Pedagogical agents are computational entities that interact with users and facilitate learning opportunities. They usually need to be programmed to follow a set of commands for an effective personalized exchange of knowledge and tasks. In this study, we evaluate the effectiveness of a policy-based model and its level of satisfaction about the interaction without and with the pedagogical agent using the bio-inspired roulette selection algorithm. The approach is quantitative, with an exploratory and descriptive study. The results revealed that our agent achieved great acceptance among the users who rated it as intelligent, friendly, and reliable. It is evidenced that the agent can influence the attitude, perception, and behavior of the user to reach better self-regulated learning.
|Title of host publication||2021 9th International Conference on Information and Education Technology, ICIET 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|State||Published - 27 Mar 2021|
|Event||9th International Conference on Information and Education Technology, ICIET 2021 - Okayama, Japan|
Duration: 27 Mar 2021 → 29 Mar 2021
|Name||2021 9th International Conference on Information and Education Technology, ICIET 2021|
|Conference||9th International Conference on Information and Education Technology, ICIET 2021|
|Period||27/03/21 → 29/03/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This article has been prepared within the framework of the project IBA-0017-2018 titled “Recommendation System for Learning Objects in Regular Basic Education, focused on competencies using Deep Learning and Big Data” and funded by the Universidad Nacional de San Agustin de Arequipa, Peru to who we are deeply grateful.
© 2021 IEEE.
- pedagogical agents