Using the grouping function of machine learning algorithm to reduce the influence of information avoidance tendency during reading behavior.

Autor:	Zhou, Juan, Wang, Siqi, Xu, Ling, Yin, Chengjiu
Předmět:	MACHINE learning EDUCATIONAL technology K-means clustering GENETIC algorithms GROUP reading COACHING psychology
Zdroj:	Smart Learning Environments; 11/27/2023, Vol. 10 Issue 1, p1-16, 16p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink