Tech-Based Motivational Design

Authors

  • Rachana Desai K J Somaiya School of Engineering, Somaiya Vidyavihar University, Mumbai
  • Suren Patwardhan K J Somaiya School of Engineering, Somaiya Vidyavihar University, Mumbai
  • Shivangi Thakkar K J Somaiya School of Engineering, Somaiya Vidyavihar University, Mumbai
  • Anushri Tambe K J Somaiya School of Engineering, Somaiya Vidyavihar University, Mumbai

DOI:

https://doi.org/10.16920/jeet/2026/v39is2/26056

Keywords:

ARCS; Educational Technology; Expectancy Value Theory (EVT); Motivation; Goal Orientation Theory (GOT); Learning Management System (LMS); Self-Determination Theory (SDT); Technological Pedagogical Content Knowledge (TPACK)

Abstract

In a digitally driven era where technology shapes how people learn, work, and connect. Despite the growing reliance on digital tools, to enhance the meaningful learning, limited frameworks exist that systematically combine technology with motivational theory. This lack of structured, theory-driven integration highlights an urgent need for models that bridge pedagogy, motivation, and technology in a cohesive manner. Integrating technology with motivational theory is not just beneficial, it is essential. In education, the intentional integration of technology, guided by motivational theory, becomes critically important. It is not only pedagogically essential but increasingly urgent for meaningful educational impact.

This paper offers the tech based motivational design: a framework that blends psychological principles with instructional technologies to foster learner engagement, autonomy, and persistence. The paper discusses about need of tech-based motivational design & suggests educational tools. A detailed mapping of specific educational tools within eight core tech categories (e.g., simulation, LMS, AI, gamification, XR, feedback systems, collaborative platforms, multimedia) illustrates how each supports distinct pedagogical techniques & specific motivational outcomes. Paper also suggests some specific tools for categories of tech-driven motivation.

To connect conceptual frameworks with real-world application, the article maps these tools to established models such as ARCS-V, Self-Determination Theory, Expectancy-Value Theory, Flow Theory, and Goal Orientation Theory.

An important outcome of this research is the introduction of an 11-step tech-based motivational design. It offers a comprehensive framework for aligning technical tools with pedagogical techniques. The suggested motivational design equips educators with the insight and structure needed to design learning experiences that are both technologically enriched and psychologically resonant. Its versatility opens scope for integration with emerging technologies such as AI-driven personalization, gamification, and immersive platforms, thereby advancing educational innovation across disciplines and contexts.

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Published

2026-02-17

How to Cite

Desai, R., Patwardhan, S., Thakkar, S., & Tambe, A. (2026). Tech-Based Motivational Design. Journal of Engineering Education Transformations, 39(Special Issue 2), 472–480. https://doi.org/10.16920/jeet/2026/v39is2/26056

Issue

Volume 39, Special Issue 2, January 2026

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

Alé, J., & Arancibia, M. L. (2025). Emerging technology-based motivational strategies: A systematic review with meta-analysis. Education Sciences, 15(2), 197.

Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students' learning strategies and motivation processes. Journal of educational psychology, 80(3), 260.

Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological review, 64(6p1), 359.

Backfisch, I., Scherer, R., Siddiq, F., Lachner, A., & Scheiter, K. (2021). Teachers’ technology use for teaching: Comparing two explanatory mechanisms. Teaching and Teacher Education, 104, 103390.

Branch, R. M. (2009). Instructional design: The ADDIE approach. New York, NY: Springer.

Connell, J. P., & Wellborn, J. G. (1991). Competence, autonomy, and relatedness: A motivational analysis of self-system processes.

Csikszentmihalyi, M., (1990). Flow: The psychology of optimal experience (Vol. 1990, p. 1). New York: Harper & Row.

David, L., & Weinstein, N. (2024). The how and how much of technology use in the classroom: A motivational approach to teachers' technology use. European Journal of Education, 59(4), e12674.

Deci, E. L., & Ryan, R. M. (2013). Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media.

Desai, R., & Patwardhan, S. (2025). Bridging Motivation and Technology: Integrating ARCS Model in LMS. Journal of Engineering Education Transformations, 518-528.

Desai, R., Rai, N., Karekar, J., & Desai, A. S. (2022). Poster presentation:A novel online assessment tool for motivation, relevance and foundation for research in Mathematics. Journal of Engineering Education Transformations, 168-174.

Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256–273.

Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.

Eickelmann, B., Gerick, J., & Vennemann, M. (2022). Teachers’ digital competencies and the role of school leadership: Results from a European study. Technology, Pedagogy and Education, 31(1), 1–17.

Guaña-Moya, J., Arteaga-Alcívar, Y., Criollo-C, S., & Cajamarca-Carrazco, D. (2024). Use of interactive technologies to increase motivation in university online courses. Education Sciences, 14(12), 1406.

Kaminskienė, L., Järvelä, S., & Lehtinen, E. (2022). How does technology challenge teacher education? International Journal of Educational Technology in Higher Education, 19(64).

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

Keller, J. M. (2016). Motivation, learning, and technology: Applying the ARCS-V motivation model. Participatory Educational Research, 3(2), 1–13.

Kryshko, O., Fleischer, J., Grunschel, C., & Leutner, D. (2023). University students’ self-efficacy for motivational regulation, use of motivational regulation strategies, and satisfaction with academic studies: Exploring between-person and within-person associations. Journal of Educational Psychology, 115(4), 571–588.

Lüftenegger, M., & Muth, J. (2024). Teachers’ mindset meaning system: Achievement goals, beliefs and classroom practices. Social Psychology of Education, 27, 2923–2942.

Ma, D. (2022). The role of motivation and commitment in teachers’ professional identity. Frontiers in Psychology, 13, 910747.

Pan, X., & Shao, H. (2020). Teacher online feedback and learning motivation: Learning engagement as a mediator. Social Behavior and Personality: an international journal, 48(6), 1-10.

Reeve, J., & Su, Y.-L. (2014). Teacher motivation. In M. Gagné (Ed.), The Oxford handbook of work engagement, motivation, and self-determination theory, pp. 349–362. Oxford University Press.

Schunk, D. H., Meece, J. L., & Pintrich, P. R. (2014). Motivation in education: Theory, research, and applications (4th ed.). Pearson.

Tondeur, J., Scherer, R., Siddiq, F., & Baran, E. (2022). Teacher educators as gatekeepers: Preparing the next generation of teachers for technology integration. British Journal of Educational Technology, 53(2), 331–347.

Valtonen, T., Sointu, E., Kukkonen, J., Kontkanen, S., Lambert, M. C., & Mäkitalo-Siegl, K. (2022). Teachers’ professional development in digital competence: The TPACK model and beyond. Education and Information Technologies, 27, 1023-1042.

Wigfield, A., Faust, L. T., Cambria, J., & Eccles, J. S. (2019). Motivation in education. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (2nd ed.). Oxford University Press.

Yurt, E., & Kasarci, I. (2024). A Questionnaire of Artificial Intelligence Use Motives: A contribution to investigating the connection between AI and motivation. International Journal of Technology in Education, 7(2).

Zajda, J. (2024). Teachers’ role in promoting engagement, motivation, and students’ achievement. In Engagement, Motivation, and Students’ Achievement (pp. 141–156). Springer.

Zheng, B., Lin, C.-H., & Kwon, J. B. (2022). The impact of digital learning platforms on student motivation and engagement in higher education. Educational Technology Research and Development, 70, 2635-2655.