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Chen, Wenli
Scripting undergraduates’ interdisciplinary collaborative learning to enhance their interdisciplinary competence
2024, Su, Guo, Chen, Wenli, Zhu, Gaoxia, Le, Chencheng, Zheng, Lishan
Interdisciplinary competence is critical to tackle the issues that cannot be addressed by a single discipline. Existing research indicates that meaningful interdisciplinary collaborative learning can potentially develop interdisciplinary competence. This research designed and implemented macro- and micro-scripts to scaffold university students’ interdisciplinary collaboration and evaluated students’ interdisciplinary competence. Results found that generally, students showed great competence in integrating disciplinary knowledge, a dimension of interdisciplinary competence.
Learners' perceived AI presences in AI-supported language learning: A study of AI as a humanized agent from community of inquiry
2022, Wang, Xinghua, Pang, Hui, Wallace, Matthew Patrick, Wang, Qiyun, Chen, Wenli
This study investigated the application of an artificial intelligence (AI) coach for second language (L2) learning in a primary school involving 327 participants. In line with Community of Inquiry, learners were expected to perceive social, cognitive, and teaching presences when interacting with the AI coach, which was considered a humanized agent. To examine how learners’ perceived AI presences were related to their language learning, this study drew on AI usage data, actual learning outcomes, and attitudinal data. Results from hierarchical regression analyses suggest that cognitive presence and learners’ affection for AI’s appearance were significant predictors of L2 enjoyment, which also positively predicted learning outcomes. The score of English shadowing (representing the quality of AI usage) positively predicted learning outcomes. Contrary to intuition, teaching presence was found to negatively predict learning outcomes. Based on cluster analysis and subsequent MANOVA results, this study indicates that the learners perceiving higher social and cognitive presences via interacting with AI and showing greater affection for AI’s appearance tended to use the AI coach more frequently, demonstrate higher L2 enjoyment, and achieve higher learning outcomes. The present study contributes to the limited but increasing knowledge of human-AI interaction in educational settings and carries implications for future efforts on the use of AI for L2 learning.
How peers communicate without words: An exploratory study of hand movements in collaborative learning using computer-vision-based body recognition techniques
2023, Lyu, Qianru, Chen, Wenli, Su, Junzhu, Heng, John Gerard Kok Hui, Liu, Shuai
Accumulating research in embodied cognition highlights the essential role of human bodies in knowledge learning and development. Hand movement is one of the most applied body motions in the collaborative ideation task when students co-construct knowledge with and without words. However, there is a limited understanding of how students in a group use their hand movements to coordinate understandings and reach a consensus. This study explored students’ hand movement patterns during the different types of knowledge co-construction discourses: quick consensus-building, integration-oriented consensus building, and conflict-oriented consensus building. Students’ verbal discussion transcripts were qualitatively analyzed to identify the type of knowledge co-construction discourses. Students’ hand motion was video-recorded, and their hand landmarks were detected using the machine learning tool MediaPipe. One-way ANOVA was conducted to compare students hand motions in different types of discourses. The results found there were different hand motion patterns in different types of collaboration discourses. Students tended to employ more hand motion during conflict-oriented consensus building discourses than during quick consensus building and integration-oriented consensus building discourses. At the group level, the collaborating students were found to present less equal hand movement during quick consensus-building than integration-oriented consensus building and conflict-oriented consensus building. The findings expand the existing understanding of embodied collaborative learning, providing insights for optimizing collaborative learning activities incorporating both verbal and non-verbal language.
Investigation 13. The Singapore experience: Synergy of national policy, classroom practice, and design research
2021, Looi, Chee-Kit, So, Hyo-Jeong, Toh, Yancy, Chen, Wenli
In recent years, there has been a proliferation of research findings on CSCL at the micro- and macro-levels, but few compelling examples of how CSCL research has impacted actual classroom practices at the meso-level have emerged. This paper critically examines the impact of adopting a systemic approach to innovative education reforms at the macro-, meso-, and micro-levels in Singapore. It presents the case for adopting design research as a methodology for CSCL integration that meets the needs of schools and discusses a specific CSCL innovation that holds the potential for sustaining transformation in classroom practices. Our driving question is: In what ways can the routine use of CSCL practices in the classroom be supported by exploring systemic factors in the school setting through design research? We will explore the synergistic conditions that led to meaningful impact (at the micro-level), mediated by systemic approaches to working with teachers in the schools (at the meso-level), guided by Singapore’s strategic planning for scalability (at the macro-level).
Exploring students’ computer‐supported collaborative argumentation with socio‐scientific issues
2024, Chen, Wenli, Han, Yiting, Tan, Jesmine Sio Hwee, Chai, Aileen Siew Cheng, Lyu, Qianru, Lyna
Background This study examined the effect of computer-supported collaborative argumentation (CSCA) on secondary school students' understanding of socio-scientific issues (SSI). Engaging students in collaborative argumentation is known to help with deepening their understanding of SSI.
Methods
In this study, a mixed-method design is used to investigate 84 students' collaborative argumentation processes and outcomes. The statistical analysis, epistemic network analysis and qualitative uptake analysis results showed that CSCA was effective in supporting secondary school students' evidence-based argumentation skills on SSI.
Findings and Conclusion
Several cases were presented to show how students engaged in CSCA to explore meaningful learning opportunities and how CSCA helped students' learning on SSI.
Implications
The findings provided insights for future innovative teaching and learning SSI in authentic classroom settings.
The role of individual preparation on coordination in computer- supported collaborative learning: A neuroscience perspective on learners’ inter-brain synchronization
2024, Chen, Wenli, Lyu, Qianru, Ho, Mavis Mei Yee, Tan, Jessica, Teo, Wei-Peng, Chai, Siew Chen Aileen, Su, Gao
Individual preparation (IP) is often applied to support collaborative learning. However, there exist mixed results of this pedagogical approach’s effectiveness. This study aims to expand the current understanding of how IP influences social coordination during collaboration. A total of 78 university students (male = 30, female = 48) aged between 21 to 40 years old collaborated in dyads in this study. Functional infrared spectroscopy (fNIRS) was used to measure brain activity in the orbitofrontal cortex (OFC) for every individual during two conditions: immediate collaboration without IP (control condition) and IP before collaboration (experimental condition). Inter-brain synchrony (IBS) between dyads was derived and compared between two conditions. Results revealed that significantly higher levels of IBS could be observed in the control-experimental comparisons. These findings suggest that introducing individual preparation can facilitate social coordination during subsequent collaborative learning.
How does feedback formulation pattern differ between more-improvement and no-improvement student groups? An exploratory study
2023, Chen, Wenli, Lyu, Qianru, Su, Junzhu, Chai, Aileen Siew Cheng, Zhang, Weiyu, Su, Guo, Li, Xinyi
Accumulating studies suggest including multiple feedback components such as evaluation and suggestion within one feedback unit is beneficial, yet how various feedback components are formulated and their learning effect remain understudied. This study examined the formulation pattern of different feedback components in the feedback given and received by groups with different levels of learning improvement. In social studies classrooms in Singapore, fourteen groups of secondary schoolers (n=61, female=61) participated in giving peer feedback during collaborative argumentation activities. Collaborative argumentation and feedback components of each group were collected and analyzed. The result reported that more-improvement groups tended to give and receive feedback that included an evaluation or position component before giving suggestions. No-improvement groups were more likely to give and receive feedback that started with a supportive standpoint of the reviewed content before opposing standpoints. The findings provide insights for the implementation of effective peer feedback in authentic classroom settings.
Steps to implementation: The role of peer feedback inner structure on feedback implementation
2023, Lyu, Qianru, Chen, Wenli, Su, Junzhu, Heng, John Gerard Kok Hui
Though implementing feedback provided by peers has been an essential step for learning efficiency in peer feedback activities, it remains challenging for students. This study aims to explore the inner structure patterns of students’ peer feedback and how they are related to feedback implementation. Sixty-nine engineering students from a Singapore university participated in the peer feedback activity. Content analysis was conducted to analyse the inner structure of feedback as well as the implementation status of each feedback. Sequential mining was applied to investigate the sequential patterns of the various feedback components. The results show a variation of inner structures in the implemented feedback and unimplemented feedback. The implemented feedback tended to be sequenced with an evaluation or a standpoint before seeking clarification or suggestions for improvement. It was also likely to contain continuous questions seeking clarifications. In comparison, the unimplemented feedback was likely to continuously indicate positive evaluations or agreements. By understanding the inner structure of peer feedback with different implementation statuses, researchers and educators can fine-tune design and instructions to support peer feedback practices.
Supporting self-directed learning and self-assessment using TeacherGAIA, a generative AI chatbot application: Learning approaches and prompt engineering
2023, Farhan Ali, Choy, Doris, Divaharan, Shanti, Tay, Hui Yong, Chen, Wenli
Self-directed learning and self-assessment require student responsibility over learning needs, goals, processes, and outcomes. However, this student-led learning can be challenging to achieve in a classroom limited by a one-to-many teacher-led instruction. We, thus, have designed and prototyped a generative artificial intelligence chatbot application (GAIA), named TeacherGAIA, that can be used to asynchronously support students in their self-directed learning and self-assessment outside the classroom. We first identified diverse constructivist learning approaches that align with, and promote, student-led learning. These included knowledge construction, inquiry-based learning, self-assessment, and peer teaching. The in-context learning abilities of large language model (LLM) from OpenAI were then leveraged via prompt engineering to steer interactions supporting these different learning approaches. These interactions contrasted with ChatGPT, OpenAI’s chatbot which by default engaged in the traditional transmissionist mode of learning reminiscent of teacher-led instruction. Preliminary design, prompt engineering and prototyping suggested fidelity to the learning approaches, cognitive guidance, and social-emotional support, all of which were implemented in a generative AI manner without pre-specified rules or “hard-coding”. Other affordances of TeacherGAIA are discussed and future development outlined. We anticipate TeacherGAIA to be a useful application for teachers in facilitating self-directed learning and self-assessment among K-12 students.