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 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 ПОШУК, ОТРИМАННЯ Й АНАЛІЗ ДАНИХ В ОСВІТІ: СУЧАСНИЙ СТАН І ПЕРСПЕКТИВИ РОЗВИТКУ

2016 ◽  
Vol 50 (6) ◽  
pp. 152
Author(s):  
Yurii O. Kovalchuk
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Learning Systems ◽  
Basic Principles ◽  
Data Mining Algorithms ◽  
Independent Direction ◽  
E Learning ◽  
The Subject ◽  
Classification And Regression ◽  
Mining Algorithms

The main tasks (classification and regression, association rules, clustering) and the basic principles of the Data Mining algorithms in the context of their use for a variety of research in the field of education which are the subject of a relatively new independent direction Educational Data Mining are considered. The findings about the most popular topics of research within this area as well as the perspectives of its development are presented. Presentation of the material is illustrated by simple examples. This article is intended for readers who are engaged in research in the field of education at various levels, especially those involved in the use of e-learning systems, but little familiar with this area of data analysis.

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.

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 Forma de ingresso X situação dos alunos no Curso Superior de Tecnologia em Sistemas para Internet do IFRS

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Mara Rosane Noble Tavares ◽  
Denise Luzia Wolff ◽  
Cristina Almeida da Silva
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Rio Grande ◽  
The Internet ◽  
Rio Grande Do Sul ◽  
Porto Alegre ◽  
System Database ◽  
Federal Institute ◽  
Academic System ◽  
Technology Systems

Resumo: Este artigo tem como objetivo identificar e analisar a relação da forma de ingresso com a situação inativa dos discentes no curso Tecnologia em Sistemas Para Internet do Instituto Federal do Rio Grande do Sul-Campus Porto Alegre. Para isso, utilizou-se o software RapidMiner e a base de dados do curso, disponível no Sistema Acadêmico da Instituição. Foram realizadas as cinco etapas do processo de KDD na base de dados do Sistema Acadêmico do curso, que foram: seleção, pré-processamento, transformação, mineração e avaliação dos dados. O KDD é o processo de descoberta de conhecimentos, que envolve etapas que vão desde a preparação da base de dados até a apresentação do conhecimento encontrado através das técnicas de mineração de dados. Atualmente, há a preocupação com o número crescente de alunos na ‘situação’ em recuperação, alunos que cursam semestres em atraso no curso. A descoberta de conhecimentos na base de dados do Sistema Acadêmico permitiu chegar à conclusão de que os alunos ingressados na instituição por vestibular, no segundo semestre de 2013 e pertencentes ao turno da manhã, apresentam uma probabilidade maior para entrar na situação de recuperação, indicando a necessidade de novas pesquisas para investigar o motivo. Palavras-chave: Mineração de Dados Educacionais. KDD. Ingresso. Situação. RapidMiner. ENTRY FORM X SITUATION OF STUDENTS IN THE HIGHER EDUCATION COURSE OF TECHNOLOGY SYSTEMS FOR THE INTERNET AT IFRS Abstract: This article aims to identify and analyze the relation between the entry way and the inactive status of students in the course of Technology Systems for the Internet at the Federal Institute of Rio Grande do Sul - Campus Porto Alegre. Thus, we used the RapidMiner software and the course database of Academic System of the Institution. The five steps of the KDD process were followed in the Academic System database of the course, which were selection, pre-processing, processing, mining and data evaluation. KDD is the process of knowledge discovery, which involves steps ranging from the preparation of the database up until presentation of knowledges found through data mining techniques. Currently there is a concern about the increasing number of students in the recovery 'situation', students who attend semesters with delay in the course. The discovery of knowledges in the Academic System database has led to the conclusion that students who entered in the institution through college entrance in the second half of 2013 and belonging to the morning shift are more likely to enter the recovery situation, indicating the need of further research to investigate the reason. Keywords: Educational Data Mining. KDD. Entrance. Situation. RapidMiner. 

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.

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