An Educational Data Mining Application by Using Multiple Intelligences

Aksoy, Esra; Narli, Serkan; Aksoy, Mehmet Akif

doi:10.4018/978-1-7998-0249-5.ch005

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Second section is quizzes grades of subjects. Next Association rules analysis that contains four sections (Aksoy, Narli, & Aksoy, 2020; M. M. Baroud, Hashim, Ahsan, Zainal, & Khalaff, 2020). First section is Math exam comparison against all subjects, second section is English exam comparison against all subjects, third section is Math quiz comparison against all subjects, fourth section is English quiz comparison against all subjects, following sections present the analysis.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Predicting Student Performance using Data Mining Techniques in Libyan High Schools

Saleh

Palaniappan

Abdalla³

2021

Edukasi

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“…[226] Kişi serbest iradesiyle aramaya rıza gösterdiğinde, durum tutanağa bağlanmalı ve ilgili kişinin de imzası alınmalıdır. [227] Bu durumda kişi, üst, eşya, konut vb. hangi arama tedbirine rıza gösterdiğini de beyan etmeli ve bu kapsamda arama işlemi yapılmalıdır.…”

Section: Ceza Muhakemesiunclassified

Ceza Muhakemesi Hukukunda Arama Koruma Tedbirinde Belirlilik İlkesi

Çöpoğlu

2019

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Ceza muhakemesi hukukunda yer alan arama koruma tedbiri, başta özel hayatın gizliliğine saygı gösterilmesi ve konut dokunulmazlığı olmak üzere bazı temel hak ve özgürlüklere doğrudan müdahale eder. Bu sebeple, temel hak ve özgürlüklerin korunması, arama tedbirinin keyfi uygulanmasının önüne geçilmesi için, arama tedbirinde belirlilik ilkesine uyularak, tedbirin sınırlarının çizilmesi zorunludur. Aksi uygulama, arama tedbirinin ve bundan elde edilen delillerin hukuka aykırı hale gelmesine yol açar.

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“…A large amount of student data can be assessed and measured in many ways for research objectives. EDM is an emerging discipline used in the educational environment to deal with big student academic data and extract useful information through data mining techniques [1,2]. Another term is learning analytics which is closely related to educational data mining.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Student’s Academic Performance through Data Mining Approach

Hussain,

Akbar,

Hassan

et al. 2024

JIWE

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The universities and institutes produce a large amount of student data that can be used in a disciplinary way and useful information can be extracted by using an automated approach. Educational Data Mining (EDM) is an emerging discipline used in the educational environment to deal with big student data and extract useful information. The data mining of students’ data can help the At-risk students as well as the stakeholders by the early warning. This study aims to predict the performance of the students based on student-related data to increase the overall performance. In existing studies, insufficient attributes and complexity of network models is a problem. The student’s current records and grades need to be analyzed. In this approach, the Levenberg Marquardt Algorithm (MLA) deep learning algorithm is used. The data consists of the class test, attendance, assignment and midterm scores. The neural network model consists of four input variables, three hidden and one output layer. The performance of the deep neural network is evaluated by accuracy, precision, recall and F1 score. The proposed model gained a higher accuracy of 88.6% than existing studies. The study successfully predicts the student's final grades using current academic records. This research will be beneficial to the students, educators and educational authorities as a whole.

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An Educational Data Mining Application by Using Multiple Intelligences

Cited by 3 publications

References 26 publications

Predicting Student Performance using Data Mining Techniques in Libyan High Schools

Predicting Student Performance using Data Mining Techniques in Libyan High Schools

Ceza Muhakemesi Hukukunda Arama Koruma Tedbirinde Belirlilik İlkesi

Prediction of Student’s Academic Performance through Data Mining Approach

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