Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education

Ray, Santosh Kumar; Saeed, Mohammed

doi:10.1007/978-3-319-76472-6_7

Cited by 56 publications

(46 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Experimental analysis indicates that genetic programming-based model is interpretable with an optimized prediction rate compared to traditional prediction algorithms. The increasing drop-out rates in school, students who earn weak classes of degree, and those who exceed the specified duration of programme motivated the work reported in [2]. Their study explored k-means and self-organizing map (SOM) in mining pieces of knowledge relating to the optimal cluster numbers in students' dataset and the correlation between the selected input features; demographic, pre-admission and first year performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…EMS is a rapidly progressing field of data mining which concentrates on the search and discovery of interestingly new patterns, techniques, tools, and models for intelligent exploratory analysis and visualization of large educational dataset. EMS is aimed at the extraction of novel and interpretable structures that will enhance comprehensibility of students, their processes and environments [2,3]. Among the important modules of EMS are students' management (SM), human resource, infrastructure management, school management, and graduands' management module.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques

Matloob¹,

Aftab²,

Iqbal³

2019

IJMECS

View full text Add to dashboard Cite

Persistent and quality graduation rates of students are increasingly important indicators of progressive and effective educational institutions. Timely analysis of students' data to guide instructors in the provision of academic interventions to students who are at risk of performing poorly in their courses or dropout is vital for academic achievement. In addition there is need for performance attributes relationship mining for the generation of comprehensible patterns. However, there is dearth in pieces of knowledge relating to predicting students' performance from patterns. This therefore paper adopts hierarchical cluster analysis (HCA) to analyze students' performance dataset for the discovery of optimal number of fail courses clusters and partitioning of the courses into groups, and association rule mining for the extraction of interesting course-status association. Agglomerative HCA with Ward's linkage method produced the best clustering structure (five clusters) with a coefficient of 92% and silhouette width 0.57. Apriori algorithm with support (0.5%), confidence (80%) and lift (1) thresholds were used in the extraction of rules with student's status as consequent. Out of the twenty one courses offered by students in the first year, seven courses frequently occur together as failed courses, and their impact on the respective students' performance status were assessed in the rules. It is conjectured that early intervention by the instructors and management of educational activities on these seven courses will increase the students' learning outcomes leading to increased graduation rate at minimum course duration, which is the overarching objective of higher educational institutions.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques

Matloob¹,

Aftab²,

Iqbal³

2019

IJMECS

View full text Add to dashboard Cite

show abstract

“…The digital revolution in developing countries is leading to development of new technologies such as ubiquitous computing devices and the Massive Open Online Courses all of which are radically transforming the mode and accessibility to teaching and learning [4]. These Massive open online courses (MOOC) are generating huge amounts of data that are relevant for Big Data Analytics [21]. [22] Underscores this by stating that the era of cloud and mobile computing is opening up many opportunities for revolutionizing education.…”

Section: Need For Big Data In Academiamentioning

confidence: 99%

Big Data Analytics in Higher Education: A Review

Murumba¹,

Micheni²

2017

THE IJES

View full text Add to dashboard Cite

Big Data provides an opportunity to educational Institutions to use their Information Technology resources strategically to improve educational quality and guide students to higher rates of completion, and to improve student persistence and outcomes. This paper explores the attributes of big data that are relevant to educational institutions, investigates the factors influencing adoption of big data and analytics in learning institutions and seeks to establish the limiting factors hindering use of big data in Institutions of higher learning. The study has been conducted through a desk search and reviewed sources of literature including scientific research journals and reports. The paper is based on desk research. The sources of literature that were reviewed included scientific research articles and journals, conference reports and theses. Online journals found on the internet were also examined with the search being broadened by Google Scholar where the following keywords were used "big data", "developing countries", "education systems" and "clustering". The paper concludes that Big Data is important since it offers Universities opportunities to their Information Technology resources strategically to improve educational quality and guide students, colleges and universities see value in analytics; and therefore recommends that these institutions carry out investments in analytics programs and in people to have relevant data science. This is because Big Data can afford educational institutions opportunities to shape a modern and dynamic education system, in which every individual student can have the maximum benefit from, and can greatly contribute towards improving the quality of education

show abstract

“…A Internet, no contexto do e-learning e do Ensino a Distância (EAD), vem permitindo o uso cada vez mais frequente de Ambientes Virtuais de Aprendizagem (AVA). Estes armazenam um grande volume de dados relativos às interações dos aprendizes nos AVAs e podem ser utilizados como insumo para diversos tipos de análise capazes de extrair informações sobre os processos de aprendizagem, dificuldades e comportamento de cada estudante [Ray & Saeed 2018].…”

Section: Introdução E Fundamentação Teóricaunclassified

Proposta de adequação da arquitetura do AVA Openredu para suporte a técnicas de análise quantitativa de dados educacionais

Vaz¹,

Dourado²,

Gomes³

et al. 2019

Anais Estendidos Do XV Simpósio Brasileiro De Sistemas De Informação (Anais Estendidos Do SBSI 2019)

View full text Add to dashboard Cite

Este trabalho propõe uma adequação para o Ambiente Virtual de Aprendizagem (AVA) Openredu para suportar técnicas de análise quantitativa de dados tais como Mineração de Dados Educacionais (MDE) e Learning Analytics (LA) a partir de um conjunto de requisitos de dados identificados em estudos anteriores. Os resultados obtidos foram (1) a remodelagem da arquitetura de dados da plataforma, que tem como objetivo possibilitar diversas aplicações com os dados armazenados, e sendo uma destas a visualização de dados associados, foi desenvolvido (2) um painel analítico.

show abstract

Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education

Cited by 56 publications

References 48 publications

A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques

A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques

Big Data Analytics in Higher Education: A Review

Proposta de adequação da arquitetura do AVA Openredu para suporte a técnicas de análise quantitativa de dados educacionais

Contact Info

Product

Resources

About