Meta-Affective Behaviour within an Intelligent Tutoring System for Mathematics

Autor: Benedict du Boulay, Genaro Rebolledo-Mendez, N. Sofia Huerta-Pacheco, Ryan S. Baker
Rok vydání: 2021
Předmět:
Zdroj: International Journal of Artificial Intelligence in Education. 32:174-195
ISSN: 1560-4306
1560-4292
DOI: 10.1007/s40593-021-00247-1
Popis: Many previous studies have highlighted the influence of learners’ affective states on learning with tutoring systems. However, the associations between learning and learners’ meta-affective capability are still unclear. The goal of this paper is to analyse meta-affective capability and its influence on learning outcomes as well as the dynamics of affect over time. Two criteria, awareness and self-regulation, were employed to define meta-affective capability. An exploratory study (n = 54) was conducted in which students at the secondary level were asked to interact with an intelligent tutoring system for mathematics and to self-report their affect during their interactions with the system. Pre-post learning outcomes were also measured. A post-hoc comparison of learning gains was made between more meta-affectively capable and less meta-affectively capable students. The results provide some empirical evidence to support the hypothesis that having meta-affective capability is positively associated with learning. Students not demonstrating meta-affective capability seemed to transition frequently from boredom to frustration (p = .0284) and from concentration to neutral (p = 0.0017). However, only a small percentage of the sample were classified as having meta-affective capability, indicating that it is important to scaffold students who are not meta-affectively capable.
Databáze: OpenAIRE
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