Reducing Stress in Math Tests Through Video-Based Question Formats: A Multimodal Aﬀective Study
type: CreativeWork
description: Foundational math tests in early undergraduate education are often associated with stress and negative emotional responses, which can lead to avoidance of quantitative coursework and limit long-term academic and career opportunities. To address this issue, we introduce a novel type of math question framed as a relatable short video story. We tested the effectiveness of this design in an experiment with 50 participants who completed a math test that contained conventional and video-based questions of comparable difficulty. Throughout the test, we continuously recorded physiological indicators of arousal and observational indicators of emotional valence. These included facial perspiration via thermal imaging, heart rate and heart rate variability via smartwatches, and facial expressions via webcam. After calibrating responses to individual baselines and normalizing the data, mixed-effects models revealed that conventional questions elicited significantly higher arousal than video questions. In addition, video-based questions were more strongly associated with positive affective responses. Importantly, these psychophysiological benefits were achieved without compromising test performance. Using Machine Learning (ML), we also demonstrated that it is possible to predict with significant accuracy (~70\%) the correctness of student responses to questions in the foundational math test. However, this is primarily due to the existence of subsets of easy/difficult questions in the said test that are solved/not solved by nearly everybody. Together, our study provides empirical support for an affective redesign of foundational math assessments, one that aligns with the media consumption habits of today’s learners and is well suited to the YouTube era.
abstract: Foundational math tests in early undergraduate education are often associated with stress and negative emotional responses, which can lead to avoidance of quantitative coursework and limit long-term academic and career opportunities. To address this issue, we introduce a novel type of math question framed as a relatable short video story. We tested the effectiveness of this design in an experiment with 50 participants who completed a math test that contained conventional and video-based questions of comparable difficulty. Throughout the test, we continuously recorded physiological indicators of arousal and observational indicators of emotional valence. These included facial perspiration via thermal imaging, heart rate and heart rate variability via smartwatches, and facial expressions via webcam. After calibrating responses to individual baselines and normalizing the data, mixed-effects models revealed that conventional questions elicited significantly higher arousal than video questions. In addition, video-based questions were more strongly associated with positive affective responses. Importantly, these psychophysiological benefits were achieved without compromising test performance. Using Machine Learning (ML), we also demonstrated that it is possible to predict with significant accuracy (~70\%) the correctness of student responses to questions in the foundational math test. However, this is primarily due to the existence of subsets of easy/difficult questions in the said test that are solved/not solved by nearly everybody. Together, our study provides empirical support for an affective redesign of foundational math assessments, one that aligns with the media consumption habits of today’s learners and is well suited to the YouTube era.
author: Kiran, Fettah 1991-
language: English
keywords: Computer science
date published: 2025-08
date modified: 2026-07-24T02:32:12Z
provider: University of Houston
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Houston

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10657/20720