Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques

Mueen, Ahmed; Zafar, Bassam; Manzoor, Umar

doi:10.5815/ijmecs.2016.11.05

Cited by 175 publications

(114 citation statements)

References 18 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…En la tabla 2, se puede observar que las probabilidades a priori para el atributo "aprueba" son similares, sin embargo, la probabilidad P(aprueba=NO) resulta mayor, debido Mueen et al (2016) participaron 60 estudiantes y el valor más alto de exactitud se obtuvo con 38 atributos y fue de 86%. En este trabajo, se obtuvieron exactitudes de las predicciones de la aprobación del curso de hasta 73%, únicamente con cinco atributos correspondientes a las calificaciones de las actividades académicas iniciales del mismo.…”

Section: Resultados Y Discusiónunclassified

Diseño de un modelo para automatizar la predicción del rendimiento académico en estudiantes del IPN / Design of a model to automate the prediction of academic performance in students of IPN

Páez

Guzmán

2018

RIDE

View full text Add to dashboard Cite

ResumenLa minería de datos educativa permite extraer conocimiento útil y comprensible a partir de datos académicos para la solución de problemas acerca de diversos procesos de enseñanza y de aprendizaje. Una de las aplicaciones más populares de la minería de datos educativa es la predicción del rendimiento académico. El principal objetivo de este trabajo fue diseñar y automatizar un modelo predictivo del rendimiento académico de estudiantes del Instituto Politécnico Nacional (IPN).Para la construcción del modelo, se analizaron las calificaciones de actividades académicas y la calificación final de 94 estudiantes inscritos en una carrera de ingeniería perteneciente al IPN. Este modelo se aplicó a 86 estudiantes para predecir su rendimiento académico.Posteriormente, se compararon estas predicciones con los resultados reales obtenidos por los estudiantes al final del curso. Se obtuvieron exactitudes de las predicciones de la aprobación

show abstract

Section: Resultados Y Discusiónunclassified

Diseño de un modelo para automatizar la predicción del rendimiento académico en estudiantes del IPN / Design of a model to automate the prediction of academic performance in students of IPN

Páez

Guzmán

2018

RIDE

View full text Add to dashboard Cite

show abstract

“…Ahmed Mueen et.al. [3] Predicting student's performance in placement in an education system has become more difficult due to huge amount of data and inaccurate data with uncertainty in educational databases. Some of the existing methodologies and their problem for the student analysis have been discussed in this survey.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Classification Algorithms in EDM using 2 Level Classification for Predicting Student’s Performance

Katare¹,

Dubey²

2017

IJCA

View full text Add to dashboard Cite

In higher education the performance of students is a most challenge work day by day in academic as well as in other curricular activities. As they all know that internet technology is growing as much as faster, but the learning approach of students are not up to the mark. The emerging research community which helps to find the solution to the said problem is Educational Data Mining. In present scenario, the huge students' data is stored in educational database. That type of database contains widely open or secret information to improve student performance. In our proposed work, we will have tested it on reputed dataset, which can be downloaded from a well known organization UCI repository and dataset name is student-mat.csv. This work has been investigated the process of classification of plethora of student's data. Classification plot data into pre-determined groups of classes. It is often mentioned to as supervised learning because the classes are determined before analyzing the data. The work will to be divided into two parts. The first part will be the entropy based feature selection, after that classification process has to be performed. For the classification, we would have used 2 level classification method i.e, SVM and KNN. Later than observe the performance prediction of students based on parameters like accuracy, sensitivity, specificity of proposed method and is to be compared with some previous methods results.

show abstract

“…Lately, DM methods and tools for analyzing data available at educational institutions, named Educational Data Mining (EDM) [24] [3]; [21] have been widely applied to enhance the goodness of educational system [2] and to solve many problems at higher education such student"s retention and dropout, enrollment management, web-based education and student performance [3].…”

Section: Data Mining In Education Fieldmentioning

confidence: 99%

Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases

Bousbahi¹

2017

IJCA

View full text Add to dashboard Cite

Universities offer a wide selection of courses to students studying in different fields; however, this has generated various unintended problem such as students struggling, dropping or failing. Universities also, own large amounts of historical data about students containing rich knowledge that can be used to facilitate a broad range of educational research and analytics study in order to enhance student performance. Knowledge discovery in databases (KDD) is the process of finding comprehensible patterns that can be interpreted as useful or interesting knowledge. One component in this process is Data Mining which is the application of machine learning algorithms for extracting patterns from data. In this context, data mining techniques are used to answer educational research questions that highlight students" success in higher education. Several challenges are facing higher education, one of which is finding the most effective sequence of modules for students for each semester. This finding may help students to improve their performance. In this research we investigate dependency between set of courses chosen by students at each level and their performance using a classification technique in order to build a performance prediction model, which is based on previous students" academic records.The finding of the study furnishes useful information to advisors in educational institution and valuable knowledge to feed analytics based systems and learning recommender systems to assist students selecting future modules to obtain optimum pathways courses.

show abstract

Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques

Cited by 175 publications

References 18 publications

Diseño de un modelo para automatizar la predicción del rendimiento académico en estudiantes del IPN / Design of a model to automate the prediction of academic performance in students of IPN

Diseño de un modelo para automatizar la predicción del rendimiento académico en estudiantes del IPN / Design of a model to automate the prediction of academic performance in students of IPN

A Comparative Study of Classification Algorithms in EDM using 2 Level Classification for Predicting Student’s Performance

Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases

Contact Info

Product

Resources

About