Learning from CLT-Informed Interactive Video: Cognitive Performance and Learning Motivation in Culinary Education

Mauren Gita Miranti, Nona Rachel Putri Talaperu, Lucia Tri Pangesthi, Hanif Naufal Ahmi, Anderson Ngelambong

Abstract


Grounded in Cognitive Load Theory (CLT), this study examined whether CLT-informed interactive video instruction was associated with improvements in students’ cognitive performance and learning motivation in vocational culinary education. A one-group pretest–posttest design was employed involving 41 eleventh-grade culinary arts students at a public vocational high school in East Java, Indonesia. Data were collected using a 30-item cognitive test and a 35-item learning motivation questionnaire, both of which demonstrated acceptable content validity and high internal consistency (α = 0.914 and α = 0.866, respectively). Because some variables did not meet normality assumptions, non-parametric analyses were used. Wilcoxon signed-rank tests were conducted to examine pre–post changes in cognitive performance and learning motivation, while Spearman’s rho was used to examine the relationship between post-intervention learning motivation and cognitive performance gain. The results showed statistically significant improvements in both cognitive performance (Z = −3.650, p < .001) and learning motivation (Z = −2.130, p = .033) following the intervention. However, no statistically significant relationship was found between learning motivation and cognitive performance (rs = −.188, p = .319). These findings suggest that CLT-informed interactive video may serve as a promising instructional medium for improving topic-specific cognitive performance and supporting learning motivation in vocational culinary education.

Keywords


Culinary Education; Cognitive Ability; Interactive Video; Learning Motivation; Vocational Education

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References


Aiken, L. R. (1985). Three coefficients for analyzing the reliability and validity of ratings. Educational and Psychological Measurement, 45(1), 131–142. https://doi.org/10.1177/0013164485451012

Almarzouki, A. F. (2024). Stress, working memory, and academic performance: A neuroscience perspective. Stress, 27(1). https://doi.org/10.1080/10253890.2024.2364333

Baxter, K. A., Sachdeva, N., & [rekan]. (2025). The application of cognitive load theory to the design of health and behavior change programs: Principles and recommendations. Health Education & Behavior, 52(4), 469–477. https://doi.org/10.1177/10901981251327185.

Chen, Z., Qian, W., & Zhang, C. (2024). The effect of digital technology usage on higher vocational student satisfaction: The mediating role of learning experience and learning engagement. Frontiers in Education, 9, 1508119. https://doi.org/10.3389/feduc.2024.1508119

Chicco, D., Sichenze, A., & Jurman, G. (2025). A simple guide to the use of Student’s t-test, Mann–Whitney U test, Chi-squared test, and Kruskal–Wallis test in biostatistics. BioData Mining, 18, Article 56. https://doi.org/10.1186/s13040-025-00465-6

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.

Deng, R., & Gao, Y. (2023). Effects of embedded questions in pre-class videos on learner perceptions, video engagement, and learning performance in flipped classrooms. Active Learning in Higher Education, 25(1). https://doi.org/10.1177/14697874231167098

Diseth, Å. (2025). Motivation and learning strategies among students in upper secondary education: Grade level differences and academic outcomes. Frontiers in Education, 10, 1679954. https://doi.org/10.3389/feduc.2025.1679954

Efendi, D. (2025). The relationship between training satisfaction and work motivation of employees in the emergency department unit of Ciputra Hospital Surabaya. International Journal of Administration, Business & Organization, 6(2), 66–78. https://doi.org/10.61242/ijabo.25.486

Fan, E., Bower, M., & Siemon, J. (2024). Video tutorials in the traditional classroom: The effects on different types of cognitive load. Technology, Knowledge and Learning, 29, 2017–2036. https://doi.org/10.1007/s10758-024-09754-1

Fiorella, L., & Mayer, R. E. (2018). What works and doesn't work with instructional video [Editorial]. Computers in Human Behavior, 89, 465–470. https://doi.org/10.1016/j.chb.2018.07.015.

Hong, J. C., & Guo, M. (2025). Improving the effectiveness of video-based flipped classrooms with question-embedding. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12303-5

Keller, J. M. (1987). Development and Use of the ARCS Model of Instructional Design. Journal of Instructional Development, 10, 2-10. http://dx.doi.org/10.1007/BF02905780.

Kok, X. F. K., Wang, P. C., Avnit, K., & Shukla, M. (2024). User engagement with interactive educational videos: Relations with task value, cognitive load, and learning satisfaction. International Journal of Instruction, 17(4), 459–482. https://doi.org/10.29333/iji.2024.17426a

Liu, Y., Ma, S., & Chen, Y. (2024). The impacts of learning motivation, emotional engagement and psychological capital on academic performance in a blended learning university course. Frontiers in Psychology, 15, 1357936. https://doi.org/10.3389/fpsyg.2024.1357936

Ljubojević, M., Vujičić, V., Stanković, M., & Đorđević, D. (2025). Improving the efficiency of multimedia learning and the quality of experience by reducing cognitive load. Applied Sciences, 15(3), 1054. https://doi.org/10.3390/app15031054

Luo, J., Boland, R., & Chan, C. (2025). How to use technology in educational innovation. In Advances in educational technologies (pp. xx–xx). Springer. https://doi.org/10.1007/978-3-031-91745-5_17

Mayer, R. E. (2024). The past, present, and future of the cognitive theory of multimedia learning. Educational Psychology Review, 36, Article 8. https://doi.org/10.1007/s10648-023-09842-1

Oudat, Q., & Othman, M. (2024). Embracing digital learning: Benefits and challenges of using Canvas in education. Journal of Nursing Education and Practice, 14, 39. https://doi.org/10.5430/jnep.v14n2p39

Perry-Kates, A., & Cohen, A. (2025). From viewers to participants: The evolution of learning through interactive video. Journal of Computer Assisted Learning, 41(3), e70061. https://doi.org/10.1111/jcal.70061

Raffi, M., Salo, M., Familoni, B., & Onyebuchi, C. (2025). Digital learning in the 21st century: Trends, challenges, and innovations in technology integration. Frontiers in Education, 10, 1562391. https://doi.org/10.3389/feduc.2025.1562391

Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, Article 101860. https://doi.org/10.1016/j.cedpsych.2020.101860

Suen, H. Y., & Hung, K. E. (2025). Enhancing learner affective engagement: The impact of instructor emotional expressions and vocal charisma in asynchronous video-based online learning. Education and Information Technologies, 30, 4033–4060. https://doi.org/10.1007/s10639-024-12956-w

Sweller, J. (2024). Cognitive load theory and individual differences. Learning and Individual Differences, 110, Article 102423. https://doi.org/10.1016/j.lindif.2024.102423

Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), The psychology of learning and motivation: Cognition in education (pp. 37–76). Elsevier Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-8

Timileyin, A. (2024). The role of cognitive load in shaping web usability requirements. SSRN. https://doi.org/10.2139/ssrn.5247018

Weinert, T., Benner, D., Dickhaut, E., Janson, A., Schöbel, S., & Leimeister, J. M. (2024). Engaging students through interactive learning videos in higher education: Developing a creation process and design patterns for interactive learning videos. Communications of the Association for Information Systems, 55, 475–506. https://doi.org/10.17705/1CAIS.05519

Wong, J. T., Richland, L. E., & Hughes, B. S. (2025). Immediate versus delayed low-stakes questioning. Technology, Knowledge and Learning, 30, 1421–1456. https://doi.org/10.1007/s10758-024-09746-1

Xu, Z. (2025). The predictive effect of extrinsic motivation on English online learning engagement. Frontiers in Psychology, 16, 1612002. https://doi.org/10.3389/fpsyg.2025.1612002

Zhang, H. (2024). Cognitive load as a mediator in self-efficacy and English learning motivation among vocational college students. PLOS ONE. https://doi.org/10.1371/journal.pone.0314088




DOI: https://doi.org/10.20961/ijpte.v10i1.113058

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