Measuring Students' Thermal Comfort and Its Impact on Learning
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| Title: | Measuring Students' Thermal Comfort and Its Impact on Learning |
|---|---|
| Language: | English |
| Authors: | Jiang, Han, Iandoli, Matthew, Van Dessel, Steven, Liu, Shichao, Whitehill, Jacob |
| Source: | International Educational Data Mining Society. 2019. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Peer Reviewed: | Y |
| Page Count: | 10 |
| Publication Date: | 2019 |
| Sponsoring Agency: | National Science Foundation (NSF) |
| Contract Number: | 1551594 1822768 |
| Document Type: | Reports - Research Speeches/Meeting Papers |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Climate, Heat, Environmental Influences, Climate Control, Measurement Techniques, Predictor Variables, Nonverbal Communication, Measurement Equipment, Learning, Learner Engagement, Foreign Countries, College Students |
| Geographic Terms: | Romania |
| Abstract: | "Thermal comfort" (TC) -- how comfortable or satisfied a person is with the temperature of her/his surroundings -- is one of the key factors influencing the "indoor environmental quality" of schools, libraries, and offices. We conducted an experiment to explore how TC can impact students' learning. University students (n = 25) were randomly assigned to different temperature conditions in an office environment (25[degrees]C [right arrow] 30[degrees]C, or 30[degrees]C [right arrow] 25[degrees]C) that were implemented using a combination of heaters and air conditioners over a 1.25 hour session. The task of the participants was to learn from tutorial videos on three different topics, and a test was given after each tutorial. The results suggest that (1) changing the room temperature by a few degrees Celsius can stat. sig. impact students' self-reported TC; (2) the relationship between TC and learning exhibited an inverted U-curve, i.e., should be neither too uncomfortable nor too comfortable. We also explored different computer vision and sensor-based approaches to measure students' thermal comfort automatically. We found that (3) TC can be predicted automatically either from the room temperature or from an infra-red (IR) camera of the face; however, (4) TC prediction from a normal (visible-light) web camera is highly challenging, and only limited predictive power was found in the facial expression features to predict thermal comfort. [For the full proceedings, see ED599096.] |
| Abstractor: | As Provided |
| Entry Date: | 2019 |
| Accession Number: | ED599249 |
| Database: | ERIC |
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