Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education

2018 ◽  
pp. 135-160 ◽  
Author(s):  
Santosh Ray ◽  
Mohammed Saeed
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Learning Analytics ◽  
Educational Data Mining

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.

Big Data in Educational Data Mining and Learning Analytics

2014 ◽  
Vol 02 (12) ◽  
pp. 7515-7520 ◽  
Author(s):  
B R Pra kash ◽  
Dr.M. Hanuman thappa ◽  
Vasantha Kavitha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Learning Analytics ◽  
Educational Data Mining

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

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.

Big Data Analyses in the Community of Inquiry and Educational Research Spheres

2018 ◽  
pp. 34-52
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Educational Research ◽  
Learning Analytics ◽  
Community Of Inquiry ◽  
Educational Data Mining ◽  
Data Analyses ◽  
Data Volume ◽  
Learning Data

Chapter 3 builds on the previous chapters and provides a summary of big data-style research within the Community of Inquiry scholarly literature, as well as examples from educational research broadly. This chapter also connects to the broader topics of machine learning, data analytics, learning analytics, and educational data mining. Constructs from the Community of Inquiry are integrated into this synthesis and overview. Unfortunately, only a fraction of the studies in educational research broadly today exhibit the tell-tale signs of big data: data volume and variety, new environments or instrumented sources of larger data, often with emerging tools and platforms critical to the analysis of the resulting datasets. A list of additional readings is provided.

Modeling the Humanities: Data Lessons from the World of Education

2016 ◽  
Vol 10 (1) ◽  
pp. 51-62
Author(s):  
Armanda Lewis
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Best Practices ◽  
Learning Analytics ◽  
Qualitative Data ◽  
Educational Data Mining ◽  
Data Modeling ◽  
Mixed Methods Approach ◽  
The World ◽  
Modeling Practices

This article will explore advances in the field of educational data modeling that have implications for modeling humanistic data. Traditional humanistic inquiry, bolstered by micro-analyses conducted by the scholar, has made way for machine-assisted methods that parse and quantify large amounts of qualitative data to reveal possible trends and focus more analogue approaches. At best, this play between human- and machine-directed approaches can lead to more profound explorations of texts. In this exploration of qualitative-quantitative methodologies that leverage human agency and machine-directed techniques, I suggest a mixed methods approach for dealing with the humanities. Specifically, this discussion will analyze the current methodological tensions related to Educational Data Mining and Learning Analytics to reveal best practices for modeling humanistic data. Principle questions of interest in this essay include: What defines literary “big” data? How can we define DH modeling and where does it depart from traditional data modeling? What role does machine-based modeling have in the context of the scholarly close read? What can we learn from educational data modeling practices that are in the midst of resolving tensions between human-machine patterning?

Sign in / Sign up

Export Citation Format

Share Document