Technologies for foreign language learning: a review of technology types and their effectiveness
Abstract
This review summarizes evidence for the effectiveness of technology use in foreign language (FL) learning and teaching, with a focus on empirical studies that compare the use of newer technologies with more traditional methods or materials. The review of over 350 studies (including classroom-based technologies, individual study tools, network-based social computing, and mobile and portable devices) revealed that, in spite of an abundance of publications available on the topic of technology use in FL learning and teaching, evidence of efficacy is limited. However, strong support for the claim that technology made a measurable impact in FL learning came from studies on computer-assisted pronunciation training, in particular, automatic speech recognition (ASR). These studies demonstrated that ASR can facilitate the improvement of pronunciation and can provide feedback effectively. Additional studies provided strong support for the use of chat in FL learning. These studies showed that, with chat, both the amount of learners’ language production and its complexity significantly increased. The literature revealed moderate support for claims that technology enhanced learners’ output and interaction, affect and motivation, feedback, and metalinguistic knowledge.
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Advances in automatic speech recognition software are one of the most promising uses of CALL, with evidence of learners showing increased confidence in oral skills when used regularly.
“On the basis of their own literature review, Golonka et al. (2014) argue that advances in AUTOMATIC SPEECH RECOGNITION software - offering finely tuned individual feedback for learning pronunciation - is one of the most promising uses of CALL, with evidence of learners showing increased confidence in their oral skills, when used regularly.”
From The motivational dimension of language teaching (2017) · cites this paper as Golonka et al. (2014)
Cites
Ranalli, J. (2008) · Computer Assisted Language Learning · doi:10.1080/09588220802447859 · Cited by 269 · see citing works
Artificial Intelligence and Second Language Learning: An Efficient Approach to Error Remediation
Dodigovic, M. (2007) · Language Awareness · doi:10.2167/la416.0 · Cited by 68 · cited 1× · see citing works
“Dodigovic (2007) performed an evaluation of an ITS designed to address seven errors common to learners of English as an L2 and found that use of an ITS resulted in an average 83% reduction in these types of errors.”
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Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103599 · Cited by 713 · see citing works
The motivational dimension of language teaching
Lamb, M. (2017) · Language Teaching · doi:10.1017/s0261444817000088 · Cited by 361 · see citing works
Digital game-based vocabulary learning: where are we and where are we going?
Zou, D., Huang, Y., & Xie, H. (2021) · Computer Assisted Language Learning · doi:10.1080/09588221.2019.1640745 · Cited by 329 · see citing works
Wei, P., Wang, X., & Dong, H. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1249991 · Cited by 89 · see citing works
Jia, F., Sun, D., Ma, Q., & Looi, C.-K. (2022) · Sustainability · doi:10.3390/su142315527 · Cited by 90 · see citing works
Tseng, Y.-C., & Lin, Y.-H. (2024) · Electronic Journal of e-Learning · doi:10.34190/ejel.21.5.3329 · Cited by 88 · see citing works
Dashtestani, R., & Hojatpanah, S. (2022) · Computer Assisted Language Learning · doi:10.1080/09588221.2020.1744664 · Cited by 86 · see citing works
Systematic review of virtual speech therapists for speech disorders
Chen, Y.-P. P., Johnson, C., Lalbakhsh, P., Caelli, T., Deng, G., Tay, D., Erickson, S., Broadbridge, P., El Refaie, A., Doube, W., et al. (2016) · Computer Speech & Language · doi:10.1016/j.csl.2015.08.005 · Cited by 83 · see citing works
The use of massively multiplayer online role-playing games in CALL: an analysis of research
Peterson, M. (2016) · Computer Assisted Language Learning · doi:10.1080/09588221.2016.1197949 · Cited by 74 · see citing works
Parlak, Ö., & Ziegler, N. (2017) · Studies in Second Language Acquisition · doi:10.1017/s0272263116000310 · Cited by 58 · see citing works
Kohnke, L., Foung, D., & Chen, J. (2022) · Sage Open · doi:10.1177/21582440221089957 · Cited by 13 · see citing works
Uther, M., Smolander, A.-R., Junttila, K., Kurimo, M., Karhila, R., Enarvi, S., & Ylinen, S. (2018) · Advances in Human-Computer Interaction · doi:10.1155/2018/7345397 · Cited by 3 · see citing works
- Authors
- Golonka, E. M., Bowles, A. R., Frank, V. M., Richardson, D. L., & Freynik, S.
- Affiliations
- University of Maryland, College Park (US)
- American Councils for International Education (US)
- United States Department of Defense (US)
- Corresponding author
- Golonka, Ewa M.
- ORCID
- Golonka, Ewa M.
- Venue
- Computer Assisted Language Learning
- Publisher
- Informa UK Limited
- Published
- 2012-09-03
- Volume
- 27
- Issue
- 1
- Pages
- 70-105
- ISSN
- 0958-8221 (ISSN-L)
- 1744-3210
- OpenAlex
- W2128389066
- Cited by
- 1,144
- References
- 2
- Citations by year
- 2026: 104 · 2025: 125 · 2024: 128 · 2023: 137 · 2022: 96 · 2021: 119 · 2020: 84 · 2019: 97 · 2018: 89 · 2017: 57 · 2016: 51 · 2015: 36 · 2014: 15 · 2013: 6
- FWCI
- 46.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Assisted Language Learning (publishedVersion)
- Funders
- Defense Language Institute Foreign Language Center
- Topics
- Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Education › Foreign Language Teaching Methods
- Keywords
- foreign language learning, educational technology, technology effectiveness, computer-assisted pronunciation training, speech recognition, social computing, metalinguistic knowledge, student motivation, feedback in language learning
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