Formulation of a predictive model for academic performance based on students' academic and demographic data

Rubiano, Sandra Milena Merchán; Garcia, Jorge Alberto Duarte

doi:10.1109/fie.2015.7344047

Cited by 17 publications

(9 citation statements)

References 11 publications

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“…[1], [2], [23], [32], [9], [19], [28], [17], [26], [23], [5], [33], [38 ], [11], [4], [20], [3], [25], [3 1], [32], [15], [34], [7], [37], [18], [31] M02 CRISP-DM [14], [6], [16] M03 SEMMA…”

Section: Code Methodology Articlementioning

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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Section: Code Methodology Articlementioning

confidence: 99%

Predicting Academic Performance through Data Mining: A Systematic Literature

Daza¹,

Guerra²,

Cervera³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

show abstract

“…A study on academic performance conducted by [10] using the students' educational and demographic data of 932 students. Decision tree classification was used, and it was found out that socio-demographic variables like marital status, social stratum, whether the student takes day or night classes, gender, and the number of siblings influence the academic performance of a student.…”

Section: Related Literaturementioning

confidence: 99%

IT Students Selection and Admission Analysis using Naïve Bayes and C4.5 Algorithm

Alejandrino¹

2020

IJATCSE

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Admission to college and selection of applications have probably become an integral part of some colleges and universities in their enrolment process, yet it is girded by controversy and skepticism. A new area of research that uses techniques of data mining is known as Educational Data Mining. It incorporates machine learning algorithms and statistical methods to help for the interpretation of student's learning habits, academic performances, and further improvements-if needed. This paper focuses on the predictive values of certain academic variables, admission tests, high school academic records as related to the performance of Information Technology (IT) students at the end of the first year. For this reason, 221 data were used, and C4.5 and Naive Bayes algorithms are applied to generate a prediction on the students' performance. The C4.5 classification gained 98.64% in 10-folds cross-validation and 96.97% in the 70% training and 30% testing percentage split compared to Naïve Bayes which only gained 89.14% and 86.36% for both 10-folds cross-validation and 70% training and 30% testing percentage split respectively. The comparative analysis of the result shows that senior high school track and academic data and admission test results are the influential attributes to the performance of IT students in their first year. This paper recommends for future studies to add different data from different years to increase the accuracy of the prediction.

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“…Limited papers fall under extended [19-20, 27, 41, 43, 55, 58-60, 63-65] and holistic [14,[22][23][24]39] categories. In [25] included distance of schools from a district office to predict school's performance and accreditation within the vicinity of the district office.…”

Section: S M Muthukrishnan Et Al J Fundam Appl Sci 2017 9(4s) 7mentioning

confidence: 99%

Systematic mapping review on student’s performance analysis using big data predictive model

Muthukrishnan

Govindasamy²,

Mustapha

2018

J. Fundam and Appl Sci.

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This paper classify the various existing predicting models that are used for monitoring and improving students' performance at schools and higher learning institutions. It analyses all the areas within the educational data mining methodology. Two databases were chosen for this study and a systematic mapping study was performed. Due to the very infant stage of this research area, only 114 articles published from 2012 till 2016 were identified. Within this, a total of 59 articles were reviewed and classified. There is an increased interest and research in the area of educational data mining, particularly in improving students' performance with various predictive and prescriptive models. Most of the models are devised for pedagogical improvements ultimately. It is a huge scarcity in producing portable predictive models that fits into any educational environment. There is more research needed in the educational big data.

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Formulation of a predictive model for academic performance based on students' academic and demographic data

Cited by 17 publications

References 11 publications

Predicting Academic Performance through Data Mining: A Systematic Literature

Predicting Academic Performance through Data Mining: A Systematic Literature

IT Students Selection and Admission Analysis using Naïve Bayes and C4.5 Algorithm

Systematic mapping review on student’s performance analysis using big data predictive model

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