Timetabling system for student self-scheduling
DOI:
https://doi.org/10.26439/ciis2018.5484Keywords:
student self-scheduling, timetabling, recommender system, course selectionAbstract
In the state of literature, few authors have investigated a schedule recommendation
system for university students; that is, a system that solves the student self-scheduling problem.
This problem is a variant of the student sectioning for the master timetabling classroom assig-
nment. In this variant, students are offered the freedom to make their own schedules; they
must perform several iterations to combine course-sections in the search for the best feasible
schedule given their preferences. A system for the generation of schedules was proposed to
help the students by presenting several schedules that consider their preferences. For this, the
Wong Evolutionary Algorithm (WEA) was proposed, this is an evolutionary algorithm that
achieved to produce several quality results in a single run. Besides, the prototype of the system
was highly accepted by the evaluated students due to the quality of the generated solutions.
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References
Al-Badarenah, A., y Alsakran, J. (2016). An Automated Recommender System for Course Selection. International Journal of Advanced Computer Science and Applications 7(3), pp. 166-175. DOI:10.14569/ijacsa.2016.070323
Babad, E., y Tayeb, A. (2003). Experimental analysis of students’ course selection. British Journal of Educational Psychology 73(3), 373-393. DOI:10.1348/000709903322275894
Babaei, H., Karimpour, J., y Hadidi, A. (2015). A survey of approaches for university course timetabling problem. Computers & Industrial Engineering 86, pp. 43-59. DOI: 10.1016/j.cie.2014.11.010
Carter, M. W., y Laporte, G. (1998). Recent developments in practical course timetabling. En: Burke, E., y Carter, M. (Eds.), Lecture Notes in Computer Science: Vol. 1408. Practice and Theory of Automated Timetabling II (pp. 3-19). Toronto: Springer. DOI:10.1007/bfb0055878
Dostert, M., Politz, A., y Schmitz, H. (2016). A complexity analysis and an algorithmic approach to student sectioning in existing timetables. Journal of Scheduling 19(3), pp. 285-293. DOI:10.1007/s10951-015-0424-2
Fatt, A. C. M., Kee, C. W., Heong, L. C., Seng, N. H., Har, K. N. S., Ni, P. S. ..., y Prakash, E. C. (2000). Software engineering approach for a timetable generator. En: TENCON 2000. Proceedings 3, pp. 147-150. IEEE. DOI:10.1109/TENCON.2000.892240
Kelly, L. K. (1979). Student Self-Scheduling —Is It Worth the Risk? NASSP Bulletin (BUL), 63(424), pp. 84-91. DOI:10.1177/019263657906342414
Kristiansen, S., y Stidsen, T. R. (2013). A Comprehensive Study of Educational Timetabing, a Survey (Informe de DTU Management Engineering Report N.o 8.2013). Recuperado de http://orbit.dtu.dk/files/60366101/A_Comprehensive_Study.pdf
Petrovic, S., y Burke, E. K. (2004). University Timetabling. En: Leung, J. (Ed.), Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Recuperado de https://
pdfs.semanticscholar.org/acf8/6f8bf8bab064a34e9c1c0b258ec0896bf46c.pdf
Qu, R., Burke, E. K., McCollum, B., Merlot, L. T., y Lee, S. Y. (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12(1), 55-89. DOI:10.1007/s10951-008-0077-5
Sahin, M., Kellegoz, T., y Kokhan, S. (2016). A multi-objective decision making model for class selection problem: a case study. The Eurasia Proceedings of Educational & Social Sciences 5, pp. 145-190. Recuperado de http://dergipark.gov.tr/epess/issue/30752/334511
Schaerf, A., y Di Gaspero, L. (2001, September). Local search techniques for educational timetabling problems. En: Proceedings of the 6th International Symposium on Operational Research (SOR-01) (pp. 13-23). Preddvor, Slovenia.
Tamayo, S., Campaña, C., y Expósito, C. (2007). Alternativa para el proceso de planificación de horarios docentes de una Universidad. Ciencias Holguín 13(4). Recuperado de http://www.ciencias.holguin.cu/index.php/cienciasholguin/article/view/411
Unelsrød, H. F. (2011). Design and Evaluation of a Recommender System for Course Selection. Tesis de maestría. Norwegian University of Science and Technology. Recuperado de https://brage.bibsys.no/xmlui/handle/11250/252564
Uslu, S., Ozturan, C., y Uslu, M. F. (2016). Course scheduler and recommendation system for students. En: 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (pp. 1-6). Bakú, Azerbaiyán: IEEE. DOI:10.1109/ICAICT. 2016.7991812
Vialardi, C., Bravo, J., Shafti, L., y Ortigosa, A. (2009). Recommendation in Higher Education,Using Data Mining Techniques. International Working Group on Educational Data Mining, pp. 190-199. Córdoba, España. Recuperado de http://www.educationaldatamining.org/EDM2009/uploads/proceedings/vialardi.pdf
Yang, S., y Jat, S. N. (2011). Genetic algorithms with guided and local search strategies for university course timetabling. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 41(1), pp. 93-106.
Zhang, X., Tian, Y., y Jin, Y. (2015). A knee point-driven evolutionary algorithm for many- objective optimization. IEEE Transactions on Evolutionary Computation 19(6), 761-776. DOI:10.1109/TEVC.2014.2378512
