The Role of Learning Analytic in Education Reform

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruihong Dai ◽  
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Education Reform ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Research Community ◽  
Educational Setting ◽  
Future Performance ◽  
Education Data

In year 2009, the nascent research community of Educational Data Mining (EDM) has been found to continually and increasingly grow. Now the education data mining has become popular and deeply studied in all universities. Specially, in United Kingdom, United State, Canada, they held several conferences annually on learning analytic discussion, which is related with Educational Data Mining. Learning analytics refers to the collection of large volume of data about students in an educational setting and to analyze the data to predict the students' future performance, identify risk and provide recommendations for improvement. LA is an increasingly emerging field, it is necessary for higher education stakeholders to become more familiar with the issues related to LA's use in education. Such a paper provides a brief introduction, methods and benefits, and challenges of LA.

Learning Analytics and Education Data Mining in Higher Education

2021 ◽  
pp. 108-126
Author(s):  
Samira ElAtia ◽  
Donald Ipperciel
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Learning Analytics ◽  
Large Scale ◽  
Educational Data Mining ◽  
Current Situation ◽  
Research Perspective ◽  
Education Data ◽  
The Impact

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.

scholarly journals Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

2016 ◽  
Vol 3 (2) ◽  
pp. 220-238 ◽  
Author(s):  
Paulo Blikstein ◽  
Marcelo Worsley
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Online Courses ◽  
Educational Data Mining ◽  
Multimodal Learning ◽  
Learning Tasks ◽  
Education Data ◽  
Multimodal Learning Analytics ◽  
High Degree ◽  
Learning Analysis

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.

The Role of Learning Analytics in Global Higher Education

2016 ◽  
pp. 282-307 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Higher Education ◽  
Social Networking ◽  
Social Networking Sites ◽  
Learning Analytics ◽  
School Administrators ◽  
Information Age ◽  
Educational Data Mining ◽  
Educational Performance ◽  
Strategic Goals

This chapter presents the role of learning analytics in global higher education, thus illustrating the theoretical and practical overview of learning analytics; learning analytics and educational data mining (EDM); learning analytics and learning management system (LMS); learning analytics and Course Signals; learning analytics and knowledge perspectives; learning analytics and social networking sites; and the significance of learning analytics in global higher education. The application of learning analytics is critical in global higher education that seeks to serve the school administrators and students, increase educational performance, sustain competitiveness, and fulfill expected accomplishment in global higher education. The chapter argues that applying learning analytics has the potential to improve educational performance and reach strategic goals in the information age.

Educational Data Mining and Learning Analytics in Higher Education

2018 ◽  
Vol 12 (1) ◽  
pp. 85
Author(s):  
Padma Mishra ◽  
Vaishali B ◽  
Sangvikar
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining

scholarly journals AN OVERVIEW OF EDUCATIONAL DATA MINING

2019 ◽  
Vol 1 (1) ◽  
pp. 56-60
Author(s):  
Thanh Ngoc Dan Nguyen ◽  
Vi Thi Thuy Ha
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Predictive Models ◽  
Educational Data Mining ◽  
Effective Management ◽  
Clustering Techniques ◽  
Learning Capacity ◽  
Education Data ◽  
And Training ◽  
Mining Tools

Higher education data is growing, but the exploitation and extraction of meaningful knowledge for management have not been paid much attention. The existing mining tools are not effective. This study aims to introduce three techniques for educational data mining: (1) Classification techniques, (2) Predictive models, (3) Clustering techniques. Simultaneously, the study also proposes some solutions to analyze and visualize data, predict students’ learning capacity and assemble  learners. Thereby, education managers could choose appropriate data mining solutions for effective management and training.

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