Main Article Content
This study aimed to investigate the attitude of rural teachers towards the problem in the current state of learning materials, school curriculum, simulation software, and argument-based inquiry activity for enhancing middle school’s scientific reasoning ability. The survey research method was used in this study, and data were collected from 42 small, medium, large, and extra-large schools in Sisaket province (northeastern of Thailand) using 105 questionnaires. The research findings reveal that the analysis of descriptive statistics and PNImod showed that the essential features of simulation, which could be improved, were displaying a relationship between multivariable, feedback, and result. Likewise, the argument-based inquiry could be empowered with an emphasis on evidence or question-based evidence, presentation of evidence supporting the claim, presentation of a counter-argument, a stipulation of a conflict of arguments, and use of mathematics, information, information technologies, or computational thinking.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Acar, Ö. (2014a). Scientific reasoning, conceptual knowledge, & achievement differences between prospective science teachers having a consistent misconception and those having a scientific conception in an argumentation-based guided inquiry course. Learning and Individual Differences, 30, 148-154. DOI: 10.1016/j.lindif.2013.12.002
Acar, Ö. (2014b). Scientific reasoning, conceptual knowledge, & achievement differences between prospective science teachers having a consistent misconception and those having a scientific conception in an argumentation-based guided inquiry course. Learning and Individual Differences, 30, 148-154. DOI: 10.1016/j.lindif.2013.12.002
Aldrich, C. (2009). Learning online with games, simulations, and virtual worlds: Strategies for online instruction. Wiley.
Amgoud, L., & Kaci, S. (2007). An argumentation framework for merging conflicting knowledge bases. International Journal of Approximate Reasoning, 45(2), 321-340. DOI: 10.1016/j.ijar.2006.06.014
Bolduc, J.-S. (2014). Narrow and broad styles of scientific reasoning: A reply to O. Bueno. Studies in History and Philosophy of Science Part A, 47(0), 104-110. DOI: 10.1016/j.shpsa.2014.03.007
De La Paz, S., Ferretti, R., Wissinger, D., Yee, L., & MacArthur, C. (2012). Adolescents’ Disciplinary use of evidence, argumentative strategies, and organizational structure in writing about historical controversies. Written Communication, 29(4), 412-454. DOI: 10.1177/0741088312461591
Dennis, C. W., Dorsey, J. A., & Gitlow, L. (2015). A call for sustainable practice in occupational therapy: Un appel à la pratique durable en ergothérapie. Canadian Journal of Occupational Therapy. DOI: 10.1177/0008417414566925
Efstathiou, C., Hovardas, T., Xenofontos, N. A., Zacharia, Z. C., deJong, T., Anjewierden, A., & van Riesen, S. A. N. (2018). Providing guidance in virtual lab experimentation: The case of an experiment design tool. Educational Technology Research and Development, 66(3), 767-791. DOI: 10.1007/s11423-018-9576-z
Engelmann, K., Neuhaus, B. J., & Fischer, F. (2016). Fostering scientific reasoning in education meta-analytic evidence from intervention studies. Educational Research and Evaluation, 22(5-6), 333-349. DOI: 10.1080/13803611.2016.1240089
Gilbert, G. E., & Prion, S. (2016). Making sense of methods and measurement: Lawshe's Content Validity Index. Clinical Simulation in Nursing, 12(12), 530-531. DOI: 10.1016/j.ecns.2016.08.002
Gillies, R. M., Nichols, K., Burgh, G., & Haynes, M. (2014). Primary students’ scientific reasoning and discourse during cooperative inquiry-based science activities. International Journal of Educational Research, 63(0), 127-140. DOI: 10.1016/j.ijer.2013.01.001
Grigg, J., Kelly, K. A., Gamoran, A., & Borman, G. D. (2013). Effects of two scientific inquiry professional development interventions on teaching practice. Educational Evaluation and Policy Analysis, 35(1), 38-56. DOI: 10.3102/0162373712461851
Harpe, S. E. (2015). How to analyze Likert and other rating scale data. Currents in Pharmacy Teaching and Learning, 7(6), 836-850. DOI: 10.1016/j.cptl.2015.08.001
Hoban, G., & Nielsen, W. (2014). Creating a narrated stop-motion animation to explain science: The affordances of “Slowmation” for generating discussion. Teaching and Teacher Education, 42(0), 68-78. DOI: 10.1016/j.tate.2014.04.007
Hodson, D. (2014). Learning Science, Learning about Science, Doing Science: Different goals demand different learning methods. International Journal of Science Education, 36(15), 2534-2553. DOI: 10.1080/09500693.2014.899722
Hsu, C.-C., Chiu, C.-H., Lin, C.-H., & Wang, T.-I. (2015). Enhancing skill in constructing scientific explanations using a structured argumentation scaffold in scientific inquiry. Computers & Education, 91, 46-59. DOI:10.1016/j.compedu.2015.09.009
Kaizer, J. S., Heller, A. K., & Oberkampf, W. L. (2015). Scientific computer simulation review. Reliability Engineering & System Safety, 138, 210-218. DOI: 10.1016/j.ress.2015.01.020
Kant, J. M., Scheiter, K., & Oschatz, K. (2017). How to sequence video modeling examples and inquiry tasks to foster scientific reasoning. Learning and Instruction, 52, 46-58. DOI: 10.1016/j.learninstruc.2017.04.005
Köksal-Tuncer, Ö., & Sodian, B. (2018). The development of scientific reasoning: Hypothesis testing and argumentation from evidence in young children. Cognitive Development, 48, 135-145. DOI: 10.1016/j.cogdev.2018.06.011
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610. DOI: 10.1177/001316447003000308
Landriscina, F. (2013). Simulation and Learning: A Model-Centered Approach: Springer New York.
Lazonder, A. W., Hagemans, M. G., & Jong, T. d. (2010). Offering and discovering domain information in simulation-based inquiry learning. Learning and Instruction, 20, 511-520.
Liu, T.-C., Kinshuk, Lin, Y.-C., & Wang, S.-C. (2012). Can verbalisers learn as well as visualisers in simulation-based CAL with predominantly visual representations? Preliminary evidence from a pilot study. British Journal of Educational Technology, 43(6), 965-980. DOI: 10.1111/j.1467-8535.2011.01262.x
Mayer, D., Sodian, B., Koerber, S., & Schwippert, K. (2014). Scientific reasoning in elementary school children: Assessment and relations with cognitive abilities. Learning and Instruction, 29(0), 43-55. DOI: 10.1016/j.learninstruc.2013.07.005
O’Hallaron, C. L. (2014). Supporting Fifth-Grade ELLs’ argumentative writing development. Written Communication, 31(3), 304-331. DOI: 10.1177/0741088314536524
OECD. (2016). PISA 2015 Results: Excellence and equity in education science performance among 15–year–olds [eBook]. DOI: 10.1787/9789264266490-6-en
Phanchalaem, K., Sujiva, S., & Tangdhanakanond, K. (2016). The state of teachers’ educational data use in Thailand. Procedia - Social and Behavioral Sciences, 217, 638-642. DOI: 10.1016/j.sbspro.2016.02.084
Polit, D. F., Beck, C. T., & Owen, S. V. (2007). Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Research in Nursing & Health, 30(4), 459-467. DOI: 10.1002/nur.20199
Psycharis, S. (2013). Examining the effect of the computational models on learning performance, scientific reasoning, epistemic beliefs and argumentation: An implication for the STEM agenda. Computers & Education, 68, 253-265. DOI: 10.1016/j.compedu.2013.05.015
Seechaliao, T. (2010). A proposed model of instructional design and development based on engineering creative problem solving principles to develop creative thnking skills of undergraduate engineering students. Chulalognkorn University, Bangkok.
Seehamat, L., Sarnrattana, U., Tungkasamit, A., & Srisawasdi, N. (2014). Needs assessment for school curriculum development about water resources management: A case study of Nam Phong Basin. Procedia - Social and Behavioral Sciences, 116, 1763-1765. DOI: 10.1016/j.sbspro.2014.01.469
Thuneberg, H., Hautamäki, J., & Hotulainen, R. (2014). Scientific reasoning, school achievement and gender: A multilevel study of between and within school effects in Finland. Scandinavian Journal of Educational Research, 1-20. DOI: 10.1080/00313831.2014.904426
Vallverdú, J. (2014). What are simulations? An epistemological approach. Procedia Technology, 13, 6-15. DOI: 10.1016/j.protcy.2014.02.003
Voss, J. F., & Means, M. L. (1991). Learning to reason via instruction in argumentation. Learning and Instruction, 1, 337-350.
Weber, D. N., Hesselbach, R., Kane, A. S., Petering, D. H., Petering, L., & Berg, C. A. (2013). Minnows as a classroom model for human environmental health. The American Biology Teacher, 75(3), 203-209. Retrieved from http://abt.ucpress.edu/content/ 75/3/203.abstract
Wongwanich, S., Sakolrak, S., & Piromsombat, C. (2014). Needs for Thai teachers to become a reflective teacher: Mixed methods needs assessment research. Procedia - Social and Behavioral Sciences, 116, 1645-1650. DOI: 10.1016/j.sbspro.2014.01.450
yavuz, o., parzych, j., & generali, m. (2017). a systematic approach to exploring college and career readiness program needs within high-poverty urban public schools. Education and Urban Society, 51(4), 443-473. DOI: 10.1177/0013124517727054
Zendler, A., & Greiner, H. (2020). The effect of two instructional methods on learning outcome in chemistry education: The experiment method and computer simulation. Education for Chemical Engineers, 30, 9-19. DOI: 10.1016/j.ece.2019.09.001
Zhang, X., Anderson, R. C., Morris, J., Miller, B., Nguyen-Jahiel, K. T., Lin, T.-J., Hsu, J. Y.-L. (2015). Improving children’s competence as decision makers: Contrasting effects of collaborative interaction and direct instruction. American Educational Research Journal. DOI: 10.3102/0002831215618663
Zhu, M., Liu, O. L., & Lee, H.-S. (2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education, 143, 103668. DOI: 10.1016/j.compedu.2019.103668