Students’ Academic Performance and Dropout Prediction

Ameen, Ahmed; Alarape, Moshood Alabi; Adewole, Kayode S.

doi:10.24191/mjoc.v4i2.6701

Cited by 25 publications

(14 citation statements)

References 34 publications

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“…Using several classifiers, including max entropy and ensembles classifiers, the exactness of the classification technique with a selected characteristic vector is validated for a variety of electrical products. In comparable research, authors have analyzed the classification performance of SMO, RF, NB, and SVM for the Guardian data [ 45 , 46 ].…”

Section: Related Researchmentioning

confidence: 99%

Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques

Alanazi

Khaliq

Ahmad

et al. 2022

IJERPH

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Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public’s mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public’s mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions.

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Section: Related Researchmentioning

confidence: 99%

Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques

Alanazi

Khaliq

Ahmad

et al. 2022

IJERPH

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“…Data mining methods are also used to examine the overlapping of the completion of individual courses and the difficulties they are likely to cause to the participants during the training [8]. The most popular data mining methods include decision trees, neural networks, logistic regression and cluster analysis [9]. The traditional classification cannot directly be utilized for estimating probability events [10].…”

Section: Introductionmentioning

confidence: 99%

Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout

Csalódi

Abonyi

2021

Mathematics

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A data-driven method to identify frequent sets of course failures that students should avoid in order to minimize the likelihood of their dropping out from their university training is proposed. The overall probability distribution of the dropout is determined by survival analysis. This result can only describe the mean dropout rate of the undergraduates. However, due to the failure of different courses, the chances of dropout can be highly varied, so the traditional survival model should be extended with event analysis. The study paths of students are represented as events in relation to the lack of completing the required subjects for every semester. Frequent patterns of backlogs are discovered by the mining of frequent sets of these events. The prediction of dropout is personalised by classifying the success of the transitions between the semesters. Based on the explored frequent item sets and classifiers, association rules are formed providing the estimates of the success of the continuation of the studies in the form of confidence metrics. The results can be used to identify critical study paths and courses. Furthermore, based on the patterns of individual uncompleted subjects, it is suitable to predict the chance of continuation in every semester. The analysis of the critical study paths can be used to design personalised actions minimizing the risk of dropout, or to redesign the curriculum aiming the reduction in the dropout rate. The applicability of the method is demonstrated based on the analysis of the progress of chemical engineering students at the University of Pannonia in Hungary. The method is suitable for the examination of more general problems assuming the occurrence of a set of events whose combinations may trigger a set of critical events.

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“…This is the case for diverse disciplinary fields, even though fields such as computer science and engineering seem to have conducted more such studies than others [7]. Most EDM review studies on predicting SAP have been conducted as either reviews [2,5,[8][9][10][11][12][13]; literature reviews [14][15][16]; systematic literature reviews [17][18][19][20]; systematic reviews [4,[21][22][23][24][25]; review syntheses [26]; or surveys [27,28]. While these review study types are not exhaustive, they represent a broad spectrum of the types of review studies that the current paper was able to locate.…”

Section: Introductionmentioning

confidence: 99%

Educational Data Mining, Student Academic Performance Prediction, Prediction Methods, Algorithms and Tools: An Overview of Reviews

Chaka¹

2021

Preprint

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This overview study set out to compare and synthesise the findings of review studies conducted on predicting student academic performance (SAP) in higher education using educational data mining (EDM) methods, EDM algorithms and EDM tools from 2013 to June 2020. It conducted multiple searches for suitable and relevant peer-reviewed articles on two online search engines, on nine online databases, and on two online academic social networks. It, then, selected 26 eligible articles from 2,050 articles. Some of the findings of this overview study are worth mentioning. First, only 2 studies explicitly stated their precise sample sizes with maths and science as the two most mentioned subject areas. Second, 16 review studies had purposes related to either EDM techniques, EDM methods, EDM models, or EDM algorithms employed to predict SAP and student success in the higher education sector. Third, there are six commonly used typologies of input variables reported by 26 review studies, of which student demographics was the most commonly utilised variable for predicting SAP. Fourth and last, seven common EDM algorithms employed for predicting SAP were identified, of which Decision Tree emerged both as the most used algorithm and as the algorithm with the highest prediction accuracy rate for predicting SAP.

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Students’ Academic Performance and Dropout Prediction

Cited by 25 publications

References 34 publications

Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques

Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques

Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout

Educational Data Mining, Student Academic Performance Prediction, Prediction Methods, Algorithms and Tools: An Overview of Reviews

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