A Brain-Friendly Teaching Inventory: A Rasch-based Model Validation
Subject Areas : آموزش زبان انگلیسی
انسیه ستاری گوارشک
1
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مونا طباطبایی یزدی
2
1 - گروه انگلیسی دانشگاه غیرانتفاعی تابران، مشهد، ایران
2 - گروه انگلیسی دانشگاه غیرانتفاعی تابران، مشهد، ایران
Keywords: validity, Rasch model, EFL teachers, Brain-friendly teaching, Scale development,
Abstract :
Teachers usually teach according to how brains naturally learn. In this way, not only do their learners learn, retain, and recall quickly, but also the teaching becomes more joyful. Increased attention to the worthwhile role of the mind in learning/teaching in recent times Due to the lack of a valid scale for estimating teachers' awareness of brain-friendly teaching, the current study intended to construct and validate a 54-item brain-friendly teaching inventory by the implementation of the Rasch model. The test was administered to 200 Iranian EFL teachers from different educational contexts. The results revealed that all the 54 items of the scale had a good fit to the Rasch model. Infit and outfit values were within the acceptable range which indicates unidimensionality of the scale. Furthermore, it is asserted that the inventory enjoyed suitable reliability. This demonstrates that the Brain-Friendly Teaching Inventory is valid and can be applied as a scale for assessing the teachers' awareness of brain-friendly teaching.
Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561-573.
Baghaei, P. & Shoahosseini, R. (2019). A note on the Rasch model and the instrument-based account of validity. Rasch Measurement Transactions, 32, 1705-1708.
Baghaei, P. (2008). The Rasch model as a construct validation tool. Rasch Measurement Transactions, 22, 1145-1146.
Baghaei, P., & Tabatabaee-Yazdi, M. (2016). The logic of latent variable analysis as validity evidence in psychological measurement. The Open Psychology Journal, 9, 168-175.
Battro, A., Fischer, K., & Lena, P. (2008). The educated brain: Essays in neuroeducation. New York: Cambridge University Press.
Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences. : Mahwah, NJ: Lawrence Erlbaum.
Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111, 1061-1071.
Cahill, L. (2000). Neurobiological mechanisms of emotionally influenced, long-term memory. Progress in Brain Research, 126, 29-37.
Caine, R., & Caine, G. (1994). Making Connections: Teaching and the Human Brain. Rev. Ed. Menlo Park, Calif: Addison Wesley.
Caine, R., & Caine, G. (1997). Unleashing the power of perceptual change: The potential of Brain-based Teaching. Alexandria, Va: Association for Supervision and Curriculum development.
Caine, R. N., & Caine, G. (1991). Making connections: Teaching and the human brain. Association for Supervision and Curriculum Development: Alexandria, VA.
Caine, R. N., Caine, G., McClinti, C., & Klimek, K. (2005). 12Brain/mind learning principles in action. Corwin Press: Thousand Oaks.
Duman, B. (2010). The effects of brain-based learning on the academic achievement of students with different learning styles. Educational Sciences: Theory and Practice,10(4),2077-2103
Fatima, H. G., Quraishi, U., & Khanam, A. (2020). Applying Brain Based Learning Modules for Learning Acceleration of 6th Grade Science Students. SJESR, 3(1), 27-34.
Gao, X., & Xu, H. (2014). The dilemma of being English language teachers: Interpreting teachers’ motivation to teach, and professional commitment in China’s hinterland regions. Language Teaching Research, 18(2), 152-168.
Ghanbari, S., Haghani, F., & Akbarfahimi, M. (2019). Practical points for brain-friendly medical and health sciences teaching. Journal of education and health promotion, 8(1). doi:10.4103/jehp.jehp_135_19
Gu, S., Lillicrap, T., Ilya, I., & Sergey, S. (2016). Continuous deep q-learning with model-based acceleration. Paper presented at the International Conference on Machine Learning.
Janesick, V. J. (2001). The assessment debate: A reference handbook. California: ABCCLIO, Inc.
Kagan, S. (2014). Brian-friendly teaching. USA: Kagan publishing.
King, R., J. (2001). Brain-friendly techniques for improving memory. Educational Leadership, 59(3), 76-79.
Lewis, P. J. (2016). Brain friendly teaching—reducing learner's cognitive load. Academic Radiology, 23(7), 877-880.
Linacre, J. M. (2009). A user’s guide to WINSTEPS: Chicago, IL: Winsteps.
Magnesen, V. A. (1983). A review of findings from learning and memory retention studies. Innovation Abstracts 5, 25. doi:https://eric.ed.gov/?id=ED234878
Moodie, I. A. N., & Feryok, A. (2015). Beyond cognition to commitment: English language teaching in South Korean primary schools. The Modern Language Journal, 99(3), 450-469.
Rasch, G. (1960/1980). Probabilistic models for some intelligence and attainment tests. In Copenhagen: Danish Institute for Educational Research 1960: (Expanded edition, Chicago: The University of Chicago Press, 1980).
Rehman, A. U., & Bokhari, M., A. (2011). Effectiveness of brain-based theory at secondary level. International Journal of Academic Research, 3(4), 354-359.
Satria, E. (2020). Improving students’ scientific skills, cognitive learning outcomes, and learning Interest in natural science in class IV by using brain based learning approach with science kit at SD Negeri 34 Kuranji Padang. Proceedings International Conference on Mathematics, Sciences and Education. doi:10.31219/osf.io/9fj6e
Siercks, E. (2012). Public school teachers' gender, years of teaching experience, knowledge, and perceptions as predictors of their implementation of brain-based learning practices in K-12 classrooms. Retrieved from https://dx.doi.org/10.32597/dissertations/1689
Smilkstein, R. (2003). We’re born to learn. Calif: Corwin Press: Thousand oaks.
Solihatin, E., & Syahrial, Z. (2019). The effects of Brain-based learning and Project-based learning strategies on student group mathematics learning outcomes student visual learning styles. Pedagogical Research, 4(4), em0047.
Sousa, D. A. (2011). How the brain learns. Thousand Oaks, CA: Corwin.
Sousa, D. A. (2015). Brain-Friendly assessment. United States: Learning Sciences.
Sprenger, M. (2006). Becoming a "wiz" a brain-based teaching. Thousand Oaks, Calif: Corwin press.
Squire, L. R., & Kandel, E. R. (2000). Neuroscience: Breaking down scientific barriers to the study of brain and mind. Science, 290, 1113-1120. doi:10.1126/science.290.5494.1113
Tabatabaee-Yazdi, M. (2020). Hierarchical Diagnostic Classification Modeling of Reading Comprehension. Sage Open, 10(2). https://doi.org/10.1177%2F2158244020931068
Tabatabaee-Yazdi, M., Motallebzadeh, K., & Baghaei, P. (2021). A Mokken Scale Analysis of an English Reading Comprehension Test. International Journal of Language Testing, 11(1), 132-143.
Tommerdhal, J. (2010). A model for bringing the gap between neuroscience and education. Oxford Review of Education, 36(1), 97-109
.Van Veen, K., Sleegers, P., & Van de Ven, P. H. (2005). One teacher's identity, emotions, and commitment to change: A case study into the cognitive–affective processes of a secondary school teacher in the context of reforms. Teaching and Teacher Education, 21(8), 917-934.
Varghese, M. G., & Pandya, S. (2016). A study on the effectiveness of brain-based-learning of students of secondary level on their academic achievement in biology, study habits and stress. International Journal of Humanities, 5(2), 103-122.
Wiggins, G. P. (1993). Assessing student performance: Exploring the purpose and limits. Netherlands: Jossy-Bass.
Willis, J. (2007). Brain-friendly strategies for the inclusion classroom: insights from a neurologist and classroom teacher. Dallas USA: Association for supervision & curriculum Dave.
Winarso, W., & Karimah, S. A. (2017). The influence of implementation brain-friendly learning through the whole brain teaching to students’ response and creative character in learning mathematics. Jurnal Pendidikan dan Pengajaran, 50(1). doi:http://dx.doi.org/10.2139/ssrn.2948569