A review on identifying influencing factors and data mining techniques best suited for analyzing students' performance

Marwaha, Anu; Ahuja, Sachin

doi:10.1109/icbdaci.2017.8070866

Cited by 3 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is found that 22 out of the 33 review articles were not characterized as systematic reviews because they did not illustrate the detailed process of the examined data collections. These non-systematic review articles mainly introduced and reviewed EDM methods and tools (Salter et al , 2017; Anoopkumar and Rahman, 2016; Venkatachalapathy et al , 2017; Sukhija et al , 2015; Bonde and Kirange, 2018; Sachin and Vijay, 2012; Roy and Garg, 2018; Ganesh and Christy, 2015; Marwaha and Ahuja, 2017; Zaffar et al , 2018; Burman et al , 2017), data sources and main applications (Romero and Ventura, 2007; Romero and Ventura, 2010; Romero and Ventura, 2013; Baker and Kisor, 2009; Algarni, 2016). They claimed that EDM methods included not only prediction, clustering, relationship mining, distillation of data for human judgment and discovery with models but also outlier detections, text mining and social network analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Educational data mining: a systematic review of research and emerging trends

Yang

Hung

et al. 2020

IDD

View full text Add to dashboard Cite

Purpose Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education. Design/methodology/approach This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research. Findings Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward. Research limitations/implications Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals. Originality/value This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Educational data mining: a systematic review of research and emerging trends

Yang

Hung

et al. 2020

IDD

View full text Add to dashboard Cite

show abstract

“…A aplicação de métodos de mineração de dados e aprendizagem de máquina na educação tem sido vista como um campo interdisciplinar emergente. Essa novaárea de pesquisaé chamada de Mineração de Dados Educacionais (Educational Data Mining -EDM) [Devasia et al 2016, Marwaha and Ahuja 2017, Hegde and Prageeth 2018. A EDMé usada para estudar os dados disponíveis no contexto educacional e extrair valor das informações ocultas em bases de dados de instituições de ensino.…”

Section: Mineração De Dados E Descoberta De Conhecimentosunclassified

Uma proposta para predição de risco de evasão de estudantes em um curso técnico em informática

Saraiva¹,

Pereira²,

Gallindo³

et al. 2019

Anais Do Workshop Sobre Educação Em Computação (WEI)

View full text Add to dashboard Cite

O alto ı́ndice de evasão em cursos de tecnologia é um desafio enfrentado por muitas instituições de ensino. Por isso, identificar os estudantes em situação de risco de evasão é relevante para que possam ser realizadas ações de incentivo à permanência e êxito. Este trabalho apresenta uma proposta para predição de evasão considerando informações acadêmicas e socioeconômicas de estudantes. Em particular, uma das etapas da proposta engloba a aplicação de algoritmos de Aprendizagem de Máquina. Um estudo de caso é apresentado considerando uma base de dados de 8 anos de um curso Técnico em Informática do IFCE. Os resultados demonstram a eficácia da proposta, alcançando 97,97% de taxa de acerto na previsão da situação dos estudantes.

show abstract

Education Analytics: Challenges and Approaches

Zhang

Li²

2018

2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)

View full text Add to dashboard Cite

A review on identifying influencing factors and data mining techniques best suited for analyzing students' performance

Cited by 3 publications

References 27 publications

Educational data mining: a systematic review of research and emerging trends

Educational data mining: a systematic review of research and emerging trends

Uma proposta para predição de risco de evasão de estudantes em um curso técnico em informática

Education Analytics: Challenges and Approaches

Contact Info

Product

Resources

About