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

scholarly journals Early Detection of Drop Outs in E-Learning Systems

2019 ◽  
Vol 2 (3) ◽  
pp. 1008-1015
Author(s):  
Neslihan Ademi ◽  
Suzana Loshkovska
Keyword(s):  
Data Mining ◽  
Early Detection ◽  
Learning Management Systems ◽  
Management Systems ◽  
Online Behavior ◽  
Event Logs ◽  
E Learning ◽  
Learning Management ◽  
Using Data ◽  
Drop Outs

After the popularity of Learning Management Systems, Data Mining and Learning Analytics have become emerging topics. Learning Management Systems such as Moodle, provide big amount of data to be used in analyzing students&amp;rsquo; online behavior. This paper represents a method for early detection of drop outs from a Bachelor degree course using data mining methods. Data is collected through Moodle logs. For early detection, event logs till the first exam is taken into consideration. Decision Tree (DT) and Bayesian Network (BN) algorithms are used for the prediction. In the end it is shown that DT algorithm gives a higher over-all accuracy but BN is better for discovering fail cases as it has higher specificity.

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.

scholarly journals Using Data Acquired from Learning Management Systems to Predict Examination Results

2019 ◽  
Author(s):  
Francis B. Lavoie ◽  
Pierre Proulx ◽  
Ryan Gosselin
Keyword(s):  
Management System ◽  
Learning Management System ◽  
Learning Management Systems ◽  
Management Systems ◽  
Learning Management ◽  
Using Data ◽  
Dynamic Relationships

This work presents a novel learning management system (LMS), named Catalyseur, which allows the instructors to easily visualize which lessons and exercises allowed the students to better perform at an examination. This LMS feature is based on a regression methodology calculating easy-to-analyze models and being able to fit dynamic relationships. These models are calculated automatically and only require as human input to upload the student results at an examination.

Integration of Public University Web Sites and Learning Management Systems

2009 ◽  
pp. 208-218
Author(s):  
Bernard Ostheimer
Keyword(s):  
Web Sites ◽  
Learning Management Systems ◽  
Content Management ◽  
Internet Technology ◽  
Management Systems ◽  
Web Content ◽  
Heterogeneous Landscape ◽  
Outer Appearance ◽  
Learning Management ◽  
The One

Internet technology has found its way into all areas of business and research. The World Wide Web is also used at universities to achieve different goals. On the one hand, it acts as a means of outer appearance, on the other hand, as an instrument of knowledge transfer and knowledge examination. Of course other purposes in addition to those named above do exist. Often different systems are used to achieve the different goals; usually, Web content management systems (WCMS) are used for the outer appearance and learning management systems (LMS) for transfer and examination of knowledge. Although these systems use the same medium (i.e., the WWW), it can be stated that often there is a heterogeneous landscape of systems. Resultant is the object of investigation of the present chapter. The chapter analyses the challenges concerning the integration of public Web sites and LMS a typical European university has to face.

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.

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