scholarly journals Research Review on Big Data Usage for Learning Analytics and Educational Data Mining: A Way Forward to Develop an Intelligent Automation System

2021 ◽  
Vol 1898 (1) ◽  
pp. 012044
Author(s):  
A Yunita ◽  
H B Santoso ◽  
Z A Hasibuan
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Research Review ◽  
Automation System ◽  
Data Usage

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 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?

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.

Learning Analytics

2018 ◽  
pp. 5158-5168
Author(s):  
Constanţa-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Radu Ioan Mogos ◽  
Stelian Stancu
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Future Research ◽  
Educational Systems ◽  
Main Section ◽  
Research Directions ◽  
Data Intensive ◽  
Academic Analytics ◽  
Future Research Directions

Reinforcement of the technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The article starts by defining LA, with relevant views on the literature. A discussion about the relationships between LA, educational data mining and academic analytics is included in the background section. In the main section of the article, the learning analytics, as an emerging trend in the educational systems is describe, by discussing the main issues, controversies, problems on this topic. Final part of the article presents the future research directions and the conclusion.

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