Application of Data Mining Techniques for Predicting Student Success in English Exit Exam

Puarungroj, Wichai; Boonsirisumpun, Narong; Pongpatrakant, Pathapong; Phromkhot, Suchada

doi:10.1145/3164541.3164638

Cited by 9 publications

(12 citation statements)

References 12 publications

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“…Recording these features can also be used to predict if a student is likely to pass an exam or fail. In [20] a study was conducted to predict the success rate of the candidate in an English exam. Besides that, scores were also predicted with the help of technology.…”

Section: A Existing Student Performance Tracking (Spt) Systemsmentioning

confidence: 99%

Blockchain based Framework for Efficient Student Performance Tracking (BloSPer)

Junejo¹,

Dziatkovskii²,

Hashmani³

et al. 2022

IJACSA

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For maintaining sustainable economy, the government of Malaysia is working towards improvising the standards of education in higher education institutes. According to reports, around 32% of enrolled students in Public Universities of Malaysia are unable to graduate on time due to unknown reasons. To ensure more students graduate on time with high quality of education, continuous monitoring of the student is essential. Continual tracking will allow the student as well as the educator to analyze the weak performer at an early stage. Tracking the student performance manually is challenging but with the advancements in information technology, keeping a track of student performance has been relatively easier. Therefore, the fundamental aim of this paper is to present a novel blockchain framework for record keeping and student performance tracking. We name this framework BloSPer (Blockchain Student Performance Tracking System). BloSPer has an edge over the existing systems as current systems face problems of single point of failure and unreliable data. The proposed framework will enable the students and educators to track the performance of the students in a more convenient and transparent manner. Due to this, it will be simpler for them to analyze the reasons of a students' poor performance. Moreover, the data gathered through the system will be more reliable and worthy for data analytics because of tamper resistance provided through blockchain. This will result in much knowledgeable decisions by the institutions regarding improving the performance of each individual candidate.

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Section: A Existing Student Performance Tracking (Spt) Systemsmentioning

confidence: 99%

Blockchain based Framework for Efficient Student Performance Tracking (BloSPer)

Junejo¹,

Dziatkovskii²,

Hashmani³

et al. 2022

IJACSA

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“…Using three different student representations, [14] in his finding using classification algorithms to predict dropouts at higher levels of educational institutions, discovered that gradient boost ensemble and random forest algorithms outperformed Naive Bayes, which underperformed due to its strong interdependence assumption. [15] used a c4.5 algorithm (j48) to predict student performance on the English exit exam and discovered that English placement test results were a significant indicator of performance on the exam. The classification accuracy of the different algorithms of the decision tree, such as reptree, c4.5 decision tree and random tree, in forecasting, if a student will graduate at the right time was compared in research published in [16].…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Development of a Predictive Model of Student Attrition Rate

Sani

Oladipo

Ogbuju

et al. 2022

J. Appl. Artif. Intell.

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Enrollment in courses is a key performance indicator in educational systems for maintaining academic and financial viability. Today, a lot of factors, comprising demographic and individual features like age, gender, academic background, financial capabilities, and academic degree of choice, contribute to the attrition rates of students at various higher education institutions. In this study, we developed prediction models for students' attrition rate in pursuing a computer science degree as well as those who have a high chance of dropping out before graduation using machine learning methodologies. This approach can assist higher education institutions in creating effective interventions to lower attrition rates and raise the likelihood that students will succeed academically. Student data from 2015 to 2022 were collected from the Federal University Lokoja (FUL), Nigeria. The data was preprocessed using existing WEKA machine learning libraries where our data was converted into attribute-related file form (ARFF). Further, the resampling techniques were used to partition the data into the training set and testing set, and correlation-based feature selection was extracted and used to develop the students' attrition model to identify the students' risk of attrition. Random Forest and decision tree machine learning algorithms were used to predict students' attrition. The results showed that Random Forest has 79.45% accuracy while the accuracy of Random tree stood at 78.09%. This is an improvement over previous results, where an accuracy of 66.14%. and 57.48% were recorded for random forest and Random tree respectively. This improvement was because of the techniques demonstrated in this study. It is recommended that applying techniques to the classification model will improve the performance of the model.

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“…[1], [2], [30], [9], [21], [19], [8 ], [31], [26], [23], [34], [36], [25] , [38], [11], [29], [24], [6], [15] , [40] PF02 Age [19], [35], [25] , [23], [29], [24], [6] , [15], [40] PF03 Marital status [19], [26], [1], [15], [24], [6]…”

Section: Pf01 Gendermentioning

confidence: 99%

Predicting Academic Performance through Data Mining: A Systematic Literature

Daza¹,

Guerra²,

Cervera³

et al. 2022

TEM Journal

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The main objective of this work is to make a systematic review of the literature on the prediction of the academic performance of university students by applying data mining techniques. For this purpose, an exhaustive search was carried out and after the analysis of the documentation collected, aspects such as: methodology, attributes, selection algorithms, techniques, tools, and metrics were considered, which served as the basis for the elaboration of this document. The results of the study showed that the most used methodology is KDD(database knowledge extraction), the most important attribute to achieve prediction is CGPA(academic performance), the most commonly used variable selection algorithm is InfoGain-AttributeEval, among the most efficient techniques are Naïve Bayes, Neural Networks (MLP) and Decision Tree (J48), the most used tools for the development of the models is the Weka software and finally the metrics necessary to determine the effectiveness of the model were Precision and Recall.

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Application of Data Mining Techniques for Predicting Student Success in English Exit Exam

Cited by 9 publications

References 12 publications

Blockchain based Framework for Efficient Student Performance Tracking (BloSPer)

Blockchain based Framework for Efficient Student Performance Tracking (BloSPer)

Development of a Predictive Model of Student Attrition Rate

Predicting Academic Performance through Data Mining: A Systematic Literature

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