On the Cognitive Load of Online Learners With Multi-Level Data Mining

被引：0
|
作者：
Liu, Lingyan [1 ]
Zhao, Bo [2 ]
Rao, Yiqiang [2 ]
机构：
[1] Sch Informat Sci & Technol, Kunming, Yunnan, Peoples R China
[2] Minist Educ, Key Lab Educ Informatizat Nationalities, Beijing, Peoples R China
来源：
INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY EDUCATION | 2022年 / 18卷 / 02期
关键词：
Cognitive Load; Grade; Learning Analytic; Multi-Level Data Mining; Online Learning Behavior Characteristics; ARCHITECTURE; MOTIVATION; PATTERNS; BEHAVIOR;
D O I：
10.4018/ijicte.314225
中图分类号：
G40 [教育学];
学科分类号：
040101 ; 120403 ;
摘要：
A lot of studies have shown that there is an "inverse U-curve" relationship between learners' grades and cognitive load. Learners' grades are closely related to their learning behavior characteristics on online learning. Is there any relationship between online learners' behavior characteristics and cognitive load? Based on this, the data of research are obtained from the professions and applied sciences on the Canvas Network platform. The multi-level data mining technology is used to analyze and mine the relationship between grades and online learners' behavior characteristics layer by layer. The results show that there is an "inverse U-curve" relationship between grades and "nevents." Therefore, the research attempts to map "nevents" to the online learners' cognitive load, which makes the online learners' cognitive load can be quantitative analysis. Research results also prove that multi-level data mining technology can be used to mine the special learning rules hidden behind the data effectively.
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页数：15
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