Semi Supervised Prediction Model in Educational Data Mining

Hmiedi, Ismail; Najadat, Hassan; Halloush, Zain; Jalabneh, Ibtihal

doi:10.1109/acit47987.2019.8991048

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Another eight studies 23,[27][28][29][30][31][32][33] collected data from Kaggle, 34 and seven of them used dataset named xAPI-Edu-Data. 35 Two studies 36,37 used StudentPerformance 38 dataset from UCI Machine Learning Repository.…”

Section: Source Of Datamentioning

confidence: 99%

A survey on educational data mining methods used for predicting students' performance

Xiao

2021

Engineering Reports

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Predicting students' performance is one of the most important issues in educational data mining (EDM), which has received more and more attention. By predicting students' performance, we can identify students' risk of academic failure and help instructors to take some actions such as guidance or interventions to help learners as early as possible, or carry out continual evaluation of learners as to optimize learning path or personalized learning resources recommendation. In this survey, we reviewed the 80 important studies on predicting students' performance using EDM methods in 2016-2021, synthesized the procedure of building prediction model of students' performance which contains four phases and 10 key steps, and compared and discussed the latest EDM methods used in all steps. We analyzed the challenges faced by previous studies in three aspects and put forward future suggestions on data collection, EDM methods used, and interpretation of prediction model. This survey provides a comprehensive understanding and practical guide for researchers in this field, and also provides direction for further research.

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Section: Source Of Datamentioning

confidence: 99%

A survey on educational data mining methods used for predicting students' performance

Xiao

2021

Engineering Reports

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“…The authors found that the linear regression achieved the highest accuracy on their dataset (hypothetical open -source data of UCLA), which had low mean squared error (MSE) and a high R2 score compared to the other implemented regression techniques. Hmiedi et al [3] made a regression model using the RF algorithm to predict the graduate admissions probabilities. This work used the same hypothetical open -source dataset from Kaggle of the University of California in Los Angeles as in [2].…”

Section: Introductionmentioning

confidence: 99%

Masters and Doctor of Philosophy admission prediction of Bangladeshi students into different classes of universities

Suvon

Siam

Ferdous

et al. 2022

IJ-AI

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Many Bangladeshi students intend to pursue higher studies abroad after completing their undergraduate degrees every year. Choosing a university for higher education is a challenging task for students. Especially, the students with average and lower academic credentials (undergraduate grades, English proficiency test scores, job, and research experiences) can hardly choose the universities that could match their profile. In this paper, we have analyzed some real unique data of Bangladeshi students who had been accepted admissions at different universities worldwide for higher studies. Finally, we have produced prediction models based on random forest (RF) and decision tree (DT) techniques, which can predict appropriate universities of specific classes for students according to their past academic performances. Two separate models have been studied in this paper, one for Masters (MS)students and another for Doctor of Philosophy (PhD)students. According to the Quacquarelli Symonds (QS) World University Rankings, the universities where the students got admitted have been divided into 9 classes for MS students and 8 classes for PhD students. Accuracy, precision, recall and F1-Score have been studied for the two machine learning algorithms. Numerical results show that both the algorithm DT and RF have the same accuracy of 89% for PhD student data and 86% for MS student data.

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Supervised Home Loan and Top-up Home Loan Risk Optimization Using Neuro-Fuzzy Model

Paul¹

2022

Advances in Intelligent Systems and Computing

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Semi Supervised Prediction Model in Educational Data Mining

Cited by 4 publications

References 7 publications

A survey on educational data mining methods used for predicting students' performance

A survey on educational data mining methods used for predicting students' performance

Masters and Doctor of Philosophy admission prediction of Bangladeshi students into different classes of universities

Supervised Home Loan and Top-up Home Loan Risk Optimization Using Neuro-Fuzzy Model

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