Course timetabling is a time consuming problem that arise every year in each university. This is a problem that deals with assigning a large number of courses to a limited number of rooms and timeslots  while satisfying a set of predefined constraints. Most of the existing timetabling systems can only be used at the university involved in the research, as each university has its own needs and requirements that differ specifically. MyCourses software tries to make a difference by allowing each university to configure which scheduling rules to use. It provides a way to schedule courses for a whole semester rather than on weekly basis. It also gives teachers the ability  to easily specify their preferences,  facultymembers to enter various university data and also provides an optional way for scheduling university courses. The MyCourses’s automation solver uses Simulated Annealing as an optimization technique for solving the NP hard scheduling problem. Simulated Annealing searches for  a better solution as well as  it  has advantage to escape from local minimum  by allowing  to move to  worse  solution  in comparison with other algorithms which always seeks a better one.

Made available in DSpace on 2014-07-29T14:57:46Z (GMT). No. of bitstreams: 1 dissertacao_jean.pdf: 321149 bytes, checksum: 11c9f94be02284e8412d026b60b596d0 (MD5) Previous issue date: 2010-08-16 / The Course Scheduling is a hard resolution problem, found in most of the learning institutions. Just like the others timetabling problems, the Course Scheduling have a strong associative characteristic, that means that its resolution is made of associations between events and resources. In the educational case, the lectures are events, while the teachers workload are resources. Techniques and methods have being used on the solution of these kind of problems, however is small the number of universities using software based solutions. This work is a starting point to software based solutions applied to the Federal University of Goiás. / O Agendamento Semanal de Aulas é um problema de difícil resolução enfrentado em grande maioria das instituições de ensino. Assim como os demais problemas de timetabling, possui como característica principal a sua natureza associativa, ou seja, sua resolução envolve a associação entre uma certa quantidade de recursos e eventos que utilizarão tais recursos. Especificamente em relação ao problema em questão, as aulas a serem ministradas podem ser caracterizadas como eventos, enquanto que a carga horária dos professores envolvidos podem ser vistas como recursos disponíveis (Programação de Horários de Aulas). Técnicas e métodos de grande relevância na ciência da computação estão relacionados na pesquisa e na solução destes tipos de problemas, contudo, a utilização de tais tecnologias no cotidiano de escolas e universidades ainda é pequena. Neste contexto, propõe-se uma abordagem para a resolução de Problemas de Programação de Horários, incluindo o Problema de Alocação de Professores a Disciplinas, e utiliza-se o Instituto de Informática da Universidade Federal de Goiás como um estudo de caso para tal.

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University course planning and scheduling is the process of determining what courses to offer, how many sections are needed, determining the best term to offer each section, assigning a faculty member to instruct each section, and scheduling each section to a timeslot to avoid conflicts. The result of this task has an impact on every student and faculty member in the department. The process is typically broken down into three major phases: course offering planning, faculty assignment to planned course sections, and course scheduling into timeslots. This thesis looks at each of these phases for the Industrial and Manufacturing department and brings them together into a decision support and scheduling system. A decision support tool is created to facilitate planning of course offerings. Operations research is applied to assign sections to faculty members using a faculty preference driven integer linear programming model in order to minimize dissatisfaction in the department. Next, the faculty-section pairs are scheduled into university timeslots using a complex integer linear programming model. This scheduling model takes into consideration the faculty member time availability and preferences and general student time slot preferences as it minimizes dissatisfaction while avoiding conflicts among labs, faculty members and courses offered for each class level.

During the past decade, there has been a dramatic increase by postsecondary institutions in providing academic programs and course offerings in a multitude of formats and venues (Biemiller, 2009; Kucsera & Zimmaro, 2010; Lang, 2009; Mangan, 2008). Strategies pertaining to reapportionment of course-delivery seat time have been a major facet of these institutional initiatives; most notably, within many open-door 2-year colleges. Often, these enrollment-management decisions are driven by the desire to increase market-share, optimize the usage of finite facility capacity, and contain costs, especially during these economically turbulent times. So, while enrollments have surged to the point where nearly one in three 18-to-24 year-old U.S. undergraduates are community college students (Pew Research Center, 2009), graduation rates, on average, still remain distressingly low (Complete College America, 2011). Among the learning-theory constructs related to seat-time reapportionment efforts is the cognitive phenomenon commonly referred to as the spacing effect, the degree to which learning is enhanced by a series of shorter, separated sessions as opposed to fewer, more massed episodes. This ex post facto study explored whether seat time in a postsecondary developmental-level algebra course is significantly related to: course success; course-enrollment persistence; and, longitudinally, the time to successfully complete a general-education-level mathematics course. Hierarchical logistic regression and discrete-time survival analysis were used to perform a multi-level, multivariable analysis of a student cohort (N = 3,284) enrolled at a large, multi-campus, urban community college. The subjects were retrospectively tracked over a 2-year longitudinal period. The study found that students in long seat-time classes tended to withdraw earlier and more often than did their peers in short seat-time classes (p < .05). Additionally, a model comprised of nine statistically significant covariates (all with p-values less than .01) was constructed. However, no longitudinal seat-time group differences were detected nor was there sufficient statistical evidence to conclude that seat time was predictive of developmental-level course success. A principal aim of this study was to demonstrate—to educational leaders, researchers, and institutional-research/business-intelligence professionals—the advantages and computational practicability of survival analysis, an underused but more powerful way to investigate changes in students over time.