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Department:  Computer Science
AMOUNT:  20000
PAGES:  90 pages, abstract, chapter 1-5 , APENDIX A source code and APENDIX B output, well reserached and supervised
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1.1            Background of the Study

Decision support system is a specific type of computerized information system that supports decision-making activities. It is intended to assist decision makers in compiling useful information from raw data, user knowledge, and domain model in order to identify and solve problems and make decisions. As such, studies of decision support and decision support systems naturally belong to an environment with multidisciplinary foundations, including (but not exclusively) database and operations research, artificial and computational intelligence, human-computer interaction, modelling and simulation, and software engineering. In particular, the research and development of techniques that enable the construction and performance of selected cognitive decision-making functions form a key to the successful application of decision support systems (Qiang, 2010). In today’s technological era, intelligent decision support has become a need for system’s framework.

Graded Point Average (GPA) is a commonly used indicator of academic performance. Many Universities set a minimum GPA that should be maintained in order to continue in the degree program. In some University, the minimum GPA requirement set for the students is 1.5. Nevertheless, for any graduate program, a GPA of 3.0 and above is considered an indicator of good academic performance. Therefore, GPA still remains the most common factor used by the academic planners to evaluate progression in an academic environment. Many factors could act as barriers to students attaining and maintaining a high GPA that reflects their overall academic performance during their tenure in University. These factors could be targeted by the faculty members in developing strategies to improve student learning and improve their academic performance by way of monitoring the progression of their performance. Therefore, performance evaluation is one of the bases to monitor the progression of student performance in higher Institution of learning.Student academic performance evaluation involves several components, each based on number of imprecise judgments arising due to human (teacher/tutor) interpretation. Both arithmetical and statistical methods have been used for aggregating information from these assessment components in educationaldomain. These commonly used methods have some limitations. For example, in a scenario where two student’s scores are 50, 60, 70, and 70, 60, 50 in three tests, respectively. The average mark obtained by each is 60 without any indication of their intelligence level. However, data indicates that one student is improving while other is deteriorating consistently (i.e. one student is learning consistently).The main aim of educational institutions is to provide students with evaluation reports regarding their test/examination as best as possible with minimum errors. Some factors other than academic have been reported to create or pose a barrier to students attaining and maintaining their high performance. With traditional grouping of students based on their average scores, it is difficult to obtain a comprehensive view of the state of the students’ performance and simultaneously discover important details from their time to time performance. Due to the increased difficulty in analysing students’ academic performance in Nigerian Universities owing to the large number of students and the volume of data to be processed, it becomes imperative to leverage computational tool known as Fuzzy logic in carrying out this analysis.

The use of fuzzy logic approach for academic performance evaluation is in general fairly new. However, it has reached a wide range of application areas in educational systems in addition to evaluation of student academic performance, including the evaluation of curriculum and that of the educators (e.g. lecturers and tutors) (Bai et al, 2006). In student performance evaluation in particular, fuzzy techniques have been adapted for evaluation based on numerical scores obtained in an assessment and for assessing prior educational achievement based on evidence such as academic certificates. Much attention has also been given to adopting fuzzy approaches for the evaluation of teaching

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