Omar Ben-Ayed Heba Samir Younis

Abstract

This study is concerned with the redesign of the class meeting patternat Qatar University. It examines the existing meeting pattern based on itsoperational efficiency, its alignment with the strategic plan of the University,and its perception by the students and the faculty members. The analysis revealsserious limitations and shows the need for a new pattern with a full non-teachingday and no one-hour lectures. A capacity analysis proves the feasibility of such apattern. Taking into consideration the specifications of the Qatari society, it wasjudged that the non-teaching day be split in two-half days. The present researchrecognizes the distinction between scheduling and class meeting patterns andaims to address the under-researched theme of having the meeting pattern as avariable rather than just an input to scheduling.

Metrics

Metrics Loading ...

##plugins.themes.bootstrap3.article.details##

Keywords

Class meeting pattern
time slot/block
course/class scheduling/ timetabling
higher education

References
• Abramson, D. (1991), Constructing school timetables using simulated annealing: Sequential and parallel algorithms, Management Science, 37(1), 98–113.
• Ali, N. S., Hodson-Carlton, K., and Ryan, M. (2004). Students’ perceptions of online learning: Implications for teaching. Nurse Educator, 29(3), 111-115.
• Alvarez-Valdes, R., Crespo, E., and Tamarit, J.M. (2002). Design and implementation of a course scheduling system using Tabu Search. European Journal of Operational Research, 137(3), 512–523.
• Bateson, D.J. (1990). Science achievement in semester and all year courses. Journal of research in science teaching, 27(3), 233–240.
• Becker, W. E., and Powers, J. R. (2001). Student performance, attrition, and class size given missing student data. Economics of Education Review, 20(4), 377-388.
• Beynon, M., Rasmequan, S., and Russ, S. (2002). A new paradigm for computer-based decision support. Decision Support Systems, 33(2), 127–142.
• Boronico, J. (2000). Quantitative modeling and technology driven departmental course scheduling. Omega, 28(3), 327–346.
• Burke, E.K., Elliman, D.G. and Weare, R.F. (1994). A genetic algorithm based university timetabling system. In Proceedings of the 2nd East-West International Conference on Computer Technologies in Education, 1(September), 35-40.
• Burke, E.K. and Petrovic, S. (2002). Recent research directions in automated timetabling. European Journal of Operational Research, 140(2), 266–280.
• Čangalović, M., and Schreuder, J.A. (1991). Exact coloring algorithm for weighted graphs applied to timetabling problems with lectures of different lengths. European Journal of Operational Research, 51(2), 248–258.
• Daskalaki, S., and Birbas, T. (2005). Efficient solutions for a university timetabling problem through integer programming. European Journal of Operational Research, 160(1), 106–120.
• De Causmaecker, P., Demeester, P., and Vanden Berghe, G. (2009). A decomposed metaheuristic approach for a real-world university timetabling problem. European Journal of Operational Research, 195(1), 307-318.
• De Werra, D., Eisenbeis, C., Lelait, S., and Stöhr, E. (2002). Circular-arc graph coloring: On chords and circuits in the meeting graph. European Journal of Operational Research, 136(3), 483–500.
• Dexter, S., Doering, A.H., and Riedel, E. (2006). Content area specific technology integration: A model for educating teachers. Journal of Technology and Teacher Education, 14(2), 325–345.
• Dills, A.K., and Hernández-Julián, R. (2008). Course scheduling and academic performance. Economics of Education Review, 27(6), 646–654.
• Dimopoulou, M., and Miliotis, P. (2001). Implementation of a university course and examination timetabling system. European Journal of Operational Research, 130(1), 202–213.
• Dimopoulou, M., and Miliotis, P. (2004). An automated university course timetabling system developed in a distributed environment: A case study. European Journal of Operational Research, 153(1), 136-147.
• Dowsland, K. (1990), Efficient automated pallet loading, European Journal of Operational Research 44(2), 232–238.
• Foulds, L.R., and Johnson, D.G. (2000). Slot Manager: A microcomputer-based decision support system for university timetabling. Decision Support Systems, 27(4), 367–381.
• Gallo, M.A., and Odu, M. (2009). Examining the relationship between class scheduling and student achievement in college algebra. Community College Review, 36(4), 299–325.
• Hammad, H.G (2014). Redesigning Qatar University Class Meeting Pattern to Improve Performance. MBA Graduation Project, Qatar University.
• Hertz, A. (1991). Tabu search for large scale timetabling problems. European Journal of Operational Research, 54(1), 39–47.
• Hughes, J. N., Luo, W., Kwok, O. M., and Loyd, L. K. (2008). Teacher-student support, effortful engagement, and achievement: A 3-year longitudinal study. Journal of educational psychology, 100(1), 1-14.
• Lee, T.D., and Genovese, E.D. (1988). Distribution of practice in motor skill acquisition: Learning and performance effects reconsidered. Research Quarterly for Exercise and Sport, 59(4), 277–287.
• Marburger, D. R. (2006). Does mandatory attendance improve student performance? The Journal of Economic Education, 37(2), 148-155.
• MirHassani, S.A. (2006). A computational approach to enhancing course timetabling with integer programming. Applied Mathematics and Computation, 175(1), 814–822.
• Oran, A.F. (2009). Time management in higher education: Reforming the credit hour system in Jordan’s universities. Education, Business and Society: Contemporary Middle Eastern Issues, 2(1), 32–43.
• Pongcharoen, P., Promtet, W., Yenradee, P., and Hicks, C. (2008). Stochastic optimization timetabling tool for university course scheduling. International Journal of Production Economics, 112(2), 903-918.
• Qatar University, Registration Department, Schedules Section (2015). Class Schedules for Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013, Fall 2013, Spring 2014, Fall 2014, Spring 2015, and Fall 2015.
• Qatar University, Office of Institutional Planning and Development (2015). Book of Trends.
• Qatar University, Registration Department, Schedules Section (2011). Class Scheduling Policy and Procedures.
• Ream, R. K., and Rumberger, R. W. (2008). Student engagement, peer social capital, and school dropout among Mexican American and non-Latino white students. Sociology of Education, 81(2), 109-139.
• Reardon, S.F., and Galindo, C. (2009). The Hispanic-White achievement gap in math and reading in the elementary grades. American Educational Research Journal, 46(3), 853–891.
• Rettig, M.D., and Canady, R.L. (1996). All around the block: The benefits and challenges of a non-traditional school schedule. School Administrator, 53(8), 8–14.
• Romer, P. (1993). Idea gaps and object gaps in economic development. Journal of monetary economics, 32(3), 543-573.
• Smith, K.A., and Ng, A. (2003). Web page clustering using a self-organizing map of user navigation patterns. Decision Support Systems, 35(2), 245–256.
• Stallaert, J. (1997). Automated timetabling improves course scheduling at UCLA. Interfaces, 27(4), 67–81.
• Thompson, G. M. (2005). Using information on unconstrained student demand to improve university course schedules. Journal of Operations Management, 23(2), 197-208.
• Toppino, T.C., and Bloom, L.C. (2002). The spacing effect, free recall, and two-process theory: A closer look. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(3), 437.
• Valouxis, C., and Housos, E. (2003). Constraint programming approach for school timetabling. Computers and Operations Research, 30(10), 1555–1572.
• Willingham, W. W., Pollack, J. M., and Lewis, C. (2002). Grades and test scores: Accounting for observed differences. Journal of Educational Measurement, 1-37.
How to Cite
Ben-Ayed, Omar, and Heba Samir Younis. 2015. “Redesigning the Schedule Time Slots for Qatar University to Cope With Local Specificities”. Studies in Business and Economics 18 (2). https://doi.org/10.29117/sbe.2015.0090.
Section
Articles