Classifying and Predicting Students’ Performance using Improved Decision Tree C4.5 in Higher Education Institutes

Sadiq, Mohammed Hikmat; Ahmed, Nawzat S.

doi:10.3844/jcssp.2019.1291.1306

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…In addition, the BT model also has the advantage of predicting class imbalance data. This result was quite different from the study conducted by Sadiq and Ahmed [14] that concluded Decision Tree model shows better results in predicting student performance in the final exam. Meanwhile, Cinaroglu [15] stated that Random Forest provides better AUC score than the Decision Tree in predicting health expenditures.…”

Section: Resultscontrasting

confidence: 99%

Building a Classification Model to Predict School Quality in Indonesia

Wibowo¹

2021

Advances in Social Science, Education and Humanities Research

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The aim of the present study was to explore the best classification model based on the predictive performance of decision tree (DT), random forest (RF), and boosted tree (BT) models used for school quality status detection in Indonesia. In order to get a better insight into the predictive abilities of these three models, 213,536 records from Basic Education Data (Dapodik), an education management information system managed by Ministry of Education and Culture, were utilized. A total of 23 predictors were extracted from Dapodik. School quality status (two classes: 'met national education standards' and 'improvement required') was an output (dependent) variable. Accuracy of the DT, RF, and BT models evaluated on the training dataset and the testing dataset was 71.51%, 99.91%, and 72.66% as well as 71.53%, 73.56%, and 72.82%, respectively. The findings indicate that the BT model had better performance than the DT dan RF models.

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Section: Resultscontrasting

confidence: 99%

Building a Classification Model to Predict School Quality in Indonesia

Wibowo¹

2021

Advances in Social Science, Education and Humanities Research

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“…The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio. Mohammed & Nawzat (2019) carried out a research in which they classified and predicted students' performance using classification algorithms in the final exam. Four classification algorithms, which are Decision Tree C4.5, Random Forest, Support Vector Machine (SVM) and Naive Bayes, were used in this research in order to classify and predict the students' performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances

Agwi,

Akpojaro

2024

AIMS Research Journal

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Rainfall as an environmental feat can change fast and yield significant influence in downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn, impact the quality of life, sewage systems, agriculture, and tourism of a nation, to mention a few. It chaotic, complex and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models – many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999–2019 for the Benin-Owena River Basin. Data is split: 70% for train, and 30% for test. Our study adapts a spatial-temporal profile hidden Markov trained deep neural network. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of classification of 0.12. Other ensembles underperformed when compared to proposed model. The study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide, lead time warnings in flood management. Keywords: Evidence, Rainfall Runoff, Southern Nigeria, Hybrid Ensemble Machine Learning Approach Nwanze Ashioba, Frances Emordi, Patrick Ejeh, Christopher Odiakaose, Christopher Odeh, Obiageli Attoh, & Maduabuchuku Azaka. (2024): Empirical Evidence for Rainfall Runoff in Southern Nigeria Using a Hybrid Ensemble Machine Learning Approach. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 73-86. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P6

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“…Alternatively, in a study conducted by Sadiq et al [10], four algorithms which were Random Forest, Support Vector Machine, Decision Tree C4.5, and Naïve Bayes were used to classify and predict students' performance. The data for the study was mined through interviews with the academic staff of three universities in Duhok province, Kurdistan, Iraq.…”

Section: Comparative Studymentioning

confidence: 99%

Forecasting the Academic Performance by Leveraging Educational Data Mining

Saeed

2024

IASC

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The study aims to recognize how efficiently Educational Data Mining (EDM) integrates into Artificial Intelligence (AI) to develop skills for predicting students' performance. The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University. The first step's initial population placements were created using Particle Swarm Optimization (PSO). Then, using adaptive feature space search, Educational Grey Wolf Optimization (EGWO) was employed to choose the optimal attribute combination. The second stage uses the SVM classifier to forecast classification accuracy. Different classifiers were utilized to evaluate the performance of students. According to the results, it was revealed that AI could forecast the final grades of students with an accuracy rate of 97% on the test dataset. Furthermore, the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50% and could be categorized as having equal information ratio gain after the semester. While the random forest provided an accuracy of 28%. These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy (12%). The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance, reducing chances of failure, and taking appropriate steps at the right time to raise the standards of education. The study also motivates academics to assess and discover EDM at several other universities.

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Classifying and Predicting Students’ Performance using Improved Decision Tree C4.5 in Higher Education Institutes

Cited by 13 publications

References 21 publications

Building a Classification Model to Predict School Quality in Indonesia

Building a Classification Model to Predict School Quality in Indonesia

Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances

Forecasting the Academic Performance by Leveraging Educational Data Mining

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