تدوین و اعتبار سنجی پرسشنامه سنجش میزان آگاهی مدرسین زبان انگلیسی از آموزش سازگاربا مغز

Subject Areas : آموزش زبان انگلیسی

انسیه  ستاری گوارشک ¹ (گروه انگلیسی دانشگاه غیرانتفاعی تابران، مشهد، ایران)
مونا  طباطبایی یزدی ² (گروه انگلیسی دانشگاه غیرانتفاعی تابران، مشهد، ایران)

Received: 2022-03-17 Accepted : 2022-03-17 Published : 2022-04-01

Keywords: مدل راش, اعتبارسنجی, آموزش سازگاربا مغز, مدرسین زبان انگلیسی, تدوین پرسشنامه,

Abstract :

معلمان معمولاً بر اساس نحوه یادگیری طبیعی مغز آموزش می دهند. به این ترتیب، نه تنها فراگیران به سرعت یاد می گیرند، حفظ می کنند و به یاد می آورند، بلکه آموزش نیز لذت بخش تر می شود. افزایش توجه به نقش ارزشمند ذهن در یادگیری وتدریس در دوران اخیر، علاوه بر فقدان پرسشنامه ای معتبر برای تخمین آگاهی معلمان از آموزش سازگاربا مغز، هدف پژوهش حاضر، تدوین و اعتبارسنجی یک پرسشنامه 54 ماده ای آموزش سازگاربا مغز با استفاده ازمدل راش است. این پرسشنامه توسط 200 معلم زبان انگلیسی زبان ایرانی درزمینه های مختلف آموزشی تکمیل گردید. نتایج نشان داد که تمامی 54 آیتم پرسشنامه تناسب خوبی با مدل راش دارند. مقادیر تناسب و عدم تناسب در محدوده قابل قبولی بودند که نشان دهنده تک بعدی بودن پرسشنامه است. این نشان می دهد که پرسشنامه آموزش سازگاربا مغزمعتبر است و می تواند به عنوان مقیاسی برای ارزیابی آگاهی معلمان از آموزش آموزش سازگاربا مغز استفاده شود.

References:

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