Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

Fuzzy-based user modelling for motivation assessment in programming learning adaptive web-based education systems

Learning programming is not an easy task and students often find this subject difficult to understand and pass. One way to improve students’ knowledge in programming is by using Intelligent Tutoring System (ITS) through Adaptive Web-Based Education Systems (AWBESs). The objective of ITS is to provide a personalized tutoring that is tailored to the student’s needs. User modelling is one of the key factors that can meet the ITS intended objectives. From the literature, it was discovered that motivation stands out as one of the critical students’ characteristics that need to be considered when designing a user model. However, from the previous studies, it was discovered that almost all the researchers and educators constructed the user model based on knowledge and skills as students’ characteristics. Thus, the aim of this study is to develop a user model based on students’ motivation known as the Motivation Assessment Model. This is a model that is able to assess students’ motivation level and deliver tutorial materials accordingly. The Motivation Assessment Model was developed based on Self-Efficacy theory that contributes to the fundamental motivation factor which influences students’ motivation during the learning process. Furthermore, to assess the motivation level, fuzzy logic technique was applied. A tutoring system was then developed based on the proposed model using ITS architecture and ADDIE instructional design model. In order to determine students’ knowledge level after using the tutoring system, pre- and post-tests were conducted on the controlled group and experimental group (30 and 31 students). The learning achievements between experimental group (mean = 3.00) and control group (mean = 2.00) indicated that the tutoring system is significantly more effective in improving students’ knowledge level compared to the traditional approach. A usability evaluation was also conducted whereby the effectiveness was evaluated at the number of errors (7.5%) and completion rate (86.5%); efficiency (mean = 4.85); satisfaction evaluated at task level (mean = 6.77) and test level (mean = 83.55). As a conclusion, the overall tutoring system usability results are accepted by students in the experimental group. The research contribution to knowledge is the development of the proposed Motivation Assessment Model for ITS