با همکاری مشترک دانشگاه پیام نور و انجمن روانشناسی تربیتی ایران

نوع مقاله : پژوهشی

نویسنده

دانشیار گروه مدیریت ورزشی، دانشگاه پیام نور، تهران، ایران

چکیده

هدف از انجام این پژوهش ویژگی‌های روان‌سنجی پرسش‌نامه ادراک دانش‌آموزان از یادگیری فعال مبتنی بر فناوری در هنرستان‌های تربیت بدنی استان البرز بود. جامعه آماری این پژوهش را دانش‌آموزان هنرستان‌های تربیت بدنی استان البرز تشکیل دادند که از بین آنها تعداد 412 پرسش‌نامه به شیوه نمونه‌گیری تصادفی طبقه‌ای جمع‌آوری شد. روش پژوهش از نوع توصیفی انتخاب شد و از آنجا که هدف از انجام این پژوهش بررسی ویژگی‌های روان‌سنجی پرسش‌نامه بود به طرح‌های همبستگی تعلق دارد. به منظور جمع‌آوری داده‌ها از پرسش‌نامه یادگیری فعال مبتنی بر فناوری شروف و همکاران (2019) که مشتمل بر 20 سؤال بوده و دارای مؤلفه‌های مشارکت تعاملی، مهارت حل مسئله، علاقه و بازخورد است، استفاده شد. به منظور تحلیل داده‌ها، از شاخص‌های توصیفی و آزمون‌های آماری ضریب آلفای کرونباخ و رایکوف برای تعیین همسانی درونی و تحلیل عاملی اکتشافی و تحلیل عاملی تأییدی برای تعیین روایی سازه در نرم‌افزارهای آماری SPSS نسخه 21، LISREL نسخه 8/8 و Stata نسخه 17 استفاده شد. نتایج نشان داد همسانی درونی پرسش‌نامه یادگیری فعال مبتنی بر فناوری (968/0) است. در خصوص روایی سازه و بر اساس میزان روابط و سطح معناداری، تمامی سؤال‌ها رابطه معناداری با عامل‌ها داشتند و توانستند پیش‌گوی خوبی برای مؤلفه خود باشند. همچنین در خصوص روابط مؤلفه‌ها با مفهوم یادگیری فعال مبتنی بر فناوری نتایج نشان داد که تمامی چهار مؤلفه توانستند پیش‌گوی خوبی برای مفهوم یادگیری فعال مبتنی بر فناوری باشند و در نتیجه در سنجش کارکردهای استفاده از فناوری در حوزه یادگیری فعال در دانش‌آموزان می‌توان از آن استفاده نمود

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Psychometric Characteristics of the Questionnaire of Students' Perception of Technology-Based Active Learning in Physical Education Colleges of Alborz Province

نویسنده [English]

  • Hossein Poursoltani Zarandi

Associate Professor of Department of Sport Management, Payame Noor University, Tehran, Iran.

چکیده [English]

The aim of this research was the psychometric characteristics of the questionnaire of students' perception of technology-based active learning in physical education colleges of Alborz province. The statistical population of this research consisted of students from physical education colleges in Alborz province, from whom 412 questionnaires were collected using stratified random sampling. The descriptive research method selected and since the purpose of this research was to investigate the psychometric properties of the questionnaire, it falls under correlation designs. In order to collect data, the technology-based active learning questionnaire of Shroff et al. (2019) used which consists of 20 questions and has the components of interactive participation, problem solving skills, interest and feedback. In order to analyze the data, descriptive indices and Cronbach's and Raykov's alpha coefficient statistical tests were used to determine internal consistency and exploratory factor analysis and confirmatory factor analysis were used to determine construct validity in SPSS.21, LISREL 8.8 and Stata.17 statistical software. The results showed that the reliability of the technology-based active learning questionnaire is (0.968). Regarding construct validity and based on the degree of relationships and significance level, all questions had a significant relationship with the factors and were able to be a good predictor for their component. Additionally, regarding the relationships of the components with the concept of technology-based active learning, the results showed that all four components could be a good predictor for the concept of technology-based active learning, and as a result, it can used to measure the functions of using technology in the field of active learning among students.

کلیدواژه‌ها [English]

  • Tool Making
  • Psychometric Characteristics
  • Active Learning
  • Student
  • Physical Education
Abramovich, S. (2022). Technology-Immune/Technology-Enabled Problem Solving as Agency of Design-Based Mathematics Education. Education Sciences, 12(8), 514.
Adam, I. (2020). Web 2.0 Tools in Classroom; Enhancing Student Engagement through Technology Enabled Active Learning. International Journal of Creative Multimedia, 1(SI 1), 40-54.
Adamson, J., & Sloan, D. (2023). Developing a technology enabled learning framework supporting staff transitioning degree module content to a blended learning approach. Innovations in Education and Teaching International, 60(1), 59-69.
Adi Badiozaman, I. F., Segar, A. R., & Hii, J. (2022). A pilot evaluation of technology–enabled active learning through a Hybrid Augmented and Virtual Reality app. Innovations in Education and Teaching International, 59(5), 586-596.
Alt, D., & Raichel, N. (2020). Problem-based learning, self-and peer assessment in higher education: Towards advancing lifelong learning skills. Research Papers in Education, 37(3), 370–394.
Babo, R., Rocha, J., Fitas, R., Suhonen, J., & Tukiainen, M. (2021). Self and peer e-assessment: A study on software usability. International Journal of Information and Communication Technology Education, 17(3), 68–85.
Bailey, R., Ries, F., Heck, S., & Scheuer, C. (2023). Active Learning: A Review of European Studies of Active Lessons. Sustainability, 15(4), 3413.
Banville, D., Desrosiers, P., & Genet-Volet, Y. (2000). Translating questionnaires and interventories using a cross-cultural translation technique. Journal teaching in physical educ, 19, 374-87.
Barbara, H. M., & William, F. (2005). Statistical methods for health care research. Lippincott Williams and Wilkins", A welters clawer company, 325-330.
Bedenlier, S., Bond, M., Buntins, K., Zawacki-Richter, O., & Kerres, M. (2020). Facilitating student engagement through educational technology in higher education: A systematic review in the field of arts and humanities. Australasian Journal of Educational Technology, 36(4), 126–150.
Brasseur, R. (2023). Virtual Site Visits: Student Perception and Preferences Towards Technology Enabled Experiential Learning. International Journal of Emerging Technologies in Learning, 18(2).
Burns, N., & Grove, S. K. (1999). Understanding Nursing Research, 2 nd Ed. Philadelphia. W. B. Saunders Company, 321.
Chang-Tik, C. (2022). Technologies and Learning Spaces for Collaborative Active Learning. In Collaborative Active Learning: Practical Activity-Based Approaches to Learning, Assessment and Feedback (pp. 331-357). Singapore: Springer Nature Singapore.
Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.
Dori, Y. J., & Belcher, J. (2005). How does technology-enabled active learning affect undergraduate students’ understanding of electromagnetism concepts? The Journal of the Learning Sciences, 14(2), 243–279.
Espey, M. (2018). Enhancing critical thinking using team-based learning. Higher Education Research & Development, 37(1), 15–29.
Ewing, L. A., & Cooper, H. B. (2021). Technology-enabled remote learning during COVID-19: perspectives of Australian teachers, students and parents. Technology, pedagogy and education, 30(1), 41-57.
Garzón, J., Baldiris, S., Gutiérrez, J., & Pavón, J. (2020). How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis. Educational Research Review, 31, 1–19. 
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives, Structural Equation Modeling, 6, 1-55.
Kim, M. J., Lee, C.-K., & Preis, M. W. (2020). The impact of innovation and gratification on authentic experience, subjective well-being, and behavioral intention in tourism virtual reality: The moderating role of technology readiness. Telematics and Informatics, 49, 1–16.
Landis, J. R., & Koch, C. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.
Liang, Y. W., Wu, M. W., Pan, Z. S., & Cen, G. (2021, August). Information and Communication Technology Enabled Active Learning in College Physics Experiment. In 2021 16th International Conference on Computer Science & Education (ICCSE) (pp. 1014-1018). IEEE.
Lin, G. S. S., Tan, W. W., Tan, H. J., Khoo, C. W., & Afrashtehfar, K. I. (2023). Innovative Pedagogical Strategies in Health Professions Education: Active Learning in Dental Materials Science. International journal of environmental research and public health, 20(3), 2041.
Lock, J., Lakhal, S., Cleveland‐Innes, M., Arancibia, P., Dell, D., & De Silva, N. (2021). Creating technology‐enabled lifelong learning: A heutagogical approach. British Journal of Educational Technology, 52(4), 1646-1662.
Maxwell, A. E. (1970). Basic statistics in behavioural research. Harmondsworth, UK: Penguin.
Moore, G., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
Mozelius, P., Fagerström, A., & Söderquist, M. (2017). Motivating factors and tangential learning for knowledge acquisition in educational games. Electronic Journal of e-Learning, 15(4), 343–354.
Nardo, J. E., Chapman, N. C., Shi, E. Y., Wieman, C., & Salehi, S. (2022). Perspectives on active learning: challenges for equitable active learning implementation. Journal of Chemical Education, 99(4), 1691-1699.
Nelson, M. J., & Hawk, N. A. (2020). The impact of field experiences on prospective preservice teachers’ technology integration beliefs and intentions. Teaching and Teacher Education, 89, 1–12.
Olvis, P. R., Disca, B. Y., Comoda, J. T., Anabo, R. G., & Subiera, N. C. (2021). The effect of technology-enabled active learning (teal) method on the learning outcomes of students in physics. In PAPSI International 3-Day Research Conference Proceedings (Vol. 2, No. 1, pp. 1-1).
Phipps, D. J., Rhodes, R. E., Jenkins, K., Hannan, T. E., Browning, N. G., & Hamilton, K. (2022). A dual process model of affective and instrumental implicit attitude, selfmonitoring, and sedentary behavior. Psychology of Sport and Exercise, 62.
Pillai, S. V., Mathew, L. S., Daniel, A., & Abhilash, V. S. (2020). Technology enabled online learning in a Digital age. Asian Journal of Management, 11(3), 266-274.
Pramod, S., Govindan, D., Ramasubramani, P., Kar, S. S., Aggarwal, R., Manoharan, N., Thabah, M. M. (2022). Effectiveness of Covishield vaccine in preventing Covid-19 – A test-negative case-control study. Vaccine, 40(24), 3294-3297.
Shroff, R. H., Ting, F. S. T., & Lam, W. H. (2019). Development and validation of an instrument to measure students’ perceptions of technology-enabled active learning. Australasian Journal of Educational Technology, 35(4).
Shroff, R. H., Ting, F. S. T., Chan, C. L., Garcia, R. C., Tsang, W. K., & Lam, W. H. (2022). Conceptualisation, measurement and preliminary validation of learners’ problem-based learning and peer assessment strategies in a technology-enabled context. Australasian Journal of Educational Technology, 1-18.
Van den Bergh, L., Ros, A., & Beijaard, D. (2013). Teacher feedback during active learning: Current practices in primary schools. British Journal of Educational Psychology, 83(2), 341–362.
Watson, J. H., & Rockinson-Szapkiw, A. (2021). Predicting preservice teachers' intention to use technology-enabled learning. Computers & Education, 168, 104207.
Wu, L., Liu, Y., How, M. L., & He, S. (2023). Investigating Student-Generated Questioning in a Technology-Enabled Elementary Science Classroom: A Case Study. Education Sciences, 13(2), 158.