Research Article
Determinants of student engagement and behavioral intention towards mobile learning platforms
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1 Information Technology Education, Faculty of Teacher Training and Education, Bina Bangsa University, Serang, Banten, INDONESIA2 Department of Information System and Magister Computer Science, Universitas Amikom Purwokerto, Purwokerto Utara, INDONESIA* Corresponding Author
Contemporary Educational Technology, 17(1), January 2025, ep558, https://doi.org/10.30935/cedtech/15774
Published Online: 30 December 2024, Published: 01 January 2025
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ABSTRACT
This study explores the factors influencing student engagement and behavioral intention towards mobile learning platforms, with a focus on widely used platforms in Indonesia, such as Ruangguru, Zenius, and Quipper. A total of 375 questionnaires were distributed, out of which 363 were deemed valid and used for analysis. The research employed structural equation modeling with partial least squares to analyze the data, aiming to uncover the key determinants driving the adoption of mobile learning. The findings highlight the significant impact of perceived usefulness (PU) on students’ attitudes toward mobile learning, emphasizing the crucial role of perceived utility in shaping positive attitudes. However, the study also reveals that the direct influence of PU on behavioral intention towards mobile learning is minimal, suggesting that attitude toward mobile learning plays a critical mediating role in this relationship. Additionally, the study demonstrates that perceived entertainment and facilitating conditions have substantial effects on shaping attitudes and behavioral intentions, underscoring the importance of enjoyment and support systems in fostering student engagement. The structural model developed in this research offers strong explanatory power, providing a comprehensive understanding of the factors that contribute to the success of mobile learning platforms. The insights gained from this study offer valuable guidance for educators and developers seeking to enhance mobile learning experiences and improve educational outcomes through targeted interventions that address these key determinants.
CITATION (APA)
Hayadi, B. H., & Hariguna, T. (2025). Determinants of student engagement and behavioral intention towards mobile learning platforms. Contemporary Educational Technology, 17(1), ep558. https://doi.org/10.30935/cedtech/15774
REFERENCES
- Açıkgül, K., & Şad, S. N. (2021). High school students’ acceptance and use of mobile technology in learning mathematics. Education and Information Technologies, 26(4), 4181–4201. https://doi.org/10.1007/s10639-021-10466-7
- Aggarwal, D. (2019). Mobile technology adoption by Indian consumers. International Journal of Recent Technology and Engineering, 8(2S6), 892–899. https://doi.org/10.35940/ijrte.B1166.0782S619
- Alanazi, A., Elias, N. F. B., Mohamed, H. B., & Sahari, N. (2024). The critical success factors influencing the use of mobile learning and its perceived impacts in students’ education: A systematic literature review. KSII Transactions on Internet and Information Systems, 18(3), 610–632. https://doi.org/10.3837/tiis.2024.03.005
- Aljaaidi, K. S., Bagais, O. A., & Sharma, R. B. (2020). Factors influencing usage of university mobile application among university students. Journal of Asian Finance Economics and Business, 7(10), 1129–1136. https://doi.org/10.13106/jafeb.2020.vol7.no10.1129
- Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. M. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673–174686. https://doi.org/10.1109/access.2019.2957206
- Almogren, A. S., & Aljammaz, N. A. (2022). The integrated social cognitive theory with the TAM model: The impact of M-learning in King Saud University art education. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1050532
- 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, 13(14), Article 7893. https://doi.org/10.3390/su13147893
- Alshammari, R., Parkes, M., & Adlington, R. (2018). Factors influencing Saudi Arabian preparatory year students’ skills and attitudes in the use of mobile devices in learning English as a foreign language. International Journal of Research Studies in Educational Technology, 7(1). https://doi.org/10.5861/ijrset.2018.3002
- Althunibat, A., Almaiah, M. A., & Altarawneh, F. (2021). Examining the factors influencing the mobile learning applications usage in higher education during the COVID-19 pandemic. Electronics, 10(21), Article 2676. https://doi.org/10.3390/electronics10212676
- Anuyahong, B. (2023). Exploring the effectiveness of mobile learning technologies in enhancing student engagement and learning outcomes. International Journal of Emerging Technologies in Learning, 18(18), 50–63. https://doi.org/10.3991/ijet.v18i18.40445
- Berlilana, & Mu’amar, A. (2024). Economic decentralization through blockchain opportunities challenges and new business models. Journal of Current Research in Blockchain, 1(2), 112–123. https://doi.org/10.47738/jcrb.v1i2.14
- Biswas, B. B., Roy, S. C., & Roy, F. (2020). Students perception of mobile learning during COVID-19 in Bangladesh: University student perspective. Aquademia, 4(2), Article ep20023. https://doi.org/10.29333/aquademia/8443
- Botero, G. G., Nguyet, D. A., Botero, J. G., Zhu, C., & Questier, F. (2022). Acceptance and use of mobile-assisted language learning by higher education language teachers. Lenguaje, 50(1), 66–92. https://doi.org/10.25100/lenguaje.v50i1.11006
- Bukharaev, N., & Altaher, A. W. (2017). Mobile learning education has become more accessible. American Journal of Computer Science and Information Technology, 5(2). https://doi.org/10.21767/2349-3917.100005
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
- Department of Technology and Vocational Education, Universitas Negeri Yogyakarta, Sleman, Indonesia, & Hakiki, M. (2024). Effectiveness of Android-based mobile learning in graphic design course for digital learning: The development research study. International Journal of Information and Education Technology, 14(4), 602–611. https://doi.org/10.18178/ijiet.2024.14.4.2083
- Ebadi, S., & Raygan, A. (2023). Investigating the facilitating conditions, perceived ease of use and usefulness of mobile-assisted language learning. Smart Learning Environments, 10(1), Article 30. https://doi.org/10.1186/s40561-023-00250-0
- El Emary, I. M. M. (2024). Assessing the adoption of Metaverse platforms: A structural equation modeling approach with mediating effects of switching costs. International Journal Research on Metaverese, 1(3), 236–263.
- Feng, L. (2023). Factors influencing undergraduate students’ intention to use online education platforms. The Euraseans Journal on Global Socio-Economic Dynamics, 6(43), 239–250. https://doi.org/10.35678/2539-5645.6(43).2023.239-250
- Ghyas, Q. M., & Kondo, F. N. (2016). Behavioural intention to use mobile entertainment services among Bangladeshi students. International Journal of E-Services and Mobile Applications, 8(2), 38–53. https://doi.org/10.4018/ijesma.2016040103
- Guo, J., Huang, F., Yong-qiang, L., & Shao-mei, C. (2020). Students perceptions of using mobile technologies in informal English learning during the COVID-19 epidemic: A study in Chinese rural secondary schools. Journal of Pedagogical Research, 4(4), 475–483. https://doi.org/10.33902/jpr.2020063786
- Habibi, A., Riady, Y., Alqahtani, T. M., Muhaimin, M., Albelbisi, N. A., Jaya, A., & Yaqin, L. N. (2022). Drivers affecting Indonesian pre-service teachers’ intention to use M-learning: Structural equation modeling at three universities. E-Learning and Digital Media, 20(6), 519–538. https://doi.org/10.1177/20427530221118775
- Hakimi, M. (2024). The impact of mobile applications on Quran education: A survey of student performance and satisfaction. Journal of Digital Learning and Distance Education, 2(8), 722–736. https://doi.org/10.56778/jdlde.v2i8.220
- Hameed, F., & Qayyum, A. (2018). Determinants of behavioral intention towards mobile learning in Pakistan: Mediating role of attitude. Business & Economic Review, 10(1), 33–62. https://doi.org/10.22547/ber/10.1.2
- Hameed, F., Qayyum, A., & Khan, F. A. (2024). A new trend of learning and teaching: Behavioral intention towards mobile learning. Journal of Computers in Education, 11, 149–180. https://doi.org/10.1007/s40692-022-00252-w
- Hamid, N. A. A., Ang, S. H., Abdullah, N. H., Ngadiman, Y., & Ahmad, M. F. (2016). The adoption of 4G mobile network services in Klang Valley. In Proceedings of 2016 6th International Workshop on Computer Science and Engineering (pp. 272–279). https://doi.org/10.18178/wcse.2016.06.042
- Hananto, A. R., & Srinivasan, B. (2024). Comparative analysis of ensemble learning techniques for purchase prediction in digital promotion through social network advertising. Journal of Digital Market and Digital Currency, 1(2). https://doi.org/10.47738/jdmdc.v1i2.7
- Handayani, W. P. P. (2023). The UTAUT implementation model in defining the behavioral intention of mobile banking users. Jurnal Manajemen Bisnis, 14(2), 361–377. https://doi.org/10.18196/mb.v14i2.18649
- Handayati, P., & Trisnawati, N. (2023). The intention to use mobile payment during the COVID-19 pandemic: The mediating role of attitude. Jurnal Pendidikan Ekonomi & Bisnis, 11(1), 42–50. https://doi.org/10.21009/jpeb.011.1.4
- Hartatik, Isnanto, R. R., & Warsito, B. (2024). Applied data science and artificial intelligence for tourism and hospitality industry in society 5.0: A review. Journal of Applied Data Sciences, 5(4), 1566–1578. https://doi.org/10.47738/jads.v5i4.300
- Hegarty, B., & Thompson, M. (2019). A teacher’s influence on student engagement: Using smartphones for creating vocational assessment ePortfolios. Journal of Information Technology Education Research, 18, 113–159. https://doi.org/10.28945/4244
- Hoang, Q., Pham, T., Dang, Q., & Nguyễn, T. (2021). Factors influencing Vietnamese teenagers’ intention to use mobile devices for English language learning. In Proceedings of the 18th International Conference of the Asia Association of Computer-Assisted Language Learning (pp. 230–245). Atlantis Press. https://doi.org/10.2991/assehr.k.211224.023
- Huang, J., & Li, H. (2022). Influencing factors of mobile learning interactive behavior: Moderated mediating effect. International Journal of Information and Education Technology, 12(8), 772–777. https://doi.org/10.18178/ijiet.2022.12.8.1683
- Huang, Y.-M. (2015). Exploring the factors that affect the intention to use collaborative technologies: The differing perspectives of sequential/global learners. Australasian Journal of Educational Technology, 31(3). https://doi.org/10.14742/ajet.1868
- Hunde, M. K., Demsash, A. W., & Walle, A. D. (2023). Behavioral intention to use e-learning and its associated factors among health science students in Mettu University, southwest Ethiopia: Using modified UTAUT model. Informatics in Medicine Unlocked, 36, Article 101154. https://doi.org/10.1016/j.imu.2022.101154
- Ibrahim, U. (2024). Assessing the impact of mobile applications on student engagement in ICT and computer science education. International Journal of Applied Educational Research, 2(1), 51–62. https://doi.org/10.59890/ijaer.v2i1.1457
- Irfan, M. (2024). The role of trust in mediating the impact of electronic word of mouth and security on cryptocurrency purchase decisions. Journal of Current Research in Blockchain, 1(3), 242–266.
- Isaías, P., Reis, F. F. D., Coutinho, C. P., & Lencastre, J. A. (2017). Empathic technologies for distance/mobile learning. Interactive Technology and Smart Education, 14(2), 159–180. https://doi.org/10.1108/itse-02-2017-0014
- Islam, A. Y. M. A., Leng, C. H., & Singh, D. (2015). Efficacy of the technology satisfaction model (TSM). International Journal of Technology and Human Interaction, 11(2), 45–60. https://doi.org/10.4018/ijthi.2015040103
- Issaramanoros, E., Khlaisang, J., & Pugsee, P. (2018). Auto mechanic students’ perceptions and readiness toward mobile learning in Thailand. International Journal of Interactive Mobile Technologies, 12(5), Article 28. https://doi.org/10.3991/ijim.v12i5.8906
- Izkair, A. S., & Lakulu, M. M. (2021). Experience moderator effect on the variables that influence intention to use mobile learning. Bulletin of Electrical Engineering and Informatics, 10(5), 2875–2883. https://doi.org/10.11591/eei.v10i5.3109
- Jiang, G., Peng, L., & Liu, R. (2015). Mobile game adoption in China: The role of TAM and perceived entertainment, cost, similarity and brand trust. International Journal of Hybrid Information Technology, 8(4), 213–232. https://doi.org/10.14257/ijhit.2015.8.4.24
- Jou, Y., Mariñas, K. A., Saflor, C. S., & Young, M. N. (2022). Investigating accessibility of social security system (SSS) mobile application: A structural equation modeling approach. Sustainability, 14(13), Article 7939. https://doi.org/10.3390/su14137939
- Julita, Z. (2023). Factor analysis of Sharia mobile banking using the UTAUT2 model in millennial generations. International Journal of Scientific Research and Management, 11(09), 5152–5163. https://doi.org/10.18535/ijsrm/v11i09.em11
- Kampa, R. K. (2023). Combining technology readiness and acceptance model for investigating the acceptance of m-learning in higher education in India. Asian Association of Open Universities Journal, 18(2), 105–120. https://doi.org/10.1108/AAOUJ-10-2022-0149
- Küçük, S., Onlu, O. B., & Kapakin, S. (2020). A model for medical students’ behavioral intention to use mobile learning. Journal of Medical Education and Curricular Development, 7. https://doi.org/10.1177/2382120520973222
- Kumar, J. A., Bervell, B., Annamalai, N., & Osman, S. (2020). Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and WhatsApp use habit. IEEE Access, 8, 208058–208074. https://doi.org/10.1109/ACCESS.2020.3037925
- Lenus, L. (2024). Predicting consumer perceptions of Metaverse shopping through insights from machine learning models. International Journal Research on Metaverese, 1(3), 199–211.
- Martínez-Ruiz, M. P., Izquierdo-Yusta, A., Pascual, C. O., & Lara, E. M. R. (2017). Do affective variables make a difference in consumers behavior toward mobile advertising? Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.02018
- Min, L. (2024). Instructors’ usage of mobile learning applications in classroom and its impact on the learners’ performance. World Journal of Educational Research, 11(1), Article 48. https://doi.org/10.22158/wjer.v11n1p48
- Murnawan, M., Lestari, S., Samihardjo, R., & Dewi, D. A. (2024). Sustainable educational data mining studies: Identifying key factors and techniques for predicting student academic performance. Journal of Applied Data Sciences, 5(3). https://doi.org/10.47738/jads.v5i3.347
- Naveed, Q. N., Alam, M. M., & Tairan, N. (2020). Structural equation modeling for mobile learning acceptance by university students: An empirical study. Sustainability, 12(20), Article 8618. https://doi.org/10.3390/su12208618
- Nikolopoulou, K., Saltas, V., & Tsiantos, V. (2023). Postgraduate students’ perspectives on mobile technology benefits and learning possibilities: Insights from Greek students. Trends in Higher Education, 2(1), 140–151. https://doi.org/10.3390/higheredu2010009
- Ozturk, A. B. (2016). Customer acceptance of cashless payment systems in the hospitality industry. International Journal of Contemporary Hospitality Management, 28(4), 801–817. https://doi.org/10.1108/ijchm-02-2015-0073
- Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605. https://doi.org/10.1111/j.1467-8535.2011.01229.x
- Paule-Ruiz, Mp., Álvarez-García, V. M., Pérez, J. M. E., Álvarez-Sierra, M., & Trespalacios-Menéndez, F. (2016). Music learning in preschool with mobile devices. Behaviour and Information Technology, 36(1), 95–111. https://doi.org/10.1080/0144929x.2016.1198421
- Poong, Y. S., Yamaguchi, S., & Takada, J. (2016). Investigating the drivers of mobile learning acceptance among young adults in the world heritage town of Luang Prabang, Laos. Information Development, 33(1), 57–71. https://doi.org/10.1177/0266666916638136
- Pramana, E. (2018). Determinants of the adoption of mobile learning systems among university students in Indonesia. Journal of Information Technology Education Research, 17, 365–398. https://doi.org/10.28945/4119
- Pratama, S. F. (2024). Analyzing the determinants of user satisfaction and continuous usage intention for digital banking platform in Indonesia: A structural equation modeling approach. Journal of Digital Market and Digital Currency, 1(3), 267–285. https://doi.org/10.47738/jdmdc.v1i3.21
- Pratama, S. F., & Prastyo, P. A. (2024). Evaluating blockchain adoption in Indonesia’s supply chain management sector. Journal of Current Research in Blockchain, 1(3), 190–213.
- Putawa, R. A., & Sugianto, D. (2024). Exploring user experience and immersion levels in virtual reality: A comprehensive analysis of factors and trends. International Journal Research on Metaverse, 1(1), 20–39. https://doi.org/10.47738/ijrm.v1i1.3
- Rahman, S. M. M., Mia, M. S., Ahmed, F., Thongrak, S., & Kiatpathomchai, S. (2020). Assessing students’ satisfaction in public universities in Bangladesh: An empirical study. Journal of Asian Finance Economics and Business, 7(8), 323–332. https://doi.org/10.13106/jafeb.2020.vol7.no8.323
- Rosman, M. R. M., Aziz, M. A. A., Osman, M. A. F., & Razlan, N. M. (2022). Self-efficacy and user behavioral intention to use online consultation management system. International Journal of Evaluation and Research in Education, 11(3), Article 1240. https://doi.org/10.11591/ijere.v11i3.22875
- Rudhumbu, N. (2022). Applying the UTAUT2 to predict the acceptance of blended learning by university students. Asian Association of Open Universities Journal, 17(1), 15–36. https://doi.org/10.1108/aaouj-08-2021-0084
- Sangsawang, T. (2024). Predicting ad click-through rates in digital marketing with support vector machines. Journal of Digital Market and Digital Currency, 1(3), 225–246. https://doi.org/10.47738/jdmdc.v1i3.20
- Setiawan, E. I., Tjendika, P., Santoso, J., Ferdinandus, F. X., Gunawan, & Fujisawa, K. (2024). Aspect-based sentiment analysis of healthcare reviews from Indonesian hospitals based on weighted average ensemble. Journal of Applied Data Sciences, 5(4), 1579–1596. https://doi.org/10.47738/jads.v5i4.328
- Shi, Y., Shu-Qin, C., & Wang, H. (2017). Mobile multimedia classroom construction for rhythmic gymnastics based on APT teaching model. International Journal of Emerging Technologies in Learning, 12(07), Article 79. https://doi.org/10.3991/ijet.v12i07.7216
- Stymne, J. (2020). Mobile learning in outdoor settings: A systematic review. In Proceedings of the 16th International Conference Mobile Learning (pp. 63–70). IADIS. https://doi.org/10.33965/ml2020_202004l008
- Sukmana, H. T., & Kim, J. I. (2024). Exploring the impact of virtual reality experiences on tourist behavior and perceptions. International Journal Research on Metaverse, 1(2), 94–112. https://doi.org/10.47738/ijrm.v1i2.8
- Suryawirawan, O. A. (2021). The effect of college students’ technology acceptance on e-commerce adoption. Bisma: Bisnis dan Manajemen, 14(1), 46–62. https://doi.org/10.26740/bisma.v14n1.p46-62
- Talan, T. (2024). Digital natives’ mobile learning adoption in terms of UTAUT-2 model: A structural equation model. Innoeduca: International Journal of Technology and Educational Innovation, 10(1), 100–123. https://doi.org/10.24310/ijtei.101.2024.17440
- Tam, N. T., Truc, N. M. T., & Thanh, H. L. T. (2024). Applying quantitative and data analysis using structural equation modeling for accessing factors influencing employee loyalty. Journal of Applied Data Sciences, 5(1), 1–15. https://doi.org/10.47738/jads.v5i1.151
- Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
- Viriyasuebphong, P., Sophea, D., & Sungsuwan, T. (2021). Factors influencing students’ behavioral intention on using mobile learning (M-learning) in tourism and hospitality major in Phnom Penh, Cambodia. Current Applied Science and Technology, 22(2). https://doi.org/10.55003/cast.2022.02.22.010
- Wahyuningsih, T., & Chen, S. C. (2024). Analyzing sentiment trends and patterns in bitcoin-related tweets using TF-IDF vectorization and k-means clustering. Journal of Current Research in Blockchain, 1(1), 48–69. https://doi.org/10.47738/jcrb.v1i1.11
- Wang, C. C. (2017). A near field communication-enabled e-learning environment for context-aware mobile Japanese conversation learning. International Journal of Applied Systemic Studies, 7(1/2/3), Article 41. https://doi.org/10.1504/ijass.2017.088900
- Wang, Z. (2022). Higher education management and student achievement assessment method based on clustering algorithm. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2022/4703975
- Widayati, T., Sulistiyani, S., Nurchayati, N., & Suprapti, S. (2023). Antecedents of user attitude towards e-commerce and future purchase intention. International Journal of Data and Network Science, 7(1), 505–512. https://doi.org/10.5267/j.ijdns.2022.8.007
- Widiar, G., Yuniarinto, A., & Yulianti, I. (2023). Perceived ease of use’s effects on behavioral intention mediated by perceived usefulness and trust. Interdisciplinary Social Studies, 2(4), 1829–1844. https://doi.org/10.55324/iss.v2i4.397
- Wirawan, G. (2024). Exploring students’ perceptions of mobile game-based social studies learning model. Yupa Historical Studies Journal, 6(2), 181–187. https://doi.org/10.30872/yupa.v6i2.3379
- Wu, C.-G., & Ho, J. (2021). The influences of technological characteristics and user beliefs on customers’ perceptions of live chat usage in mobile banking. The International Journal of Bank Marketing, 40(1), 68–86. https://doi.org/10.1108/ijbm-09-2020-0465
- Wu, S.-J., Chang, T.-C., Lee, Z.-H., Liu, F.-L., & Xu, Y.-Y. (2023). Constructing a student engagement and learning development model in mobile learning by SEM. In Proceedings of the International Conferences on E-Society 2023 and Mobile Learning 2023 (pp. 259–266). https://doi.org/10.33965/ES_ML2023_202302L032
- Xu, Q., Hou, X., Ting-chao, X., & Zhao, W. (2022). Factors affecting medical students’ continuance intention to use mobile health applications. Journal of Multidisciplinary Healthcare, 15, 471–484. https://doi.org/10.2147/jmdh.s327347
- Yadulla, A. R., Nadella, G. S., Maturi, M. H., & Gonaygunta, H. (2024). Evaluating behavioral intention and financial stability in cryptocurrency exchange app: Analyzing system quality, perceived trust, and digital currency. Journal of Digital Market and Digital Currency, 1(2), 103–124. https://doi.org/10.47738/jdmdc.v1i2.12
- Zainuddin, N. F. F. B., Bakar, Z. B. A., Mohammad, N. M. B., & Mohamed, R. B. (2022). The effect of the aesthetically mobile interfaces on students’ learning experience for primary education. International Journal of Advanced Computer Science and Applications, 13(10). https://doi.org/10.14569/ijacsa.2022.0131028
- Zhou, H., Liu, J., & Cui, X. (2021). Research on influencing factors of adoption behavior of mobile readers based on meta-analysis. Mathematical Problems in Engineering. https://doi.org/10.1155/2021/5082594