Explaining students' learning activity: Effects of motivation, environment, and self-efficacy
DOI:
https://doi.org/10.59672/ijed.v7i1.6057Keywords:
Learning activity, Learning environment, Motivation, Self-efficacy, Vocational studentsAbstract
This research investigated the influence of motivation, learning environment, and self-efficacy on students' learning activity through an explanatory research approach. The population consisted of 3,481 vocational students in Palu City, Central Sulawesi, Indonesia, from which 359 respondents were selected using the Slovin formula and a simple random sampling. Data were collected using a Likert-scale questionnaire and analyzed through Partial Least Squares–Structural Equation Modeling with SmartPLS 3. The assessment of the measurement model confirmed adequate validity and reliability, as all constructs met the recommended thresholds for outer loading (> 0.70), composite reliability (> 0.70), and average variance extracted (> 0.50). The structural model analysis revealed that motivation exerted a positive and significant effect on learning activity (β = 0.395; t = 4.506; p < 0.001), while self-efficacy emerged as the strongest predictor (β = 0.470; t = 6.919; p < 0.001). Conversely, the learning environment did not demonstrate a significant direct effect on learning activity (β = 0.062; t = 0.696; p = 0.487). The results indicate that internal psychological factors, particularly self-efficacy and motivation, play a more substantial role than external environmental factors in shaping students' learning activity.
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Adolfo, R., & Ducot, L. (2025). Classroom environment and its influence on learners’ academic achievement. Psychology and Education A Multidisciplinary Journal, 40(5), 632–647. https://doi.org/10.70838/pemj.400504
Al-khresheh, M. H., & Alkursheh, T. (2024). An integrated model exploring the relationship between self-efficacy, technology integration via Blackboard, English proficiency, and Saudi EFL students’ academic achievement. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-02783-2
Aljehani, S. B. (2024). Enhancing student learning outcomes: The interplay of technology integration, pedagogical approaches, learner engagement, and leadership support. 418–437. https://doi.org/10.53555/kuey.v30i4.1485
Alkhalifah, Z. (2022). The impact of self-efficacy, learning preferential, learning motivation and academic achievement on EFL students in Saudi Arabia. World Journal of English Language, 13(1), 225. https://doi.org/10.5430/wjel.v13n1p225
AlTwijri, L., & Alghizzi, T. M. (2024). Investigating the integration of artificial intelligence in English as foreign language classes for enhancing learners’ affective factors: A systematic review. In Heliyon (Vol. 10, Issue 10). Elsevier BV. https://doi.org/10.1016/j.heliyon.2024.e31053
Ariza, J. Á. (2023). Bringing active learning, experimentation, and student‐created videos in engineering: A study about teaching electronics and physical computing integrating online and mobile learning. Computer Applications in Engineering Education, 31(6), 1723–1749. https://doi.org/10.1002/cae.22673
Bakri, H., Mukhlisin, M., Sukma, I., & Faraby, M. D. (2025). Enhancing learning activity and learning outcomes in the electrical installation course using augmented reality. Jurnal Inovasi Teknologi Pendidikan, 12(4), 455–467.
Basileo, L. D., Otto, B., Lyons, M. E., Vannini, N., & Toth, M. D. (2024). The role of self-efficacy, motivation, and perceived support of students’ basic psychological needs in academic achievement. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1385442
Bernal, A. I. I. S., & Palma, R. (2025). Effective teaching, student engagement, classroom learning environment: A structural model of motivation in learning the filipino language. International Journal of Innovative Research in Multidisciplinary Education, 4(1). https://doi.org/10.58806/ijirme.2025.v4i2n03
Chen, C., Zhu, Y., Hong, Y., Xiao, F., & Li, L. (2026). How depression affects academic self-efficacy among college students: The mediating effect of professional identity and the moderating effect of belief in a just world. BMC Public Health, 26(1), 208. https://doi.org/10.1186/s12889-025-26074-w
da Costa, L. A., & Reis, M. J. C. S. (2025). Motivational teaching techniques in secondary and higher education: A systematic review of active learning methodologies. In Digital, 5(3), p. 40). https://doi.org/10.3390/digital5030040
Dai, X., Wang, R., feng Huang, X., Xiao, W., Huang, Y., Li, Y., Wu, Y., Yuan Guan, C., Kazembe, R. Q., Zhang, Y., & Gao, B. (2024). An analytical model of college students’ self-assessed satisfaction with the effectiveness of online learning: a structural equation model integrating LICE and S-O-R models. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1248729
Dai, X., Wang, Y., Yu, J., Qiu, B., Wang, R., & Na, M. (2026). Motivation and engagement as pathways: how AI-augmented online assessment shapes English-speaking competency in vocational EFL classrooms. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1730953
Dheghu, Y. P., Sanga, M. H., Situmorang, R., Latumahina, O. ., & Wora, A. . (2026). ESP, metacognition, and financial reporting practice: The moderating role of learning motivation. Indonesian Journal of Educational Development (IJED), 6(4), 1542–1555. https://doi.org/10.59672/ijed.v6i4.5469
Endo, T., Yamamoto, A., & Watanabe, T. (2016). Bias factor method using random sampling technique. Journal of Nuclear Science and Technology, 51(3). https://doi.org/10.1080/00223131.2015.1126541
Fakhkhari, H., Bounabat, B., & Bennani, M. (2021). Structural Equation Model (SEM) for evaluating factors affecting ICT4D project success. Journal of Computer Science, 17(8), 724–737. https://doi.org/10.3844/jcssp.2021.724.737
Firdaus, H., Syafrizal, S., & Nulhakim, L. (2024). Improving learning quality through the implementation of electronic teaching materials. Journal of Education and Learning (EduLearn), 18(3), 727–733. https://doi.org/10.11591/edulearn.v18i3.21362
Fitria, F., Yahya, M., Syahrul, S., Purnamawati, P., & Nur, H. (2023). The impact of a conducive learning environment on learning motivation and student achievement in vocational schools. In Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research (pp. 199–206). https://doi.org/10.2991/978-2-38476-084-8_27
Gomez, R., Stavropoulos, V., & Griffiths, M. D. (2020). Confirmatory factor analysis and exploratory structural equation modelling of the factor structure of the Depression Anxiety and Stress Scales-21. PLoS ONE, 15(6). https://doi.org/10.1371/journal.pone.0233998
Hammoudi, M. M., Grira, S., & Alseksek, Y. (2025). Improving engineering students’ motivation for success in statistics and data science: An innovative visualization of four mathematical models in higher education in united arab emirates. International Journal of Engineering Pedagogy (IJEP), 15(2), 122–152. https://doi.org/10.3991/ijep.v15i2.50611
Hendra, R., Habibi, A., Ridwan, A., Sembiring, D., Wijaya, T., Denmar, D. & Widana, I. (2025). The impact of perfectionism, self-efficacy, academic stress, and workload on academic fatigue and learning achievement: Indonesian perspectives. Open Education Studies, 7(1), 20250071. https://doi.org/10.1515/edu-2025-0071
Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hong, H., Vispoel, W. P., & Martinez, A. J. (2024). Applying SEM, exploratory SEM, and bayesian SEM to personality assessments. Psych, 6(1), 111–134. https://doi.org/10.3390/psych6010007
Hu, J., & Xiao, W. (2025). What are the influencing factors of online learning engagement? A systematic literature review. In Frontiers in Psychology, Vol. 16, p. 1542652). Frontiers Media. https://doi.org/10.3389/fpsyg.2025.1542652
Iliescu, D., Greiff, S., & Ion, A. (2025). Evidence based approaches for enhancing vocational education worldwide. In npj Science of Learning, 10(1), p. 20. Nature Portfolio. https://doi.org/10.1038/s41539-025-00317-2
Jičínská, L., Sedláčková, P., Kolek, L., Tetourová, T., Volná, K., Lukavský, J., & Brom, C. (2021). Extrinsically integrated instructional quizzes in learning games: an educational disaster or not? Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.678380
Juita, R., Syahza, A., & Suarman, S. (2025). The moderating role of learning environment in self-efficacy, learning motivation, and academic achievement relationship. Tarbawi Jurnal Keilmuan Manajemen Pendidikan, 11(1), 63–76. https://doi.org/10.32678/tarbawi.v11i01.11157
Khalil, A. I., Saati, H. S., Singali, D., Alsomali, L., & Shereda, H. M. A. (2025). Relationship between Emotional Intelligence (EI), Locus of Control (LOC), and academic achievements among nursing students: Across sectional study. Wiadomości Lekarskie, 7, 1279–1290. https://doi.org/10.36740/wlek/205299
Kong, K. H., & Kong, W. (2024). The role of self-efficacy in motivating middle school students’ learning. International Journal of Education and Humanities, 17(1), 273–275. https://doi.org/10.54097/qsq0q663
Lachenbruch, P. A., Lwanga, S. K., & Lemeshow, S. (1991). Sample size determination in health studies: a practical manual. Journal of the American Statistical Association, 86(416), 1149. https://doi.org/10.2307/2290547
Lago, M. M. A. (2025). Correlating student satisfaction on classroom environment and academic performance in tourism and hospitality education. International Journal of Research and Innovation in Social Science, 1762–1782. https://doi.org/10.47772/ijriss.2025.914mg00132
Lei, H., Chen, C., & Luo, L. (2024). The examination of the relationship between learning motivation and learning effectiveness: a mediation model of learning engagement. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-02666-6
Li, J., & Xue, E. (2023). Dynamic interaction between student learning behaviour and learning environment: Meta-analysis of student engagement and its influencing factors. Behavioral Sciences, 13(1), 59. https://doi.org/10.3390/bs13010059
Li, Y., & Singh, C. (2022). Inclusive learning environments can improve student learning and motivational beliefs. Physical Review Physics Education Research, 18(2). https://doi.org/10.1103/physrevphyseducres.18.020147
Lin, T., & Zhu, X. (2024). Academic self-efficacy, grit, and teacher support as predictors of psychological well-being of Chinese EFL students. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1332909
Liu, X. (2024). Effect of teacher–student relationship on academic engagement: the mediating roles of perceived social support and academic pressure. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1331667
Lourenço, A. A., & Paiva, M. O. (2025). Key factors in academic achievement: The impact of procrastination, volitional control, and self-regulated learning. Creative Education, 16(2), 144–166. https://doi.org/10.4236/ce.2025.162009
Mendoza, N. B., & Yan, Z. (2025). From beliefs to behaviors: Conceptualizing and assessing students’ practices that reflect a growth mindset. Social Psychology of Education, 28(1). https://doi.org/10.1007/s11218-025-10032-w
Meng, Y., Yaacob, N. R. N., Mydin, Y. O., & Li, F. (2026). Psychometric validation of the brief Chinese psychological well-being scale in a sample of Mainland Chinese adolescents. BMC Psychology. https://doi.org/10.1186/s40359-025-03952-x
Metli, A. (2026). The cognitive foundations of STEM achievement: A cross-national multilevel and SEM investigation. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1746719
Mohamed, A. M., Shaaban, T. S., Bakry, S. H., Guillén‐Gámez, F. D., & Strzelecki, A. (2024). Empowering the faculty of education students: Applying ai’s potential for motivating and enhancing learning. Innovative Higher Education, 50(2), 587–609. https://doi.org/10.1007/s10755-024-09747-z
Mukhlisin, Asrifan, A., Miguel, L., & Barros, O. De. (2025). The effectiveness of augmented reality in enhancing learning outcomes in a microcontroller course. Jurnal Inovasi Teknologi Pendidikan, 12(2), 179–191.
Mukhlisin, Tasri Ponta, & Muhammad Syafar. (2023). Aplikasi pembelajaran mata kuliah embedded system berbasis mobile augmented reality (Mar) (Embedded system course learning application based on mobile augmented reality (Mar)). In Instek (Vol. 8, pp. 46–55).
Mulisa, F. (2022). Sampling techniques involving human subjects: Applications, pitfalls, and suggestions for further studies. International Journal of Academic Research in Education, 8(1), 74–83. https://doi.org/10.17985/ijare.1225214
Mustapha, R., Surat, S., & Rahmatullah, A. S. (2025). The Role of Motivation, Self-Efficacy, and Peer Support in Driving Student Engagement: Bridging Theory and Practice. 1(2), 1–10. https://doi.org/10.64008/gpej.v1i2.23
Naithani, U., & Guleria, V. (2024). Integrative computational approaches for discovery and evaluation of lead compound for drug design. Frontiers in Drug Discovery, 4. https://doi.org/10.3389/fddsv.2024.1362456
Obiosa, N. (2020). Effects of students’ motivation and engagement on students’ satisfaction in a lecture: Empirical analysis. International Journal of Instruction, 13(3), 861–876. https://doi.org/10.29333/iji.2020.13357a
Pan, Z., Wang, K., Zhu, J., & Smith, M. K. (2026). Levels, stickers, and strategies: How gamified math tasks foster elementary student engagement. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13866-1
Pasaribu, D., Tanjung, L. M., Yantina, R., Utami, T. H., & Angin, L. M. P. (2023). The effect of the pbl model to increase student learning activities in integrated thematic learning in elementary schools. Journal of Educational Analytics, 2(2), 263–272. https://doi.org/10.55927/jeda.v2i2.4340
Pham, T., Baeza, A., Xing, C., Blue, L., Anderson, P. J., & Chappell, R. (2025). Measuring social determinants influencing Australian university students’ completion: Confirmatory factor analysis. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1568521
Pondang, K. A., Madulid, A., Lotilla, J., Agot, K., Rufil, G., Cabalhin, J., & Agustin, L. (2025). The relationship between learning environment and self- efficacy among senior high school students. International Journal of Research and Innovation in Social Science, 1819–1832. https://doi.org/10.47772/ijriss.2025.9020148
Purnadewi, G. A. A., Arnawa, N., & Tatminingsih, S. (2023). The influence of school culture, learning interest, and learning motivation on science learning outcomes. Indonesian Journal of Educational Development (IJED), 4(2), 126-138. https://doi.org/10.59672/ijed.v4i2.3040
Rimasiute-Knabikiene, R., & Diržytė, A. (2025). Profiles of e-learners based on learning motivation: Differences in peer-to-peer confirmation and mental health. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1622805
Russell, J. M. (2025). Significant statistics: An introduction to statistics. In Virginia Tech Publishing eBooks. https://doi.org/10.21061/significantstatistics
Santoso, A. (2023). Rumus slovin : Panacea masalah ukuran sampel ? (Slovin's formula: Panacea of sample size problems?)Suksma Jurnal Psikologi Universitas Sanata Dharma, 4(2), 24–43. https://doi.org/10.24071/suksma.v4i2.6434
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. G. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Psychology Archives, 8, 23–74. https://doi.org/10.23668/psycharchives.12784
Soriano-Sánchez, J. G., López, R. Q., & Rodríguez, D. A. (2026). The impact of game-based learning on motivation, self-efficacy, and academic achievement in the natural sciences: A meta-analysis. Education Sciences, 16(1), 122. https://doi.org/10.3390/educsci16010122
Suwardika, G., Sopandi, A. T., Indrawan, I. P. O., & Masakazu, K. (2026). AI-enhanced flipped classroom design thinking to improve self-efficacy in open distance learning. Indonesian Journal of Educational Development (IJED), 6(4), 1159–1172. https://doi.org/10.59672/ijed.v6i4.5702
Tu, Y., Hwang, G., & Hu, D. (2025). Effects on the learning achievement, approaches to learning, and multi-stage reflection quality of students with different levels of digital self-efficacy in a data literacy course: An ARCS-based self-reflective online learning model. Computers & Education, 238, 105397. https://doi.org/10.1016/j.compedu.2025.105397
Urhahne, D., & Wijnia, L. (2023). Theories of motivation in education: an integrative framework. Educational Psychology Review, 35(2). https://doi.org/10.1007/s10648-023-09767-9
Voorhees, C. M., Brady, M. K., Calantone, R. J., & Ramirez, E. (2015). Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134. https://doi.org/10.1007/s11747-015-0455-4
Wei, R., & Cao, D. (2025). Learning effectiveness in ERP systems training: A structural equation model of self-efficacy, passion, and resilience in higher education. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1590309
Widana, I. W., Suarta, I. M., & Citrawan, I. W. (2019). Work motivation and creativity on the teacher's ability to develop HOTS-based assessments. International Journal of Social Sciences and Humanities, 3(3), 188-200. https://doi.org/10.29332/ijssh.v3n3.378
Wu, J., Tlili, A., Salha, S., Mizza, D., Saqr, M., López‐Pernas, S., & Huang, R. (2025). Unlocking the potential of artificial intelligence in improving learning achievement in blended learning: a meta-analysis. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1691414
Xiong, Y., Yu, J., & Wu, H. (2026). The relationship between psychological resilience and employability among higher vocational college students: the chain mediating effects of perceived social support and career decision-making self-efficacy. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1707655
Zheng, Y., & Xiao, A. (2024). A structural equation model of online learning: Investigating self-efficacy, informal digital learning, self-regulated learning, and course satisfaction. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1276266
Zhu, J.-B., & Luo, Y. (2024). The prediction of science achievement with science identity and science learning self-efficacy among china’s upper-secondary students. Journal of Baltic Science Education, 23(2), 390–410. https://doi.org/10.33225/jbse/24.23.390
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