The Application of Data Mining for Predicting Academic Performance Using K-means Clustering and Naïve Bayes Classification

Ali, Zainab Mohammed; Hassoon, Noor Hasan; Ahmed, Wasan Saad; Abed, Hazim Noman

doi:10.37200/ijpr/v24i3/pr200962

Cited by 10 publications

(8 citation statements)

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“…𝑘-mean is one of the most popular and applied methods. It is applied in education to group duplicate data into the same group for improvement of teaching and learning performance and academic achievement [29][30][31], such as learning courses, learning styles, student's behaviour, and other related aspects.…”

Section: Methodsmentioning

confidence: 99%

Educational Engineering for Models of Academic Success in Thai Universities During the COVID-19 Pandemic: Learning Strategies for Lifelong Learning

Nuankaew

2021

Int. J. Eng. Ped.

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The Coronavirus epidemic 2019 has a serious impact on the education system of Thailand. Therefore, the research aims (1) to study and compare the academic achievement of higher education students affected by the situation of Corona Virus Disease 2019 (COVID-19) epidemic, (2) to construct an academic achievement model with educational engineering technology to support the learning management process of higher education institutions, and (3) to evaluate the academic achievement model. The research approaches were carried out according to the theory of data mining development using the CRISP-DM methodology (Cross-Industry Standard Process for Data Mining). The data collection was divided into two main parts according to the educational situation. The first part is a normal situation with data collected from 506 students form four courses during the second semester in the academic year of 2019. On the other hand, the second part is an abnormal situation with data collected from 475 students from four courses during the first semester in the academic year of 2020. From the research results and findings, the researchers believe that both of the traditional and online teaching and learning management enabled learners to make an academic achievement. It is imperative that the teacher is aware of the learner’s importance to graduate on time. For the future, the researcher needs to present the findings to the stakeholders in order to prepare for the unexpected situation.

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Section: Methodsmentioning

confidence: 99%

Educational Engineering for Models of Academic Success in Thai Universities During the COVID-19 Pandemic: Learning Strategies for Lifelong Learning

Nuankaew

2021

Int. J. Eng. Ped.

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“…Techniques Highest accuracy appeared [43] J48 100% [46] NavieBayes(NB) 98.86% [40] X-Means 86.17% [35] Support Vector Machine (SVM) 97.98% [14] Ctree 90.37% [49] Decision Tree(DT) 67% [38] Random Forest(RF) 96.4% [34] Logistic Regression(LA) 96.98% [45] Neural Network(NN) 96% [33] K-means 98.9% [36] Rule-Based 71.3% [6] CART 98.3% [4] RepTree 61.4% [6] Iterative Dichotomiser 3(ID3) 95.9% [39] IBK 82.1% [39] Simple Logistic 93.27% [44] JRip 83.46% [35] K-Medoids 84.04%…”

Section: Discussionmentioning

confidence: 99%

“…The study in 2020 [33] by Zainab Mohammed et al used WEKA tool to predict the academic instructors' performance by Using K-means Clustering and Naive Bayes classifications. They used data set at the UCI website that contained a total of 5820 evaluation scores provided by the university students for the evaluation of the academic instructors' performance and attributes such as instructor's name, course code values, and the course attendance rate.…”

Section: A Prediction Of Students' Performancementioning

confidence: 99%

A Comparison of EDM Tools and Techniques

Alshehri¹,

Alhakami²,

Baz³

et al. 2020

IJACSA

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“…It is emphasized that machine learning is multidisciplinary approach in the field of higher education management system and having a broad concern about knowledge discovery and pattern identification. The researcher developed a proposed model using Naïve Bayes classifier to predict the quality of education to evaluate the rules which are studied for educational performance (Ali et al, 2020) This research studies on educational data mining and discovers a new knowledge for academic counseling and improvement using clustering algorithms on educational data. This research study represents the Naïve Bayes classification approach to predict students' performance (Dake and Gyimah, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive model for admission uncertainty in high education using Naïve Bayes classifier

Rawal

Lal

2023

JIBR

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Purpose The uncertainty of getting admission into universities/institutions is one of the global problems in an academic environment. The students are having good marks with highest credential, but they are not sure about getting their admission into universities/institutions. In this research study, the researcher builds a predictive model using Naïve Bayes classifiers – machine learning algorithm to extract and analyze hidden pattern in students’ academic records and their credentials. The main purpose of this research study is to reduce the uncertainty for getting admission into universities/institutions based on their previous credentials and some other essential parameters. Design/methodology/approach This research study presents a joint venture of Naïve Bayes Classification and Kernel Density Estimations (KDE) to predict the student’s admission into universities or any higher institutions. The researcher collected data from the Kaggle data sets based on grade point average (GPA), graduate record examinations (GRE) and RANK of universities which are essential to take admission in higher education. Findings The classification model is built on the training data set of students’ examination score such as GPA, GRE, RANK and some other essential features that offered the admission with a predictive accuracy rate 72% and has been experimentally verified. To improve the quality of accuracy, the researcher used the Shapiro–Walk Normality Test and Gaussian distribution on large data sets. Research limitations/implications The limitation of this research study is that the developed predictive model is not applicable for getting admission into all courses. The researcher used the limited data attributes such as GRE, GPA and RANK which does not define the admission into all possible courses. It is stated that it is applicable only for student’s admission into universities/institutions, and the researcher used only three attributes of admission parameters, namely, GRE, GPA and RANK. Practical implications The researcher used the Naïve Bayes classifiers and KDE machine learning algorithms to develop a predictive model which is more reliable and efficient to classify the admission category (Admitted/Not Admitted) into universities/institutions. During the research study, the researcher found that accuracy performance of the predictive Model 1 and that of predictive Model 2 are very close to each other, with predictive Model 1 having truly predictive and falsely predictive rate of 70.46% and 29.53%, respectively. Social implications Yes, it is having a significant contribution for society; students and parents can get prior information about the possibilities of admission in higher academic institutions and universities. Originality/value The classification model can reduce the admission uncertainty and enhance the university’s decision-making capabilities. The significance of this research study is to reduce human intervention for making decisions with respect to the student’s admission into universities or any higher academic institutions, and it demonstrates many universities and higher-level institutions could use this predictive model to improve their admission process without human intervention.

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The Application of Data Mining for Predicting Academic Performance Using K-means Clustering and Naïve Bayes Classification

Cited by 10 publications

References 0 publications

Educational Engineering for Models of Academic Success in Thai Universities During the COVID-19 Pandemic: Learning Strategies for Lifelong Learning

Educational Engineering for Models of Academic Success in Thai Universities During the COVID-19 Pandemic: Learning Strategies for Lifelong Learning

A Comparison of EDM Tools and Techniques

Predictive model for admission uncertainty in high education using Naïve Bayes classifier

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