基于自编码器的无监督聚类方法分析电子学习对印度学生在 Covid-19 大流行期间心理健康的影响

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Pritha Banerjee, Chandan Jana, Jayita Saha, Chandreyee Chowdhury
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引用次数: 0

摘要

由于 "Covid-19 "大流行,印度的教育系统已转向远程教育,即在线学习模式。虽然有作品研究了教学对印度学生的影响,但关于在线学习模式的影响以及相关的心理状态,尤其是在整个国家正经历危机的时候,在文献中却找不到。我们的目标是分析数据,找到一些模式,从而了解在线学习的效果,并尝试找出压力水平。我们在 2021 年 4 月至 5 月期间以问卷形式从 500 名本科大学生中收集了数据集。我们在本文中的贡献是:(i) 发布了一个学生反馈数据集;(ii) 设计了一个数据处理管道,包括自动编码器和聚类方法。数据集是文本格式的,因此为了进行分析,我们使用二进制词袋的概念将数据集转换为数字格式。通过自动编码器进行降维,以实现有效的潜在空间表示。最后,为了从这个降维特征空间中找出模式,我们采用了无监督学习算法--kMeans 和 DBSCAN。对聚类过程的全面分析表明,无论学生的背景是城市还是农村,纯在线教育中社会交流的缺失都会造成孤立。然而,由于数据显示相当多的学生欢迎在线学习的概念,因此在线教育可以作为线下课堂的补充。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An autoencoder based unsupervised clustering approach to analyze the effect of E-learning on the mental health of Indian students during the Covid-19 pandemic

An autoencoder based unsupervised clustering approach to analyze the effect of E-learning on the mental health of Indian students during the Covid-19 pandemic

Due to the Covid-19 pandemic, the education system in India has changed to remote that is, online study mode. Though there are works on the effect of teaching learning on Indian students, the effect of online mode and associated mental state, particularly when the entire country is going through a crisis could not be found in the literature. Our goal is to analyze data and find some pattern through which we can understand the effectiveness of the online study and also try to figure out the stress level. The dataset we collected from 500 undergraduate college students during April-May, 2021 is in questionnaire format. Our contribution in this paper are - (i) publishing a dataset of student feedbacks, and (ii) designing a data processing pipeline involving autoencoders followed by clustering approach. The dataset is in text format so for our analysis we have converted the dataset into a numerical format using the concept of a binary bag of words. Dimensionality reduction is applied through autoencoder for an effective latent space representation. Finally, for finding patterns out of this dimensionally reduced feature space, we have applied unsupervised learning algorithms - kMeans and DBSCAN. A thorough analysis of the clustering process reveals that the absence of social communication in purely online education provokes isolation irrespective of the urban or rural background of the students. However, it could supplement offline classes as a substantial number of students welcomed the concept of online learning as reported in the data.

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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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