Educational Data Mining Techniques and Applications

2021 ◽  
pp. 234-251
Author(s):  
M. Govindarajan
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
At Risk Students ◽  
Educational Data Mining ◽  
Learning Needs ◽  
Institutional Performance ◽  
Clear Cut ◽  
Curriculum Renewal ◽  
Improving Teaching And Learning

Educational data mining (EDM) creates high impact in the field of academic domain. EDM is concerned with developing new methods to discover knowledge from educational and academic database and can be used for decision making in educational and academic systems. EDM is useful in many different areas including identifying at risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This chapter discusses educational data mining, its applications, and techniques that have to be adopted in order to successfully employ educational data mining and learning analytics for improving teaching and learning. The techniques and applications discussed in this chapter will provide a clear-cut idea to the educational data mining researchers to carry out their work in this field.

Educational Data Mining Review

2017 ◽  
pp. 149-165 ◽  
Author(s):  
Rashmi Agrawal ◽  
Neha Gupta
Keyword(s):  
Data Mining ◽  
Network Formation ◽  
At Risk Students ◽  
Educational Data Mining ◽  
Learning Needs ◽  
Institutional Performance ◽  
Data Mining Techniques ◽  
Curriculum Renewal ◽  
Analysis Of Social Networks ◽  
Educational Field

In today's era, educational data mining is a discipline of high importance for teaching enhancement. EDM techniques can reveal useful information to educators to help them design or modify the structure of courses. EDM techniques majorly include machine learning and data mining techniques. In this chapter of the book, we will deliberate upon various data mining techniques that will help in identifying at-risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, optimizing subject curriculum renewal. Various applications of data mining are also discussed by quoting example of various case studies. Analysis of social networks in educational field to understand student network formation in classrooms and the types of impact these networks have on student is also discussed.

scholarly journals Privacy concerns in educational data mining and learning analytics

2020 ◽  
Vol 28 ◽  
Author(s):  
Isak Potgieter
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Educational Data Mining ◽  
Analysis Data ◽  
Privacy Concerns ◽  
Ethical Implications ◽  
Individual Privacy ◽  
Future Of Education ◽  
The Impact

Education at all levels is increasingly augmented and enhanced by data mining and analytics, catalysed by the growing prevalence of automated distance learning. With an unprecedented capacity to scale both horizontally (individuals reached) and vertically (level of analysis), data mining and analytics are set to be a transformative part of the future of education. We reflect on the assumptions behind data mining and the potential consequences of learning analytics, with reference to an issue brief prepared for the U.S. Department of Education entitled Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics. We argue that the associated gains conceal subtle, but important risks. Data-ism, an underpinning paradigm, assigns unjustified veracity to data-driven science and the application of personalised analytics may compromise individual privacy, agency and inventiveness. This holds serious ethical implications, particularly when considering the impact on minors, rendering wholesale adoption premature.

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.

scholarly journals Educational Data Mining and Learning Analytics

2014 ◽  
pp. 61-75 ◽  
Author(s):  
Ryan Shaun Baker ◽  
Paul Salvador Inventado
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining
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