1007/s11165-014-9427-9

Krist, C., Schwarz, C. V, & Reiser, B. J. (2019). Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning. Journal of the Learning Sciences, 28(2), 160-205. https://doi.org/10.1080/10508406.2018.1510404

Lazenby, K., & Becker, N. M. (2021). Evaluation of the students’ understanding of models in science (SUMS) for use in undergraduate chemistry. Chemistry Education Research and Practice., 22(1), 62-76. https://doi.org/10.1039/D0RP00084A

Lehrer, R., & Schauble, L. (2015). The Development of Scientific Thinking. In Handbook of Child Psychology and Developmental Science (pp. 1–44). https://doi.org/https://doi.org/10.1002/9781118963418.childpsy216

Mashahizade, H. (2023) Assessment the level of elementary school teachers' model and modeling understanding in science education [Unpublished master’s thesis]. Shahid chamran University of Ahvaz, Ahvaz, Iran.

National Research Council. (2012). A framework for K–12 science education: Practices crosscutting concepts, and core ideas. National Academies Press.

NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: National Academies Press.

Nguyen, H., & Santagata, R. (2021). Impact of computer modeling on learning and teaching systems thinking. Journal of Research in Science Teaching, 58(5), 661-688. https://doi.org/10.1002/tea.21674

Nielsen, S. S., & Nielsen, J. A. (2021). A Competence-Oriented Approach to Models and Modelling in Lower Secondary Science Education: Practices and Rationales Among Danish Teachers. Research in Science Education, 51(2), 565-593. https://doi.org/10.1007/s11165-019-09900-1

Nimon, K., Henson, R., & Gates, M. (2010). Revisiting Interpretation of Canonical Correlation Analysis: A Tutorial and Demonstration of Canonical Commonality Analysis. Multivariate Behavioral Research - MULTIVARIATE BEHAV RES, 45, 702-724. https://doi.org/10.1080/00273171.2010.498293

Pituch, K. A., & Stevens, J. (2016). Applied multivariate statistics for the social sciences. In TA - TT - (Sixth edit). Routledge. https://doi.org/LK-https://worldcat.org/title/952863240

Prezenski, S., Brechmann, A., Wolff, S., & Russwinkel, N. (2017). A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.01335

Rosenberg, J., & Lawson, M. (2019). An Investigation of Students’ Use of a Computational Science Simulation in an Online High School Physics Class. Education Sciences, 9(49), 1-19. https://doi.org/10.3390/educsci9010049

Samon, S., & Levy, S. T. (2021). The Role of Physical and Computer-Based Experiences in Learning Science Using a Complex Systems Approach. Science & Education, 30(3), 717-753. https://doi.org/10.1007/s11191-020-00184-w

Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632–654. https://doi.org/10.1002/tea.20311

Schwarz, C. V, & White, B. Y. (2005). Metamodeling Knowledge: Developing Students' Understanding of Scientific Modeling. Cognition and Instruction, 23((2)), 165. https://doi.org/10.1207/s1532690xci2302_1

Sins, P. H. M., Savelsbergh, E. R., van Joolingen, W. R., & van Hout‐Wolters, B. H. A. M. (2009). The Relation between Students’ Epistemological Understanding of Computer Models and their Cognitive Processing on a Modelling Task. International Journal of Science Education, 31(9), 1205–1229. https://doi.org/10.1080/09500690802192181

Sjøberg, M., Furberg, A., & Knain, E. (2023). Undergraduate biology students’ model-based reasoning in the laboratory: Exploring the role of drawings, talk, and gestures. Science Education, 107(1), 124-148. https://doi.org/https://doi.org/10.1002/sce.21765

Sun, D., Looi, C.-K., & Wenting, X. (2014). Collaborative Inquiry with a Web-Based Science Learning Environment: When Teachers Enact It Differently. Educational Technology & Society, 14, 390-403.

Treagust, D. F., Chittleborough, G., & Mamiala, T. L. (2002). Students’ understanding of the role of scientific models in learning science. International Journal of Science Education, 24(4), 357-368. https://doi.org/10.1080/09500690110066485

Tytler, R., Prain, V., Aranda, G., Ferguson, J., & Gorur, R. (2020). Drawing to reason and learn in science. Journal of Research in Science Teaching, 57(2), 209-231. https://doi.org/https://doi.org/10.1002/tea.21590

Wade-Jaimes, K., Demir, K., & Qureshi, A. (2018). Modeling strategies enhanced by metacognitive tools in high school physics to support student conceptual trajectories and understanding of electricity. Science Education, 102(4), 711–743. https://doi.org/10.1002/sce.21444

Xiang, L., & Passmore, C. (2015). A Framework for Model-Based Inquiry Through Agent-Based Programming. Journal of Science Education and Technology, 24(2), 311-329. https://doi.org/10.1007/s10956-014-9534-4