The Feasibility of Using Bande à Part to Aid French Language Learners


  • Ross Sundberg Concordia University, Centre for the Study of Learning and Performance
  • Walcir Cardoso Concordia University, Centre for the Study of Learning and Performance



mobile-assisted learning, L2 French, music application, computer-assisted language learning, technology acceptance model


This pilot study examines users’ perceptions of Bande à Part, a music application designed for learners of French. The technology acceptance model (TAM) was adopted to investigate users’ perceptions of the app’s usability and potential for second language (L2) learning. The model’s two constructs, perceived usefulness and perceived ease of use, and one added factor, perceived enjoyment, formed the main predictors of users’ intentions to continue using the app. Mean scores for the predictors were: perceived usefulness = 4.27/6, perceived ease of use = 3.88/6, and perceived enjoyment = 3.95/6, which are confirmed by the survey results that show that 10 of 13 participants intend to continue using the app. Qualitative results suggest that the app enhances users’ ability to notice targeted forms in the musical input (e.g., liaison, gender) and, corroborating the quantitative data, suggest that users find the features in the app useful. Several comments also indicate that the ease of use could be improved (e.g., improved mobile device access). This study helps to establish the TAM in Computer-Assisted Language Learning (CALL) literature and forms the basis for future work evaluating how songs aid L2 acquisition.

Author Biographies

Ross Sundberg, Concordia University, Centre for the Study of Learning and Performance

Ross Sundberg is a PhD candidate in Education specializing in Applied Linguistics and the development of CALL programs. He has developed a mobile music application for French learners and has worked on both English and French versions of the Spaceteam-ESL language learning game.

Walcir Cardoso, Concordia University, Centre for the Study of Learning and Performance

Walcir Cardoso is a Professor of Applied Linguistics at Concordia University. He conducts research on the L2 acquisition of phonology, morphosyntax and vocabulary, and the effects of computer technology (e.g., clickers, text-to-speech synthesizers, automatic speech recognition, intelligent personal assistants) on L2 learning.


Barcomb, M., & Cardoso, W. (2020). Rock or lock? Gamifying a course management system for pronunciation instruction. Focus on English /r/ and /l/. CALICO Journal, 37(2), 127–147.

Besson, M., Chobert, J., & Marie, C. (2011). Transfer of training between music and speech: Common processing, attention, and memory. Frontiers in Psychology, 2(94), 1–12.

Box, G., & Tidwell, P. (1962). Transformation of the independent variables. Technometrics, 4, 531–550.

Celce-Murcia, M., Brinton, D., & Goodwin, J. (2010). Teaching pronunciation: A course book with reference guide (2nd ed.). New York: Cambridge University Press.

Collins, L., & Muñoz, C. (2016). The foreign language classroom: Current perspectives and future considerations. Modern Language Journal, 100(S1), 133–147.

Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Dörnyei, Z. (2009). The L2 motivational self system. In Z. Dörnyei & E. Ushioda (Eds.), Motivation, language identity and the L2 self (pp. 9–42). Blue Ridge Summit: Multilingual Matters.

Dörnyei, Z., & Taguchi, T. (2010). Questionnaires in second language research: Construction, administration, and processing. New York and London: Routledge.

Engh, D. (2013). Why use music in English language learning? A survey of the literature. English Language Teaching, 6(2), 113–127.

Farahat, T. (2012). Applying the technology acceptance model to online learning in the Egyptian universities. Procedia—Social and Behavioral Sciences, 64, 95–104.

François, C., Chobert, J., Besson, M., & Schön, D. (2012). Music training for the development of speech segmentation. Cerebral Cortex, 23(9), 2038–2043.

Gagnon, M., Orruño, E., Asua, J., Abdeljelil, A., & Emparanza, J. (2012). Using a modified technology acceptance model to evaluate healthcare professionals’ adoption of a new telemonitoring system. Telemedicine and e-Health, 18(1), 54–59.

Gatbonton, E., & Segalowitz, N. (2005). Rethinking communicative language teaching: A focus on access to fluency. Canadian Modern Language Review, 61(3), 325–353.

Hosmer, D., Jr., Lemeshow, S., & Sturdivant, R. (2013). Applied logistic regression (3rd ed.). Hoboken: Wiley.

Hsu, H., & Chang, Y. (2013). Extended TAM model: Impacts of convenience on acceptance and use of Moodle. Online Submission, 3(4), 211–218.

Hsu, C. (2015). Learning motivation and adaptive video caption filtering for EFL learners using handheld devices. ReCALL, 27(1), 84–103.

Hsu, L. (2016). An empirical examination of EFL learners’ perceptual learning styles and acceptance of ASR-based computer-assisted pronunciation training. Computer Assisted Language Learning, 29(5), 881–900.

Huang, J., Lin, Y., & Chuang, S. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. Electronic Library, 25(5), 585–598.

Hwang, Y., & Yi, M. (2002). Predicting the use of web-based information systems: Intrinsic motivation and self-efficacy. Proceedings of the 8th AMCIS (pp. 1076–1081). Association for Information Systems.

King, W., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & management, 43(6), 740–755.

Kline, P. (1999). The handbook of psychological testing. Routledge.

Kraus, N., & Chandrasekaran, B. (2010). Music training for the development of auditory skills. Nature Reviews Neuroscience, 11, 599–605.

Lee, Y., Kozar, K., & Larsen, K. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.

Levey, S., Levey, T., & Fligor, B. J. (2011). Noise exposure estimates of urban MP3 player users. Journal of Speech, Language, and Hearing Research, 54, 263–277.

Lin, C. (2014). Learning English reading in a mobile-assisted extensive reading program. Computers & Education, 78, 48–59.

Lindgren, E., & Muñoz, C. (2013). The influence of exposure, parents, and linguistic distance on young European learners’ foreign language comprehension. International Journal of Multilingualism, 10(1), 105–129.

Long, M. (2015). Second language acquisition and task-based language teaching. Chichester: Wiley-Blackwell.

Ludke, K. (2016). Singing and arts activities in support of foreign language learning: An exploratory study. Innovation in Language Learning and Teaching, 1–16.

Marangunic, N., & Granic, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95.

Parasuraman, A. (2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.

Patel, A. (2003). Language, music, syntax and the brain. Nature Neuroscience, 6(7), 674–681.

Patel, A. (2011). Why would musical training benefit the neural encoding of speech? The OPERA hypothesis. Frontiers in Psychology, 2, 142.

Porter, C., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007.

Reeves, T. C., Herrington, J., & Oliver, R. (2005). Design research: A socially responsible approach to instructional technology research in higher education. Journal of Computing in Higher Education, 16(2), 96–115.

Ruel, E., Wagner III, W., & Gillespie, B. (2015). The practice of survey research. Los Angeles: Sage.

Schon, D., Boyer, M., Moreno, S., Besson, M., Peretz, I., & Kolinsky, R. (2008). Songs as an aid for language acquisition. Cognition, 106(2), 975–983.

Sundberg, R., & Cardoso, W. (2019). Learning French through music: The development of the Bande à Part app. Computer Assisted Language Learning, 32(1–2), 49–70.

Tsai, Y. (2015). Applying the technology acceptance model (TAM) to explore the effects of a course management system (CMS)-assisted EFL writing instruction. CALICO Journal, 32(1), 153–171.

Van Belle, G. (2011). Statistical rules of thumb (vol. 699). New York: John Wiley & Sons.

Venkatesh, V., & Davis, F. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481.

Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

Wilcox, W. (1995). Music cues from classroom singing for second language acquisition: Prosodic memory for pronunciation of target vocabulary by adult non-native English speakers. Doctoral dissertation, University of Kansas, Lawrence.

Yih, M., & Nah, E. (2009). Writing web logs in the ESL classroom: A study of student perceptions and the technology acceptance model. Asian Journal of University Education, 5(1), 47–70.

Zhang, S., Zhao, J., & Tan, W. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science and Technology, 13(3), 312–317.



How to Cite

Sundberg, R. ., & Cardoso, W. . (2022). The Feasibility of Using Bande à Part to Aid French Language Learners. CALICO Journal, 39(2), 196–218.