Educational data mining for students' performance based on fuzzy C‐means clustering

Liu, Yu; Gou, Jianping; Fan, Zongwen

doi:10.1049/joe.2019.0938

Cited by 17 publications

(13 citation statements)

References 36 publications

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“…Como dito na Etapa 1, obteve-se 50 trabalhos selecionados resultantes das buscas realizadas (Tabelas 3 e 4). E40 [Limsathitwong et al 2018] E27 [Jamesmanoharan et al 2014] E41 [Murakami et al 2018] E28 [Yukselturk et al 2014] E42 [Perez et al 2018] E29 [Pradeep et al 2015] E43 [Solis et al 2018] E30 [Marbouti et al 2016] E44 [Alban and Mauricio 2019] E31 [Meedech et al 2016] E45 [Li et al 2019] E32 [Ahuja and Kankane 2017] E46 [Beltran et al 2019] E33 [Pereira and Zambrano 2017] E47 [Lottering et al 2020] E34 [Rocha et al 2017] E48 [Utari et al 2020] E35 [Rodrıguez-Maya et al 2017] E49 [Viloria et al 2020] E36 [Rovira et al 2017] E50 [Yaacob et al 2020 Como mostram as Tabelas 3 e 4, dentre os trabalhos selecionados, 22 são nacionais e 28 internacionais. Pode-se dizer que há um equilíbrio entre as quantidades de trabalhos encontrados para cada cenário.…”

Section: Resultados E Discussõesunclassified

Uma análise de trabalhos de mineração de dados educacionais no contexto da evasão escolar

Santos

Saraiva

Oliveira

2021

Anais Do XXXII Simpósio Brasileiro De Informática Na Educação (SBIE 2021)

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Este artigo apresenta uma análise de trabalhos de mineração de dados educacionais (Educational Data Mining - EDM) nacionais e internacionais que tratam da temática da evasão escolar. As buscas por trabalhos foram realizadas com o propósito de responder a sete questões de pesquisa sobre ferramentas/bibliotecas, algoritmos e bases de dados considerados nos trabalhos. Dada a grande quantidade de informações analisadas, foi utilizada a ferramenta Tableau para uma melhor compreensão e comparação dos fatores estudados. Os resultados servem como subsídio para otimizar a tomada de decisão de analistas de dados no planejamento e desenvolvimento de projetos de EDM.

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Section: Resultados E Discussõesunclassified

Uma análise de trabalhos de mineração de dados educacionais no contexto da evasão escolar

Santos

Saraiva

Oliveira

2021

Anais Do XXXII Simpósio Brasileiro De Informática Na Educação (SBIE 2021)

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“…Studies under EDM proposed several data mining approaches ( Chanlekha and Niramitranon 2018 ; Kaviyarasi and Balasubramanian 2018 ; Predić et al, 2018 ; Zaffar et al, 2018 ; Fernandes et al, 2019 ; Inyang et al, 2019 ; Li et al, 2019 ) to predict academic performance. In contrast to AIED, specific subject-related data were collected (e.g.…”

Section: Related Workmentioning

confidence: 99%

Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation

Afzaal

Nouri

Zia

et al. 2021

Front. Artif. Intell.

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Formative feedback has long been recognised as an effective tool for student learning, and researchers have investigated the subject for decades. However, the actual implementation of formative feedback practices is associated with significant challenges because it is highly time-consuming for teachers to analyse students’ behaviours and to formulate and deliver effective feedback and action recommendations to support students’ regulation of learning. This paper proposes a novel approach that employs learning analytics techniques combined with explainable machine learning to provide automatic and intelligent feedback and action recommendations that support student’s self-regulation in a data-driven manner, aiming to improve their performance in courses. Prior studies within the field of learning analytics have predicted students’ performance and have used the prediction status as feedback without explaining the reasons behind the prediction. Our proposed method, which has been developed based on LMS data from a university course, extends this approach by explaining the root causes of the predictions and by automatically providing data-driven intelligent recommendations for action. Based on the proposed explainable machine learning-based approach, a dashboard that provides data-driven feedback and intelligent course action recommendations to students is developed, tested and evaluated. Based on such an evaluation, we identify and discuss the utility and limitations of the developed dashboard. According to the findings of the conducted evaluation, the dashboard improved students’ learning outcomes, assisted them in self-regulation and had a positive effect on their motivation.

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“…It could help the teacher to create a teaching method that can benefit the students to perform better and to increase and sustain the number of student and at the same time can prevent them from leaving the school unexpected (Li, Gou, & Fan, 2019). It could help the management to find a way to reduce the number of potential students who are low achiever as early as possible.…”

Section: Comparison Quizzes Is Math Against All Subjectsmentioning

confidence: 99%

Predicting Student Performance using Data Mining Techniques in Libyan High Schools

Saleh

Palaniappan

Abdalla³

2021

Edukasi

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Student performance in schools have been always the key factor for the teacher ability to teach and what brings good reputation to the school. Recently schools in Libya are facing an issue trying to figure out why students perform poorly in certain subjects and how can they know how they will perform next in the future in coming semesters in perspective subject. There are several methods proposed to predict the studentâ€™s performance, using data mining. This paper proposes using Math and English as key factors to predict the performance of the students. results and findings of the presented method in terms of predicting studentsâ€™ performance based on their grades in Math and English. The results are divided in to three main sections clustering analysis using k-mean algorithm, classification analysis was done using two rounds first using Gain Ratio Evaluations to find out the top attributes that used by J84 algorithm in second round of classification, and rule association analysis using A priori algorithm. Rule association analysis is applied for the clusters generate by clustering analysis to generate the rules associated with each cluster. For each section, a list of inputs is presented with the scale used for the values followed by the results of the algorithm and explanation for the finding.

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Educational data mining for students' performance based on fuzzy C‐means clustering

Cited by 17 publications

References 36 publications

Uma análise de trabalhos de mineração de dados educacionais no contexto da evasão escolar

Uma análise de trabalhos de mineração de dados educacionais no contexto da evasão escolar

Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation

Predicting Student Performance using Data Mining Techniques in Libyan High Schools

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