Toward personalized scaffolding and fading of motivational support in L2 learner–dialogue agent interactions: an exploratory study
Open accessAbstract
This article proposes a computer-based approach to effectively enhance second language learners' willingness to communicate in the target language. To do so, we implemented a conversational agent embedding a dialogue management model based on two conversational strategies (i.e., communication strategies and affective backchannels), serving as scaffolds for enhancing learners' willingness to communicate in the target language. Here, we report on differences observed among second language learners' preferences for both conversational strategies according to their initial level of willingness to communicate and on variations of their willingness with respect to such differences. Although we found that most students generally preferred a combination of both strategies, learners' preferences and the effects of the support provided by these strategies varied according to their level of willingness to communicate. Learners with lower willingness to communicate tended to prefer affective backchannels, whereas those with higher willingness to communicate seemed to favor communication strategies. These results were consistent with post-test results, which showed that learners' expected willingness to communicate tended to be higher after interacting with systems embedding their preferred strategies. In sum, these results are preliminary evidence of the meaningfulness of accounting for such learners' preferences in adaptively using and fading the strategies employed by conversational agents to motivate second language learners to communicate in the target language.
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The use of chatbots in higher education increased learners' intrinsic and extrinsic motivation in language learning.
“increased their intrinsic and extrinsic motivation in language learning; and encouraged them to invest time and effort ([42,43]).”
The use of chatbots in higher education encouraged learners to invest time and effort in language learning.
“increased their intrinsic and extrinsic motivation in language learning; and encouraged them to invest time and effort ([42,43]).”
Chatbots that operated as teaching assistants provided academic knowledge, offered formative feedback, and enabled scaffolded learning.
“The chatbots that operated as teaching assistants provided academic knowledge ([41,[48], [49], [50]]), offered formative feedback to EFL learners [45], and enabled scaffolded learning ([22,27,31,42,51]).”
Interactive tools such as chatbots kept learners motivated and engaged, while allowing them to learn EFL in an exciting and relaxing atmosphere.
“Interactive tools such as chatbots kept learners motivated and engaged, while allowing them to learn EFL in an exciting and relaxing atmosphere ([26,41,42,45]).”
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- Dr. Ayedoun was the recipient of a Poster Presentation Award at the 18th International Conference of Artificial Intelligence in Education (AIED2017). He is a member of JSAI and IAIED.
- Dr. Hayashi was the recipient of the Best Overall Paper Award of the 25th International Conference on Computers in Education (ICCE2017). He is a member of IPSJ, JSAI, JSiSE, HIS, and ACM.
- Dr. Seta was the recipient of best paper awards from the Japanese Society for Information and Systems in Education in 2012 and 2015, as well as the Best Overall Paper Award of the 25th International Conference on Computers in Education (ICCE2017). He is a member of JSAI, IEICE, IPSJ, JSiSE, JCSS, and IAIED.
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- Authors
- Ayedoun, E., Hayashi, Y., & Seta, K.
- Affiliations
- Osaka Prefecture University (JP)
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2020-07-01
- Volume
- 13
- Issue
- 3
- Pages
- 604-616
- ISSN
- 1939-1382 (ISSN-L)
- 2372-0050
- OpenAlex
- W3020383060
- Cited by
- 24
- References
- 80
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 3 · 2023: 9 · 2022: 1
- FWCI
- 0.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Learning Technologies (publishedVersion, cc-by)
- Funders
- JSPS KAKENHI (17K18017, 18H03345, 18J22445)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- second language acquisition, willingness to communicate, chatbots, dialogue management, scaffolding, fading, communication strategies, learner preferences, adaptive systems, student motivation, human-agent interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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