Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance
Abstract
This study investigated the impact of a chatbot-based micro-learning system on students’ learning motivation and performance. A quasi-experiment was conducted with 99 first-year students taking part in a basic computer course on number system conversion. The students were assigned to a traditional learning group or a chatbot-based micro-learning group. After the experiment, both groups achieved a comparable performance, suggesting that students are sufficiently competent to learn independently in the chatbot-based learning environment without the need for continuous face-to-face delivery. Moreover, students in the chatbot learning group attained significantly higher intrinsic motivation than the traditional learning group with perceived choice and perceived value as core predictors of intrinsic motivation. Further analysis with the Johnson-Neyman procedure revealed differences on interaction between the perceived choice and the learning environments. For students with a high initial perceived choice (>=5.1), chatbot-based learning further enhances their post choice motivation whereas for students with a low initial perceived choice (<=3.0), the traditional classroom is more suitable to enhance their post choice motivation. The implications of the findings can help instructors to incorporate chatbot-based learning in the classroom.
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The chatbot was ineffective when students developed computing knowledge, e.g., conversion of numerical systems.
“However, the chatbot was ineffective when students developed computing knowledge, e.g., conversion of numerical systems [18].”
From A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education (2023) · cites this paper as [18]
Chatbot-assisted instructional videos and micro-learning systems could effectively promote intrinsic motivation.
“Furthermore, chatbot-assisted instructional videos and micro-learning systems could also effectively promote intrinsic motivation [18,44].”
From A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education (2023) · cites this paper as [18]
The study whose intervention lasted for only 40 min found no significant differences in learning performance between the chatbot and control groups.
“The study [18] whose intervention lasted for only 40 min found no significant differences in learning performance between the chatbot and control groups.”
From A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education (2023) · cites this paper as [18]
Pressure was a significant predictor of motivation in the chatbot-assisted learning context, whereas perceived competency was an influencing factor in the traditional context.
“Specifically, pressure was a significant predictor of motivation in the chatbot-assisted learning context, whereas perceived competency was an influencing factor in the traditional context [18].”
From A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education (2023) · cites this paper as [18]
Chatbots acted as learning partners by chatting and interacting with learners through voice and text.
“Chatbots also acted as learning partners by chatting and interacting with learners through voice ([26,28,52,47,53]) and text ([27,41]).”
Voice-based chatbots positively influenced EFL learners' motivation.
“In addition, voice-based chatbots positively influenced EFL learners’ motivation ([27,52]).”
The vast majority of chatbots have been employed as teaching agents and assessed using the experimental approach, and the findings largely point to improved learning performance and satisfaction.
“The vast majority of chatbots have been employed as teaching agents and assessed using the experimental approach, and the findings largely point to improved learning performance and satisfaction (e.g., Pereira et al., 2019; Chen et al., 2020; Yin et al., 2021; Khalil and Rambech, 2022).”
From Implementing the bashayer chatbot in Saudi higher education: measuring the influence on students' motivation and learning strategies (2023) · cites this paper as Yin et al., 2021
The more meaningful the learning environment is for students, the more motivated to learn, and the more involved in learning activities, they will be.
“The more meaningful the learning environment is for students, the more motivated to learn, and the more involved in learning activities, they will be (Fryer et al., 2019; Yin et al., 2021).”
From Implementing the bashayer chatbot in Saudi higher education: measuring the influence on students' motivation and learning strategies (2023) · cites this paper as Yin et al., 2021
The study's results on learning motivation align with previous research on chatbot systems' potential to support learning motivation.
“These results align with previous research studies on chatbot systems' potential to support learning motivation (Nghi et al., 2019; Chen et al., 2020; Yin et al., 2021; Fidan and Gencel, 2022; Khalil and Rambech, 2022).”
From Implementing the bashayer chatbot in Saudi higher education: measuring the influence on students' motivation and learning strategies (2023) · cites this paper as Yin et al., 2021
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- Authors
- Yin, J., Goh, T.-T., Yang, B., & Xiaobin, Y.
- Affiliations
- Hubei University (CN)
- Victoria University of Wellington (NZ)
- School of Education, Hubei University, Wuhan, China
- School of Foreign Studies, Hubei University, Wuhan, China
- Venue
- Journal of Educational Computing Research
- Publisher
- SAGE Publications
- Published
- 2020-08-21
- Volume
- 59
- Issue
- 1
- Pages
- 154-177
- ISSN
- 0735-6331 (ISSN-L)
- 1541-4140
- OpenAlex
- W3080510869
- Cited by
- 349
- References
- 5
- Citations by year
- 2026: 47 · 2025: 104 · 2024: 97 · 2023: 62 · 2022: 25 · 2021: 14
- FWCI
- 9.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Computing Research (publishedVersion)
- Funders
- National Education Science Plan 2015 National General Project of China (BCA150053)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
- Keywords
- chatbots, microlearning, student motivation, intrinsic motivation, perceived choice, perceived value, number system conversion, computer courses, first-year students, learning performance
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