Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve

Eylem Çimen; Aliye Hilal Cevher Aydın; Ahmet Yusuf Cevher

doi:10.63063/jsat.1925483

TR EN

Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve

Abstract

Bu çalışma, Open University Learning Analytics Dataset (OULAD) temelinde öğrenci başarısı ve ders terk riskini öğrenme analitiği yaklaşımıyla incelemeyi amaçlamaktadır. Araştırmada, demografik değişkenler, değerlendirme göstergeleri ve sanal öğrenme ortamı etkileşimleri birlikte ele alınarak bu değişkenlerin öğrenci başarısını açıklama ve ders terk olasılığını tahmin etme potansiyeli değerlendirilmiştir. Çalışma, ikincil veri kullanımına dayalı nicel bir araştırma olarak tasarlanmış; OULAD veri setindeki öğrenci kayıtları, değerlendirme sonuçları ve VLE etkileşim verileri üzerinden betimsel ve tahmine dayalı analizler yürütülmüştür. Bulgular, demografik değişkenlerin tek başına sınırlı bir açıklayıcılığa sahip olduğunu, buna karşılık değerlendirme performansı, aktif gün sayısı, toplam etkileşim düzeyi ve özellikle erken dönem çevrim içi davranışların hem başarı hem de terk riski açısından daha güçlü göstergeler sunduğunu ortaya koymuştur. Ayrıca demografik, assessment ve VLE değişkenlerinin birlikte kullanılmasının daha bütüncül ve güçlü bir analitik çerçeve sağladığı belirlenmiştir. Sonuç olarak çalışma, OULAD veri setinin yalnızca öğrenci performansının tahmini için değil, aynı zamanda yükseköğretimde veri temelli erken uyarı ve destek sistemlerinin geliştirilmesi için de anlamlı bir yöntemsel temel sunduğunu göstermektedir.

Keywords

Öğrenme analitiği, OULAD veri seti, öğrenci performans tahmini, ders terk tahmini, erken müdahale

Ethical Statement

“Bu çalışmada, Yükseköğretim Kurulu tarafından yayımlanan Yapay Zekâ Kullanımına Dair Etik Rehber ile TÜBİTAK Üretken Yapay Zekâ Rehberi esas alınarak dil ve üslubun geliştirilmesi sürecinde OpenAI GPT-5 tabanlı üretken yapay zekâ aracından yararlanılmıştır.”

A Learning Analytics Approach Using the OULAD Dataset: A Preliminary Study on Student Success and Dropout Prediction

Abstract

This study aims to examine student success and course dropout risk based on the Open University Learning Analytics Dataset (OULAD) through a learning analytics approach. In the research, demographic variables, assessment indicators, and virtual learning environment (VLE) interactions are considered together, and the potential of these variables to explain student success and predict the likelihood of course dropout is evaluated. The study is designed as quantitative research based on secondary data usage; descriptive and predictive analyses are conducted using student records, assessment results, and VLE interaction data within the OULAD dataset. The findings reveal that demographic variables alone have limited explanatory power, whereas assessment performance, number of active days, overall level of interaction, and especially early-stage online behaviors provide stronger indicators for both success and dropout risk. Additionally, it is determined that the combined use of demographic, assessment, and VLE variables provides a more comprehensive and robust analytical framework. As a result, the study demonstrates that the OULAD dataset offers a meaningful methodological foundation not only for predicting student performance but also for developing data-driven early warning and support systems in higher education.

Keywords

Learning analytics, OULAD dataset, student performance prediction, dropout prediction, early intervention

Ethical Statement

In this study, an OpenAI GPT-5–based generative artificial intelligence tool was utilized in the process of improving language and style, based on the Ethical Guidelines on the Use of Artificial Intelligence published by the Council of Higher Education and the TÜBİTAK Generative Artificial Intelligence Guide.

References

J. Kuzilek, M. Hlosta, and Z. Zdrahal, "Open University Learning Analytics dataset," Scientific Data, vol. 4, Art. no. 170171, 2017, https://doi.org/10.1038/sdata.2017.171
O. Viberg, M. Hatakka, O. Bälter, and A. Mavroudi, "The current landscape of learning analytics in higher education," Computers in Human Behavior, vol. 89, pp. 98-110, 2018, https://doi.org/10.1016/j.chb.2018.07.027
L. P. Macfadyen and S. Dawson, "Mining LMS data to develop an “early warning system” for educators: A proof of concept," Computers & Education, vol. 54, no. 2, pp. 588-599, 2010, doi: 10.1016/j.compedu.2009.09.008. https://doi.org/10.1016/j.compedu.2009.09.008
H. A. Alhakbani and F. M. Alnassar, "Open learning analytics: A systematic review of benchmark studies using Open University Learning Analytics Dataset (OULAD)," in Proc. 2022 7th International Conference on Machine Learning Technologies (pp. 81–86), ACM, 2022, https://doi.org/10.1145/3529399.3529413
L. Jin et al., "Predictive modelling with the Open University Learning Analytics Dataset (OULAD): A systematic literature review," in A. M. Olney et al. (Eds.), Artificial Intelligence in Education, AIED 2024, Proceedings (pp. 477–484), Springer, 2024, https://doi.org/10.1007/978-3-031-64315-6_46 N. Jha, I. Ghergulescu, and A.-N. Moldovan, "OULAD MOOC dropout and result prediction using ensemble, deep learning and regression techniques," in Proc. 11th International Conference on Computer Supported Education (CSEDU 2019) (Vol. 2, pp. 154–164), SCITEPRESS, 2019, https://doi.org/10.5220/0007767901540164
F. Qiu et al., "Predicting students’ performance in e-learning using learning process and behaviour data," Scientific Reports, vol. 12, Art. no. 453, 2022, https://doi.org/10.1038/s41598-021-03867-8 M. W. Rodrigues, S. Isotani, and L. E. Zárate, "Educational data mining: A review of evaluation process in the e-learning," Telematics and Informatics, vol. 35, no. 6, pp. 1701-1717, 2018, https://doi.org/10.1016/j.tele.2018.04.015
N. Tomašević, N. Gvozdenović, and S. Vranes, "An overview and comparison of supervised data mining techniques for student exam performance prediction," Computers & Education, vol. 143, Art. no. 103676, 2020, https://doi.org/10.1016/j.compedu.2019.103676.
C. Herodotou et al., "The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study," The Internet and Higher Education, vol. 45, Art. no. 100725, 2020, https://doi.org/10.1016/j.iheduc.2020.100725
B. Alnasyan, M. Basheri, and M. Alassafi, "The power of deep learning techniques for predicting student performance in virtual learning environments: A systematic literature review," Computers and Education: Artificial Intelligence, vol. 6, Art. no. 100231, 2024, https://doi.org/10.1016/j.caeai.2024.100231
J. Chen, X. Zhou, J. Yao, and S. Tang, "Application of machine learning in higher education to predict students’ performance, learning engagement and self-efficacy: A systematic literature review," Asian Education and Development Studies, vol. 14, no. 2, pp. 205-240, 2025, https://doi.org/10.1108/AEDS-08-2024-0166

F. A. Al-azazi and M. Ghurab, "ANN-LSTM: A deep learning model for early student performance prediction in MOOC," Heliyon, vol. 9, no. 4, Art. no. e15382, 2023, https://doi.org/10.1016/j.heliyon.2023.e15382
B. Alnasyan et al., "Kanformer: An attention-enhanced deep learning model for predicting student performance in virtual learning environments," Social Network Analysis and Mining, vol. 15, Art. no. 25, 2025, https://doi.org/10.1007/s13278-025-01446-7
B. Ujkani, D. Minkovska, and N. Hinov, "Course success prediction and early identification of at-risk students using explainable artificial intelligence," Electronics, vol. 13, no. 21, Art. no. 4157, 2024, https://doi.org/10.3390/electronics13214157
A. Shaikhanova et al., "Interpretable predictive modeling for educational equity: A workload-aware decision support system for early identification of at-risk students," Big Data and Cognitive Computing, vol. 9, no. 11, Art. no. 297, 2025, https://doi.org/10.3390/bdcc9110297
S. Slade and P. Prinsloo, "Learning analytics: Ethical issues and dilemmas," American Behavioral Scientist, vol. 57, no. 10, pp. 1510-1529, 2013, https://doi.org/10.1177/0002764213479366 D. Bañeres et al., "An early warning system to identify and intervene online dropout learners," International Journal of Educational Technology in Higher Education, vol. 20, Art. no. 3, 2023, https://doi.org/10.1186/s41239-022-00371-5
S. Sohail, A. Alvi, and A. Khanum, "Interpretable and adaptable early warning learning analytics model," Computers, Materials & Continua, vol. 71, no. 2, pp. 3211-3225, 2022, https://doi.org/10.32604/cmc.2022.023560
N. R. Aljohani, A. Fayoumi, and S.-U. Hassan, "Predicting at-risk students using clickstream data in the virtual learning environment," Sustainability, vol. 11, no. 24, Art. no. 7238, 2019, doi: 10.3390/su11247238. https://doi.org/10.3390/su11247238
Y. Liu et al., "Predicting student performance using clickstream data and machine learning," Education Sciences, vol. 13, no. 1, Art. no. 17, 2023, https://doi.org/10.3390/educsci13010017
N. S. Raj and V. G. Renumol, "Early prediction of student engagement in virtual learning environments using machine learning techniques," E-Learning and Digital Media, vol. 19, no. 6, pp. 537-554, 2022, https://doi.org/10.1177/20427530221108027
K. Jawad, M. A. Shah, and M. Tahir, "Students’ academic performance and engagement prediction in a virtual learning environment using random forest with data balancing," Sustainability, vol. 14, no. 22, Art. no. 14795, 2022, https://doi.org/10.3390/su142214795
Z. Shou et al., "Predicting student performance in online learning: A multidimensional time-series data analysis approach," Applied Sciences, vol. 14, no. 6, Art. no. 2522, 2024, https://doi.org/10.3390/app14062522
A. Al-Zawqari, D. Peumans, and G. Vandersteen, “A flexible feature selection approach for predicting students’ academic performance in online courses,” Computers and Education: Artificial Intelligence, vol. 3, Art. no. 100103, 2022, https://doi.org/10.1016/j.caeai.2022.100103
J. C. Gámez-Granados et al., "An algorithm based on fuzzy ordinal classification to predict students’ academic performance," Applied Intelligence, vol. 53, pp. 27537-27559, 2023, https://doi.org/10.1007/s10489-023-04810-2
E. Kalita et al., "LSTM-SHAP based academic performance prediction for disabled learners in virtual learning environments: A statistical analysis approach," Social Network Analysis and Mining, vol. 15, Art. no. 65, 2025, https://doi.org/10.1007/s13278-025-01484-1
A. A. Mubarak, H. Cao, and I. M. Hezam, "Deep analytic model for student dropout prediction in massive open online courses," Computers & Electrical Engineering, vol. 93, Art. no. 107271, 2021, https://doi.org/10.1016/j.compeleceng.2021.107271
K. O. Adefemi, M. B. Mutanga, and V. Jugoo, "Hybrid deep learning models for predicting student academic performance," Mathematical and Computational Applications, vol. 30, no. 3, Art. no. 59, 2025, https://doi.org/10.3390/mca30030059
A. Ben Said, A.-S. G. Abdel-Salam, and K. A. Hazaa, "Performance prediction in online academic course: A deep learning approach with time series imaging," Multimedia Tools and Applications, vol. 83, pp. 55427-55445, 2024, https://doi.org/10.1007/s11042-023-17596-9
R. Asif et al., "Analyzing undergraduate students’ performance using educational data mining," Computers & Education, vol. 113, pp. 177-194, 2017, https://doi.org/10.1016/j.compedu.2017.05.007
E. Fernandes et al., "Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil," Journal of Business Research, vol. 94, pp. 335-343, 2019, https://doi.org/10.1016/j.jbusres.2018.02.012
M. Bilal et al., "The role of demographic and academic features in a student performance prediction," Scientific Reports, vol. 12, Art. no. 13365, 2022, https://doi.org/10.1038/s41598-022-15880-6
Q. Huang et al., "Improving academic performance predictions with dual graph neural networks," Complex & Intelligent Systems, vol. 10, pp. 5815-5834, 2024, https://doi.org/10.1007/s40747-024-01344-z

Details

Primary Language

Turkish

Subjects

Data Mining and Knowledge Discovery

Journal Section

Research Article

Authors

Eylem Çimen
0009-0006-7499-0926
Türkiye

Aliye Hilal Cevher Aydın
0000-0003-1764-9722
Türkiye

Ahmet Yusuf Cevher ^*
0000-0002-0233-3226
Türkiye

Publication Date

June 30, 2026

Submission Date

April 8, 2026

Acceptance Date

May 31, 2026

Published in Issue

Year 2026 Volume: 4 Number: 1

DOI

https://doi.org/10.63063/jsat.1925483

IZ

https://izlik.org/JA76TP89KN

APA

Çimen, E., Cevher Aydın, A. H., & Cevher, A. Y. (2026). Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve. Journal of Studies in Advanced Technologies, 4(1), 31-49. https://doi.org/10.63063/jsat.1925483

AMA

1.Çimen E, Cevher Aydın AH, Cevher AY. Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve. JSAT. 2026;4(1):31-49. doi:10.63063/jsat.1925483

Chicago

Çimen, Eylem, Aliye Hilal Cevher Aydın, and Ahmet Yusuf Cevher. 2026. “Öğrenci Başarı Ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve”. Journal of Studies in Advanced Technologies 4 (1): 31-49. https://doi.org/10.63063/jsat.1925483.

EndNote

Çimen E, Cevher Aydın AH, Cevher AY (June 1, 2026) Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve. Journal of Studies in Advanced Technologies 4 1 31–49.

IEEE

[1]E. Çimen, A. H. Cevher Aydın, and A. Y. Cevher, “Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve”, JSAT, vol. 4, no. 1, pp. 31–49, June 2026, doi: 10.63063/jsat.1925483.

ISNAD

Çimen, Eylem - Cevher Aydın, Aliye Hilal - Cevher, Ahmet Yusuf. “Öğrenci Başarı Ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve”. Journal of Studies in Advanced Technologies 4/1 (June 1, 2026): 31-49. https://doi.org/10.63063/jsat.1925483.

JAMA

1.Çimen E, Cevher Aydın AH, Cevher AY. Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve. JSAT. 2026;4:31–49.

MLA

Çimen, Eylem, et al. “Öğrenci Başarı Ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve”. Journal of Studies in Advanced Technologies, vol. 4, no. 1, June 2026, pp. 31-49, doi:10.63063/jsat.1925483.

Vancouver

1.Eylem Çimen, Aliye Hilal Cevher Aydın, Ahmet Yusuf Cevher. Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve. JSAT. 2026 Jun. 1;4(1):31-49. doi:10.63063/jsat.1925483

Aim & Scope

Author Guidelines

The contents of the articles submitted to the Journal of Studies in Advanced Technologies must be original, not previously published anywhere or sent for publication. The submitting author (especially corresponding author) must ensure that all authors have read and approved the submitted version of manuscript. The journal reserves the right to make appropriate corrections in language and spelling where it deems necessary. Manuscripts can be sent back to the corresponding author for revision when necessary. The articles published in the journal become the property of the journal and the copyright of the articles is taken in the name of "The Journal of Studies in Advanced Technologies". Even partially or completely, the published articles cannot be used anywhere without showing the source. Manuscripts previously published in any language are not considered for publication in the journal.
All of the following files should be uploaded to the system at the stage of submitting an article to our journal:
• Similarity Report
• Submission Declaration
• Copyright Transfer Form
• Ethics committee approval (If your study requires ethics committee approval, you must upload your article with ethics committee approval, if not, indicate it in a section of the article)

The Journal of Studies in Advanced Technologies requires the author(s) to check the entire text of each article sent to the journal with the "iThenticate®" plagarism program, except for the references section, and upload the article scanned in this program to the "Similarity Reports" field. The similarity rate determined by the Editorial Board is maximum 20% for research articles, 25% for review articles, and even within these limits, the similarity rate in a single source is maximum 5%. Articles with similarity rates above the specified rates will not be evaluated.

Name(s) and surname(s), institutional titles of the author(s); The institution(s) and e-mail address(es) of the author(s) should be stated in the "Application Statement" file and should not be given in the manuscript to be uploaded.

The Journal of Studies in Advanced Technologies (JSAT) accepts free format submission.

We do not have strict formatting requirements, but all manuscripts must contain the required sections: Abstract, Keywords, Introduction, Materials & Methods, Results, Conclusions, Figures and Tables with Captions, Funding Information, Author Contributions, Conflict of Interest and other Ethics Statements.

Your references may be in any style, provided that you use the consistent formatting throughout. It is essential to include author(s) name(s), journal or book title, article or chapter title (where required), year of publication, volume and issue (where appropriate) and pagination. DOI numbers (Digital Object Identifier) are not mandatory but highly encouraged. The bibliography software package EndNote, Zotero, Mendeley, Reference Manager are recommended.

When submitted manuscript reaches the revision stage, the journal publication template will be sent to the author(s) and requested to format the manuscript according to journal template.

Ethical Principles and Publication Policy

ETHICAL CONSIDERATIONS
All articles submitted to Journal of Studies in Advanced Technologies should be based on original work and must not be published before. Likewise, the authors should not submit the same manuscript to other journals for consideration at the same time. Each manuscript is first checked by the Editorial office and goes through a blind peer-review process, with at least two referees. Plagiarism, duplication, false authorship/denied authorship, research/data fabrication, article slicing, slicing, copyright infringement, and concealment of conflict of interest are considered unethical behaviors.
All articles that do not comply with accepted ethical standards are removed from the publication. This includes articles with possible irregularities and inconsistencies detected after publication.
In case of possible scientific unethical behavior and ethical publication violation, Committee on Publication Ethics (COPE) Guidelines are taken into account

PUBLICATION POLICY
Manuscripts to be sent to the journal must first be approved by the Editorial Board. In addition, the articles are evaluated by at least two referees. Manuscripts should not exceed 20 pages in total, including the main text, references, charts, figures, and photographs. The responsibility of the articles published in the journal belongs to the author. When submitting a manuscript to the journal, the authors are deemed to have accepted that their studies have not been previously published or sent for publication in another scientific publication.

PUBLICATION LANGUAGE
Publication languages are Turkish and English.

PUBLICATION FREQUENCY
The journal is published twice a year (June and December)

COPYRIGHT
The authors are responsible for their opinions and suggestions in the articles and the accuracy of the resources they used. No copyright is paid to the articles published in the journal. The authors agree to transfer the copyright of the articles accepted for publication to Journal of Studies in Advanced Technology (JSAT).

OPEN ACCESS POLICY
Open access increases global access to information. Journal of Studies in Advanced Technologies (JSAT) adopts an open access policy.
Journal of Studies in Advanced Technologies (JSAT) can be used, reproduced, and shared, provided that the source is cited in the context of open access policy. The official website of the journal is https://dergipark.org.tr/en/pub/jsat. Readers can access all published articles in the journal from this link.

ARTICLE EVALUATION PROCESS

This journal uses double-blind review, which means that both the reviewer and author identities are concealed from the reviewers, and vice versa.
The manuscripts submitted to the journal are subject to peer-reviewing as well as a preliminary evaluation of the Editors and Editorial Board if necessary. Evaluations are made in accordance with the following criteria;
· Compliance with the scope and subject areas of the journal
Plagiarism check
· Compliance with the scientific rules of the journal
· Publication language (abstract, adequacy of keywords, structured abstract translation, etc.),

After the preliminary evaluation process is completed, two main and one substitute reviewers are invited to review, depending on the subject and content of the manuscripts.
First, the manuscript's abstract is sent to the reviewers. The reviewers must declare within 7 days whether they accept reviewing or not. If the reviewers accept the invitation, a full-text file of the manuscript is sent to them. The review period is a maximum of one month. The reviewers evaluate the manuscript on a standard evaluation form sent to them. In addition, if the reviewers wish, they can submit their notes stating their suggestions and opinions.

There are 4 options at the end of evaluation process for the reviewers
· Accept
· Minor revision. (Whether or not the revisions are made is decided by the referee and/or the editorial board).
· Major revision. (After the revision is received by the author(s), it is evaluated for the second round).
· Cannot be published.
If revisions are requested by the referees, the article is directed to the author(s) for necessary actions. The author(s) should submit their revisions within a month. When submitting the revised texts, the author(s) should submit a report containing the list of revisions made according to the suggestions of the editor and the reviewers as a separate file. Revisions made by the author(s) are sent to the reviewers for another round of evaluation which is a 15-day evaluation period. According to the re-evaluation of the reviewers; authors can be asked to revise the article again, the article can be published or rejected with the approval of the editorial board. The final statement of evaluation is sent to the responsible author.

In line with the opinions of the reviewers, 3rd or 4th reviewers can be appointed with the same procedure described above.
Peer reviews guide the Editors and Editorial Board to make their Final Decision. The final decision always rests with the Editors and the Editorial Board.

Indexes

Journal Boards

Editör

Akif Göktuğ BOZKURT

Analytical Spectrometry, Sensor Technology, Food Engineering, Nanotechnology, Food Chemistry and Food Sensory Science, Food Technology

Arif Cem TOPUZ

Natural Language Processing, Open and Distance Learning, Instructional Design, Instructional Technologies

Dergi Kurulu

AN

Alper NUHOĞLU

Environmental Biotechnology, Environmental Engineering, Waste Management, Reduction, Reuse and Recycling, Wastewater Treatment Processes

AB

Ali BALTAKESMEZ

Photonics, Optoelectronics and Optical Communications, Material Physics

AY

Asıf YOKUŞ

Neural Networks, Physics Education, Mathematical Economics, Applied Mathematics

OE

Okan EŞTÜRK

MF

Mahmut FIRAT

Natural Resource Management, Fluid Mechanics and Thermal Engineering, Civil Engineering, Water Resources and Water Structures, Hydrology

Ahmet AKKÖSE

Food Engineering, Food Sciences, Meat Technology

AY

Ali YEŞILDAĞ

Analytical Chemistry

Yayın ve Danışma Kurulu

Nihat Emre BOREKCI

Human Resources Management, Leadership, Organisational Behaviour, Management and Organization Education

ÜY

Ümit YAŞAR

Metabolic Medicine, Medical Biochemistry and Metabolomics (Other), Veterinary Sciences

Yaşar NUHOĞLU

Terrestrial Ecology, Environmental Pollution and Prevention, Air Pollution Modelling and Control

ÜD

Ümmühan DANIŞ

Chemical Sciences, Engineering, Environmental Engineering

AA

Abdullah ATEŞ

Digital Processor Architectures, Optimization in Manufacturing

HÇ

Hasan Ufuk ÇELEBIOĞLU

Proteomics and Intermolecular Interactions, Industrial Microbiology

AD

Ali Rıza DENIZ

Condensed Matter Physics

AA

Ayşe Burcu ATALAY

Food Sciences

Bilge ERTEKIN FILIZ

Food Sciences, Fermentation Technology, Drying Technologies

Ezgi DEMIR ÖZER

Food Engineering, Sustainable Gastronomy, Food Sciences, Food Technology

GK

Gülsen KILINÇ

Operator Algebras and Functional Analysis

Mehmet Murat CEYLAN

Food Engineering, Oil Technology

SD

Semahat DORUK

Education, Microfluidics and Nanofluidics

Serbay DURAN

Modelling and Simulation, Mathematical Physics (Other), Numerical Analysis, Applied Mathematics, Dynamical Systems in Applications

SO

Sevda OCAK

Environmental Engineering, Air Pollution Modelling and Control, Air Pollution and Gas Cleaning , Air Pollution Processes and Air Quality Measurement

SD

Sevgin DIBLAN

Food Packaging, Preservation and Processing

Lütfi Bilal TAŞYÜREK

Physical Sciences, Photonics, Optoelectronics and Optical Communications, Semiconductors

MG

Mahmud GÜNGÖR

YB

Yalçın BOZKURT

Zootechny

HD

Hülya DURUR

Mathematical Sciences

Mizanpaj Editörü

Nihat Emre Börekçi

Nihat Emre BOREKCI

Human Resources Management, Leadership, Organisational Behaviour, Management and Organization Education

Dil Editörü

Abdurrezak Emre KAYA

English As A Second Language, Translation Studies, Linguistics

Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve

Öğrenci Başarı ve Ders Terk Riskinin Tahmininde Öğrenme Analitiği: OULAD Veri Seti Temelli Yorumlanabilir Bir Analitik Çerçeve

Abstract

Keywords

Ethical Statement

A Learning Analytics Approach Using the OULAD Dataset: A Preliminary Study on Student Success and Dropout Prediction

Abstract

Keywords

Ethical Statement

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Aim & Scope

Author Guidelines

Ethical Principles and Publication Policy

Indexes

Other Indexes

Journal Boards

Editör

Dergi Kurulu

Yayın ve Danışma Kurulu

Mizanpaj Editörü

Dil Editörü