Validity and Reliability of the Intrinsic Motivation Inventory Subscales within a self-directed blended learning environment

Dorothy Joy Laubscher(1*), Chantelle Bosch(2),

(1) North-West University
(2) North-West University
(*) Corresponding Author




DOI: https://doi.org/10.26858/est.v10i1.59195

Abstract


Blended learning environments, where face-to-face and online learning are integrated, are gaining traction in education, offering opportunities for self-directed learning and intrinsic motivation. This study explores the validity and reliability of the Intrinsic Motivation Inventory (IMI) in a self-directed blended learning context. A cross-sectional design involving 651 final-year teacher students reveals high reliability in interest/enjoyment, competence, and autonomy subscales (Cronbach’s alpha: 0.94, 0.94, 0.85, respectively). A confirmatory factor analysis (CFA) indicates a favourable model fit (TLI: 0.970, CFI: 0.975, RMSEA: 0.056), reinforcing the IMI's appropriateness in this setting. Factor loadings demonstrate convergent validity, which emphasises the importance of interest, competence, and autonomy in fostering intrinsic motivation. The use of convenience sampling and the exclusion of the belonging factor due to low reliability are identified as limitations in this study. Future research might explore the use of diverse populations, longitudinal studies, additional constructs, and qualitative insights. This study contributes to educational research by validating the IMI in self-directed blended learning, emphasising the need for engaging experiences, competence, and autonomy to enhance intrinsic motivation. 

Keywords


Self-directed learning, blended learning, Intrinsic Motivation Inventory, open educational resources (OER), Validity, Reliability.

Full Text:

PDF

References


Asarkaya, Ç., & Akaarir, S. (2021). The effect of ethical leadership on intrinsic motivation and employees job satisfaction. Working Paper Series Dergisi, 2(1), 14-30.

Bailey, D., Almusharraf, N., & Hatcher, R. (2021). Finding satisfaction: Intrinsic motivation for synchronous and asynchronous communication in the online language learning context. Education and Information Technologies, 26, 2563-2583.

Cheng, X., Bai, J., Pan, S. Q., Li, Y. Q., & Yang, X. (2023). Assessing Chinese anatomists’ perceptions and attitudes toward blended learning through faculty development training programs. PeerJ, 11, e16283.

Cobo-Rendón, R., Bruna Jofre, C., Lobos, K., Cisternas San Martin, N., & Guzman, E. (2022). Return to university classrooms with Blended Learning: a possible post-pandemic COVID-19 scenario. In Frontiers in Education (Vol. 7). Frontiers Media SA.

Cocca, A., Veulliet, N., Niedermeier, M., Drenowatz, C., Cocca, M., Greier, K., & Ruedl, G. (2022). Psychometric Parameters of the Intrinsic Motivation Inventory Adapted to Physical Education in a Sample of Active Adults from Austria. Sustainability, 14(20), 13681.

Deci, E. L., Ryan, R. M., Deci, E. L., & Ryan, R. M. (1985). Conceptualizations of intrinsic motivation and self-determination. Intrinsic motivation and self-determination in human behavior, 11-40.

Dwilestari, S., Zamzam, A., Susanti, N. W. M., & Syahrial, E. (2021). The students’self-directed learning in english foreign language classes during the covid-19 pandemic. Journal Lisdaya, 17(2), 38-46.

Gibbens, B. (2019). Measuring student motivation in an introductory biology class. The American Biology Teacher, 81(1), 20-26.

Hair, J.P., Black, J.P., Babin, J.P., & Anderson, R.E. (2019). Multivariate Data Analysis, Eighth Edition. Harlow: Cengage Learning.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis: Pearson new international edition. Essex, UK: Pearson Education Limited.

Hair, Jr. J. F., Wolfinbarger, M. F., Ortnau, D. J., & Bush, R. P. (2017). Essentials of marketing research. McGraw-Hill.

Kibga, E., Sentongo, J., & Gakuba, E. (2021). Effectiveness of hands-on activities to develop chemistry learners’ curiosity in community secondary schools in Tanzania. Journal of Turkish Science Education, 18(4), 605-621.

Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers.

Lai, K. (2021). Fit difference between nonnested models given categorical data: measures and estimation. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 99-120.

Lepper, M. R., & Malone, T. W. (2021). Intrinsic motivation and instructional effectiveness in computer-based education. In Aptitude, learning, and instruction (pp. 255-286). Routledge.

Malhotra, N. K., Nunan, D., & Birks, D. F. (2017). Marketing research: An applied approach. Pearson.

Malik, M. A. R., Choi, J. N., & Butt, A. N. (2019). Distinct effects of intrinsic motivation and extrinsic rewards on radical and incremental creativity: The moderating role of goal orientations. Journal of Organizational Behavior, 40(9-10), 1013-1026.

Mendoza, A., & Venables, A. (2023). Attributes of Blended Learning Environments Designed to Foster a Sense of Belonging for Higher Education Students. Journal of Information Technology Education. Research, 22, 129.

Mohd Saad, M. R., Mamat, S., Hidayat, R., & Othman, A. J. (2023). Integrating Technology-Based Instruction and Mathematical Modelling for STEAM-Based Language Learning: A Sociocultural and Self-Determination Theory Perspective. International Journal of Interactive Mobile Technologies, 17(14).

Morris, T. H. (2019). Self-directed learning: A fundamental competence in a rapidly changing world. International Review of Education, 65(4), 633-653.

Niu, Z., Cole, C. D., & Schneider, R. (2021). Motivation to Share on a Mobile Social Network: A Qualitative Study of Motivation Behavior. e-Journal of Social & Behavioural Research in Business, 12(1), 1-13.

Okwuduba, E. N., Nwosu, K. C., Okigbo, E. C., Samuel, N. N., & Achugbu, C. (2021). Impact of intrapersonal and interpersonal emotional intelligence and self-directed learning on academic performance among pre-university science students. Heliyon, 7(3).

Ostrow, K. S., & Heffernan, N. T. (2018). Testing the validity and reliability of intrinsic motivation inventory subscales within ASSISTments. In Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part I 19 (pp. 381-394). Springer International Publishing.

Peng, R., & Fu, R. (2021). The effect of Chinese EFL students’ learning motivation on learning outcomes within a blended learning environment. Australasian Journal of Educational Technology, 37(6), 61-74.

Rasul, S., & Schwaiger, E. M. (2023). Learning Climate, Intrinsic Motivation and Psychological Wellbeing among Clinical Psychology Trainees in Pakistan. Journal of Professional & Applied Psychology, 4(2), 152-166.

Ryan, R. M., & Deci, E. L. (2022). Self-determination theory. In Encyclopedia of quality of life and well-being research (pp. 1-7). Cham: Springer International Publishing.

Sailer, M., & Sailer, M. (2021). Gamification of in‐class activities in flipped classroom lectures. British Journal of Educational Technology, 52(1), 75-90.

Schweder, S., & Raufelder, D. (2022). Examining positive emotions, autonomy support and learning strategies: self-directed versus teacher-directed learning environments. Learning Environments Research, 25(2), 507-522.

Schweder, S., & Raufelder, D. (2024). Does changing learning environments affect student motivation? Learning and Instruction, 89, 101829.

Singh, J., Steele, K., & Singh, L. (2021). Combining the best of online and face-to-face learning: Hybrid and blended learning approach for COVID-19, post vaccine, & post-pandemic world. Journal of Educational Technology Systems, 50(2), 140-171.

Sujati, H., & Akhyar, M. (2020). Testing the construct validity and reliability of curiosity scale using confirmatory factor analysis. Journal of Educational and Social Research, 20(4).

Toukiloglou, P., & Xinogalos, S. (2023). A Systematic Literature Review on Adaptive Supports in Serious Games for Programming. Information, 14(5), 277.

Verbeij, T., Pouwels, J. L., Beyens, I., & Valkenburg, P. M. (2021). The accuracy and validity of self-reported social media use measures among adolescents. Computers in Human Behavior Reports, 3, 100090.

Prudon, P. (2015). Confirmatory factor analysis as a tool in research using questionnaires: a critique. Comprehensive Psychology, 4, 03-CP.

Wu, H., Li, S., Zheng, J., & Guo, J. (2020). Medical students’ motivation and academic performance: the mediating roles of self-efficacy and learning engagement. Medical education online, 25(1), 1742964


Article Metrics

Abstract view : 17 times | PDF view : 0 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Dorothy Joy Laubscher, Chantelle Bosch

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Editorial Office

Journal of Educational Science and Technology
Graduate Program Universitas Negeri Makassar

   

address icon red

 Jl Bonto Langkasa Gunungsari Baru Makassar, 90222 Kampus PPs UNM Makassar Gedung AD Ruang 406 Lt 4, Indonesia  
  jurnalestunm@gmail.com | est.journal@unm.ac.id 
  https://ojs.unm.ac.id/JEST/index 
   085299898201 (WA) 
 

EST Index by: