Prescriptive analytics: Computer science student projection analysis based on system dynamics modelling and simulation

Azman, Natasha Amiera (2023) Prescriptive analytics: Computer science student projection analysis based on system dynamics modelling and simulation. Project Report. Melaka, Malaysia, Universiti Teknikal Malaysia Melaka. (Submitted)

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Abstract

The purpose of this project is to find an approach to acquire a prediction of the number of students enrolled to help in the process of decision and policy making at universities as well as provide an approximate number of graduates which can be considered ‘ICT experts’ that can be supplied to the industry. Through the help of system dynamics, a model has been created to show the flow of enrolment as well as to study the effects of a change of scenario to the future. Data on enrolment of students into courses under FTMK between 2015 to 2022 is acquired and fed into the model. The model is initially able to replicate the flow of enrolment of students through courses. After succeeding in doing so, several scenarios are applied as an analysis of the model’s capabilities. It is found that the model was able to create simulated values that have an error rate of less than 25% which is exceptionally good. A baseline scenario was done to be used as a comparison with the other scenarios which are enrolment of 120 students from 2023 onwards, increasing 10% from the first scenario and increasing 20% from the first scenario. It is found that the model is able to create a forecast of an increase of graduates in the future and becomes almost static in the future after 2028. This is most probably due to the ability of the management to manage the enrolment of students more properly because humans evolve to achieve a skill through experience. Therefore, the model is able to produce a prediction that is somewhat useful.

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