Impact of pre-knowledge and engagement in robot-supported collaborative learning through using the ICAPB model

Several challenges exist in computer-supported collaborative learning environments, such as the potential for distraction and student boredom and isolation, which may adversely affect the quality of collaborative learning and knowledge construction. On the other hand, as an innovative learning tool,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers and education 2024-08, Vol.217, p.105069, Article 105069
Hauptverfasser: Zhao, Jia-Hua, Yang, Qi-Fan, Lian, Li-Wen, Wu, Xian-Yong
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Several challenges exist in computer-supported collaborative learning environments, such as the potential for distraction and student boredom and isolation, which may adversely affect the quality of collaborative learning and knowledge construction. On the other hand, as an innovative learning tool, physical robots are seen as successful collaborative learning facilitators that can raise student engagement, strengthen social presence, and boost learning results. Meanwhile, tasks designed based on Bloom's taxonomy further ensure students' attention and cognitive growth in robot-supported collaborative learning (RSCL) environments. Although some researchers have explored how to maintain engagement in previous studies on robots, it is still difficult due to the lack of a commonly employed annotation method for evaluating engagement. Therefore, this study proposed the interactive, constructive, active, passive, and behavioral (ICAPB) engagement coding model, combining cognitive and behavioral engagement, to comprehensively analyze the relationship between pre-knowledge, student engagement, and learning achievement in the RSCL environment. An experiment was conducted in a first-aid course at a university to evaluate the effectiveness of this approach. The study involved a total of 36 students using a collaborative robotic system with Bloom's taxonomy. The results showed that pre-knowledge, whether at a high or low level, did not significantly affect students' posttest scores. Instead, student engagement significantly positively impacted their learning achievement. •A collaborative robotic system with Bloom taxonomy was used in first-aid courses.•An ICAPB coding model with cognitive and behavioral engagement was proposed.•An experiment using the two-way ANCOVA was conducted for 36 university students.•The results showed pre-knowledge did not significantly affect post-test scores.•Student engagement significantly impacted learning achievement.
ISSN:0360-1315
1873-782X
DOI:10.1016/j.compedu.2024.105069