Mining Educational Data to Analyze Students' Behavior and Performance

Kurdi, Moustafa M.; Al-Khafagi, Hatim; Elzein, Imad A.

doi:10.1109/icca-ticet.2018.8726203

Cited by 6 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A technological solution in this area can have several approaches [28], but considering the large volume of data handled by computer systems in universities and the advancement of machine learning techniques, our approach would be framed in educational data science (EDS) [29][30][31][32], specifically in EDM, a discipline widely used in the analysis of education and student learning [31,[33][34][35][36][37][38][39][40][41].…”

Section: Step 10: Intermediate Evaluationmentioning

confidence: 99%

A New Methodological Framework for Project Design to Analyse and Prevent Students from Dropping Out of Higher Education

2022

View full text Add to dashboard Cite

The problem of university dropout is a recurring issue in universities that affects students, especially in the first year of studies. The situation is aggravated by the COVID-19 pandemic, which has imposed a virtual education, generating a greater amount of data in addition to historical information, and thus, a greater demand for strategies to design projects based on Educational Data Mining (EDM). To deal with this situation, we present a framework for designing EDM projects based on the construction of a problem tree. The result is the proposal of a framework that merges the six phases of the CRISP-DM methodology with the first stage of the Logical Framework Methodology (LFM) to increase university retention. To illustrate this framework, we have considered the design of a project based on data mining to prevent students from dropping out of a Peruvian university.

show abstract

Section: Step 10: Intermediate Evaluationmentioning

confidence: 99%

A New Methodological Framework for Project Design to Analyse and Prevent Students from Dropping Out of Higher Education

2022

View full text Add to dashboard Cite

show abstract

“…Berdasarkan permasalahan yang ada, maka paper ini mengusulkan sebuah database akademik terpadu guna mendukung sistem monitoring dan evaluasi perkulihan mahasiswa. database ini kedepannya juga dapat digunakan sebagai sumber data untuk analisis dan penemuan pengetahuan sehingga dapat memprediksi tingkah laku dan performance mahasiswa [5] [6] [7].…”

Section: Pendahuluanunclassified

“…Hasil Hasil pengujian menunjukkan nilai sesuai dengan rancangan skenario uji database ini berarti bahwa database akademik yang terbentuk dapat menyediakan data dan informasi untuk monitoring dan evaluasi perkuliahan. Database ini ke depannya juga dapat digunakan untuk untuk menganalisis, prilaku dan performance mahasiswa [5] serta untuk memprediksi performance mahasiswa [6].…”

Section: E Implemetasi Database Terpaduunclassified

System Data Base Terpadu Untuk Mendukung Sistem Monitoring Perkuliahan

Eriya

Sari

2020

JURNAL MULTIMEDIA NETWORKING INFORMATICS

View full text Add to dashboard Cite

Student achievements and academic processes need to be monitored and evaluated continuously to see the success of students and study programs in achieving predetermined educational goals. This monitoring and evaluation certainly required complete and comprehensive data for all academic activities. The problem that often arises is the lack of comprehensive data. For this reason, an integrated academic database is needed. An integrated database system is a centralized data storage medium for managing data and information. Integrated database development using database design methodology. With this database system, it will facilitate study programs in obtaining quality information for decision making and policies related to the academic process and study program future.

show abstract

“…In this sense it is necessary to have adequate tools that allow for the detection of student dropout in higher education institutions. Therefore, taking technology as an ally for solving student dropout problems, and considering the excessive volume of data administered by computer systems in universities, our approach is framed in a technological solution based on educational data sciences (EDC) [16,17], especially educational data mining (EDM), a discipline widely studied by researchers in order to address the analysis of education and learning in university students, such as in [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Predictive Models with Balanced Classes Using the SMOTE Method for the Forecast of Student Dropout in Higher Education

2022

View full text Add to dashboard Cite

Based on the premise that university student dropout is a social problem in the university ecosystem of any country, technological leverage is a way that allows us to build technological proposals to solve a poorly met need in university education systems. Under this scenario, the study presents and analyzes eight predictive models to forecast university dropout, based on data mining methods and techniques, using WEKA for its implementation, with a dataset of 4365 academic records of students from the National University of Moquegua (UNAM), Peru. The objective is to determine which model presents the best performance indicators to forecast and prevent student dropout. The study aims to propose and compare the accuracy of eight predictive models with balanced classes, using the SMOTE method for the generation of synthetic data. The results allow us to confirm that the predictive model based on Random Forest is the one that presents the highest accuracy and robustness. This study is of great interest to the educational community as it allows for predicting the possible dropout of a student from a university career and being able to take corrective actions both at a global and individual level. The results obtained are highly interesting for the university in which the study has been carried out, obtaining results that generally outperform the results obtained in related works.

show abstract

Mining Educational Data to Analyze Students' Behavior and Performance

Cited by 6 publications

References 12 publications

A New Methodological Framework for Project Design to Analyse and Prevent Students from Dropping Out of Higher Education

A New Methodological Framework for Project Design to Analyse and Prevent Students from Dropping Out of Higher Education

System Data Base Terpadu Untuk Mendukung Sistem Monitoring Perkuliahan

Comparison of Predictive Models with Balanced Classes Using the SMOTE Method for the Forecast of Student Dropout in Higher Education

Contact Info

Product

Resources

About