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

Abstract

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.