scholarly journals Editorial: Datasets for Learning Analytics

2016 ◽  
Vol 3 (2) ◽  
pp. 307-311 ◽  
Author(s):  
Stefan Dietze ◽  
George Siemens ◽  
Davide Taibi ◽  
Hendrik Drachsler
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Special Section ◽  
Educational Data Mining ◽  
Data Sets ◽  
Mining Communities ◽  
New Research ◽  
Machine Readable ◽  
Access Data ◽  
Research Analysis

The European LinkedUp and LACE (Learning Analytics Community Exchange) project have been responsible for setting up a series of data challenges at the LAK conferences 2013 and 2014 around the LAK dataset. The LAK datasets consists of a rich collection of full text publications in the domain of Learning Analytics and Educational Data Mining. The LAK dataset offers publicly available, machine-readable versions of research articles from the Learning Analytics and Educational Data Mining communities in various formats, where the main goal is to facilitate research, analysis, and smart explorative applications. Based on the insights gained from these data challenges, the idea was born to make more Learning Analytics data sets publicly available for researchers to get to a more open access data-driven research community within Learning Analytics. With this special section, we publish four data sets that answered the call for data sets by the journal. It is our vision to collect more data sets like these in this initial collection and reward their creators through citing the datasets and connecting new research outcomes to them.

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.

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.

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

Educational data mining meets learning analytics

2012 ◽  
Author(s):  
Ryan S. J. d. Baker ◽  
Simon Buckingham Shum ◽  
Erik Duval ◽  
John Stamper ◽  
David Wiley
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining
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