Abstract
It is well understood that affect interacts with and influences the learning process [2, 7, 9]. The impact of affect on learning is not straightforward. For example, D’Mello et al. explore how confusion, which superficially might be considered a negative affective state, is likely to promote learning under appropriate conditions [3]. In addition, the way the students perceive the tasks and the support they receive can impact their experience, agency and self-efficacy which in turn has been shown to relate to persistence and long-term outcomes [1]. It is important therefore, to deepen our understanding of the role of affect in learning in general and in particular students’ perceptions of their own learning with digital environments that provide feedback.
In previous work [4] we described the development of affect-aware support and the effect of such support in relation to learning during a whole classroom intervention with a sequence of fraction learning tasks within the iTalk2Learn platform. In contrast, in this paper, we report on a study that asked students to self-report their affective states while undertaking fractions tasks.
In previous work [4] we described the development of affect-aware support and the effect of such support in relation to learning during a whole classroom intervention with a sequence of fraction learning tasks within the iTalk2Learn platform. In contrast, in this paper, we report on a study that asked students to self-report their affective states while undertaking fractions tasks.
Original language | English |
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Title of host publication | Artificial Intelligence in Education. |
Subtitle of host publication | Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, Proceedings, Part II |
Editors | Maria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova |
Publisher | Springer, Cham |
Pages | 483-487 |
Number of pages | 5 |
Volume | 13356 |
ISBN (Electronic) | 978-3-031-11647-6 |
ISBN (Print) | 978-3-031-11646-9 |
DOIs | |
Publication status | E-pub ahead of print - 26 Jul 2022 |
Event | 23rd International Conference on Artificial Intelligence in Education, AIED 2022 - Durham, United Kingdom Duration: 27 Jul 2022 → 31 Jul 2022 https://link.springer.com/book/10.1007/978-3-031-11647-6 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 13356 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 23rd International Conference on Artificial Intelligence in Education, AIED 2022 |
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Abbreviated title | AIED 2022. |
Country/Territory | United Kingdom |
City | Durham |
Period | 27/07/22 → 31/07/22 |
Internet address |