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 Development of daily activity and lifestyle data visualization tool for the college student learning analytics
Tác giả hoặc Nhóm tác giả: Ren-Hao Pan, Van Lam Ho, Phan Dinh Van, Hsiu-Chen Hsu, Robert K Lai, and Chien-Lung Chan
Nơi đăng: The International Workshop on Learning Analytics and Educational Data Mining (LAEDM 2016); Số: 09/2016;Từ->đến trang: 1-5;Năm: 2016
Lĩnh vực: Khoa học; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
In this study, 30 college students used a wearable tracking device for 24-hour measurement of daily physical activity and lifestyle factors over a period of 56 days. The collected data included heart rate, number of steps, activity level, sleep-related information, and calories burned. These data were stored on our Hadoop-based in-memory data warehousing platform. A Behavior Visualization System (BVS) was developed to illustrate the students’ lifestyle and behaviors using dynamic visualization tools such as line charts, bar charts, etc. A descriptive statistics module, data filtering and exporting tool were also included for advanced activities analysis for specific individuals or groups. An association analysis of physical activity and learning performance was conducted using the BVS, with the criterion that the device was worn for over 20 hours per day. The results revealed that examination performance was associated with sleep duration during the one day prior to examination (Pearson correlation coefficient 0.365, p<0.05). This finding suggested that using the wearable device for daily physical activity and sleep tracking may have a beneficial effect on students’ learning.
ABSTRACT
In this study, 30 college students used a wearable tracking device for 24-hour measurement of daily physical activity and lifestyle factors over a period of 56 days. The collected data included heart rate, number of steps, activity level, sleep-related information, and calories burned. These data were stored on our Hadoop-based in-memory data warehousing platform. A Behavior Visualization System (BVS) was developed to illustrate the students’ lifestyle and behaviors using dynamic visualization tools such as line charts, bar charts, etc. A descriptive statistics module, data filtering and exporting tool were also included for advanced activities analysis for specific individuals or groups. An association analysis of physical activity and learning performance was conducted using the BVS, with the criterion that the device was worn for over 20 hours per day. The results revealed that examination performance was associated with sleep duration during the one day prior to examination (Pearson correlation coefficient 0.365, p<0.05). This finding suggested that using the wearable device for daily physical activity and sleep tracking may have a beneficial effect on students’ learning.
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