Cover not available

Article In: Australian Review of Applied Linguistics: Online-First Articles

Chinese as a second language (CSL) learners’ satisfaction with mobile learning

ORCiD logo with link | Sichuan International Studies University
ORCiD logo with link | The University of Queensland
ORCiD logo with link | The University of Queensland
This content is being prepared for publication; it may be subject to changes.
Abstract
Satisfaction is a positive feeling experienced by learners when they have attained their goals. In mobile-assisted language learning (MALL), satisfaction correlates, to some extent, with MALL learners’ academic achievements. However, the factors behind Chinese as a second language (CSL) learners’ satisfaction with MALL have not yet been extensively examined, nor is satisfaction itself well understood. This study aims to address this gap by examining whether and in what ways expectation-confirmation, performance expectancy, effort expectancy, social influence, and facilitating conditions shape learner satisfaction. Adopting a mixed-methods design, the study draws on questionnaire data from 160 CSL learners at Chinese universities, complemented by in-depth interview data from a subsample of 30 participants. Results show that expectation-confirmation, performance expectancy, and effort expectancy are the strongest predictors of CSL learners’ satisfaction. While social influence exerts a modest voluntary effect and facilitating conditions play a relatively minor role, Chinese proficiency emerges as a significant moderator by shaping the strength of the relationships between expectation-confirmation and satisfaction and between performance expectancy and satisfaction. Individual learner traits emerge as an additional factor from interview responses, extending the knowledge beyond the theoretical framework. These findings provide valuable insights for enhancing CSL learners’ MALL experience.
Article outline
  • 1.Introduction
  • 2.Literature review
    • 2.1Learner satisfaction: A Positive Psychology perspective
    • 2.2The Unified Theory of acceptance and Use of technology
      • 2.2.1Performance expectancy, effort expectancy and satisfaction
      • 2.2.2Social influence and learner satisfaction
      • 2.2.3Facilitating conditions and satisfaction
    • 2.3Integrating the Expectation-Confirmation model
    • 2.4Moderating role of language proficiency level
  • 3.Methodology
    • 3.1Study context
    • 3.2Mixed methods design
    • 3.3Participants
    • 3.4Instruments: Online questionnaire and semi-structured interview
    • 3.5Data collection process
    • 3.6Data analysis
  • 4.Results
    • 4.1Quantitative results for RQ1: CSL learners’ satisfaction level
    • 4.2Quantitative results for RQ2: Factors influencing learner satisfaction
      • 4.2.1Data reliability and validity
      • 4.2.2Factors corroborated through path analysis
    • 4.3Qualitative findings for RQ2
      • 4.3.1Learners’ perceptions of performance expectancy and effort expectancy
      • 4.3.2Social influence: Influencing satisfaction in a non-traditional way
      • 4.3.3Acknowledgement of and need for facilitating conditions
      • 4.3.4Individual learner traits: Manifested through MALL engagement
    • 4.4Quantitative results for RQ3: The moderating role of proficiency level
  • 5.Discussion
  • 6.Conclusion
  • Data availability statement
  • Funding statement
  • Ethics approval statement
  • Conflicts of interest
  • Acknowledgements
  • Notes
  • Author queries
  • References
References (37)
References
Alshurideh, M., Al Kurdi, B., Salloum, S. A., Arpaci, I., & Al-Emran, M. (2023). Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 31(3), 1214–1228. Google Scholar logo with link to Google Scholar
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation confirmation model. MIS Quarterly, 25(3), 351–370. Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
Dewaele, J. M., & Meftah, R. (2025). The emotional profiles of self-perceived ‘good’ foreign language learners. Innovation in Language Learning and Teaching, 1–19. Google Scholar logo with link to Google Scholar
DeVellis, R. F., & Thorpe, C. T. (2022). Scale development: theory and applications (5th ed.). Sage.Google Scholar logo with link to Google Scholar
Dörnyei, Z. (1990). Conceptualizing motivation in foreign-language learning. Language learning, 40(1), 45–78. Google Scholar logo with link to Google Scholar
(1998). Motivation in second and foreign language learning. Language teaching, 31(3), 117–135. Google Scholar logo with link to Google Scholar
(2005). The psychology of the language learner: individual differences in second language acquisition (1st ed.). L. Erlbaum.Google Scholar logo with link to Google Scholar
Eerdemutu, L., Dewaele, J. M., & Wang, J. (2024). Developing a short language classroom engagement scale (LCES) and linking it with needs satisfaction and achievement. System, 120, 103189. Google Scholar logo with link to Google Scholar
Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18(3), 382–388. Google Scholar logo with link to Google Scholar
Gao, B. (2023). A uses and gratifications approach to examining users’ continuance intention towards smart mobile learning. Humanities & Social Sciences Communications, 10(1), 726–13. Google Scholar logo with link to Google Scholar
García de Blanes Sebastián, M., Sarmiento Guede, J. R., Azuara Grande, A., & Filipe, A. F. (2025). UTAUT-2 predictors and satisfaction: implications for mobile-learning adoption among university students. Education and Information Technologies. 30, 3201–3237. Google Scholar logo with link to Google Scholar
Garson, G. D. (2014). Structural equation modeling (2014 preview ed.). G. David Garson and Statistical Publishing Associates.Google Scholar logo with link to Google Scholar
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. Google Scholar logo with link to Google Scholar
Hoi, V. N., & Mu, G. M. (2021). Perceived teacher support and students’ acceptance of mobile-assisted language learning: Evidence from Vietnamese higher education context. British Journal of Educational Technology, 52(2), 879–898. Google Scholar logo with link to Google Scholar
Hsu, L. (2023). EFL learners’ self-determination and acceptance of LMOOCs: the UTAUT model. Computer Assisted Language Learning, 36(7), 1177–1205. Google Scholar logo with link to Google Scholar
Li, J., Chen, M., & Zheng, Q. (2024). The dynamics and mediating factors of students’ emotional and social engagement in a project-based writing course. System, 121, 103237. Google Scholar logo with link to Google Scholar
Lin, L., Gong, Y., & Xu, N. (2021). Online Self-Regulated Learning Profiles: A Study of Chinese as a Foreign Language Learners. Frontiers in Psychology, 12, 797786. Google Scholar logo with link to Google Scholar
MacIntyre, P. D., & Tammy, G. (2012). Emotions That Facilitate Language Learning: The Positive-broadening Power of the Imagination. Studies in Second Language Learning & Teaching, 2(2), 193–213. Google Scholar logo with link to Google Scholar
Muñoz-Carril, P.-C., Hernández-Sellés, N., Fuentes-Abeledo, E.-J., & González-Sanmamed, M. (2021). Factors influencing students’ perceived impact of learning and satisfaction in Computer Supported Collaborative Learning. Computers & Education, 174, 104310. Google Scholar logo with link to Google Scholar
Ouyang, F., & Dai, X. (2022). Using a three-layered social-cognitive network analysis framework for understanding online collaborative discussions. Australasian Journal of Educational Technology, 38(1), 164–181. Google Scholar logo with link to Google Scholar
Park, Y.-J., Kim, J. Y., Ku, B., Kim, H., & Park, Y.-B. (2016). Confirmatory and Exploratory Factor Analysis for Validating the Phlegm Pattern Questionnaire for Healthy Subjects. Evidence-Based Complementary and Alternative Medicine, 2016(2016), Article 2696019. Google Scholar logo with link to Google Scholar
Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A.-E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education. Computers & Education, 182, 104468. Google Scholar logo with link to Google Scholar
Strauss, A. L., & Corbin, J. M. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Sage Publications.Google Scholar logo with link to Google Scholar
Shu, F., Liu, Q., Wang, Q., Tu, F., Li, H., & Zhao, C. (2025). Examining Factors Influencing Online Adult Learners’ Satisfaction with Blended Synchronous Learning. Education and Information Technologies, 30(8), 10257–10280. Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
Wang, X., & Jiang, W. (2024). Exploring the affordances of WeChat for Chinese cultural knowledge learning among learners of Chinese in an international exchange program. Computer Assisted Language Learning, 37(3), 558–584. Google Scholar logo with link to Google Scholar
(2025). Learners’ enjoyment in mobile task-based language learning: a longitudinal study on novice CFL learners in an Australian university. Language Teaching Research. Google Scholar logo with link to Google Scholar
Yang, H., Cai, J., Yang, H. H., & Wang, X. (2023). Examining key factors of beginner’s continuance intention in blended learning in higher education: Examining key factors of beginners’ continuance intention in blended learning in higher education. Journal of Computing in Higher Education, 35(1), 126–143. Google Scholar logo with link to Google Scholar
Yalçın, Y., & Dennen, V. P. (2024). An investigation of the factors that influence online learners’ satisfaction with the learning experience. Education and Information Technologies, 29(4), 3807–3836. Google Scholar logo with link to Google Scholar
Zhang, Y., & Liu, G. L. (2025). Examining the impacts of learner backgrounds, proficiency level, and the use of digital devices on informal digital learning of English: an explanatory mixed-method study. Computer Assisted Language Learning, 38(5–6), 1113–1140. Google Scholar logo with link to Google Scholar
Zhang, Z., & Yang, R. (2025). Personalised Language Learning Through Technology: Examining How Digital Literacy Shapes Proficiency and Communication Strategiess. Journal of Computer Assisted Learning, 41(4). Google Scholar logo with link to Google Scholar
Zhao, T., Hu, Z., Wang, J., & Ye, L. (2026). Explaining language learners’ continuance intention to use GenAI beyond the classroom: integrating control-value appraisals of language learning with the expectation-confirmation model. Education and Information Technologies. Google Scholar logo with link to Google Scholar
Zhou, S., Wang, T., Yu, M., & Shi, X. (2025). The role of phonetic radical information in compound character recognition during sentence reading. Lingua, 319, 103921. Google Scholar logo with link to Google Scholar
Mobile Menu Logo with link to supplementary files background Layer 1 jbe-platform.quadraat-sans-bold jbe-platform.quadraat-sans-bold-dark jbe-platform.quadraat-sans-bold-light jbe-platform.quadraat-sans-bold prag Twitter_Logo_Blue