Deep Learning applied to Learning Analytics and Educational Data Mining: A Systematic Literature Review

Coelho, Orlando Bisacchi; Silveira, Ismar Frango

doi:10.5753/cbie.sbie.2017.143

Cited by 34 publications

(24 citation statements)

References 21 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Artificial Neural Networks (ANNs) are the most prominent practice utilized in the Educational Data Mining (EDM) domain (Coelho & Silveira, 2017). Although there have been issues associated with ANNs, especially when extracting human-interpretable patterns from the predicted results, most of these concerns were resolved in the last decade, with the emergence of Deep ANNs (Coelho & Silveira, 2017;LeCun et al, 2015).…”

Section: Deep Learning For Educational Data Sciencementioning

confidence: 99%

Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

326

145

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Section: Deep Learning For Educational Data Sciencementioning

confidence: 99%

Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

326

145

View full text Add to dashboard Cite

show abstract

“…In our literature review, we did not find studies that help to analyze student performance based on visualization of a set of transcripts. Some works use data from transcripts to forecast student problems, so they can try to avoid them [4] [5]. CourseViewer [14] visualizes a student transcript as a graph of subjects, subject prerequisites and grades, where subjects are nodes, prerequisites are edges connecting subjects, and node colors represent grades.…”

Section: A Educational Data Miningmentioning

confidence: 99%

Use of reorderable matrices and heatmaps to support data analysis of students transcripts

Barros¹,

Mendes²,

Silva³

2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

View full text Add to dashboard Cite

For a course coordinator, the analysis of several students’ transcripts to identify the situation of subjects or students is often an old-fashioned process executed through a textual and numerical approach. This work is part of a larger project aimed at choosing appropriate visual representations to help course coordinators to analyze sets of students transcripts. In this work, we developed a system that allows the visualization of student transcripts through a heatmap of student grades per subject. The heatmap represent grades based on a user-defined color scale. To assist in the analysis, it is possible to reorder subjects and students using the optimal leaf order algorithm, or even to reorder according to the grades of a specific subject or student. In addition, some features have been developed to meet visual guidelines, such as overview, zoom, filter and details-on-demand.

show abstract

“…Apart from various data analytic techniques, deep artificial neural networks (ANNs) are foremost applied in the prediction of various measures and attributes . Deep Learning, consisting of various forms of neural networks and constituting of several computational layers, facilitates the model to learn from existing instances, overriding the conventional hand‐feature engineering practices .…”

Section: Introductionmentioning

confidence: 99%

“…Substantial evidence of Deep Learning approaches in the learning analytics domain, to predict students' performance, can rarely be observed in the literature. Recently, LeCun and Silveira administered a systematic literature review to examine the studies correlating learning analytics with deep learning techniques. They traced some significant areas, including student performance their learning evaluation and their handwriting recognition, where deep learning techniques surpassed the traditional machine learning and other baseline approaches.…”

Section: Introductionmentioning

confidence: 99%

Virtual learning environment to predict withdrawal by leveraging deep learning

et al. 2019

View full text Add to dashboard Cite

The current evolution in multidisciplinary learning analytics research poses significant challenges for the exploitation of behavior analysis by fusing data streams toward advanced decision-making. The identification of students that are at risk of withdrawals in higher education is connected to numerous educational policies, to enhance their competencies and skills through timely interventions by academia. Predicting student performance is a vital decision-making problem including data from various environment modules that can be fused into a homogenous vector to ascertain decisionmaking. This research study exploits a temporal sequential classification problem to predict early withdrawal of students, by tapping the power of actionable smart data in the form of students' interactional activities with the online educational system, using the freely available Open University Learning Analytics data set by employing deep long short-term memory (LSTM) model. The deployed LSTM model outperforms baseline logistic regression and artificial neural networks by 10.31% and 6.48% respectively with 97.25% learning accuracy, 92.79% precision, and 85.92% recall. K E Y W O R D S classification, deep learning, long short-term memory (LSTM), students-at-risk. smart data, virtual learning environment (VLE) Int J Intell Syst. 2019;34:1935-1952.wileyonlinelibrary.com/journal/int

show abstract

Deep Learning applied to Learning Analytics and Educational Data Mining: A Systematic Literature Review

Abstract: Abstract. This work presents, to the extent of the authors' knowledge, the first systematic literature review of the application of Deep

Cited by 34 publications

References 21 publications

Predicting academic performance of students from VLE big data using deep learning models

Predicting academic performance of students from VLE big data using deep learning models

Use of reorderable matrices and heatmaps to support data analysis of students transcripts

Virtual learning environment to predict withdrawal by leveraging deep learning

Contact Info

Product

Resources

About