Çevrimiçi Eğitimde Ders Çizelgelemesini Yönetmek İçin Web Tabanlı Bir Karar Destek Sistemi

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1

Mevlüt Uysal , Onur Ceran , Mustafa Tanrıverdi , Erdal Özdoğan , Mutlu Tahsin Üstündağ

https://doi.org/10.2339/politeknik.1517479

Öz

COVID-19 pandemisi, geleneksel yüz yüze eğitimden çevrimiçi öğrenmeye ani bir geçişi zorunlu kılmış ve ders çizelgeleme yönetimi ile verimli bant genişliği kullanımını sağlama konusunda önemli zorluklar ortaya çıkarmıştır. Bu makale, çevrimiçi eğitim bağlamında ders programlamayı optimize etmek için tavlama benzetimi algoritmasını kullanan web tabanlı bir Karar Destek Sistemi’nin (KDS) geliştirilmesini ve uygulanmasını sunmaktadır. Üniversitenin Öğrenci Bilgi Sistemi (ÖBS) ve Öğrenim Yönetim Sistemi (ÖYS) ile sorunsuz bir şekilde entegre olan KDS, otomatik ders programı oluşturma ve gerçek zamanlı veri senkronizasyonu sağlamaktadır. Program koordinatörleri gerekli düzenlemeleri yapabilirken, öğrenciler ve öğretim üyeleri kullanıcı dostu bir arayüz aracılığıyla ders programlarına erişebilmektedir. Deneysel sonuçlar, manuel olarak oluşturulan programlara kıyasla eşzamanlı bağlantıların dağılımında önemli bir iyileşme olduğunu, maksimum sunucu yüklerinin %66'ya varan oranda azaldığını ve standart sapmaların önemli ölçüde düştüğünü göstermektedir. Önerilen KDS, çevrimiçi eğitime geçişin getirdiği acil zorlukları ele almanın yanı sıra gelecekteki ihtiyaçlar için ölçeklenebilir bir çözüm sunarak hem öğrenciler hem de öğretim üyeleri için çevrimiçi öğrenme deneyimini iyileştirmektedir.

Anahtar Kelimeler

Ders Çizelgeleme, Çevrimiçi Eğitim, Karar Destek Sistemi, Tavlama Benzetimi Algoritması

A Web-based Decision Support System for Managing Course Timetabling in Online Education

Öz

The COVID-19 pandemic precipitated an abrupt transition from traditional face-to-face instruction to online learning, posing significant challenges in managing course timetabling and ensuring efficient bandwidth utilization. This paper presents the development and implementation of a web-based Decision Support System (DSS) that employs a simulated annealing algorithm to optimize course scheduling in an online education context. Seamlessly integrated with the university's Student Information System (SIS) and Learning Management System (LMS), the DSS enables automated timetable generation and real-time data synchronization. Program coordinators can make necessary adjustments, while students and instructors access their schedules through a user-friendly interface. Experimental results demonstrate a substantial improvement in the distribution of concurrent connections compared to manually generated timetables, significantly reducing peak server loads by up to 66% and standard deviations. The proposed DSS addresses the immediate challenges of the shift to online education while offering a scalable solution for future needs, thereby enhancing the online learning experience for both students and instructors.

Anahtar Kelimeler

Course Timetabling, Online Education, Decision Support System, Simulated Annealing Algorithm

Kaynakça

• [1] A. Aristovnik, K. Karampelas, L. Umek, and D. Ravšelj, “Impact of the COVID-19 pandemic on online learning in higher education: a bibliometric analysis,” Frontiers in Education, 8, 1225834, (2023).
• [2] E. Geçer and H. Bağci, “Examining students’ attitudes towards online education during COVID-19: evidence from Turkey (Análisis de las actitudes de los estudiantes hacia la educación en línea durante la pandemia de COVID-19. Evidencia de un estudio realizado en Turquía),” Cultura y Educacion, 34(2), 297–324, (2022).
• [3] V. R. Ivanova, “Online Training in Higher Education: an Alternative during COVID-19. Strengths and Weaknesses of Online Training,” Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika, 29(3), 263–275, (2021).
• [4] X. Wang, W. Chen, H. Qiu, A. Eldurssi, F. Xie, and J. Shen, “A Survey on the E-learning platforms used during COVID-19,” in 11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020, Institute of Electrical and Electronics Engineers Inc., 808–814, (2020).
• [5] M. G. Güler and E. Geçici, “A decision support system for scheduling the shifts of physicians during COVID-19 pandemic,” Computers and Industrial Engineering, 150, (2020).
• [6] F. Biwer et al., “Changes and Adaptations: How University Students Self-Regulate Their Online Learning During the COVID-19 Pandemic,” Frontiers in Psychology, 12, (2021).
• [7] T. Favale, F. Soro, M. Trevisan, I. Drago, and M. Mellia, “Campus traffic and e-Learning during COVID-19 pandemic,” Computer Networks, 176, (2020).
• [8] J. Cullinan, D. Flannery, J. Harold, S. Lyons, and D. Palcic, “The disconnected: COVID-19 and disparities in access to quality broadband for higher education students,” International Journal of Educational Technology in Higher Education, 18(1), (2021).
• [9] R. Bansal, A. Gupta, R. Singh, and V. K. Nassa, “Role and impact of digital technologies in E-learning amidst COVID-19 pandemic,” in Proceedings - 2021 4th International Conference on Computational Intelligence and Communication Technologies, CCICT 2021, Institute of Electrical and Electronics Engineers Inc.,194–202, (2021).
• [10] G. Korkmaz and Ç. Toraman, “Are We Ready for the Post-COVID-19 Educational Practice? An Investigation into What Educators Think as to Online Learning,” International Journal of Technology in Education and Science, 4(4), 293–309, (2020).
• [11] R. A. Oude Vrielink, E. A. Jansen, E. W. Hans, and J. van Hillegersberg, “Practices in timetabling in higher education institutions: a systematic review,” Annals of Operations Research, 275(1), 145–160, (2019).
• [12] A. Rezaeipanah, S. S. Matoori, and G. Ahmadi, “A hybrid algorithm for the university course timetabling problem using the improved parallel genetic algorithm and local search,” Applied Intelligence, 51(1), 467–492, (2020).
• [13] H. Altunay and T. Eren, “A literature review for course scheduling problem,” Pamukkale University Journal of Engineering Sciences, 23(1), 55–70, (2017).
• [14] M. Hosny, “Metaheuristic Approaches for Solving University Timetabling Problems: A Review and Case Studies from Middle Eastern Universities,” Smart Innovation, Systems and Technologies, 111, 10–20, (2019).
• [15] E. K. Burke, B. McCollum, A. Meisels, S. Petrovic, and R. Qu, “A graph-based hyper-heuristic for educational timetabling problems,” European Journal of Operational Research, 176(1), 177–192, (2007).
• [16] M. Chen, X. Tang, T. Song, C. Wu, S. Liu, and X. Peng, “A Tabu search algorithm with controlled randomization for constructing feasible university course timetables,” Computers and Operations Research, 123, (2020).
• [17] S. A. Mirhassani and F. Habibi, “Solution approaches to the course timetabling problem,” Artificial Intelligence Review, 39(2), 133–149, (2013).
• [18] R. Bellio, S. Ceschia, L. Di Gaspero, A. Schaerf, and T. Urli, “Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem,” Computers and Operations Research, 65, 83–92, (2016).
• [19] H. Erdoğan Akbulut, F. Ozçelik, and T. Saraç, “A simulated annealing algorithm for the faculty-level university course timetabling problem,” Pamukkale University Journal of Engineering Sciences, 30(1), 17–34, (Feb. 2024).
• [20] K. Xiang, X. Hu, M. Yu, and X. Wang, “Exact and heuristic methods for a university course scheduling problem,” Expert Systems with Applications, 248, 123383, (2024).
• [21] D. Romaguera, J. Plender-Nabas, J. Matias, and L. Austero, “Development of a Web-based Course Timetabling System based on an Enhanced Genetic Algorithm,” Procedia Computer Science, 234, 1714–1721, (2024).
• [22] A. Kiefer, R. F. Hartl, and A. Schnell, “Adaptive large neighborhood search for the curriculum-based course timetabling problem,” Annals of Operations Research, 252(2), 255–282, (2017).
• [23] E. Rappos, E. Thiémard, S. Robert, and J. F. Hêche, “A mixed-integer programming approach for solving university course timetabling problems,” Journal of Scheduling, 25(4), 391–404, (2022).
• [24] M. Mokhtari, M. Vaziri Sarashk, M. Asadpour, N. Saeidi, and O. Boyer, “Developing a Model for the University Course Timetabling Problem: A Case Study,” Complexity, (2021).
• [25] G. Colajanni and P. Daniele, “A new model for curriculum-based university course timetabling,” Optimization Letters, 15(5), 1601–1616, (2021).
• [26] S. Daskalaki and T. Birbas, “Efficient solutions for a university timetabling problem through integer programming,” European Journal of Operational Research, 160(1), 106–120, (2005).
• [27] M. Lindahl, A. J. Mason, T. Stidsen, and M. Sørensen, “A strategic view of University timetabling,” European Journal of Operational Research, 266(1), 35–45, (2018).
• [28] N. C. F. Bagger, G. Desaulniers, and J. Desrosiers, “Daily course pattern formulation and valid inequalities for the curriculum-based course timetabling problem,” Journal of Scheduling, 22(2), 155–172, (2019).
• [29] L. R. Foulds and D. G. Johnson, “SlotManager: A microcomputer-based decision support system for university timetabling,” Decision Support Systems, 27(4), 367–381, (2000).
• [30] J. Miranda, P. A. Rey, and J. M. Robles, “udpSkeduler: A Web architecture based decision support system for course and classroom scheduling,” Decision Support Systems, 52(2), 505–513, (2012).
• [31] A. W. Siddiqui, S. A. Raza, and Z. M. Tariq, “A web-based group decision support system for academic term preparation,” Decision Support Systems, 114, 1–17, (2018).
• [32] T. İnan and A. Fevzi BABA, “Ticari Gemiler İçin Seyir Süresi ve Yakıt Tüketiminin Azaltılması Amaçlı, Hava ve Deniz Şartlarına Göre Rota Optimizasyonu Sistemi (Ege Denizi Örneği),” Politeknik Dergisi, 24(3), 879–892, (2021).
• [33] E. Şener, A. S. Sağlam, and F. Çavdur, “Otonom-Paylaşımlı Araç Yönetim Sistemi,” Politeknik Dergisi, 26(1), 81–92, (2023).
• [34] Ç. Kılıkçıer and E. Yılmaz, “Trafik Işığı Tespiti Yapan Bir Sürücü Güvenlik Destek Sistemi,” Politeknik Dergisi, 21(2), 419–426, (2018).
• [35] C. Barnhart, D. Bertsimas, A. Delarue, and J. Yan, “Course Scheduling Under Sudden Scarcity: Applications to Pandemic Planning,” Manufacturing and Service Operations Management, 24(2), 727–745, (2021).
• [36] A. B. Şimşek, “A course timetabling formulation under circumstances of online education,” Journal of Turkish Operations Management, 2(5), 781–791, (2021).
• [37] C. Cardonha, D. Bergman, and R. Day, “Maximizing student opportunities for in-person classes under pandemic capacity reductions,” Decision Support Systems, 154, 113697, (2022).
• [38] N. M. Arratia-Martinez, C. Maya-Padron, and P. A. Avila-Torres, “University Course Timetabling Problem with Professor Assignment,” Mathematical Problems in Engineering, 2021(1), 6617177, (2021).
• [39] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, 220(4598), 671–680, (1983).