Advanced

Instructing a Teachable Agent with Low or High Self-Efficacy – Does Similarity Attract?

Tärning, Betty LU ; Silvervarg, Annika; Gulz, Agneta LU and Haake, Magnus LU (2019) In International Journal of Artificial Intelligence in Education 29(1).
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
International Journal of Artificial Intelligence in Education
volume
29
issue
1
publisher
International AIED Society
external identifiers
  • scopus:85061210023
ISSN
1560-4306
DOI
10.1007/s40593-018-0167-2
language
English
LU publication?
yes
id
727b2db6-93e6-4000-bcdd-abb8fa62960e
date added to LUP
2018-11-18 16:40:36
date last changed
2019-02-18 14:23:09
@article{727b2db6-93e6-4000-bcdd-abb8fa62960e,
  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.},
  author       = {Tärning, Betty and Silvervarg, Annika and Gulz, Agneta and Haake, Magnus},
  issn         = {1560-4306},
  language     = {eng},
  number       = {1},
  publisher    = {International AIED Society},
  series       = {International Journal of Artificial Intelligence in Education},
  title        = {Instructing a Teachable Agent with Low or High Self-Efficacy – Does Similarity Attract?},
  url          = {http://dx.doi.org/10.1007/s40593-018-0167-2},
  volume       = {29},
  year         = {2019},
}