Features of Group Online Learning Behaviours Based on Data Mining

With the development of information technology, how to scientifically and properly organizing and guiding learners to learn actively and efficiently has become a research subject for domestic and foreign scholars. However, existing research on online learning behaviours studied little about learning...

Full description

Saved in:
Bibliographic Details
Published in:International journal of emerging technologies in learning 2022, Vol.17 (4), p.34-48
Main Authors: Tanbo, Zhu, Lei, Wang, Die, Wang
Format: Article
Language:English
Subjects:
Behavior
Data mining
Distance learning
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the development of information technology, how to scientifically and properly organizing and guiding learners to learn actively and efficiently has become a research subject for domestic and foreign scholars. However, existing research on online learning behaviours studied little about learning attitudes, learning preferences, student-student interaction, teacher-student interaction and so on. To this end, this paper studies the features of group online learning behaviours based on data mining. In this paper, a K-means-based group online learning behaviour feature selection model and an AdaBoost-based group online learning behaviour classification model were constructed, and the processing methods, execution processes and algorithm functions of the two models were described in detail. Finally, the effectiveness of the constructed models was verified through an experiment.
ISSN:1863-0383
1863-0383
DOI:10.3991/ijet.v17i04.29583