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Department:  Computer Science
AMOUNT:  3000
PAGES:  82 pages, abstract, chapter 1-5 , APENDIX A source code and APENDIX B output, well reserached and supervised
  information system
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design a decision support system for student performance analysis using a fuzzy rule based system Abstract One of the major challenges encountered by schools in several countries today mainly is to handle the difficulties of students during the learning process, which in many cases might result in the lack of motivation among lecturers. Thus, evaluating student academic performance plays an important role in academics. Classifying student’s performance using conventional techniques cannot give the desired level of precision and accuracy, deploying a decision support system with the use of soft computing techniques such as fuzzy logic may prove to be beneficial. A student can be classified into one of the available categories based on his or her behavioral and qualitative features. The research work presents a decision support system with Fuzzy Logic techniques to model academic profile and performance of students within the school environ, use of Soft Computing methodology is justified for its real-time applicability in education system. The proposed system was designed and implemented using object oriented programming language PHP5 and MYSQL database and java script. The proposed system adopted Rapid Application Development Methodology (RAD), while carrying analysis for the design and implementation of the proposed system, both primary and secondary method of data collection was used. CHAPTER ONE INTRODUCTION 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 using a computer, in particular in Intelligent Tutoring Systems (ITS) and ComputerAssisted Instruction (CAI). For instance, fuzzy approaches were proposed for determining the level of a student's understanding of a certain subject matter in the context of ITS, and a fuzzy approach was proposed to assess student performance based on several criteria with a strong suggestion that the method be applied toCAI (Yadav et al, 2009). Interesting work has been reported along this line of research. Thisincludes evaluation of journal grades, evaluation of vocational education performance, collaborative assessment, and performance appraisal systems of academics in higher education (Gokmen et al, 2010). The focus of attention of this research work is an evaluation of student academic performance. It proposes the use of a fuzzy logic techniques and fuzzy rule induction approach to obtain user-comprehensible knowledge from historical data to justify any evaluation. This research work shows the advantages of the approach in student performance evaluation as it can be built not only based on information in a given dataset but also allowing expert knowledge to be added if such knowledge is available. Information induced from the dataset, especially that not formerly known by experts in the domain, can be very useful in developing fuzzy models for practical applications 1.2 Statement of the Problem Evaluation of student academic performance usually consists of several components, each involving a number of judgments often based on imprecise data. This imprecision arises from human (teacher/tutor) interpretation of human (students) performance. Arithmetical and statistical methods have been used for aggregating information from these assessment components. These methods have been accepted by many educational institutions around the world although there are limitations with these traditional approaches. In this proposed study, it is argued that the current method of classifying and grading student academic performanceusing arithmetical and statistical techniques does not necessarily offer the best way to evaluate human acquisition of knowledge and skills. It is expected that reasoning based on fuzzy models will provide an alternative way of handling various kinds of imprecise data, which often reflects the way people think and make judgments. 1.3 Aim and Objectives of the Study The aim of this study is to design a decision support system for student performance analysis using a fuzzy rule based system. The objectives are: To design a novel model using Fuzzy logic 

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