Abstract
This study examines the effects of teachable agents’ expressed self-efficacy on students. A total of 166 students, 10- to 11-years-old, used a teachable agent-based math game focusing on the base-ten number system. By means of data logging and questionnaires, the study compared the effects of high vs. low agent self-efficacy on the students’ in-game performance, their own math self-efficacy, and their attitude towards their agent. The study further explored the effects of matching vs. mismatching between student and agent with respect to self-efficacy. Overall, students who interacted with an agent with low self-efficacy performed better than students interacting with an agent with high self-efficacy. This was especially apparent for students who had reported low self-efficacy themselves, who performed on par with students with high self-efficacy when interacting with a digital tutee with low self-efficacy. Furthermore, students with low self-efficacy significantly increased their self-efficacy in the matched condition, i.e. when instructing a teachable agent with low self-efficacy. They also increased their self-efficacy when instructing a teachable agent with high self-efficacy, but to a smaller extent and not significantly. For students with high self-efficacy, a potential corresponding effect on a self-efficacy change due to matching may be hidden behind a ceiling effect. As a preliminary conclusion, on the basis of the results of this study, we propose that teachable agents should preferably be designed to have low self-efficacy.
Highlights
Digital agents are becoming increasingly common in educational software
The results clearly show that it did not matter whether students taught a digital tutee with high or low SE when it came to what they thought of their tutee or for their own SE
It is possible that students in a teacher role take more responsibility for a digital tutee with low SE precisely because this tutee expresses a low trust in her own ability to learn, and possibly comes across as someone who is in need of more help than a digital tutee with high SE
Summary
Digital agents are becoming increasingly common in educational software. They can be used to simulate different pedagogical roles, such as teachers, mentors, instructors, coaches, learning companions, and tutees. Once the role is decided, there are a number of design choices that must be made: the agent’s age, gender, and ethnicity (indicated through visual and behavior markers), range and level of knowledge, communicative style, etc. These choices have been shown to influence students’ performance and learning (Veletsianos 2009; Arroyo et al 2009), motivation (Plant et al 2009), and self-efficacy (SE), i.e. the belief in one’s capacity to succeed with a task or in a domain (Ebbers 2007). The subject-specific nature of SE is key; a person can think highly of her ability to perform and make progress in, say, ice hockey but not in programming, or in Spanish but not in math
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Artificial Intelligence in Education
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
