Application of educational data mining on analysis of students' online learning behavior

2017 ◽  
Author(s):  
Wang Jie ◽  
Lv Hai-yan ◽  
Cao Biao ◽  
Zhao Yuan
Keyword(s):  
Data Mining ◽  
Online Learning ◽  
Educational Data Mining ◽  
Learning Behavior

A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments

2021 ◽  
pp. 278-292
Author(s):  
Eric Araka ◽  
Robert Oboko ◽  
Elizaphan Maina ◽  
Rhoda K. Gitonga
Keyword(s):  
Data Mining ◽  
Online Learning ◽  
Learning Environments ◽  
Domain Knowledge ◽  
Educational Data Mining ◽  
Online Learning Environments ◽  
Student Activities ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
E Learning

Self-regulated learning is attracting tremendous researches from various communities such as information communication technology. Recent studies have greatly contributed to the domain knowledge that the use self-regulatory skills enhance academic performance. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Modern learning management systems (LMS) allow storage of datasets on student activities. Subsequently, it is now possible to use Educational Data Mining to extract learner patterns which can be used to support SRL. This chapter discusses the current tools for measuring and promoting SRL on e-learning platforms and a conceptual model grounded on educational data mining for implementation as a solution to promoting SRL strategies.

Mining Students' Learning Behavior in Moodle System

2014 ◽  
Vol 7 (4) ◽  
pp. 12-26 ◽  
Author(s):  
K. Touya ◽  
Mohamed Fakir
Keyword(s):  
Data Mining ◽  
Web Application ◽  
Educational Data Mining ◽  
Educational Environment ◽  
Learning Behavior ◽  
System Database ◽  
Hidden Knowledge ◽  
E Learning ◽  
Mining Algorithms ◽  
Interesting Area

In the last few years, Educational Data Mining has become an interesting area exploited to discover and extract hidden knowledge of students from educational environment data. During the establishment of this work an attempt was made to manage the extracted information using mining techniques. These methods took place in order to get groups of students with similar characteristics. The application of classification, clustering and association rules mining algorithms on the data stored on the e-learning (Moodle system) database allowed to extract knowledges that help to understand students' behaviors and patterns. Additionally, the development of a Web application for the educators is a tool to monitor their students learning behavior by monitoring the number of assignments taken, the number of quizzes taken, the number of forum post and read by students, etc. The knowledge obtained can help the instructors to make decision about their students' interacting with the courses activities in Moodle system, and to create an efficient educational environment. In this research, a Data Mining tool called RapidMiner was used for mining the data from the Moodle system database, and a web application written in PHP was established to aid teachers with statistics.

scholarly journals Educational Data Mining for Student Learning Pattern Analysis using Clustering Algorithms

2020 ◽  
Vol 9 (6) ◽  
pp. 481-488
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Data Mining Algorithm ◽  
Learning Behavior ◽  
Study Program ◽  
Data Mining Algorithms ◽  
Students First ◽  
And Performance

The exponential increase in universities’ electronic data creates the need to derive some useful information from these massive amounts of data. The progression in the data mining field causes it conceivable to educational data to improve the nature of educational processes. This study, thus, uses data mining methods to study the learning behavior and performance of university students. It focused on two aspects of the performance of the students. First, predicting students' learning behavior at the end of a complete year of the study program. Second, predict student performance with the help of the data model proposed by this study. Finally, provide course material recommendations using the data mining algorithm. Three data mining algorithms were considered which are K-Means, FCM, and KFCM., and maximum accuracy of 90.22% was achieved by KFCM. The study indicates that in terms of time and memory usages K-means algorithm give better results. This creates an opportunity for identifying students that may graduate with poor results or may not graduate at all, so early intercession might be possible.

Time Really Matters: Understanding the Temporal Dimension of Online Learning Using Educational Data Mining

2018 ◽  
Vol 57 (5) ◽  
pp. 1326-1347 ◽  
Author(s):  
Hengtao Tang ◽  
Wanli Xing ◽  
Bo Pei
Keyword(s):  
Data Mining ◽  
Online Learning ◽  
Online Teaching ◽  
Learning Experience ◽  
Educational Data Mining ◽  
Temporal Dimension ◽  
Online Participation ◽  
Longitudinal Patterns ◽  
New Perspective ◽  
Using Data

Learning and participation are inseparable in online environments. To improve online learning, much effort has been devoted to encouraging online participation. However, previous research has investigated participation from a variable-based perspective, looking only for relationships between participation and other variables. Time can change and even reverse this relationship, however. Online learning is a cumulative process, but participation at several critical moments is more significant for learning than is participation at other points. To fully understand how learning unfolds over time, it is necessary to shift to a new perspective on learning. Adopting an event-based view on which the units of analysis are separate but interrelated learning events, this study investigates longitudinal patterns in online participation. Using data mining techniques for education, the study validated longitudinal patterns of participation as an accurate measure for differentiating learner performance. In addition, the first segment of this online learning experience was identified as the most critical moment in which educators should provide efficient interventions to help their learners maintain active participation. Additional design, pedagogical, and methodological implications for online teaching and facilitation practices are discussed at the end.

scholarly journals An Educational Data Mining Model for Supervision of Network Learning Process

2018 ◽  
Vol 13 (11) ◽  
pp. 67
Author(s):  
Jianhui Chen ◽  
Jing Zhao
Keyword(s):  
Data Mining ◽  
Online Learning ◽  
Learning Process ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Learning System ◽  
Teaching Management ◽  
Management Platform ◽  
E Learning ◽  
Mining Model

To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was established. Statistical analysis and visualization in data mining techniques, association rule algorithms, and clustering algorithms were applied. The teaching data of a college English teaching management platform was systematically analyzed. A related conclusion was drawn on the relationship between students' English learning effects and online learning habits. The results showed that this method could effectively help teachers judge students' online learning results, understand their online learning status, and improve their online learning process. Therefore, the model can improve the effectiveness of students' online learning.

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