SIGCSE TS 2025 (series) / Posters /
Predicting Student Reasoning for Self-Reported Affect in Game-Based Learning Environments
This program is tentative and subject to change.
Fri 28 Feb 2025 10:00 - 12:00 at Exhibit Hall C - Posters - Posters #2
Student affect is widely recognized as a major influence on learning gains and engagement, which has lead to the development of many automated detectors for affect. However, in order to effectively respond to student affect, we must know how students are interpreting it. This study proposes a novel automated detector, which models when students are attributing their epistemic emotion to task difficulty. The goal is to use detectors like this one to better understand how to respond to students’ affective states (in this case, boredom, confusion, frustration and nervousness). We then discuss the implications of this novel detector for real-time support in a game-based learning environments.
This program is tentative and subject to change.
Fri 28 FebDisplayed time zone: Eastern Time (US & Canada) change
Fri 28 Feb
Displayed time zone: Eastern Time (US & Canada) change
10:00 - 12:00 | |||
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