Big Data in Educational Data Mining andLearning Analytics

@article{Prakash2015BigDI,
  title={Big Data in Educational Data Mining andLearning Analytics},
  author={B. R. Prakash and Dr. M. Hanumanthappa and Vasantha Kavitha},
  journal={International Journal of Innovative Research in Computer and Communication Engineering},
  year={2015},
  volume={2},
  pages={7515-7520}
}
Educational data mining and learning analytics are used to research and build models in several areas that can influence learning systems. Higher education institutions are beginning to use analytics for improving the services they provide and for increasing student grades and retention. With analytics and data mining experiments in education starting to proliferate, sorting out fact from fiction and identifying research possibilities and practical applications are not easy. This issue brief is… Expand
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References

SHOWING 1-10 OF 15 REFERENCES
Learning analytics and educational data mining: towards communication and collaboration
TLDR
This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields. Expand
Educational Data Mining: A Review of the State of the Art
TLDR
The most relevant studies carried out in educational data mining to date are surveyed and the different groups of user, types of educational environments, and the data they provide are described. Expand
Mining Sakai to Measure Student Performance : Opportunities and Challenges in Academic Analytics ( Research in Progress )
In this paper we lay out ongoing work on the Open Academic Analytics Initiative (OAAI) 1 , a project aimed at developing, deploying and releasing an open-source environment for academic analyticsExpand
A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks
TLDR
A scalable data mining method is developed, whose objective is to infer information on the collaboration during the collaboration process in a domain-independent way and to improve collaboration process management and learning in an open collaborative educational web environment. Expand
A Data Repository for the EDM Community: The PSLC DataShop
TLDR
In recent years, educational data mining has emerged as a burgeoning new area for scientific investigation because of the increasing availability of fine-grained, extensive, and longitudinal data on student learning. Expand
Mining Student Behavior Models in Learning-by-Teaching Environments
Abstract. This paper discusses our approach to building models and analyzing student behaviors in different versions of our learning by teaching environment where students learn by teaching aExpand
Activity sequence modelling and dynamic clustering for personalized e-learning
TLDR
This article presents an approach based on the modeling of learners’ problem solving activity sequences, and on the use of the models in targeted, and ultimately automated clustering, resulting in the discovery of new, semantically meaningful information about the learners. Expand
Using learning analytics to assess students' behavior in open-ended programming tasks
TLDR
This paper logs hundreds of snapshots of students' code during a programming assignment, and employs different quantitative techniques to extract students' behaviors and categorize them in terms of programming experience. Expand
Automatically Detecting a Student's Preparation for Future Learning: Help Use is Key
We present an automated detector that can predict a student’s later performance on a paper test of preparation for future learning, a post-test involving learning new material to solve problemsExpand
Big data: The next frontier for innovation, competition, and productivity
The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth,Expand
...
1
2
...