FOSTERING MOTIVATION IN TVET STUDENTS: THE ROLE OF LEARNER-PACED SEGMENTS AND COMPUTATIONAL THINKING IN DIGITAL VIDEO LEARNING

Wan Nor Ashiqin Wan Ali, Wan Ahmad Jaafar Wan Yahaya, Syed Zulkarnain Syed Idrus, Mohd Noorul Fakhri Yaacob

Abstract


In recent years, digital video courseware has emerged as an effective tool for enhancing learning experiences, particularly in Technical and Vocational Education and Training (TVET) programs. As technological advancements continue to reshape industries, there is an increasing demand for students to develop both digital competence and problem-solving abilities, such as Computational Thinking (CT). Despite its importance, the integration of CT principles with personalized, self-paced learning approaches, such as learner-paced digital video courseware, has not been extensively explored. This study aims to address this gap by examining how learner-paced predefined segments and CT algorithmic thinking can impact TVET students' perceived motivation. This quasi-experimental study compares two courseware modes: learner-paced and system-paced predefined segments, to evaluate their effects on student motivation. Content delivered in the learner-paced mode allows students to progress through segments at their own pace, while the system-paced mode follows a fixed sequence. The findings from ANOVA analysis revealed that students in the learner-paced mode exhibited significantly higher motivation levels than those in the system-paced mode. These results highlight the benefits of integrating self-paced learning with CT strategies, suggesting that such instructional designs can enhance motivation and engagement in TVET education. The study advocates for the adoption of these approaches to improve student outcomes and better equip them with the skills needed for the digital workforce.


Keywords


CT algorithmic thinking; digital video learning; learner-paced predefined segment; perceived motivation; system predefined segment

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References


Adnan, A. H. M., Rahmat, A. M., Mohtar, N. M., & Anuar, N. (2021). Industry 4.0 critical skills and career readiness of ASEAN TVET tertiary students in Malaysia, Indonesia and Brunei. Journal of Physics: Conference Series, 1793(1), 0–11. https://doi.org/10.1088/1742-6596/1793/1/012004

Adorni, G., Artico, I., Piatti, A., Lutz, E., Gambardella, L. M., Negrini, L., ... & Assaf, D. (2024). Development of algorithmic thinking skills in K-12 education: A comparative study of unplugged and digital assessment instruments. Computers in Human Behavior Reports, 15, 100466.

Åkerfeldt, A., Kjällander, S., & Petersen, P. (2024). A research review of computational thinking and programming in education. Technology, Pedagogy and Education, 33(3), 375-390.

Alamri, H., Lowell, V., Watson, W., & Watson, S. L. (2020). Using personalized learning as an instructional approach to motivate learners in online higher education: Learner self-determination and intrinsic motivation. Journal of Research on Technology in Education, 52(3), 322–352. https://doi.org/10.1080/15391523.2020.1728449

Bell, B. S., & Kozlowski, S. W. J. (2008). Active learning: Effects of core training design elements on self-regulatory processes, learning, and adaptability. Journal of Applied Psychology, 93(2), 296–316. https://doi.org/10.1037/0021-9010.93.2.296

Bell, L., & Bull, G. (2010). Digital video and teaching. Contemporary Issues in Technology and Teacher Education, 10, 1–6. http://www.editlib.org/p/34120

Bounou, A., Lavidas, K., Komis, V., Papadakis, S., & Manoli, P. (2023). Correlation between high school students’ computational thinking and their performance in STEM and language courses. Education Sciences, 13(11), 1101.

Brown, K. G. (2001). Using computers to deliver training: Which employees learn and why?. Personnel Psychology, 54(2), 271–296. https://doi.org/10.1111/j.1744-6570.2001.tb00093.x

Castro-Alonso, J. C., Koning, B. B. De, Fiorella, L., & Paas, F. (2021). Five strategies for optimizing instructional materials: Instructor- and learner-managed cognitive load. Educational Psychology Review, 33, 1379–1407.

Chen, L., Zeng, S., & Wang, W. (2021). The influence of emotion and learner control on multimedia learning. Learning and Motivation, 76, 101762. https://doi.org/10.1016/j.lmot.2021.101762

Clark, R. C., & Mayer, R. E. (2023). E-learning and The Science of Instruction: Proven guidelines for consumers and designers of multimedia learning. New Jersey, United States: John Wiley & Sons

Ebrahimzadeh, M. & Alavi, S. (2017). The effect of digital video games on EFL students’ language learning motivation. Teaching English with Technology, 17(2), 87–112.

Fanchamps, N. L., Slangen, L., Hennissen, P., & Specht, M. (2021). The influence of SRA programming on algorithmic thinking and self-efficacy using Lego robotics in two types of instruction. International Journal of Technology and Design Education, 31, 203-222.

Fisher, S. L., Wasserman, M. E., & Orvis, K. A. (2010). Trainee reactions to learner control: an important link in the e-learning equation. International Journal of Training and Development, 14(3), 198–208. https://doi.org/10.1111/j.1468-2419.2010.00352.x

Guo, P. J., Kim, J., & Rubin, R. (2014, March). How video production affects student engagement: An empirical study of MOOC videos. In Proceedings of the first ACM conference on Learning@ scale conference (pp. 41-50).

Hannafin, M. J. (1984). Guidelines for using locus of instructional control in the design of computer-assisted instruction. Journal of Instructional Development, 7(3), 6–10. https://doi.org/10.1007/BF02905753

Hatsidimitris, G. & Kalyuga, S. (2013). Guided self-management of transient information in animations through pacing and sequencing strategies. Educational Technology Research and Development, 61(1), 91–105. https://doi.org/10.1007/s11423-012-9276-z

Juškevičiene, A. & Dagiene, V. (2018). Computational thinking relationship with digital competence. Informatics in Education, 17(2), 265–284. https://doi.org/10.15388/infedu.2018.14

Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2–10. https://doi.org/10.1007/BF02905780

Keller, J. M. (2009). Motivational design for learning and performance: The ARCS model approach. Berlin, Germany: Springer Science & Business Media

Keller, J. M. (2012). ARCS model of motivation. Encyclopedia of the Sciences of Learning, 65(1683), 304-305.

Keller, J. M. (2016). Motivation, learning, and technology: Applying the ARCS-V motivation model. Participatory Educational Research, 3(2), 1–15. https://doi.org/10.17275/per.16.06.3.2

Lee, M. & Lee, J. (2021). Enhancing computational thinking skills in informatics in secondary education: The case of South Korea. Educational Technology Research and Development, 69(5), 2869-2893.

Malaysia, K. P. (2013). Malaysia education blueprint 2013-2025. Education, 27(1), 1-268.

Mayer, R.E. (2002). Multimedia learning. Psychology of Learning and Motivation, 41, 85 – 139. https://doi.org/10.1016/s0079-7421(02)80005-6

Mayer, R. E. (2014). Research-based principles for designing multimedia instruction. In V. A. Benassi, C. E. Overson, & C. M. Hakala (Eds.), Applying science of learning in education: Infusing psychological science into the curriculum (pp. 59–70). Society for the Teaching of Psychology.

Mayer, R. E., & Chandler, P. (2001). When learning is just a click away: Does simple user interaction foster deeper understanding of multimedia messages? Journal of Educational Psychology, 93(2), 390–397. https://doi.org/10.1037/0022-0663.93.2.390

Mayer, R. E., Dow, G. & Mayer, S. (2003). Multimedia learning in an interactive self- explaining environment: What works in the design of agent-based microworlds? Journal of Educational Psychology, 95, 806–812.

Mayer, R.E., Wells, A., Parong, J. & Howarth, J. T. (2019). Learner control of the pacing of an online slideshow lesson: does segmenting help? Applied Cognitive Psychology, 33(5), 930–935. https://doi.org/10.1002/acp.3560

McCaslin, S. E., & Young, M. (2015). Increasing student motivation and knowledge in mechanical engineering by using action cameras and video productions. Advances in Production Engineering & Management, 10(2), 87.

Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments: Special issue on interactive learning environments: Contemporary issues and trends. Educational psychology review, 19, 309-326.

Paf, M. & Dincer, B. (2021). A Study of the Relationship between Secondary School Students' Computational Thinking Skills and Creative Problem-Solving Skills. Turkish Online Journal of Educational Technology, 20(4), 1-15.

Pallant, J. (2011). SPSS survival manual (4th ed.). New South Wales, Australia: Allen & Unwin.

Rodgers, O., & Dhonnchadha, L. N. (2016). Digital Video Creation in the LSP Classroom. The EuroCALL Review, 26(1), 43-58.

Samsudin, A. (2020). Effects of persuasive technology with segmenting principle in mobile flipped classroom on university students’ performance, engagement, and perceived motivation [unpublished doctoral thesis]. Universiti Sains Malaysia.

Shin, M. H. (2018). Effects of Project-Based Learning on Students' Motivation and Self-Efficacy. English teaching, 73(1), 95-114.

Snelson, C., Yang, D., & Temple, T. (2021). Addressing the Challenges of Online Video Analysis in Qualitative Studies: A Worked Example from Computational Thinking Research. The Qualitative Report, 26(6), 1974-1988. https://doi.org/10.46743/2160-3715/2021.4734

Sorgenfrei, C. & Smolnik, S. (2016). The effectiveness of e-learning systems: A review of the empirical literature on learner control. Decision Sciences Journal of Innovative Education, 14(2), 154–184. https://doi.org/10.1111/dsji.12095

Stiller, K. D., Freitag, A., Zinnbauer, P., & Freitag, C. (2009). How pacing of multimedia instructions can influence modality effects: A case of superiority of visual texts. Australasian Journal of Educational Technology, 25(2), 184-203. https://doi.org/10.14742/ajet.1149

Sun, J. N. & Hsu, Y. C. (2013). Effect of interactivity on learner perceptions in Web-based instruction. Computers in Human Behavior, 29(1), 171–184. https://doi.org/10.1016/j.chb.2012.08.002

Tekdal, M. (2021). Trends and development in research on computational thinking. Education and Information Technologies, 26(5), 6499-6529.

Klaus, S. (2018). The future of jobs report 2018. Centre for the New Economy and Society. World Economic Forum. https://www3.weforum.org/docs/WEF_Future_of_Jobs_2018.pdf

Yusop, S. R. M., Rasul, M. S., Mohamad Yasin, R., Hashim, H. U., & Jalaludin, N. A. (2022). An assessment approaches and learning outcomes in technical and vocational education: A systematic review using PRISMA. Sustainability, 14(9), 5225.

Zhou, P., Tang, Y., Zhang, Y., Yu, Y., & Li, Y. (2024). Does computational thinking really have an impact on academic performance? A systematic review. In 2024 International Symposium on Educational Technology (ISET) (pp. 153-157).


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