The Investigation of Student Dropout Prediction Model in Thai Higher Education Using  Educational Data Mining:  A Case Study of Faculty of   Science, Prince of Songkla Uni-versity

Pattanaphanchai, Jarutas; Leelertpanyakul, Koranat; Theppalak, Napa

doi:10.29196/jubpas.v27i1.2191

Cited by 11 publications

(14 citation statements)

References 18 publications

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“…In 2019, the study [27] by Pattanaphanchai, Leelertpanyakul and Theppalak proposed a model to predict students' dropout patterns using WEKA tool. The dataset is collected from Faculty of Science, Prince of Songkla University of five years.…”

Section: B Detecting Undesirable Student Bahaviorsmentioning

confidence: 99%

Educational Data Mining Applications and Techniques

Alshareef¹,

Alhakami²,

Alsubait³

et al. 2020

IJACSA

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Educational data mining (EDM) uses data mining techniques to analyze huge amounts of student data in the educational environments. The main purpose of EDM is to analyze and solve educational issues and, consequently, improve educational processes. With the emergence of EDM applications in the educational environments, several techniques have been identified to implement these applications. This paper reviews the relevant studies in EDM including datasets and techniques used in those studies and identifies the most effective techniques. The most prevalent applications include predicting student performance, detecting undesirable student behaviors, grouping students and student modeling. These applications aim to help decision makers in the educational institutions to understand student situations, improve students' performance, identify learning priorities for different groups of students and develop learning process. The prediction accuracy is selected as the evaluation criteria for the effectiveness of educational data mining techniques. The results show that Bayesian Network and Random Forest are the most effective techniques for predicting student performance, Social Network Analysis is the best technique for detecting undesirable student behaviors, Clustering and Social Network Analysis are the most effective techniques for grouping students and student modeling, respectively. This study recommends conducting more comprehensive and extended studies to evaluate the effectiveness of EDM techniques with an extended evaluation criteria.

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Section: B Detecting Undesirable Student Bahaviorsmentioning

confidence: 99%

Educational Data Mining Applications and Techniques

Alshareef¹,

Alhakami²,

Alsubait³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…In the [11] study, the researchers used the dataset from Prince of Songkla University to predict dropout. They had collected four academic year of student data from Faculty of Science.…”

Section: A Preprocessing Techniquesmentioning

confidence: 99%

“…This study also aims to cite evidence in support of feature selection method as part of preprocessing step to increase the classification accuracy of a predictive model which has been omitted in some DM studies; like in the following similar studies [10], [11], and [12]. In view of this, two predictive models using classification technique with different feature datasets is proposed-model 1 will used all the dataset attributes queried from the university database and model 2 will used the ranking of important features.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Feature Selection in Identifying Predicting Factors of Student Retention

Febro¹

2019

IJACSA

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Student retention is an important issue faced by Philippine higher education institutions. It is a key concern that needs to be addressed for the reason that the knowledge they gain can contribute to the economic and community development of the country aside from financial stability and employability. University databases contain substantial information that can be queried for knowledge discovery that will aid the retention of students. This work aims to analyze factors associated with student's success among first-year students through feature selection. This is a critical step prior to modelling in data mining, as a way to reduce computational process and improve prediction performance. In this work, filter methods are applied on datasets queried from university database. To demonstrate the applicability of this method as a pre-processing step prior to data modelling, predictive model is built using the selected dominant features. The accuracy result jumps to 92.09%. Also, through feature selection technique, it was revealed that postadmission variables are the dominant predictors. Recognizing these factors, the university could improve their intervention programs to help students retain and succeed. This only shows that doing feature selection is an important step that should be done prior to designing any predictive model.

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“…Data mining has been shown a successful benefit in the business domain and it can be a suitable tool to benefit in the educational domain for finding useful information hidden in the huge dataset. The classification method constructs a model based on the training set of known class labels data to classify unknown objects [1].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Detect Student Dropout at University

2021

IJATCSE

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In universities, student dropout is a major concern that reflects the university's quality. Some characteristics cause students to drop out of university. A high dropout rate of students affects the university's reputation and the student's careers in the future. Therefore, there's a requirement for student dropout analysis to enhance academic plan and management to scale back student's drop out from the university also on enhancing the standard of the upper education system. The machine learning technique provides powerful methods for the analysis and therefore the prediction of the dropout. This study uses a dataset from a university representative to develop a model for predicting student dropout. In this work, machine- learning models were used to detect dropout rates. Machine learning is being more widely used in the field of knowledge mining diagnostics. Following an examination of certain studies, we observed that dropout detection may be done using several methods. We've even used five dropout detection models. These models are Decision tree, Naïve bayes, Random Forest Classifier, SVM and KNN. We used machine-learning technology to analyze the data, and we discovered that the Random Forest classifier is highly promising for predicting dropout rates, with a training accuracy of 94% and a testing accuracy of 86%.

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The Investigation of Student Dropout Prediction Model in Thai Higher Education Using Educational Data Mining: A Case Study of Faculty of Science, Prince of Songkla Uni-versity

Cited by 11 publications

References 18 publications

Educational Data Mining Applications and Techniques

Educational Data Mining Applications and Techniques

Utilizing Feature Selection in Identifying Predicting Factors of Student Retention

A Machine Learning Approach to Detect Student Dropout at University

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