2021, April. Understanding health and behavioral trends of successful students through machine learning models. In International Conference on Human Interaction and Emerging Technologies (pp. 516-525). Springer, Cham.

2021, April. Understanding health and behavioral trends of successful students through machine learning models. In International Conference on Human Interaction and Emerging Technologies (pp. 516-525). Springer, Cham.
复制标题

2021 年,四月。

DOI:
10.1007/978-3-030-74009
复制
发表时间:
2021
期刊:
International Conference on Human Interaction and Emerging Technologies
影响因子:
--
通讯作者:
Mankoff, J.
Mankoff, J.
中科院分区:
--
文献类型:
--
作者:
Kim, A.;Nikseresht, F.;Dutcher, J.M.;Tumminia, M.;Villalba, D.;Cohen, S.;Creswell, K.;Creswell, D.;Dey, A.K.;Mankoff, J.

文献摘要

相似文献

本研究分析了大学生在一个学期中不同时期的生理、心理、生活方式和人格因素的模式,并建立了它们与学生学业成绩的关系模型。分析的数据是通过智能手机和Fitbit收集的。使用从收集到的数据衍生的机器学习模型来观察学生的行为与他们的GPA、生活方式、身体健康、心理健康和人格属性相关的程度。使用了相互一致的方法,其中不是查看结果的准确性,而是使用模型参数和特征的权重来寻找共同的行为趋势。从模型创建的结果可以确定,被定义为GPA较高的学业成功的最重要指标是学生花费时间的地方。生活方式和个性因素被认为比心理和身体因素更重要。这项研究将深入了解不同因素对学生学业成绩的影响以及这些因素对学生学习成绩的影响时间。
This study analyzes patterns of physical, mental, lifestyle, and personality factors in college students in different periods over the course of a semester and models their relationships with students’ academic performance. The data analyzed was collected through smartphones and Fitbit. The use of machine learning models derived from the gathered data was employed to observe the extent of students’ behavior associated with their GPA, lifestyle, physical health, mental health, and personality attributes. A mutual agreement method was used in which rather than looking at the accuracy of results, the model parameters and weights of features were used to find common behavioral trends. From the results of the model creation, it was determined that the most significant indicator of academic success defined as a higher GPA, was the places a student spent their time. Lifestyle and personality factors were deemed more significant than mental and physical factors. This study will provide insight into the impact of different factors and the timing of those factors on students’ academic performance .