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Educational data mining and learning analytics: An updated survey

C. Romero, S. Ventura

TL;DR

The current state of the art in data mining in education is provided by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, themain objectives, and the future trends in this research area.

Abstract

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.

Educational data mining and learning analytics: An updated survey

TL;DR

The current state of the art in data mining in education is provided by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, themain objectives, and the future trends in this research area.

Abstract

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.
Paper Structure (17 sections, 5 figures, 11 tables)

This paper contains 17 sections, 5 figures, 11 tables.

Table of Contents

  1. INTRODUCTION
  2. BACKGROUND
  3. EDM/LA KNOWLEDGE DISCOVERY CYCLE
  4. Educational Environment
  5. Educational Data
  6. Preprocessing
  7. Methods and techniques
  8. Interpretation and application of the new knowledge
  9. EDUCATIONAL ENVIRONMENTS AND DATA
  10. Traditional face-to-face education
  11. Computer-based educational systems
  12. Blended Learning Systems
  13. TOOLS AND DATASETS
  14. METHODS AND APPLICATIONS
  15. CONCLUSIONS AND FUTURE TRENDS
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Main areas related to EDM/LA.
  • Figure 2: Number of papers and main events about EDM/LA terms in Google Schoolar by year (01-01-2019).
  • Figure 3: EDM/LA knowledge discovery cycle process.
  • Figure 4: Different levels of granularity and their relationship to the amount of data.
  • Figure 5: Types of educational environments and systems.