Emerging trends in applications of big data in educational data mining and learning analytics

2017 ◽  
Author(s):  
Sagardeep Roy ◽  
Shailendra Narayan Singh
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Emerging Trends

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.

Exploring data mining: facets and emerging trends

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sumeer Gul ◽  
Shohar Bano ◽  
Taseen Shah
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Web Mining ◽  
Human Life ◽  
Educational Data Mining ◽  
Knowledge Discovery In Databases ◽  
Information Mining ◽  
Content Type ◽  
Big Data Mining ◽  
Emerging Trends

Purpose Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data. Design/methodology/approach An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field. Findings The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences. Practical implications The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful. Originality/value The paper tries to highlight the current trends and facets of data mining.

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.

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