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Multi-agent and multi-modal robotics-facilitated learning for Python programming and self-regulated learning.

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Title: Multi-agent and multi-modal robotics-facilitated learning for Python programming and self-regulated learning.
Authors: Huang, Anna Y. Q.1 anna.yuqing@gmail.com, Lin, Chien-Chang1 cclin3123@gmail.com, Jiang, Yu-Xian1 ab3223323@gmail.com, Yang, Stephen J. H.1,2 jhyang@csie.ncu.edu.tw
Source: Educational Technology & Society. Jul2026, Vol. 29 Issue 3, p336-360. 25p.
Subject Terms: *Self-regulated learning, *Computer programming education, *Flow charts, *Academic support programs, Python programming language, Multiagent systems, Robotics, Intelligent personal assistants
Abstract: This study introduces RobotPython, a software-based system with virtual robot assistants designed to support self-regulated learning (SRL)-based Python learning activities for programming novices in an undergraduate non-computer science context. RobotPython includes two virtual robot assistants: the Coding Scaffolding Guidance Assistant and the Flowchart Reflection Assistant, which guide students to learn Python in the performance and self-reflection phases of SRL respectively. Both groups received SRL-based instruction; the key difference was that the experimental group was supported by RobotPython, whereas the control group was supported by teaching assistants (TAs). The 22 students in the experimental group, who used RobotPython as an aid to instruction, achieved significantly higher scores in programming concepts and coding than the 28 students in the control group. The impact of RobotPython on SRL abilities was nuanced: the effectiveness of RobotPython for goal-setting, help-seeking, and self-evaluation was moderated by students' baseline abilities (e.g., task strategies and time management), rather than showing a uniform improvement across all subscales. These results suggest that RobotPython-supported SRL-based instruction is associated with higher programming concept understanding and coding performance, while its impact on SRL abilities is conditional upon students' existing characteristics. They also reveal that programming concept understanding mediates the relationship between SRL abilities and coding performance. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
Description
Abstract:This study introduces RobotPython, a software-based system with virtual robot assistants designed to support self-regulated learning (SRL)-based Python learning activities for programming novices in an undergraduate non-computer science context. RobotPython includes two virtual robot assistants: the Coding Scaffolding Guidance Assistant and the Flowchart Reflection Assistant, which guide students to learn Python in the performance and self-reflection phases of SRL respectively. Both groups received SRL-based instruction; the key difference was that the experimental group was supported by RobotPython, whereas the control group was supported by teaching assistants (TAs). The 22 students in the experimental group, who used RobotPython as an aid to instruction, achieved significantly higher scores in programming concepts and coding than the 28 students in the control group. The impact of RobotPython on SRL abilities was nuanced: the effectiveness of RobotPython for goal-setting, help-seeking, and self-evaluation was moderated by students' baseline abilities (e.g., task strategies and time management), rather than showing a uniform improvement across all subscales. These results suggest that RobotPython-supported SRL-based instruction is associated with higher programming concept understanding and coding performance, while its impact on SRL abilities is conditional upon students' existing characteristics. They also reveal that programming concept understanding mediates the relationship between SRL abilities and coding performance. [ABSTRACT FROM AUTHOR]
ISSN:11763647
DOI:10.30191/ETS.202607_29(3).SP10