Investigating the Influence of Learning Perception and Autonomy on Mobile Learning Outcomes in Higher Education
DOI:
https://doi.org/10.52152/800018Keywords:
Mobile learning; Perceived flexibility; Structural equation model; Learning continuity; Mediating effectAbstract
Although numerous studies have explored mobile learning, relatively little attention has been given to the impact of learning perception and learning autonomy on mobile learning performance. The primary aim of this study is to examine the relationships between perceived flexibility advantage, perceived interest advantage, learning autonomy, and mobile learning performance. Additionally, the study seeks to explore the mediating effect of learning continuance on mobile learning performance. A total of 456 college students with prior mobile learning experience participated in this study. Data were analyzed using partial least squares structural equation modeling analysis and SPSS-AMOS PROCESS. The findings indicate that perceived flexibility advantage, perceived interest, and learning autonomy positively influence mobile learning performance. Furthermore, the results reveal that learning continuance mediates the relationships between perceived flexibility advantage and mobile learning performance, perceived interest and mobile learning performance, as well as learning autonomy and mobile learning performance. Notably, the study also finds that the total effect of perceived interest on mobile learning performance is the most significant, while the direct effect of learning continuance on mobile learning performance is the largest.
References
Abdelazim, A. M., Gaber, D. A., Adam,K. M., El-Ashkar, A. M., & Abdelmalak, H. W. (2023). Use of mobile learning applications as an innovative method for the teaching of biochemistry.
Biochemistry and Molecular Biology Education, 51(6), 627-634. https://doi.org/10.1002/bmb.21774
Agarwal,R., & Karahanna, E. (2000). Time flies when you ’re having fun: cognitive absorption
and beliefs about information technology usage. MIS Quarterly, 24(4), 665 –694. https://doi.org/10.2307/3250951
Alghazi, S. S., Kamsin, A., Almaiah, M. A., Wong, S. Y., & Shuib, L. (2021). For sustainable application of mobile learning: an extended UTAUT model to examine the effect of technical factors on the usage of mobile devices as a learning tool. Sustainability, 13(4), 1856. https://doi.org/10.3390/su13041856
Alraimi, K. M., Zo, H. J., & Ciganek,A. P. (2015). Understanding the MOOCs continuance: the role of openness and reputation. Computers & Education, 80, 28-38. https://doi.org/10.1016/j.compedu.2014.08.006
Alzahrani,A., Adnan, M., Aljohani, M., Alarood,A. A., & Uddin, M. I. (2022). Memory load and performance-based adaptive smartphone e-learning framework for e-commerce applications in online learning. Journal of Internet Technology, 23(6), 1353-1365. https://doi.org/10.53106/160792642022112306018
Amberg, M., Hirschmeier, M., & Wehrmann,J. (2004). The compass acceptance model for the analysis and evaluation of mobile services. International Journal of Mobile Communications, 2(3), 248-259. https://doi.org/10.1504/IJMC.2004.005163
Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet- based MBA courses. Journal of Management Education, 24(1), 32-54.
https://doi.org/10.1177/105256290002400104
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation- confirmation model. Mis Quarterly, 25(3), 351-370. https://doi.org/Doi 10.2307/3250921
Cheng, S. L., Chang, J. C., Quilantan-Garza,K., & Gutierrez, M. L. (2023). Conscientiousness, prior experience, achievement emotions and academic procrastination in online learning environments. British Journal of Educational Technology, 54(4), 898-923.
https://doi.org/10.1111/bjet.13302
Chin, K. Y., Chang, H. L., & Wang, C. S. (2024). Applying a wearable MR-based mobile learning system on museum learning activities for university students. Interactive Learning Environments, 32(9), 5744-5765. https://doi.org/10.1080/10494820.2023.2228843
Chow, W. S., & Shi, S. (2014). Investigating students' satisfaction and continuance intention toward e-learning: an extension ofthe expectation-confirmation model. 4th World Conference on Learning Teaching and Educational Leadership (Wclta-2013), 141, 1145-1149. https://doi.org/10.1016/j.sbspro.2014.05.193
Conrad, C., Deng, Q., Caron, I., Shkurska, O., Skerrett, P., & Sundararajan, B. (2022). How student perceptions about online learning difficulty influenced their satisfaction during Canada's Covid-19 response. British Journal of Educational Technology, 53(3), 534-557. https://doi.org/10.1111/bjet.13206
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
Diep, A. N., Zhu, C., Struyven, K., & Blieck, Y. (2017). Who or what contributes to student satisfaction in different blended learning modalities? British Journal of Educational Technology, 48(2), 473-489. https://doi.org/10.1111/bjet.12431
Gruber, M. J., Gelman, B. D., & Ranganath, C. (2014). States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron, 84(2), 486-496. https://doi.org/10.1016/j.neuron.2014.08.060
Hanif M. (2020). Students’ self-regulated learning in iconic mobile learning system in English cross-disciplined program. Anatolian Journal of Education, 5 (2) :121-130.
https://doi.org/10.29333/aje.2020.5210a
Hardway, C., Seitchik, A. E., Kurdziel, L. B. F., Stroud, M. J., LaTorre, J.T., & LeBert, C.
(2018). Online and classroom simulations: does video use inspire interest, comprehensibility, or achieve learning outcomes? Journal ofEducational Computing Research, 56(7), 1056-1075. https://doi.org/10.1177/0735633117732961
He, L. M., & Li, C. M. (2023). Continuance intention to use mobile learning for second language acquisition based on the technology acceptance model and self-determination theory. Frontiers in Psychology, 14, 1185851. https://doi.org/10.3389/fpsyg.2023.1185851
Hsieh, J. S. C., Huang, Y. M., & Wu, W. C. V. (2017). Technological acceptance of LINE in flipped EFL oral training. Computers in Human Behavior, 70, 178-190.
https://doi.org/10.1016/j.chb.2016.12.066
Hsu, H. T., & Lin, C. C. (2024). Factors influencing students' listening learning performance in mobile vocabulary-assisted listening learning: an extended technology acceptance model. Journal of Computer Assisted Learning, 40(4), 1511-1525. https://doi.org/10.1111/jcal.12969
Huang, E. (2008). Use and gratification in e-consumers. Internet Research, 18(4), 405-426. https://doi.org/10.1108/10662240810897817
Huang, R. T. (2023). Explore the moderating impact of learners' anticipated emotions on mobile learning outcome: a moderated mediation model. Innovations in Education and Teaching International, 60(6), 872-882. https://doi.org/10.1080/14703297.2022.2076717
Huang, R. T., & Yu, C. L. (2019). Exploring the impact of self-management of learning and personal learning initiative on mobile language learning: a moderated mediation model. Australasian Journal of Educational Technology, 35(3), 118-131.
https://doi.org/10.14742/ajet.4188
Huang, R. T., Jabor, M. K., Tang, T. W., & Chang, S. C. (2022). Examine the moderating role of mobile technology anxiety in mobile learning: a modified model of goal-directed behavior. Asia Pacific Education Review, 23(1), 101-113. https://doi.org/10.1007/s12564-021-09703-y
Kim, D., Lee, I. H., & Park, J. H. (2019). Latent class analysis of non-formal learners' self- directed learning patterns in open educational resource repositories. British Journal of Educational Technology, 50(6), 3420-3436. https://doi.org/10.1111/bjet.12746
Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation- confirmation model for web portal context. Information & Management, 42(5), 683-693. https://doi.org/10.1016/j.im.2004.04.003
Liu, H. H., Ye, Y. C., & Jiang, H. L. (2021). Self-efficacy in home-based online learning environments. Journal of Internet Technology, 22(3), 557-567.
https://doi.org/10.3966/160792642021052203006
Liu, Y., Hu, H. W., Wang, L. Y., Mao,Y., Yang, K. J., Ma, L. M., & Li, H. Y. (2023). Medical education environment perception and learning engagement in undergraduate nursing students: the mediating effect of self-regulated learning ability. Nurse Education in Practice, 72, 103793. https://doi.org/10.1016/j.nepr.2023.103793
Long, T. T., Zhao, G. Q., Yang, X. Y., Zhao, R. C., & Chen, Q. Q. (2021). Bridging the belief- action gap in a teachers'professional learning community on teaching of thinking. Professional Development in Education, 47(5), 729-744. https://doi.org/10.1080/19415257.2019.1647872
Mariam, S., Khawaja, K. F., Qaisar, M. N., & Ahmad, F. (2023). Blended learning sustainability in business schools: role of quality of online teaching and immersive learning experience. International Journal of Management Education, 21(2), 100776.
https://doi.org/10.1016/j.ijme.2023.100776
Mashhadi,A., Hussein,M. A., & Fahad,A. K. (2023). Mobile learning for teacher professional development: an empirical assessment of an extended technology acceptance model. Porta Linguarum, 2023c, 349-369. https://doi.org/10.30827/portalin.vi2023c.29658
Miao, J., Chang, J. M., & Ma, L. (2022). Teacher-student interaction, student-student interaction and social presence: their impacts on learning engagement in online learning environments. Journal of Genetic Psychology, 183(6), 514-526.
https://doi.org/10.1080/00221325.2022.2094211
Mohammadyari, S., & Singh, H. (2015). Understanding the effect of e-learning on individual performance: The role of digital literacy. Computers & Education, 82, 11-25.
https://doi.org/10.1016/j.compedu.2014.10.025
Oluwajana, D., Adeshola,I., & Clement, S. (2023). Does the use of a web-based collaborative platform reduce cognitive load and influence project-based student engagement? Current Psychology, 42(10), 8265-8278. https://doi.org/10.1007/s12144-021-02145-0
Patil, H., & Undale, S. (2023). Willingness of university students to continue using e-learning platforms after compelled adoption of technology: test of an extended UTAUT model. Education and Information Technologies, 28(11), 14943-14965.
https://doi.org/10.1007/s10639-023-11778-6
Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self-efficacy: flow-on effects for online learning behavior. Internet and Higher Education, 29, 91-97. https://doi.org/10.1016/j.iheduc.2016.01.001
Qashou,A. (2021). Influencing factors in M-learning adoption in higher education. Education and Information Technologies, 26(2), 1755-1785.
https://doi.org/10.1007/s10639-020-10323-z
Salhab R., Daher W. (2023). University students’ engagement in mobile learning. European journal of investigation in health. Psychology and Education, 13(1), 202-216. https://doi.org/10.3390/ejihpe13010016
Shang, S. S., & Lyv, W. F. (2024). Continuance intention to use MOOCs: the effects of psychological stimuli and emotions. Asia-Pacific Education Researcher, 33(1), 27-45. https://doi.org/10.1007/s40299-022-00705-x
Sun, X. G., Fu, Y., Zheng, W. Y., Huang, Y. X., & Li, Y. Q. (2022). Big educational data analytics, prediction and recommendation: a survey. Journal of Circuits Systems and Computers, 31(9), 22300070. https://doi.org/10.1142/S0218126622300070
Sung,Y. T., Chang,K. E., & Yang,J. M. (2015). How effective are mobile devices for language learning? a meta-analysis. Educational Research Review, 16, 68-84.
https://doi.org/10.1016/j.edurev.2015.09.001
Sweeney, C.J., Soutar, N.G. (2001). Consumer perceived value: the development of a multiple item scale. Journal of Retailing, 77(2), 203-220.
https://doi.org/10.1016/S0022-4359(01)00041-0
Taghizadeh, S. K., Rahman, S. A., Nikbin, D., Alam, M. M. D., Alexa, L., Suan, C. L., & Taghizadeh, S. (2022). Factors influencing students' continuance usage intention with online
learning during the pandemic: a cross-country analysis. Behavior & Information Technology, 41(9), 1998-2017. https://doi.org/10.1080/0144929x.2021.1912181
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
Wei, H.C., Chou, C. (2020). Online learning performance and satisfaction: do perceptions and readiness matter? Distance Education, 41(1), 48-69.
https://doi.org/10.1080/01587919.2020.1724768
Won, S., Kapil, M. E., Drake, B. J., & Paular, R. A. (2024). Investigating the role of academic, social, and emotional self-efficacy in online learning. Journal of Experimental Education, 92(3), 485-501. https://doi.org/10.1080/00220973.2023.2183375
Xu, K., Li Y.N. (2021) Factors influencing learning engagement of Chinese university students under the covid-19 pandemic: focus on online learning. Journal of China Studies, 24(2), 65- 81.https://doi.org/10.20288/JCS.2021.24.2.65
Yang, X. (2024). Mobile learning application characteristics and learners' continuance intentions: the role of flow experience. Education and Information Technologies, 29(2), 2259- 2275. https://doi.org/10.1007/s10639-023-11910-6
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