| Summary: | 碩士 === 國立高雄第一科技大學 === 營建工程研究所 === 104 === University is the last learning venue for the majority of the students prior to entering society, with the majority of the courses taking place in college classrooms; the conditions of the physical environment inside the classrooms may affect the students’ body and mind status if spending a long period of time inside the classrooms. If the students are exposed to a course environment with poor indoor environmental quality, it may result in conditions where the students feel tired and sleepy, thereby leading to reduced learning effect. However, college classrooms are installed with air-conditioners to adjust the temperature, yet the windows may be tightly shut to avoid the air-conditioning from going out, which may result in poor indoor air quality. Therefore, this research used a hot environment and the air environment as the main exploring items, so as to analyze whether the conditions of hot environments and air environments has an influence towards the students’ drowsiness rate, which is then comprehensively explored with the addition of other possible factors influencing drowsiness, thereby finding out the other possible causes that may influence the students’ drowsiness rate besides the hot environment and the air environment.
This research applied the Automatic Sampling Method to measure the thermal comfort and indoor air quality of the indoor environmental quality as the main exploring items, taking a total of 5 classrooms, including 2 theater classrooms, 2 regular classrooms and one big classroom as the experimental venues for continuous monitoring. All 5 classrooms are equipped with webcams to observe and calculate the conditions of students’ drowsy conditions during class; a 3-in-1 carbon dioxide meter, fixed-type air quality detector and black bulb thermometer were installed to measure items including dry bulb temperature, relative humidity, black bulb temperature, CO2 concentration as well as indoor air quality (IAQ). Moreover, classrooms were also installed with ventilation fans and control panels, where the varying conditions of air temperature, relative humidity and CO2 concentration inside the classrooms were observed through 5 different ventilation models.
After the experiment, the images from the classes were compared to calculate the students’ drowsiness rate during the classes; one-way analysis of variance was conducted on these based on the hot environment, air environment, teaching category, time category and venue category respectively. The outcome of the analysis indicated that although the result for the hot environment and air environment were extremely noticeable, only the dry bulb temperature was noticeable, while the humidity as well as the ventilation models were not evident. Regression analysis was applied to proceed with the comprehensive discussion, and it was discovered that the factors and the drowsiness rate demonstrated a low degree of relativity, while CO2 and PMV demonstrated positive relativity towards the drowsiness rate, and that air quality demonstrated a negative relativity towards the drowsiness rate. It can be seen that hot environments and air environments both have influences towards the occurrence of the drowsiness rate, but the influences are lower. If discussed according to the time, the highest drowsiness rate within a week occurs during courses on Mondays, and the highest drowsiness rate within a day occurs during courses before or after noon period. Courses at night are mainly masters or doctors graduate programs, where the relative occurrence of drowsiness is rather low or even zero.
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