Predicting Students’ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department

Badr, Ghada; Algobail, Afnan; Almutairi, Hanadi; Almutery, Manal

doi:10.1016/j.procs.2016.04.012

Cited by 49 publications

(39 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data mining compiles the knowledge produced by both Statistics and Artificial Intelligence, while at the same time remaining accessible to educators (Hand, 1998 ; Baker and Yacef, 2009 ). In regard to Higher Education dropout, three of the possible techniques have proven helpful to understanding the problem, since they are used to raise and solve classification problems in which a certain number of variables are used as predictors (acting as a criterion variable); these techniques are association rules (López et al, 2015 ; Badr et al, 2016 ), Naive Bayes (Moseley and Mead, 2008 ; Moreno-Salinas and Stephens, 2015 ; Shaleena and Paul, 2015 ) and Decision Trees (Escobar et al, 2016 ; Hasbun et al, 2016 ; Liang et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Freshmen Program Withdrawal: Types and Recommendations

et al. 2017

View full text Add to dashboard Cite

University program dropout is a problem that has important consequences not only for the student that leaves but also for the institution in which the withdrawal occurs. Therefore, higher education institutions must study the problem in greater depth to establish appropriate prevention measures in the future. However, most research papers currently focus primarily on the characteristics of students who leave university, rather than on those who choose to pursue alternative courses of study and therefore fail to take into account the different kinds of abandonment. The aim of this paper is to identify the different types of dropout to define their characteristics and propose some recommendations. Thus, an ex post facto study was carried out on a sample of 1,311 freshmen from a university in the north of Spain using data gathered using an ad-hoc designed questionnaire, applied by telephone or an online survey, and completed with data available in the university data warehouse. A descriptive analysis was performed to characterize the sample and identify five different groups, including 1. Students persisting in their initiated degree 2. Students who change of program (within the same university) 3. Students transferring to a different university 4. Students enrolling in non-higher-education studies 5. Students that quit studying. Also, data mining techniques (decision trees) were applied to classify the cases and generate predictive models to aid in the design of differentiated intervention strategies for each of the corresponding groups.

show abstract

Section: Introductionmentioning

confidence: 99%

Freshmen Program Withdrawal: Types and Recommendations

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Also, academic language skills have been used frequently in the list of features to predict student achievement (Nghe et al 2007;Abu Saa 2016;Badr et al 2016;Asif et al 2017). Academic Language is the language being used in textbooks, spoken in classrooms, and presented on tests and examinations.…”

Section: Academic Language Skillsmentioning

confidence: 99%

Predicting Academic Outcomes: A Survey from 2007 Till 2018

2020

View full text Add to dashboard Cite

The tremendous growth of educational institutions’ electronic data provides the opportunity to extract information that can be used to predict students’ overall success, predict students’ dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students’ needs, and much more. This paper aims to review the latest trends in predicting students’ performance in higher education. We provide a comprehensive background for understanding Educational Data Mining (EDM). We also explain the measures of determining academic success and highlight the strengths and weaknesses of the most common data mining (DM) tools and methods used nowadays. Moreover, we provide a rich literature review of the EDM work that has been published during the past 12 years (2007–2018) with focus on the prediction of academic performance in higher education. We analyze the most commonly used features and methods in predicting academic achievement, and highlight the benefits of the mostly used DM tools in EDM. The results of this paper could assist researchers and educational planners who are attempting to carry out EDM solutions in the domain of higher education as we highlight the type of features that the previous researches found to have significant impact on the prediction, as well as the benefits and drawbacks of the DM methods and tools used for predicting academic outcomes.

show abstract

“…Data dihasilkan setiap saat dari berbagai sumber, seperti social network, transaksi bisnis, dan data klinis. Data tersebut tersimpan di database sebagai data mentah, dan data tersebut bisa menjadi informasi yang bermanfaat [3] (Ghada Badr). Salah satunya adalah informasi mengenai lama masa studi mahasiswa dengan menggunakan indikator performa mahasiswa.…”

Section: Aplikasi Artificial Neural Network (Ann) Untuk Memprediksi Munclassified

Aplikasi Artificial Neural Network (ANN) untuk Memprediksi Masa Studi Mahasiswa Program Studi Teknik Informatika Universitas Muhammadiyah Gresik

Bhakti¹

2019

Jurnal Eksplora Informatika

View full text Add to dashboard Cite

Kualitas mahasiswa merupakan bagian penting dalam institusi pendidikan. Universitas perlu melakukan evaluasi performa mahasiswa untuk menjaga kualitas mahasiswa. Salah satu variabel indikator performa mahasiswa adalah informasi tentang lama masa studi mahasiswa. Prediksi lama masa studi dibutuhkan pihak manajemen Universitas dalam menentukan kebijakan preventif terkait pencegahan dini kasus Drop Out (DO). Artificial Neural Network (ANN) adalah suatu metode yang meniru jaringan syaraf biologis untuk mempelajari sesuatu. Salah satu implementasi ANN yang banyak digunakan adalah untuk memprediksi. Penelitian ini melakukan prediksi masa studi mahasiswa dengan menggunakan ANN dengan metode pembelajaran backpropagation. Variabel yang digunakan adalah nilai Indeks Prestasi Semester (IPS) 4 semester awal mahasiswa. Data dibagi menjadi data latih dan data uji. Dari hasil pelatihan dan pengujian didapatkan nilai Mean Square Error (MSE) dan Koefisien Relasi (R). MSE digunakan untuk melihat kesalahan rata-rata antara output jaringan dengan target. Nilai R digunakan untuk melihat kuat atau tidaknya hubungan linier antara 2 variabel. Nilai MSE dan koefisien relasi pelatihan adalah 0,016175 dan 0,94353 sedangkan nilai MSE dan koefisien relasi pengujian adalah 0,12188 dan 0,56071. Dari hasil penelitian dapat disimpulkan bahwa ANN dapat digunakan untuk memprediksi masa studi mahasiswa.

show abstract

Predicting Students’ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department

Cited by 49 publications

References 2 publications

Freshmen Program Withdrawal: Types and Recommendations

Freshmen Program Withdrawal: Types and Recommendations

Predicting Academic Outcomes: A Survey from 2007 Till 2018

Aplikasi Artificial Neural Network (ANN) untuk Memprediksi Masa Studi Mahasiswa Program Studi Teknik Informatika Universitas Muhammadiyah Gresik

Contact Info

Product

Resources

About