The Effects of Three Scaffoldings on Computer-Supported, Robot-Assisted Collaborative Programming in Higher Education

Abstract

Computer-supported, robot-assisted collaborative programming (CSRACP) is a computer-supported collaborative learning (CSCL) mode used in programming education. CSRACP allows students to work in groups with the support of robots to achieve programming functions and complete complex tasks. This research applied multimodal learning analytics (MMLA) to examine the effects of three pedagogical scaffoldings (i.e., conceptual, task-oriented, and meta-cognitive scaffolding) on student pairs’ CSRACP activities in higher education. The results revealed that students under conceptual scaffolding had the low-level cognitive engagement, task-oriented regulation, and low-level socio-emotional expression, with the lowest programming task score; students under task-oriented scaffolding had the operation-driven cognitive engagement, observation-oriented regulation, and low-level socio-emotional expression, with the highest programming task score; and students under meta-cognitive scaffolding had the communication-driven cognitive engagement, exploration-oriented regulation and high-level socio-emotional expression, with the medium programming task score. Based on these findings, pedagogical and analytical implications were proposed to promote CSRACP in higher education.

Keywords:

• Collaborative programming
• computer-supported collaborative learning
• pedagogical scaffolding
• multimodal learning analytics
• higher education

Acknowledgements

We thank the students participated in this research. We thank Zhiyu Ji for the data collection and preliminary analysis.

Author confirmation

All authors have approved the manuscript for submission and the content of the manuscript has not been published, or submitted for publication elsewhere.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data is available upon request from the corresponding author.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [62177041]; Zhejiang Province educational science and planning research project [2022SCG256]; and Zhejiang University graduate education research project [20220310].

Notes on contributors

Weiqi Xu

Weiqi Xu is a doctoral student in educational technology program in the College of Education at Zhejiang University. His research interests are learning analytics and educational data mining, collaborative programming, AI in education.

Xinran Dong

Xinran Dong an undergraduate student in the College of Education at Zhejiang University. Her research interests are higher education, learning analytics and AI in education.

Fan Ouyang

Fan Ouyang is a research professor in the College of Education at Zhejiang University. Her research interests are computer-supported collaborative learning, learning analytics and educational data mining, online and blended learning, and artificial intelligence in education.