Digital Learning Design Innovation: Evaluating Behavioral Intention to Use Microlearning in Child Protection Professional Training in Indonesia
DOI:
https://doi.org/10.51278/aj.v8i2.1741Keywords:
Microlearning, Technology Acceptance Model, Behavioral Intention, Professional Training, Child ProtectionAbstract
The high prevalence of child protection cases necessitates continuous professional learning; however, conventional training models often lead to training fatigue due to time constraints, heavy workloads, and the demanding nature of frontline responsibilities. This study aims to evaluate learners’ acceptance of microlearning-based e-learning as an instructional design innovation for professional training in child protection in Indonesia. Guided by the Technology Acceptance Model (TAM), the study examines the influence of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) on Behavioral Intention (BI). A quantitative approach was employed involving 136 respondents from cross-sectoral professional backgrounds within child protection ecosystems, selected through purposive sampling. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that the proposed model demonstrates strong predictive power (R² = 0.661). PEOU shows a strong positive effect on PU, and both PEOU and PU significantly influence BI. Notably, PEOU contributes more substantially than PU in shaping learners’ intention to adopt the platform. These findings indicate that system usability, intuitive interaction, and flexibility of learning access are critical determinants of adoption among professional learners. Furthermore, ease of use is found to support the reduction of cognitive load, enabling learners to focus more effectively on complex and sensitive child protection materials. The study concludes that the success of digital learning innovations in professional contexts depends not only on perceived instructional benefits but also on the ergonomic quality of user experience (UX) that accommodates professionals’ work rhythms and learning needs
References
A. Granić, & Nikola Marangunic. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology.
Abdalla, R. A. M. (2024). Examining awareness, social influence, and perceived enjoyment in the TAM framework as determinants of ChatGPT. Personalization as a moderator. Journal of Open Innovation: Technology, Market, and Complexity, 10(3). https://doi.org/10.1016/j.joitmc.2024.100327
Al-Fraihat, D., Joy, M., & E, J. S. (2017). Identifying success factors for e-learning in higher education. In: 12th International Conference on e-Learning (ICEL 2017), Orlando, Florida, 01-02 Jun 2017. Proceedings of the 12th International Conference on E-Learning (ICEL 2017).
Al‐rahmi, A. M., Al‐rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al‐adwan, A. S. (2021). Exploring the factors affecting mobile learning for sustainability in higher education. Sustainability (Switzerland), 13(14). https://doi.org/10.3390/su13147893
Baah, C., Govender, I., & Subramaniam, P. R. (2024). Enhancing Learning Engagement: A Study on Gamification’s Influence on Motivation and Cognitive Load. Education Sciences, 14(10). https://doi.org/10.3390/educsci14101115
Badan Pusat Statistik (BPS). (2023). Survey Angkatan Kerja Nasional. https://www.bps.go.id/id/statistics-table/2/MjAwOSMy/persentase-pekerja-anak-usia-10-17-tahun-menurut-jenis-kelamin.html
Binford, W. (2023). What Frontline Professionals Need to Combat Child Maltreatment Online. In International Journal on Child Maltreatment: Research, Policy and Practice (Vol. 6, Number 2). https://doi.org/10.1007/s42448-023-00164-x
Bobbitt, L. J., Cimino, C., Garvey, K. V., Craft, L. S., Eichenseer, N. A., & Nelson, G. E. (2023). An app a day: Results of pre- and post-surveys of knowledge, attitudes, and practices (KAP) regarding antimicrobial stewardship principles among nurses who utilized a novel learning platform. Antimicrobial Stewardship and Healthcare Epidemiology, 3(1). https://doi.org/10.1017/ash.2023.131
Bryzgalina, E. V., Alekseeva, D. A., & Dryaeva, E. D. (2021). Digital pedagogy: Experience of advanced training. Vysshee Obrazovanie v Rossii, 30(5). https://doi.org/10.31992/0869-3617-2021-30-5-161-167
Chang, C. C., & Yang, S. T. (2023). Interactive effects of scaffolding digital game-based learning and cognitive style on adult learners’ emotion, cognitive load and learning performance. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00385-7
Chung, C. J., Hwang, G. J., & Lai, C. L. (2019). A review of experimental mobile learning research in 2010–2016 based on the activity theory framework. Computers and Education, 129. https://doi.org/10.1016/j.compedu.2018.10.010
Collins, T. M. (2014). The relationship between children’s rights and business. International Journal of Human Rights, 18(6). https://doi.org/10.1080/13642987.2014.944805
Conde-Caballero, D., Castillo-Sarmiento, C. A., Ballesteros-Yánez, I., Rivero-Jiménez, B., & Mariano-Juárez, L. (2024). Microlearning through TikTok in Higher Education. An evaluation of uses and potentials. Education and Information Technologies, 29(2). https://doi.org/10.1007/s10639-023-11904-4
Crane, A., & Kazmi, B. A. (2010). Business and children: Mapping impacts, managing responsibilities. Journal of Business Ethics, 91(4). https://doi.org/10.1007/s10551-009-0132-y
Cronin, J., & Durham, M. L. (2024). Microlearning: A Concept Analysis. CIN - Computers Informatics Nursing, 42(6), 413–420. https://doi.org/10.1097/CIN.0000000000001122
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3). https://doi.org/10.2307/249008
Davis, F. D., & Granić, A. (2024). SpringerBriefs in Human-Computer Interaction The Technology Acceptance Model 30 Years of TAM. https://link.springer.com/chapter/10.1007/978-3-030-45274-2_2
Davis Jr, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Sloan School of Management, Massachusetts Institute of Technology. Science, 146(3652).
de Gagne, J. C., Park, H. K., Hall, K., Woodward, A., Yamane, S., & Kim, S. S. (2019). Microlearning in health professions education: Scoping review. In JMIR Medical Education (Vol. 5, Number 2). https://doi.org/10.2196/13997
Deng, X., & Yu, Z. (2023). An extended hedonic motivation adoption model of TikTok in higher education. Education and Information Technologies, 28(10), 13595–13617. https://doi.org/10.1007/s10639-023-11749-x
Effendi, & Hartono Zhuang. (2005). E-Learning Konsep dan Aplikasi. ANDI.
Fathema, N., Shannon, D., & Ross, M. (2015). Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of Learning Management Systems (LMSs) In Higher Education Institutions. Journal of Online Learning and Teaching , 11(2).
Gómez, D., Bermeo, A., Prado, D., & Cedillo, P. (2021). Microlearning Method to Building Learning Capsules for Older Adults: A Case Study for COVID-19 Prevention at Home. ETCM 2021 - 5th Ecuador Technical Chapters Meeting. https://doi.org/10.1109/ETCM53643.2021.9590793
Hair, J. F., Ringle, C. M., Hult, G. T. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM). In International Journal of Research & Method in Education (Number 2).
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2). https://doi.org/10.2753/MTP1069-6679190202
Hug, T. (2006). Microlearning: A New Pedagogical Challenge (Introductory Note). Microlearning: Emerging Concepts, Practices and Technologies after E-Learning.
Ibrahim, R., Leng, N. S., Yusoff, R. C. M., Samy, G. N., Masrom, S., & Rizman, Z. I. (2018). E-learning acceptance based on technology acceptance model (TAM). Journal of Fundamental and Applied Sciences, 9(4S), 871. https://doi.org/10.4314/jfas.v9i4s.50
Indrayani, N., Cahyono, B. Y., Mukminatien, N., & Ivone, F. M. (2024). Exploring Informal Digital Language Learning: How Learning Frequency Counts. Journal of Languages and Language Teaching, 12(3). https://doi.org/10.33394/jollt.v12i3.11366
International Labour Organization (ILO). (2015). Commercial Sexual Exploitation and Trafficking of Children “in a nutshell”: A Resource for Pacific Island Countries (Vol. 40). www.ifrro.org
Iqbal, M., Iskandarini, I., & ... (2025). The Influence of Learning Management System (LMS)-Based Training and Career Development on Employee Competence at PT. Perkebunan Nusantara IV Regional …. Journal Analytica Islamica.
Isibika, I. S., Zhu, C., De Smet, E., & Musabila, A. K. (2023). The influence of user-perceived benefits on the acceptance of microlearning for librarians’ training. Research in Learning Technology, 31. https://doi.org/10.25304/rlt.v31.2930
Kovachev, D., Cao, Y., Klamma, R., & Jarke, M. (2011). Learn-as-you-go: New ways of cloud-based micro-learning for the mobile web. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7048 LNCS. https://doi.org/10.1007/978-3-642-25813-8_6
Lai-Kwon, J., Dushyanthen, S., Seignior, D., Barrett, M., Buisman-Pijlman, F., Buntine, A., Woodward-Kron, R., McArthur, G., & Kok, D. L. (2023). Designing a wholly online, multidisciplinary Master of Cancer Sciences degree. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04537-1
Lamimi, I. J., Alaoui, S. M., & Ouelfatmi, M. (2024). Bite-Sized Learning on TikTok: Exploring the Platform’s Educational Value within the Framework of TAM (Technology Acceptance Theory). Open Journal of Social Sciences, 12(04), 228–245. https://doi.org/10.4236/jss.2024.124015
Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Understanding Psychosocial Barriers to Healthcare Technology Adoption: A Review of TAM Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology and UTAUT Frameworks. In Healthcare (Switzerland) (Vol. 13, Number 3). https://doi.org/10.3390/healthcare13030250
Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers and Education, 53(4). https://doi.org/10.1016/j.compedu.2009.06.014
Lehikko, A., Nykänen, M., Lukander, K., Uusitalo, J., & Ruokamo, H. (2024). Exploring interactivity effects on learners’ sense of agency, cognitive load, and learning outcomes in immersive virtual reality: A mixed methods study. Computers and Education: X Reality, 4. https://doi.org/10.1016/j.cexr.2024.100066
Littlejohn, A., & Pegler, C. (2007). Preparing for blended e-Learning. In Preparing for Blended e-Learning. https://doi.org/10.4324/9780203961322
Lohman, L. (2024). How can you deliver microlearning when learners don’t want it? Designing microlearning for socially oriented learners. Educational Technology and Society, 27(1). https://doi.org/10.30191/ETS.202401_27(1).SP03
Lolang, E., & Putra Pratama, Muh. (2025). Microlearning-Based Mobile Learning Module in Optimizing Learning Management System (LMS). JPI (Jurnal Pendidikan Indonesia), 14(2). https://doi.org/10.23887/jpi-undiksha.v14i2.90779
Maričić, M., Anđić, B., Soeharto, S., Mumcu, F., Cvjetićanin, S., & Lavicza, Z. (2025). The exploration of continuous teaching intention in emerging-technology environments through perceived cognitive load, usability, and teacher’s attitudes. Education and Information Technologies, 30(7). https://doi.org/10.1007/s10639-024-13141-9
Martin, T. (2022). A Literature Review on The Technology Acceptance Model. International Journal of Academic Research in Business and Social Sciences, 12(11). https://doi.org/10.6007/ijarbss/v12-i11/14115
Mayer, R. E., & Fiorella, L. (2022). The Cambridge Handbook of Multimedia Learning: Introduction to Multimedia Learning. In The Cambridge Handbook of Multimedia Learning.
Moslemi Nezhad Arani, S., & Atasoy, A. (2025). Exploring learners’ psychology and engagement in mobile language applications through self-determination theory. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13834-9
Muali, C., & Karlina, L. (2025). The Effect of Microlearning Integration in Digital Platforms on Student Engagement: An Experimental Study in Higher Education. Journal of Education Technology, 9(1), 21–30. https://doi.org/10.23887/jet.v9i1.926
Munro, E. (2019). Decision-making under uncertainty in child protection: Creating a just and learning culture. Child and Family Social Work, 24(1). https://doi.org/10.1111/cfs.12589
Noe, R. A., Clarke, A. D. M., & Klein, H. J. (2014). Learning in the Twenty-First-Century Workplace. In Annual Review of Organizational Psychology and Organizational Behavior (Vol. 1). https://doi.org/10.1146/annurev-orgpsych-031413-091321
Nur Morat, B., Izzati Idrus, N., Mohd Salleh, S., & Dimisyqiyani, E. (2024). Technological Access, Cognitive Load, and Motivation in University Students’ Keyboarding Readiness: A Framework. International Journal of Research and Innovation in Social Science, VIII(IX). https://doi.org/10.47772/ijriss.2024.8090124
Ogange, B., & Mishra, S. (2021). Introduction to microlearning 2. http://oasis.col.org/bitstream/handle/11599/3877/2021_Allela_Introduction_ to_Microlearning_Course.pdf ?sequence=8&isAllowed=y#:~:text=Microlearning%20 supports%20flexible%20and%20self,the%20end%20of%20each%20lesson
Orwoll, B., Diane, S., Henry, D., Tsang, L., Chu, K., Meer, C., Hartman, K., & Roy-Burman, A. (2018). Gamification and Microlearning for Engagement With Quality Improvement (GAMEQI): A Bundled Digital Intervention for the Prevention of Central Line–Associated Bloodstream Infection. American Journal of Medical Quality, 33(1). https://doi.org/10.1177/1062860617706542
Parton, N. (2024). THE CHANGING AND CHALLENGING NATURE OF CHILD AND FAMILY SOCIAL WORK AND ITS RESEARCH. In The Routledge Handbook of Child and Family Social Work Research: Knowledge-Building, Application, and Impact. https://doi.org/10.4324/9781003241492-6
Rai N, & Thapa B. (2015). A study on purposive sampling method in research. Kathmandu: Kathmandu School of Law, 5(1), 8–15. http://study.com/academy/lesson/what-is-sampling-in-research-definition-methods-importance.html,
Rof, A., Bikfalvi, A., & Marques, P. (2024). Exploring learner satisfaction and the effectiveness of microlearning in higher education. Internet and Higher Education, 62. https://doi.org/10.1016/j.iheduc.2024.100952
Sacristano, A., Genovese, C., & Di Nicola, S. (2025). Informal Learning and Cognitive Processes: A Psychological Analysis between Theories and Applications. European Journal of Education and Pedagogy, 6(4). https://doi.org/10.24018/ejedu.2025.6.4.962
Şahin, M., & Kartal, O. Y. (2026). A systemic vulnerability in child protection: the interprofessional gap in abuse and neglect recognition rooted in university curricula. Frontiers in Public Health, 14. https://doi.org/10.3389/fpubh.2026.1760670
Salas, E., Tannenbaum, S. I., Kraiger, K., & Smith-Jentsch, K. A. (2012). The Science of Training and Development in Organizations. Psychological Science in the Public Interest, 13(2). https://doi.org/10.1177/1529100612436661
Sanderson, P. E. (2002). E-Learning: strategies for delivering knowledge in the digital age. The Internet and Higher Education, 5(2). https://doi.org/10.1016/s1096-7516(02)00082-9
Sankaranarayanan, R., Leung, J., Abramenka-Lachheb, V., Seo, G., & Lachheb, A. (2023). Microlearning in Diverse Contexts: A Bibliometric Analysis. TechTrends, 67(2). https://doi.org/10.1007/s11528-022-00794-x
Santini, F. de O., Sampaio, C. H., Rasul, T., Ladeira, W. J., Kar, A. K., Perin, M. G., & Azhar, M. (2025). Understanding students’ technology acceptance behaviour: A meta-analytic study. Technology in Society, 81. https://doi.org/10.1016/j.techsoc.2024.102798
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers and Education, 128. https://doi.org/10.1016/j.compedu.2018.09.009
Sichel, D. E. . (1997). The computer revolution : an economic perspective. Brookings Institution Press.
Şimşek, A. S., Cengiz, G. Ş. T., & Bal, M. (2025). Extending the TAM framework: Exploring learning motivation and agility in educational adoption of generative AI. Education and Information Technologies, 30(15). https://doi.org/10.1007/s10639-025-13591-9
Sitzmann, T., & Weinhardt, J. M. (2018). Training Engagement Theory: A Multilevel Perspective on the Effectiveness of Work-Related Training. Journal of Management, 44(2). https://doi.org/10.1177/0149206315574596
Sönmez, A., & Özdamar, N. (2024). Examining the Factors Related to Learners’ Intention and Usage Continuity of Online Learning. Open Praxis, 16(2), 195–207. https://doi.org/10.55982/openpraxis.16.2.570
Suartama, I. K., Yasa, I. N., & Triwahyuni, E. (2024). Instructional Design Models for Pervasive Learning Environment: Bridging Formal and Informal Learning in Collaborative Social Learning. Education Sciences, 14(12). https://doi.org/10.3390/educsci14121405
Sulistiyani, S., Pratikto, H., & Rahayu, W. P. (2024). BELAJAR GO GREEN: “INOVASI LEARNING MANAGEMENT SYSTEM BERBASIS MICROLEARNING UNTUK PEMBELAJARAN ECOPRENEURSHIP". Research and Development Journal of Education, 10(2), 681. https://doi.org/10.30998/rdje.v10i2.23549
Sun, G., Cui, T., Yong, J., Shen, J., & Chen, S. (2018). MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning. IEEE Transactions on Services Computing, 11(2). https://doi.org/10.1109/TSC.2015.2473854
Sweller, J. (2024). Cognitive load theory and individual differences. Learning and Individual Differences, 110. https://doi.org/10.1016/j.lindif.2024.102423
Taylor, A. dung, & Hung, W. (2022). The Effects of Microlearning: A Scoping Review. Educational Technology Research and Development, 70(2). https://doi.org/10.1007/s11423-022-10084-1
UNSW Media, & Childlight Global Child Safety Institute. (2024). Research Reveals the Global Scale of Child Sexual Abuse and Exploitation for The First Time. Https://Www.Unsw.Edu.Au/Newsroom/News/2024/05/More-than-300-Million-Child-Victims-of-Online-Sexual-Abuse-Globally-Report. https://www.unsw.edu.au/newsroom/news/2024/05/more-than-300-million-child-victims-of-online-sexual-abuse-globally-report
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3). https://doi.org/10.2307/30036540
Walsh, K., Eggins, E., Hine, L., Mathews, B., Kenny, M. C., Howard, S., Ayling, N., Dallaston, E., Pink, E., & Vagenas, D. (2022). Child protection training for professionals to improve reporting of child abuse and neglect. Cochrane Database of Systematic Reviews, 2022(7). https://doi.org/10.1002/14651858.CD011775.pub2
Welsh, E. T., Wanberg, C. R., Brown, K. G., & Simmering, M. J. (2003). E-learning: emerging uses, empirical results and future directions. International Journal of Training and Development, 7(4). https://doi.org/10.1046/j.1360-3736.2003.00184.x
Yao, S. Y., & Ho, Y. Y. (2024). Evaluating the Usefulness of Microlearning to Adult Students in Higher Education: An Empirical Study in Singapore. Adult Learning. https://doi.org/10.1177/10451595241280672
Žvanut, B., Pucer, P., Ličen, S., Trobec, I., Plazar, N., & Vavpotič, D. (2011). The effect of voluntariness on the acceptance of e-learning by nursing students. Nurse Education Today, 31(4), 350–355. https://doi.org/10.1016/j.nedt.2010.07.004
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Safira Ryanatami, Elfindah Princes

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

