Prediction Graduate Student Use Naive Bayes Classifier

Meiriza, Allsela; Lestari, Endang; Putra, Pacu; Monaputri, Ayu; Lestari, Dini Ayu

doi:10.2991/aisr.k.200424.056

Cited by 10 publications

(15 citation statements)

References 2 publications

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“…Kurniawan et al (2020) develop a graduation prediction system based on C4.5 algorithms. Meiriza et al (2020) leverage naive Bbayes classifier to analyse the influence of demographic features and academic performance on college graduation. Due to the small amount of relevant research data, no researcher has attempted to predict by deep learning algorithms for the time being.…”

Section: Methods For Predicting Difficulty In Graduationmentioning

confidence: 99%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

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Section: Methods For Predicting Difficulty In Graduationmentioning

confidence: 99%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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“…In this research article, the researcher used the Kernel Density Estimation (KDE), which is a nonparametric way to estimate the probability of variables such as GRE score, grade point average (GPA) and RANK of the universities or higher academic institutions. The fundamental approach of KDE is handling data smoothing problem where inferences about sample are made, which is based on finite data sample (Lestari, 2020). The KDE is one of the probability techniques for estimation that must be enabled for the user to better analyze and study probability distribution than any traditional histogram (Kabra and Bichkar, 2011).…”

Section: Introductionmentioning

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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“…For example, in college, this method is used to predict student graduation (Meiriza et al, 2020), to select students on admission selection (Alejandrino et al, 2020) and to predict students who would fail and drop out of school (Márquez-Vera et al, 2013). High school is used to solve students' academic predictions (Musau et al, 2019), to predict students' performance (Pattiasina & Rosiyadi, 2020), and to predict students' final grades (Khan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The Relevance of the Vocational Education Curriculum to the Business World and Education in Vocational High Schools

Khuluqo¹,

Istaryatiningtias²

2022

Proceedings of the 1st International Conference on Economic and Education, ICON 2021, 14 - 15 December 2021, Padang-West Sumatr

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Teachers in Vocational High Schools mostly do not have much experience involved in the field. This makes teachers unable to organize good lessons or help develop students into what industry needs or seeks. The purpose of this research is to determine the relevance of the vocational education curriculum to the business world and education in Vocational High Schools. The method used in this research is qualitative research which can describe the results of research accurately by using data provided by schools related to the Implementation of Vocational Education Curriculum in the Industrial Era 4.0 in Vocational High Schools.

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Prediction Graduate Student Use Naive Bayes Classifier

Cited by 10 publications

References 2 publications

Educational Anomaly Analytics: Features, Methods, and Challenges

Educational Anomaly Analytics: Features, Methods, and Challenges

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

The Relevance of the Vocational Education Curriculum to the Business World and Education in Vocational High Schools

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