A Review on Predicting Student's Performance Using Data Mining Techniques
TLDR
An overview on the data mining techniques that have been used to predict students performance and how the prediction algorithm can be used to identify the most important attributes in a students data is provided.About:
This article is published in Procedia Computer Science.The article was published on 2015-01-01 and is currently open access. It has received 558 citations till now. The article focuses on the topics: Educational data mining.read more
Citations
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Journal ArticleDOI
Predicting Academic Performance of Students from VLE Big Data using Deep Learning Models
TL;DR: A deep artificial neural network is deployed on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures for early intervention of such cases, to assist institutes in formulating a necessary framework for pedagogical support.
Journal ArticleDOI
Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods
TL;DR: There is a strong relationship between learner’s behaviors and their academic achievement, and the proposed model based on data mining techniques with new data attributes/features, which are called student's behavioral features proves the reliability of this proposed model.
Journal ArticleDOI
Predicting academic success in higher education: literature review and best practices
Eyman Alyahyan,Dilek Düştegör +1 more
TL;DR: This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success, and will provide to educators an easier access to datamining techniques, enabling all the potential of their application to the field of education.
Proceedings ArticleDOI
Predicting academic performance: a systematic literature review
Arto Hellas,Petri Ihantola,Andrew Petersen,Vangel V. Ajanovski,Mirela Gutica,Timo Hynninen,Antti Knutas,Juho Leinonen,Chris Messom,Soohyun Nam Liao +9 more
TL;DR: In this paper, the authors present a systematic literature review of work in the area of predicting student performance, which shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used.
Journal ArticleDOI
Educational Data Mining: A review of evaluation process in the e-learning
TL;DR: The review of EDM research of the teaching and learning process considering the educational perspective allowed to present perspectives, identify trends and observe potential research directions, such as behavioral research, collaboration, interaction and performance in the development of teaching-learning activities.
References
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Journal ArticleDOI
Educational Data Mining: A Review of the State of the Art
TL;DR: 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.
Journal ArticleDOI
Systematic literature reviews in software engineering - A tertiary study
Barbara Kitchenham,Rialette Pretorius,David Budgen,O. Pearl Brereton,Mark Turner,Mahmood Niazi,Stephen Linkman +6 more
TL;DR: SLRs appear to have gone past the stage of being used solely by innovators but cannot yet be considered a main stream software engineering research methodology, such as often failing to assess primary study quality.
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Predicting students' final performance from participation in on-line discussion forums
TL;DR: To determine how the selection of instances and attributes, the use of different classification algorithms and the date when data is gathered affect the accuracy and comprehensibility of the prediction, a new Moodle module for gathering forum indicators was developed and different executions were carried out.
Posted Content
Data Mining Approach for Predicting Student Performance
Edin Osmanbegovic,Mirza Suljic +1 more
TL;DR: In this article, different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment.
Proceedings ArticleDOI
Visualizing patterns of student engagement and performance in MOOCs
TL;DR: An exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs is reported on, able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear.