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Using the Grouping Function of Machine Learning Algorithm to Reduce the Influence of Information Avoidance Tendency during Reading Behavior

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Bibliographic Details
Title: Using the Grouping Function of Machine Learning Algorithm to Reduce the Influence of Information Avoidance Tendency during Reading Behavior
Language: English
Authors: Zhou, Juan (ORCID 0000-0002-2995-4559), Wang, Siqi, Xu, Ling, Yin, Chengjiu
Source: Smart Learning Environments. 2023 10.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 16
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Graduate Students, Reading, Group Activities, Student Behavior, Users (Information), Social Psychology, Motivation, Behavior, Attention Control, Control Groups
DOI: 10.1186/s40561-023-00281-7
ISSN: 2196-7091
Abstract: Information avoidance has been studied in medicine, economics, and psychology, and has recently been discussed in educational technology. In this study, the authors developed a grouping method to reduce students' information avoidance in reading through group work. This two-step group method includes the k-means and genetic algorithm to explore the grouping method based on students' marking tendencies. To examine the effect of this method, an experiment was conducted in a web-system development course with 33 graduate students. The results showed that information avoidance occurred less in the experimental group than in the control group. The students of the two-step grouping method evaluated group work as more helpful for their study than the students who attended the usual group work.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1401473
Database: ERIC
Description
Abstract:Information avoidance has been studied in medicine, economics, and psychology, and has recently been discussed in educational technology. In this study, the authors developed a grouping method to reduce students' information avoidance in reading through group work. This two-step group method includes the k-means and genetic algorithm to explore the grouping method based on students' marking tendencies. To examine the effect of this method, an experiment was conducted in a web-system development course with 33 graduate students. The results showed that information avoidance occurred less in the experimental group than in the control group. The students of the two-step grouping method evaluated group work as more helpful for their study than the students who attended the usual group work.
ISSN:2196-7091
DOI:10.1186/s40561-023-00281-7