scholarly journals Educational Data Mining To Improve The Academic Performance in Higher Education

2020 ◽  
Vol 4 (2) ◽  
pp. 13-18
Author(s):  
Alyaa A. Mahdi
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Business Processes ◽  
Educational Data Mining ◽  
Educational Institutions ◽  
Learning And Teaching ◽  
Global Innovation ◽  
Daily Data ◽  
E Learning ◽  
Innovative Techniques

Globalization and Innovation are mainly consider the great interest public sector and private business in the world especially in the higher education institutions. Educational Data Mining is mainly one of the business processes nowadays that attempt to bring the global innovation through improving and enhancing their processes and procedures to fulfill all the requirements and needs of the students as well as the institutions. The Educational Data Mining considered mostly concern with any research concerning the applications of the data mining and developing innovative techniques for data mining (DM) in the educational sector. This study mainly combined the use of the powerful online E-learning management system (Moodle) with data mining tools to improve the performance and effectiveness of the learning and teaching manners by using the innovative daily data that collected from the educational institutions.

scholarly journals A Study of the Applications of Data Mining Techniques in Higher Education

2015 ◽  
pp. 1-4
Author(s):  
SUSHIL VERMA ◽  
R. S. THAKUR ◽  
SHAILESH JALORI
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Business Processes ◽  
Large Data ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Higher Education System ◽  
Student’S Performance ◽  
Quality In Higher Education

Data mining is used to extract meaningful information and to develop significant relationships among variables stored in large data set. Few years ago, the information flow in education field was relatively simple and the application of technology was limited. However, as we progress into a more integrated world where technology has become an integral part of the business processes, the process of transfer of information has become more complicated. Today, one of the biggest challenges that educational institutions face is the explosive growth of educational data and to use this data to improve the quality of managerial decisions and student’s performance. The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of Unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students’ performance. The paper aims to purpose the use of Data mining techniques to improve the efficiency of higher educational institutions. If data mining techniques such as clustering, dicision tree and association can be applied to higher education processes, it can help improve student’s performance.

scholarly journals Predicting Academic Outcomes: A Survey from 2007 Till 2018

2020 ◽  
Author(s):  
Sarah Alturki ◽  
Ioana Hulpuș ◽  
Heiner Stuckenschmidt
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Success ◽  
Academic Outcomes ◽  
Educational Data Mining ◽  
Dropout Rate ◽  
Educational Institutions ◽  
Learning Material ◽  
The Past ◽  
Extract Information

Abstract The tremendous growth of educational institutions’ electronic data provides the opportunity to extract information that can be used to predict students’ overall success, predict students’ dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students’ needs, and much more. This paper aims to review the latest trends in predicting students’ performance in higher education. We provide a comprehensive background for understanding Educational Data Mining (EDM). We also explain the measures of determining academic success and highlight the strengths and weaknesses of the most common data mining (DM) tools and methods used nowadays. Moreover, we provide a rich literature review of the EDM work that has been published during the past 12 years (2007–2018) with focus on the prediction of academic performance in higher education. We analyze the most commonly used features and methods in predicting academic achievement, and highlight the benefits of the mostly used DM tools in EDM. The results of this paper could assist researchers and educational planners who are attempting to carry out EDM solutions in the domain of higher education as we highlight the type of features that the previous researches found to have significant impact on the prediction, as well as the benefits and drawbacks of the DM methods and tools used for predicting academic outcomes.

scholarly journals USE OF E-LEARNING TOOLS ON HIGHER EDUCATION INSTITUTIONS

2020 ◽  
Vol 8 (12) ◽  
pp. 805-810
Author(s):  
Alka Sharma ◽  
◽  
Hina Jain Gupta ◽  
Keyword(s):  
Higher Education ◽  
Quality Education ◽  
Educational Institutions ◽  
Learning Tools ◽  
Higher Educational Institutions ◽  
E Learning ◽  
Higher Educational ◽  
Almost All ◽  
The Impact ◽  
Quantitative And Qualitative Methods

In the last two decades, technology has evolved at a great pace and has influenced almost all spheres of life and education is no exception to it. Nowadays, most of the educational institutions are using various tools and equipments to impart education to the students. This paper has tried to explore the impact of e-education tools on thestudents in higher educational institutions. The sample consists of students enrolled in higher educational institutions. Both quantitative and qualitative methods have been adopted for data collection including questionnaires, semi-structured &open-ended interviews. Use of computer and internet was found to be one of the most important e-learning tools. The findings are expected to assist the higher educational institutions in framing their policies to impart quality education to the students.

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.

Harnessing Cloud Computing Services for E-Learning Systems in Higher Education

2019 ◽  
Vol 15 (2) ◽  
pp. 18-30
Author(s):  
Abderrahim El Mhouti ◽  
Mohamed Erradi
Keyword(s):  
Higher Education ◽  
Cloud Computing ◽  
Learning Systems ◽  
Learning And Teaching ◽  
Computing Services ◽  
E Learning ◽  
Cloud Computing Services ◽  
Learning Platforms ◽  
Basic Infrastructure ◽  
Common Architecture

The use of e-learning suggests the use of ICT to enhance the quality of learning and teaching. However, many higher education institutions, does not have e-learning platforms, resources and infrastructure necessary to implement this type of training. This is due to the need for high cost of basic infrastructure and applications challenges related projects it has to face. This article puts forward an overview on what is the current state of the use of cloud computing in e-learning in higher education context, where the use of computers is increasingly intensive. The article analyzes e-learning systems challenges and trends, the convenience of cloud computing for e-learning and the key benefits of e-learning on the cloud. The article exposes also some application solutions using cloud computing in e-learning for higher education, by presenting the most common architecture that has been adopted. Finally, this article discusses issues related to the implementation of cloud-based e-learning systems and presents some potential ways to overcome them.

scholarly journals Innovative practices in ODL — the experience of Open University Malaysia

2012 ◽  
Vol 7 (1) ◽  
pp. 79-85 ◽  
Author(s):  
Chiam Chooi Chea ◽  
Lim Tick Meng ◽  
Phang Siew Nooi
Keyword(s):  
Higher Education ◽  
Teaching And Learning ◽  
Internet Technology ◽  
Knowledge Generation ◽  
Innovative Technology ◽  
Open University ◽  
Learning And Teaching ◽  
Face To Face ◽  
E Learning ◽  
Face To Face Learning

With the advancements in communications technology brought about by the advent of the Internet and World Wide Web, attention has been drawn to Open and Distance Learning (ODL) as a mode for teaching and learning. In Malaysia, the establishment of ODL universities such as Open University Malaysia (OUM) has expanded the role of ICT in learning and knowledge generation. By leveraging on Internet technology, ODL universities are able to transmit education across the country and even globally. ODL sets about making quality e-learning and e-content more accessible to both facilitators and learners. Utilising this method, new opportunities are continuously created to make higher education more accessible to those who seek to improve and upgrade themselves. This paper examines OUM's practice of using the innovative technology of online learning and teaching to make higher education easily accessible to those that seek it. With greater advancements in technology, the future of higher education may lie more with ODL than with traditional face-to-face learning.

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.

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