Using the Grouping Function of Machine Learning Algorithm to Reduce the Influence of Information Avoidance Tendency during Reading Behavior
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| Titel: | Using the Grouping Function of Machine Learning Algorithm to Reduce the Influence of Information Avoidance Tendency during Reading Behavior |
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| Sprache: | English |
| Autoren: | Zhou, Juan (ORCID |
| Quelle: | Smart Learning Environments. 2023 10. |
| Verfügbarkeit: | 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 |
| Publikationsdatum: | 2023 |
| Publikationsart: | 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 |
| Dokumentencode: | EJ1401473 |
| Datenbank: | ERIC |
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Nájsť tento článok vo Web of Science | 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. |
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| ISSN: | 2196-7091 |
| DOI: | 10.1186/s40561-023-00281-7 |