Educational Data Mining on Learning Management Systems Using SCORM

2014 ◽  
Author(s):  
Deka Ganesh Chandra ◽  
Anupama C. Raman
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Management Systems ◽  
Learning Management

Role of Indian Print and Digital Media in Learning Analytics

2022 ◽  
pp. 199-218
Author(s):  
Chandana Aditya
Keyword(s):  
Data Mining ◽  
Digital Media ◽  
News Media ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Educational Programs ◽  
Mass Scale ◽  
Management Systems ◽  
Learning Management

There is a pressing need for data management and learning management systems. Educational data mining and learning analytics are two related aspects of educational technology that promote an overall effective teaching-learning system. The news media has the potential to act as a tool of learning analytics since they can easily access information at a mass scale. There are instances of leading newspapers organizing different educational programs where students from all the social layers have an opportunity to participate. A review of the programs reveals that all the programs collect and analyze educational data, which can form a research base of learning analytics. This chapter presents the description of three such educational programs organized by the leading media houses of India. This chapter also reflects on the contribution to learning management systems and educational data mining for the improvement of the overall educational system.

Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems

2011 ◽  
pp. 120-131
Author(s):  
Owen McGrath
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Open Source Software ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Management Systems ◽  
Open Learning ◽  
User Activity ◽  
Learning Management ◽  
Usage Analysis

Free and Open Source Software (FOSS)/Open Educational Systems development projects abound in higher education today. Many universities worldwide have adopted open source software like ATutor and Moodle as an alternative to commercial or homegrown systems. The move to open source learning management systems entails many special considerations, including usage analysis facilities. The tracking of users and their activities poses major technical and analytical challenges within web-based systems. This paper examines how user activity tracking challenges are met with data mining techniques, particularly web usage mining methods, in four different open learning management systems: ATutor, LON-CAPA, Moodle, and Sakai. As examples of data mining technologies adapted within widely used systems, they represent important first steps for moving educational data mining outside the research laboratory. Moreover, as examples of different open source development contexts, exemplify the potential for programmatic integration of data mining technology processes in the future. As open systems mature in the use of educational data mining, they move closer to the long-sought goal of achieving more interactive, personalized, adaptive learning environments online on a broad scale.

Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems

2010 ◽  
Vol 2 (1) ◽  
pp. 65-75
Author(s):  
Owen McGrath
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Open Source Software ◽  
Adaptive Learning ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Management Systems ◽  
Open Learning ◽  
User Activity ◽  
Learning Management

Free and Open Source Software (FOSS)/Open Educational Systems development projects abound in higher education today. Many universities worldwide have adopted open source software like ATutor and Moodle as an alternative to commercial or homegrown systems. The move to open source learning management systems entails many special considerations, including usage analysis facilities. The tracking of users and their activities poses major technical and analytical challenges within web-based systems. This paper examines how user activity tracking challenges are met with data mining techniques, particularly web usage mining methods, in four different open learning management systems: ATutor, LON-CAPA, Moodle, and Sakai. As examples of data mining technologies adapted within widely used systems, they represent important first steps for moving educational data mining outside the research laboratory. Moreover, as examples of different open source development contexts, exemplify the potential for programmatic integration of data mining technology processes in the future. As open systems mature in the use of educational data mining, they move closer to the long-sought goal of achieving more interactive, personalized, adaptive learning environments online on a broad scale.

Application of EDM to Understand the Online Students' Behavioral Pattern

2019 ◽  
Vol 12 (3) ◽  
pp. 154-168 ◽  
Author(s):  
Luis Naito Mendes Bezerra ◽  
Márcia Terra da Silva
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Behavioral Pattern ◽  
Management Systems ◽  
Online Course ◽  
Educational Context ◽  
Behavioral Patterns ◽  
Limited Capacity ◽  
Online Access

In distance learning, the professor cannot see that the students are having trouble with a subject, and can fail to perceive the problem in time to intervene. However, in learning management systems (LMS's) a large volume of data regarding online access, participation and progress can be registered and collected allowing analysis based on students' behavioral patterns. As traditional methods have a limited capacity to extract knowledge from big volumes of data, educational data mining (EDM) arises as a tool to help teachers interpreting the behavior of students. The objective of the present article is to describe the application of educational data mining technics aiming to obtain relevant knowledge of students' behavioral patterns in an LMS for an online course, with 1,113 students enrolled. This paper applies two algorithms on educational context, decision tree and clustering, unveiling unknown relevant aspects to professors and managers, such as the most important examinations that contribute to students' approval as well as the most significant attributes to their success.

scholarly journals Using Data Mining In Learning Management Systems Amidst Covid-19

2020 ◽  
Vol 6 (3) ◽  
pp. 213
Author(s):  
Froilan D Mobo
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Learning Management Systems ◽  
The Philippines ◽  
Management Systems ◽  
Official Gazette ◽  
Learning Management ◽  
The Republic ◽  
Using Data ◽  
The One

<p>The Second Semester of Academic Year 2019-2020 was temporarily suspended due to the widespread COVID-19 last March 16, 2020, forcing the President of the Republic of the Philippines, Hon. Rodrigo Roa Duterte imposed an Enhanced Community Quarantine in Luzon which is known as a lockdown closing all the border points of each town and provinces. One of the major problem encountered during the lockdown is the suspension of classes because as per IATF guidelines you need to stay home, the said Memorandum Order was posted in the official gazette, (Medialdea, 2020)</p><p>The dataset on the features of the Learning Management Systems using Moodle is that Professors will be the one who will set the topics, quizzes, and exercises for his class even the assessment methods on the system. To prevent from slowing down the network,  the Team of Seaversity the developer of the learning management systems headed by C/E Ephrem Dela Cernan conducts a ZOOM Training to all Faculty to be familiarized more on the Learning Management Systems of the Philippine Merchant Marine Academy. </p><p>The Moodle Learning Management Systems is a user-friendly environment because of its features and users can easily adjust from the traditional face to face teaching going to e-Learning approach because of it’s all capabilities as a data mining methods such as statistics, association rule mining, pattern mining visualization, categorization, clustering, and text mining., (AlAjmi &amp; Shakir, 2013)</p>

scholarly journals Detecting Learning Styles in Learning Management Systems Using Data Mining

2016 ◽  
Vol 24 (4) ◽  
pp. 740-749 ◽  
Author(s):  
Madura Prabhani Pitigala Liyanage ◽  
K.S. Lasith Gunawardena ◽  
Masahito Hirakawa
Keyword(s):  
Data Mining ◽  
Learning Styles ◽  
Learning Management Systems ◽  
Management Systems ◽  
Learning Management ◽  
Using Data

Educational Data Mining Applied to a Massive Course

2020 ◽  
Vol 18 (4) ◽  
pp. 17-30
Author(s):  
Luis Naito Mendes Bezerra ◽  
Márcia Terra Silva
Keyword(s):  
Data Mining ◽  
Distance Learning ◽  
Undergraduate Students ◽  
Student Behavior ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Management Systems ◽  
Behavior Patterns ◽  
Current Context ◽  
Attribute Type

In the current context of distance learning, learning management systems (LMSs) make it possible to store large volumes of data on web browsing and completed assignments. To understand student behavior patterns in this type of environment, educators and managers must rethink conventional approaches to the analysis of these data and use appropriate computational solutions, such as educational data mining (EDM). Previous studies have tested the application of EDM on small datasets. The main contribution of the present study is the application of EDM algorithms and the analysis of the results in a massive course delivered by a Brazilian University to 181,677 undergraduate students enrolled in different fields. The use of key algorithms in educational contexts, such as decision trees and clustering, can reveal relevant knowledge, including the attribute type that most significantly contributes to passing a course and the behavior patterns of groups of students who fail.

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