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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
FullText Text:
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  Data: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
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  Data: "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.]
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