Development and Validation of an Instrument for Assessing Student´s Attitudes Towards the Use of Video-based Media in Physical Education

Authors

DOI:

https://doi.org/10.17309/tmfv.2025.6.14

Keywords:

physical education, video-based media, attitudes, scale validation, Technology Acceptance Model, questionnaire

Abstract

Background. The integration of digital media, particularly video-based technologies, has become an important element of contemporary education. While teachers’ attitudes toward digital media are well researched, students’ attitudes toward the use of video in physical education remain underexplored. Existing instruments capture only selected components of attitudes and do not fully represent the cognitive, affective, and behavioral structure. Therefore, a validated and comprehensive instrument is needed to assess students’ attitudes toward video-based media in PE.

Objectives. To develop, refine, and validate a questionnaire measuring students’ attitudes toward the use of video-based media in physical education, based on an extended Technology Acceptance Model (TAM).

Materials and Methods. The initial questionnaire consisted of 33 items covering cognitive, affective, and behavioral attitude components. A sample of 202 eighth-grade students (M = 13.26; SD = 0.54) participated. A series of confirmatory factor analyses (CFA) was conducted to optimize the factor structure. Internal consistency (ω, α), factor loadings, model fit indicators (CFI, RMSEA), and convergent/discriminant validity were evaluated.

Results. The final scale includes 21 items forming a three-component structure: cognitive (ω = .74), affective (ω = .81), and behavioral (ω = .87). The factor model demonstrated acceptable fit (CFI = .82; RMSEA = .10). Significant correlations among subcomponents confirmed convergent validity, while the absence of substantial associations with demographic characteristics supported discriminant validity.

Conclusions. The developed scale is a reliable and valid instrument for comprehensively measuring students’ attitudes toward the use of video-based media in physical education. It can be applied in future research on technology acceptance and in designing pedagogical interventions aimed at optimizing the use of video in PE lessons.

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Author Biographies

Maik Beege, University of Education Freiburg

Department of Psychology
Kunzenweg 21, 79117 Freiburg, Germany
maik.beege@ph-freiburg.de

Anne-Christin Roth, University of Education Freiburg

Department for Sport and Sport Science
Sandfangweg 4, 79102 Freiburg, Germany
anne.roth@ph-freiburg.de

Jana Bergmann, TU Dortmund University

Institute for Sport and Sports Science
Otto-Hahn-Str. 3, 44227 Dortmund, Germany
jana.bergmann@tu-dortmund.de

Britta Schröder, TU Dortmund University

Institute for Sport and Sports Science
Otto-Hahn-Str. 3, 44227 Dortmund, Germany
britta2.schroeder@tu-dortmund.de

References

KMK [Kultusministerkonferenz] (2016). Bildung in der digitalen Welt. Strategie der Kultusministerkonferenz. Berlin: Kultusministerkonferenz

KMK [Kultusministerkonferenz] (2021). Lehren und Lernen in der digitalen Welt. Ergänzung zur Strategie der Kultusministerkonferenz “Bildung in der digitalen Welt”. Berlin: Kultusministerkonferenz

Rideout, V., & Robb, M.B. (2020). The Common Sense census: Media use by kids age zero to eight, 2020. San Francisco, CA: Common Sense Media.

Oblinger, D.G. & Oblinger, J.L. (2005). Is it age oder IT: First steps toward understanding the net generation. In D.G. Oblinger & J.L. Oblinger (Ed.), Educating the Net Generation (pp. 2.1-2.20). EDUCAUSE.

Jones, C., Ramanau, R., Cross, S. & Healing, G. (2010). Net generation or Digital Natives: Is there a distinct new generation entering university? Computers & Education, 54, (3), 722-732. DOI: https://doi.org/10.1016/j.compedu.2009.09.022

Wendeborn, T. (2019). Digitalisierung als (weiteres) Themenfeld für die Sportpraxis? Status quo einer notwendigen Diskussion. SportPraxis. Digitale Medien im Sportunterricht. 60, 4-6.

Braumüller, B., & Hartmann-Tews, I. (2017). Jugendliche als mediatisierte Stubenhocker? Eine Analyse der Zusammenhänge zwischen sportlichem und medialem Handeln von Jugendlichen aus Geschlechterperspektive. Diskurs Kindheits- und Jugendforschung / Discourse. Journal of Childhood and Adolescence Research, 12(1), 49-70. DOI: https://doi.org/10.3224/diskurs.v12i1.05

Petko, D., Cantieni, A., & Prasse, D. (2018). Was beeinflusst die Einstellungen von Schülerinnen und Schülern zum Lernen mit digitalen Medien? Eine Analyse der Befragungen von PISA 2012 in der Schweiz. Schweizerische Zeitschrift für Bildungswissenschaften, 40 (2018) 2, S. 373-390 DOI: https://doi.org/10.24452/sjer.40.2.5066

Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 25, 4961-5002. DOI: https://doi.org/10.1007/s10639-020-10197-1

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. DOI: https://doi.org/10.1111/bjet.12864

Jastrow, F., Greve, S., Thumel, M., Diekhoff, H., & Suessenbach, J. (2022). Digital technology in physical education: a systematic review of research from 2009 to 2020. German Journal of Exercise and Sport Research, 52(4), 504-528. DOI: https://doi.org/10.1007/s12662-022-00848-5

Mödinger, M., Woll, A. & Wagner, I. (2021). Video-based visual feedback to enhance motor learning in physical education-a systematic review. German Journal of Exercise and Sport Research, 52(3), 447-460. DOI: https://doi.org/10.1007/s12662-021-00782-y

Schmidt, R.A. (1975). A schema theory of discrete motor skill learning. Psychological review, 82(4), 225-260. DOI: https://doi.org/10.1037/h0076770

Dowrick, P.W. (2012). Self modeling: expanding the theories of learning. Psychology in the Schools, 49(1), 30-41. DOI: https://doi.org/10.1002/pits.20613

Casey, A., & Jones, B. (2011). Using digital technology to enhance student engagement in physical education. Asia-Pacifc Journal of Health, Sport and Physical Education, 2(2), 51-66. DOI: https://doi.org/10.1080/18377122.2011.9730351

O’Loughlin, J., Chróinín, D. N., & O’Grady, D. (2013). Digital video: the impact on children’s learning experiences in primary physicaleducation. European Physical Education Review, 19(2), 165-182. DOI: https://doi.org/10.1177/1356336X13486050

Mummendey, H. & Grau, I. (2914). Die Fragebogenmethode. Hogrefe.

Rosenberg, M.J. & Hovland, C.I. (1960). Cognitive, affective, and behavioral components of attitudes. In Hovland, C.I. & Rosenberg, M.J. (Hrsg.), Attitude organization and change: An analysis of consistency among attitude components, New Haven, CT: Yale University Press, S. 1-14.

Zimbardo & Gerrig (1996). Psychologie. (7th ed). Springer.

Cuéllar, L. (2022). Attitudes and Beliefs. In: Arsuffi, L. (Ed.) Social Psychology in Forensic Practice, 91-115. DOI: https://doi.org/10.4324/9781315560243-5

Johnson, B., Martinez-Berman, L., & Curley, C. (2022, September 15). Formation of Attitudes: How People (Wittingly or Unwittingly) Develop Their Viewpoints. Oxford Research Encyclopedia of Psychology. DOI: https://doi.org/10.1093/acrefore/9780190236557.013.812

Jain V. (2014). 3D model of attitude. International Journal of Advanced Research in Management and Social Sciences, 3(3), 1-12.

Fishbein, M. & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Farley, S.D., & Stasson, M.F. (2003). Relative influences of affect and cognition on behavior: Are feelings more related to blood donation intentions? Experimental Psychology, 50(1), 55-62. DOI: https://doi.org/10.1027//1618-3169.50.1.55

Li, F., Chen, J., & Baker M. (2014). University students’ attitudes toward physical education teaching. Journal of Teaching in Physical Education, 33(2), 186-212. DOI: https://doi.org/10.1123/jtpe.2012-0187

Ntovolis, Y., Barkoukis, V., Michelinakis, E., & Tsorbatzoudis, H. (2015). An application of the trans-contextual model of motivation in elementary school physical education. Physical Educator, 72(5), 123-141. DOI: https://doi.org/10.18666/TPE-2015-V72-I5-5111

Subramaniam, P.R., & Silverman, S. (2000). The development and validation of an instrument to assess student attitude toward physical education. Measurement in Physical Education and Exercise Science, 4(1), 29-43. DOI: https://doi.org/10.1207/S15327841Mpee0401_4

Haible, S., Volk, C., Demetriou, Y., Honer, O., Thiel, A., Trautwein, U., & Sudek, G. (2019). Promotion of physical activity-related health competence in physical education: Study protocol for the GEKOS cluster randomized controlled trial. BMC Public Health, 19(396), 1-15. DOI: https://doi.org/10.1186/s12889-019-6686-4

Subramaniam, P.R., & Silverman, S. (2007). Middle school students’ attitudes toward physical education. Teaching and Teacher Education, 23(5), 602-611. DOI: https://doi.org/10.1016/j.tate.2007.02.003

Digelidis, N., Papaioannou, A., Laparidis, K., & Christodoulidis, T. (2003). A one-year intervention in 7th grade physical education to change motivational climate and attitudes towards physical education. Psychology of Sport and Exercise, 4(3), 195-210. DOI: https://doi.org/10.1016/S1469-0292(02)00002-X

Hair, J.F., LDS Gabriel, M., Silva, D.D., & Braga, S. (2019). Development and validation of attitudes measurement scales: fundamental and practical aspects. RAUSP Management Journal, 54, 490-507. DOI: https://doi.org/10.1108/RAUSP-05-2019-0098

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 19(2), 319-340. DOI: https://doi.org/10.2307/249008

Venkatesh, V., Thong, J.Y. & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178. DOI: https://doi.org/10.2307/41410412

Alsharida, R., Hammood, M., & Al-Emran, M. (2021). Mobile Learning Adoption: A Systematic Review of the Technology Acceptance Model from 2017 to 2020. International Journal of Emerging Technologies in Learning (IJET), 16(5), 147-162. DOI: https://doi.org/10.3991/ijet.v16i05.18093

Phillips, S.R., & Silverman, S. (2012). Development of an instrument to assess fourth and fifth grade students’ attitudes toward physical education. Measurement in Physical Education and Exercise Science, 16(4), 316-327. DOI: https://doi.org/10.1080/1091367X.2012.693359

Price, A., Beckey, A., & Collins, D. (2024). Developing a love for playing games: A clarification of why Digital Video Games Approach is not gamification. Physical Education and Sport Pedagogy, 29(6), 558-572. https://doi.org/10.1080/17408989.2022.2125946 DOI: https://doi.org/10.1080/17408989.2022.2125946

Greve, S., Thumel, M., Jastrow, F., Krieger, C., Schwedler, A. & Süßenbach, J. (2022) The use of digital media in primary school PE - student perspectives on product-oriented ways of lesson staging. Physical Education and Sport Pedagogy, 27(1), 43-58. DOI: https://doi.org/10.1080/17408989.2020.1849597

Mackenbrock, J. & Kleinert, J. (2023). Motivational effects of digital media on students in physical education: a scoping review. Journal of Physical Education and Sport, 23(8), Art 243, pp. 2115-2126.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 19(2), 319-340. DOI: https://doi.org/10.2307/249008

Park, S.Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Educational Technology & Society, 12(3), 150-162.

Al-Rahmi, A.M., Shamsuddin, A., Alturki, U., Aldraiweesh, A., Yusof, F.M., Al-Rahmi, W.M., & Aljeraiwi, A.A. (2021). The influence of information system success and technology acceptance model on social media factors in education. Sustainability, 13(14), 7770. DOI: https://doi.org/10.3390/su13147770

Rosenberg, M. J. & Hovland, C. I. (1960). Cognitive, affective, and behavioral components of attitudes. In Hovland, C. I. & Rosenberg, M. J. (Hrsg.), Attitude organization and change: An analysis of consistency among attitude components, New Haven, CT: Yale University Press, S. 1–14.

Chao C-M (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front. Psychol, 10:1652. DOI: https://doi.org/10.3389/fpsyg.2019.01652

Al-Emran, M., & Granić, A. (2021). Is it still valid or outdated? A bibliometric analysis of the technology acceptance model and its applications from 2010 to 2020. In Recent advances in technology acceptance models and theories (pp. 1-12). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-64987-6_1

Selim, H.M. (2003). An empirical investigation of student acceptance of course web sites. Computers & Education, 40, 343-360. DOI: https://doi.org/10.1016/S0360-1315(02)00142-2

Roth, A.-C. (2022). Digitalisierung aus der Sicht von Sportlehrer*innen. Eine Rekonstruktion metaphorischer Konzepte als soziale Deutungsmuster. Zeitschrift für sportpädagogische Forschung, 10(2), 183-200. https://doi.org/10.5771/2196-5218-2022-2 DOI: https://doi.org/10.5771/2196-5218-2022-2-183

Cacioppo, J.T., & Berntson, G.G. (1994). Relationship between attitudes and evaluative space: A critical review, with emphasis on the separability of positive and negative substrates. Psychological bulletin, 115(3), 401-423. DOI: https://doi.org/10.1037//0033-2909.115.3.401

Reschke, K., & Jude, N. (2022). Implizite Theorien: Messinstrumente in verschiedenen Kontexten. Zeitschrift für Pädagogische Psychologie, 1-15. DOI: https://doi.org/10.1024/1010-0652/a000341

Garms-Homolová, V. (2020). Messung von Einstellungen. In Sozialpsychologie der Einstellungen und Urteilsbildung (pp. 47-59). Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-662-62434-0_4

Neumann, J., Hoffmann, L., & Baumgarten, K. (2018). Digitalisierung in Bildungseinrichtungen des Handels. Fallstudien als IST-Stands-Analyse im BMBF-Verbundprojekt VOM_Handel. Dresden: Technische Universität Dresden.

DeVellis, R.F., & Thorpe, C.T. (2022). Scale development: Theory and applications (5th ed.). Sage Publications.

Natasia, S.R., Wiranti, Y.T., & Parastika, A. (2022). Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197, 512-520. DOI: https://doi.org/10.1016/j.procs.2021.12.168

Lance, C.E., & Vandenberg, R.J. (2002). Confirmatory factor analysis. In F. Drasgow & N. Schmitt (Eds.), Measuring and analyzing behavior in organizations: Advances in measurement and data analysis (pp. 221-254). Jossey-Bass.

Koran, J. (2016). Preliminary proactive sample size determination for confirmatory factor analysis models. Measurement and Evaluation in Counseling and Development, 49(4), 296-308. DOI: https://doi.org/10.1177/0748175616664012

Weigold, A., Weigold, I.K., & Natera, S.N. (2019). Response rates for surveys completed with paper-and-pencil and computers: using meta-analysis to assess equivalence. Social Science Computer Review, 37(5), 649-668. DOI: https://doi.org/10.1177/0894439318783435

Swartz, R.J., De Moor, C., Cook, K.F., Fouladi, R.T., Basen-Engquist, K., Eng, C., & Carmack Taylor, C.L. (2007). Mode effects in the center for epidemiologic studies depression (CES-D) scale: personal digital assistant vs. paper and pencil administration. Quality of Life Research, 16, 803-813. DOI: https://doi.org/10.1007/s11136-006-9158-0

Murray, J.S. (2018). Multiple imputation: a review of practical and theoretical findings. Statistical Science, 33, 142-159. DOI: https://doi.org/10.1214/18-STS644

Mertler, C.A., & Vannatta, R.A. (2001). Advanced and multivariate statistical methods: Practical applications and interpretation. Pyrczak Publishing.

Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2009). Multivariate Data Analysis. 7th Edition. Pearson Prentice Hall

Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246. DOI: https://doi.org/10.1037//0033-2909.107.2.238

Browne, M.W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21, 230-258. DOI: https://doi.org/10.1177/0049124192021002005

McDonald, R.P. (1999). Test theory: A unified treatment. Erlbaum.

Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. DOI: https://doi.org/10.1007/BF02310555

American Educational Research Association (AERA), American Psychological Association (APA) & National Council on Measurement in Education (NCME). (2014). Standards for Educational and Psychological Testing. Washington, DC: American Psychological Association.

Hartig, J., Frey, A. & Jude, N. (2020). Validität von Testwertinterpretationen. In H. Moosbrugger & A. Kelava (Hrsg.), Testtheorie und Fragebogenkonstruktion (S. 529-544). Heidelberg: Springer Berlin. DOI: https://doi.org/10.1007/978-3-662-61532-4_21

Kane, M.T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50, 1-73. DOI: https://doi.org/10.1111/jedm.12000

Luke, M.D., & Sinclair, G.D. (1991). Gender differences in adolescents’ attitudes toward school physical education. Journal of Teaching in physical Education, 11(1), 31-46. DOI: https://doi.org/10.1123/jtpe.11.1.31

Ramírez-Correa, P. E., Arenas-Gaitán, J., & Rondán-Cataluña, F. J. (2015). Gender and acceptance of e-learning: a multi-group analysis based on a structural equation model among college students in Chile and Spain. PloS one, 10(10), e0140460. DOI: https://doi.org/10.1371/journal.pone.0140460

Greiff, S., Stadler, M., Sonnleitner, P., Wolff, C., & Martin, R. (2015). Sometimes less is more. Comparing the validity of complex problem solving measures. Intelligence, 50, 100-113. DOI: https://doi.org/10.1016/j.intell.2015.02.007

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Published

2025-11-30

How to Cite

Beege, M., Roth, A.-C., Bergmann, J., & Schröder, B. (2025). Development and Validation of an Instrument for Assessing Student´s Attitudes Towards the Use of Video-based Media in Physical Education. Physical Education Theory and Methodology, 25(6), 1435–1449. https://doi.org/10.17309/tmfv.2025.6.14

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