Educational Data Mining: A review of evaluation process in the e-learning

2018 ◽  
Vol 35 (6) ◽  
pp. 1701-1717 ◽  
Author(s):  
Marcos Wander Rodrigues ◽  
Seiji Isotani ◽  
Luiz Enrique Zárate
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Evaluation Process ◽  
E Learning

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 Increasing the Adaptivity of an Intelligent Tutoring System with Educational Data Mining: A System Overview

2016 ◽  
Vol 11 (03) ◽  
pp. 67 ◽  
Author(s):  
Igor Jugo ◽  
Božidar Kovačić ◽  
Vanja Slavuj
Keyword(s):  
Data Mining ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Educational Data Mining ◽  
Knowledge Domain ◽  
Systems Architecture ◽  
Tutoring Systems ◽  
E Learning ◽  
Mining Tools

Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our systems architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.

scholarly journals Educational data mining using cluster analysis and decision tree technique: A case study

2020 ◽  
Vol 12 ◽  
pp. 184797902090867
Author(s):  
Snježana Križanić
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Decision Tree ◽  
Student Behavior ◽  
Educational Data Mining ◽  
Education Institution ◽  
Data Mining Techniques ◽  
Event Logs ◽  
Hidden Knowledge ◽  
E Learning

Data mining refers to the application of data analysis techniques with the aim of extracting hidden knowledge from data by performing the tasks of pattern recognition and predictive modeling. This article describes the application of data mining techniques on educational data of a higher education institution in Croatia. Data used for the analysis are event logs downloaded from an e-learning environment of a real e-course. Data mining techniques applied for the research are cluster analysis and decision tree. The cluster analysis was performed by organizing collections of patterns into groups based on student behavior similarity in using course materials. Decision tree was the method of interest for generating a representation of decision-making that allowed defining classes of objects for the purpose of deeper analysis about how students learned.

scholarly journals Comparison of Some Classification Algorithms for the Analysis of Students Academic Performance in Educational Data Mining Using Orange

2021 ◽  
pp. 318-324
Author(s):  
Vanthana V
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Random Forest ◽  
Educational Data Mining ◽  
Evaluation Process ◽  
Support Vector ◽  
Classification Algorithms ◽  
Academic Improvement ◽  
Academic Information ◽  
Tools And Techniques

In the modern education system, many higher education institutions prefer data mining tools and techniques to analyze the academic improvement of their students. To support that many data mining techniques and tools are available. This paper uses the classification concept to analyze the student’s academic performance. This paper presents the comparison result of five classification algorithms – Decision Tree, Naïve Bayesian, K-Nearest Neighbour, Support Vector Machine and Random Forest which is applied to the data collected from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. The data mining tool used was ORANGE. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in the dataset. The results showed that Random Forest out performs the other classifiers based on accuracy.

scholarly journals Mapeamento sistemático da literatura brasileira sobre educational data mining e learning analytics

2020 ◽  
Vol 6 (1) ◽  
pp. 58-70
Author(s):  
Francisco Alan Oliveira Santos ◽  
Mardoqueu Sousa Telvina ◽  
Márcio Fabiano Oliveira Moura Santos
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
E Learning
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