Instructing a Teachable Agent with Low or High Self-Efficacy – Does Similarity Attract?
(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:
https://lup.lub.lu.se/record/727b2db6-93e6-4000-bcdd-abb8fa62960e
- author
- Tärning, Betty LU ; Silvervarg, Annika ; Gulz, Agneta LU and Haake, Magnus LU
- organization
- publishing date
- 2019
- 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
- 2022-04-25 18:47:03
@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}},
doi = {{10.1007/s40593-018-0167-2}},
volume = {{29}},
year = {{2019}},
}