نقش شایستگی‌های دیجیتالی معلمان، تمایل به تدریس آنلاین و مشکلات یادگیری دانش‌آموزان در پیش‌بینی رفتار تدریس آنلاین

نوع مقاله : علمی- پژوهشی

نویسنده

استادیار، سازمان پژوهش و برنامه‌ریزی آموزشی، تهران، ایران

10.22055/edus.2022.40050.3335

چکیده

هدف از پژوهش حاضر بررسی این موضوع بود که آیا شایستگی‌های دیجیتالی معلمان، تمایل به استفاده از تدریس آنلاین و مشکلات یادگیری آنلاین دانش‌آموزان، رفتار تدریس آنلاین را پیش‌بینی می‌کند. روش پژوهش توصیفی و از نوع همبستگی بود. جامعه آماری پژوهش، شامل کلیه معلمان مقاطع ابتدایی و متوسطه شهر تهران در سال تحصیلی 1401-1400 بود که با استفاده از روش نمونه‌گیری در دسترس 324 نفر از آن‌ها به عنوان نمونه پژوهش انتخاب شدند. اعضای نمونه به مقیاس‌های خودگزارشی شایستگی دیجیتالی معلم، تمایل به استفاده از تدریس آنلاین و رفتار تدریس آنلاین معلم و پرسشنامه مشکلات یادگیری آنلاین دانش‌آموزان ادراک شده توسط معلم به صورت برخط پاسخ دادند. نتایج حاصل از ضریب همبستگی نشان داد رفتار تدریس آنلاین با شایستگی‌های دیجیتالی معلمان و تمایل به استفاده از تدریس آنلاین رابطه مثبت و معنی‌دار داشت و با مشکلات یادگیری آنلاین دانش‌آموزان این رابطه منفی و معنی‌دار به دست آمد. همچنین نتایج تحلیل رگرسیون چندگانه به روش همزمان نشان داد شایستگی‌های دیجیتالی معلمان، تمایل به استفاده از تدریس آنلاین و مشکلات یادگیری آنلاین دانش‌آموزان قادرند در قالب یک مدل پیش‌بین به طور معنی‌داری تغییرات رفتار تدریس آنلاین معلمان را به عنوان متغیر ملاک تبیین و پیش‌بینی کنند. یافته‌های این پژوهش می‌تواند به درک بهتر تأثیر ویژگی‌های معلمان شامل شایستگی‌های دیجبتالی و تمایل به استفاده از تدریس آنلاین و ویژگی‌های دانش‌آموزان شامل مشکلات یادگیری آنلاین بر رفتار تدریس آنلاین کمک نماید.

کلیدواژه‌ها


عنوان مقاله [English]

The role of teachers' digital competencies, use intention of online teaching and students' Online learning difficulties in predicting online teaching behavior

نویسنده [English]

  • Sara Ebrahimi
Assistant Professor, Faculty member of Organization for Educational Research and Planning, Tehran, Iran.
چکیده [English]

Corona epidemics have forced educational systems in many countries to use online education and adapt to digital learning environments.Despite many benefits of online education, such as unlimited time and space,resource sharing and collaboration, openness and personalization of learning,but it can be frustrating and stressful because many teachers lack the skills, resources and competencies of online education.A review of research evidence shows that a large amount of research activity is devoted to teachers' digital competencies, but information on how this feature,along with other teacher features such as use intention of online teaching and student features such as online learning difficulties affects on their online teaching behavior, are not available.Thus,the aim of this study was to investigate whether teachers'digital competencies,use intention of online teaching and students' online learning difficulties predict online teaching behavior.
In this correlation study, the population was all teachers of primary and secondary schools in Tehran in the academic year1400-1401,which324teachers were selected with use of convenience sampling.They responded online to the Teachers' Digital Competence Scale,Teachers' Use Intention of Online Teaching Scale,Teachers'Online Teaching Behavior Scale&Teachers'Perceived Online Learning Difficulties of Students Questionnaire.Pearson correlation coefficient and multiple regression analysis were used to analyze the data.
The results showed that the online teaching behavior had a positive andsignificant relationshipwith teachers'digital competencies and the use intention of online teaching, and this relationship was negatively and significantlywith students'online learning difficulties.Also, the resultsof multiple regression analysis showed that teachers'digital competencies, use intention of online teaching and students'online learning difficulties, are able tosignificantly explain changes in teachers'online teaching behavior as predictor variable in a predictive model.
Based on the research findings, it is necessaryfor teachers in online teaching to improve their digital skills and competencies in accessing and using resources, analyzing data related to students' learning characteristics, and combining digital resourceswith educational content to produce more comprehensible content, in fact, modify and improve online teaching behaviors.Also,online teaching has more requirements for teachers than traditional classroom teaching. People use digital technology and resources to varying degrees;Therefore, it is necessary for teachers in teaching activities to strengthen their desire to use online education and increase their awareness of integrating information technology with educational activities by avoiding mechanical transfer to offline-online education.Overall, the present study adds insights into improving online teaching behavior&can help to better understand the effect of teachers'characteristics including digital competencies and use intention of online teaching and students' characteristics including online learning difficulties on online teaching behavior.

کلیدواژه‌ها [English]

  • online teaching behavior
  • students' online learning difficulties
  • teachers' digital competencies
  • use intention of online teaching
Bai, X. M., & Gu, X. Q. (2020). What makes It difficult to use technology to its full potential in the classroom? A study on the factors influencing teachers’ behavioral intention to teach with information technology based on cognitive & affective perspectives. Open Edu. Res, 26, 86–94.
Bicer, A., & Capraro, R. M. (2016). Longitudinal effects of technology integration & teacher professional development on students’ mathematics achievement. Eurasia J. Math. Sci, 13, 815–833.
Cai, H. H. (2021). Research on the correlation of teachers' e-readiness & students' learning effect: The mediation effect of learner control & academic emotions. J. East China Normal University: Educational Sci, 7, 27–37.
Chai, X., Gong, S., Duan, T., Zhong, L., & Jiao, Y. (2011). A social-cognitive approach to motivational contagion between teacher & student. Adv. Psychol. Sci, 19, 1166–1173.
Clarebout, G., & Elen, J. (2006). Tool use in computer-based learning environments: towards a research framework. Comput. Hum. Behav, 22,389–411.
Crow, J., & Murray, J. A. (2020). Online distance learning in biomedical sciences: community, belonging & presence, in biomedical visualisation. Advances in Experimental Medicine & Biology. Editor P. Rea, 1235.
Delahunty, J., Verenikina, I., & Jones, P. (2014). Socio-emotional connections: identity, belonging & learning in online interactions. A literature review. Technol. Pedagogy Edu, 23, 243–265.
Ebrahimi, S. (2020). Evaluation of teachers’ generalized anxiety disorder during the COVID-19 pandemic. Journal of Educational Sciences, 27(2), 45-68. [Persian]
Fredricks, J. A., Filsecker, M., & Lawson, M. A. (2016). Student engagement, context, & adjustment: addressing definitional, measurement, & methodological issues. Learn. Instruction, 43, 1–4.
Ge, W. S., & Han, S. B. (2017). A standard framework for teachers’ teaching competence in the digital age. Mod. Distance Edu. Res, 145, 59–67.
Goodhue, D., & Thompson. (1995). Task-technology fit & individual performance. MIS Quarterly, 19(2), 213-236.
Hall, R., & Batty, D. (2020). I can’t get motivated: The students struggling with online learning. United Kingdom: The Guardian.
Hill, P. (2020). Revised outlook for higher ed’s online response to COVID-19.
Hong, A. J., & Kim, H. J. (2018). College students’ digital readiness for academic engagement Scale: Scale development & validation. APER, 27, 303–312.
Huang, R. H. (2011). Education informatization helps current educational changes: opportunities & challenges. China Educ. Tech, 288, 36–40.
Kabakci Yurdakul, I., & Coklar, A. N. (2014). Modeling preservice teachers’ TPACK competencies based on ICT usage. JCAL, 30(4), 363–376.
Knutsson, O., Blåsjö, M., Hållsten, S., & Karlström, P. (2012). Identifying different registers of digital literacy in virtual learning environments. Internet Higher Edu, 15, 237–246.
Lan, G. S., Guo, Q., Zhang, Y., Kong, X. K., & Guo, X. J. (2020). Digital literacy framework for educators in the European Union: Interpretation of key points & insights. Mod. Distance Edu. Res, 32, 23–32.
Lei, W. P. (2018). Three misconceptions of education information technology policy research. Educ. Res. Exp, 6, 1–6.
Li, S. L. (2005). Introductory remarks on analysis of classroom instructional behaviors. Theor. Pract. Educ, 25, 48–51.
Li, W., Gao, W., & Sha, J. (2020). Perceived teacher autonomy support & school engagement of Tibetan students in elementary & middle schools: Mediating effect of self-efficacy & academic emotions. Front. Psychol,11,50.
Li, W., Gao, W. Y., & Fu, W. D. (2021). When does teacher dupport reduce depression in students? The moderating role of students’ status as left-behind children. Front. Psychol, 12, 608359.
Li, W., Gao, W. Y., Fu, W. D., & Chen, Y. (2021). A moderated mediation model of the relationship between primary & secondary school teachers’ digital competence & online teaching behavior. Digital Competence & Online Teaching, 6, 1-11.
Lowenthal, P. R., & Dennen, V. P. (2017). Social presence, identity, & online learning: research development & needs. Distance Edu, 38, 137–140.
Ma, J. (2020). A study of the mechanisms influencing college students’ learning engagement in a Hybrid Teaching Environment: A perspective of teaching behavior. Distance Edu. China, 2, 57–67.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teach. Coll. Rec, 108, 1017–1054.
Mosayebi, M., Rezapour Mirsaleh, Y., & Behjati Ardakani, F. (2021). The problems & challenges of virtual education in elementary school during the outbreak of coronavirus. Quarterly Journal of Education Studies, 7-27, 65-79. [Persian]
Nguyen, N. T., Thai, T. V., Pham, H. T., & Nguyen, G. C. T. (2020). Cdio approach in developing teacher training program to meet requirement of the industrial revolution 4.0 in Vietnam. Int. J. Emerg. Technol, 15, 108–123.
Owens, J. K., & Hudson, A. K. (2021). Prioritizing teacher emotions: shifting teacher Training to a digital environment. Education Tech Res.Dev, 69,59–62.
Perifanou, M., Economides, A. A., & Tzafilkou, K. (2021). Teachers’ digital skills readiness during Covid-19 pandemic. Int. J. Emerg. Technol, 16, 238–251.
Porras-Hernández, L. H., Hernández, M. L., & Alva, M. (2010). Integración de tic al currículum de telesecundaria: incidiendo en procesos del pensamiento desde el enfoque comunicativo funcional de la lengua. Revista Mexicana De Investigación Educativa, 15, 515–551.
Porras-Hernández, L. H., & Salinas-Amescua, B. (2013). Strengthening TPACK: A broader notion of context & the use of teacher’s narratives to reveal knowledge construction. J. Educ. Comput. Res, 48, 223–244.
Santos, J. M., & Castro, R. D. R. (2021). Technological Pedagogical Content Knowledge (TPACK) in action: application of learning in the classroom by pre-service teachers (Pst). Social Sci. Humanities Open, 3 (1), 100110.
Shi, L. F., & Cui, Y. K. (1999). Teaching theory: principles, strategies, & research on classroom teaching. Shanghai: East China Normal University Press.
Slater, H., Davies, N. M., & Burgess, S. (2012). Do teachers matter? measuring the variation in teacher effectiveness in England. Oxf. Bull. Econ. Stat, 74,629–645.
Starkey, L. (2019). A review of research exploring teacher preparation for the digital age. Cambridge J. Edu. 50, 37–56.
Tahmasebizadeh, Z., Rahimidoost, Gh., & Khalifeh, Gh. (2020). Designing & validating technologial competencies scale for primary teachers. Journal of Educational Sciences, 27(1), 241-262. [Persian]
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Inf. Syst. Res, 5, 91–108.
Tondeur, J., Scherer, R., Baran, E., Siddiq, F., Valtonen, T., & Sointu, E. (2019). Teacher educators as gatekeepers: preparing the next generation of teachers for technology integration in education. Br. J. Educ. Technol, 50, 1189–1209.
Uerz, D., Volman, M., &Kral, M. (2018). Teacher educators’competences in fostering student teachers’ proficiency in teaching & learning with technology: an overview of relevant research literature. Teach. Teach.Edu,70, 12–23.
Wang, J. X., Wei, Y. T., & Zong, M. (2020). The Current situation, problems & reflection of online teaching for primary & secondary school teachers during a large-scale epidemic based on the investigation & analysis of classes suspended but learning continues in hubei province. China Educ. Tech, 5, 15–21.
Yang, G. X., & Hu, J. J. (2014). In the view of ecological sight interpret IT support classroom teaching. China Educ. Tech, 331, 100–104+110.
Yu, S. Q., & Wang, H. M. (2020). How to better organize online learning in extreme situations such as epidemics. China Educ. Tech. 5 (6–14), 33.
Zeng, Z. M., & Zheng, A. A. (2019). Study on factors influencing learners’ willingness to continuously use MOOC platform in Chinese universities. Chin. J. Ict Edu, 16, 28–33.
Zhai, X. (2021). Advancing automatic guidance in virtual science inquiry: from ease of use to personalization. Edu. Tech Res. Dev, 69, 255–258.
Zhang, J. (2014). Connotations & Features of TPACK from Three Different Perspectives. Distance Edu. J, 32, 87–95.
Zhang, K. (2018). A study on elementary school teachers’ intention to use online teaching resources. Zhengzhou: Henan University, 113107. Dissertation’s thesis.
Zhang, Y., Zhu, Y., Bai, Q. Y., Li, X. Y., & Zhu, Y. H. (2016). Research of the teaching interaction behavior characteristics of primary mathematics in the smart classroom. China Educ. Tech, 353, 43–48.
Zhao, C. L., Hu, P., Ling, Y. Z. M., Jiang, Z. H., Huang, Y., & Shu, F. F. (2019). Using NLP opinion mining to study teaching behaviors in open online courses. Distance Edu. China, 556, 62–70+97.
Zhu, W. M., Wang, X., & Wang, J. X. (2018). A comparative study on acceptance of innovative teaching model for teachers in rural weak schools: From the perspective of technology-task fitting. E-education Res, 39, 117–122.