Computer simulation on composition of curricular components of an academic process

Abstract

This study proposes a methodology to develop a mathematical model that may help university students choose the best composition of curriculum components. The aim is to provide support for students' decisions in the disciplines registration process, and hence reduce the time needed in order to complete their course. This paper examines the Federal University of Rio de Janeiro (UNIRIO) Information Systems (IS) Master of Science (MSc) students' composition of curricular components, and is based on past UNIRIO IS graduates who could not finish the course within the given time frame as defined in the pedagogical project. Within the School of Applied Informatics (SAI) there is a tutor whose duty is to guide students in their academic life by helping them choose appropriate curriculum components. Students with complicated academic backgrounds pose great difficulty for the tutors. Therefore, SAI finds there is a need to improve and increase the efficiency of the enrolment process into appropriate curricular components. This paper can be divided into two phases; the first provides a literature review that integrates business process modeling, and simulation to business management and the foundation of mathematical models. Initially, we consider the Markov model that is a state transition model. Thus, the evolution of a process in the future is conditioned on the present and past, and is only dependent on its present value. These models have become useful in decision analysis when a problem involves evolution over the time, and when an event can occur repeatedly in an analysed period of time. The second phase of this paper presents a case study using the SAI curriculum. The current semester is analysed, in order to refute or affirm the observations obtained in the previous phase. Quantitative variables will be determined in subscribe (prospective) students in disciplines process in order to collect data for these variables to create subsidies for the Markov model. Once the statistical analysis of the data has been completed, we intend simulate scenarios for final analysis. After the simulations are finished, the scenario analysis will allow the tutor to handle the options for students and offer the best composition of curriculum components. Thereafter, this paper intends to offer a model optimized for the mentoring of students in the SAI.