Annals of An Application of Fuzzy Logic to Computational Thinking

European Journal of Electrical Engineering and Computer Science, 2019

A Fuzzy logic controller is a problem-solving control system that provides means for representing approximate knowledge. The output of a fuzzy controller is derived from the fuzzifications of crisp (numerical) inputs using associated membership functions. The crisp inputs are usually converted to the different members of the associated linguistic variables based on their respective values. This point is evident enough to show that the output of a fuzzy logic controller is heavily dependent on its memberships of the different membership functions, which can be considered as a range of inputs [4]. Input membership functions can take various forms trapezoids, triangles, bell curves, singleton or any other shape that accurately enables the distribution of information within the system, in as much as the shape provides a region of transition between adjacent membership functions.

The assessment system generally requires transparency and objectivity to assess student performance in terms of abstraction. Fuzzy logic method has been used as one of the best methods to reduce this uncertainty. Therefore, we have conducted a literature review to examine the application of fuzzy logic in assessing the performance of different students. The Preferred Reporting Items for the Systematics Review and Meta-Analysis Method (PRISMA) were used as the basic method for conducting systematic reviews and meta-analyses. The articles reviewed were 38 articles from 2008 until 2018. All articles were classified based on the author, year of publication, type of journal or conference, sample size, context, data type, fuzzy technique, and basic findings. The results of this review show the positive effects of using fuzzy logic on student performance assessment. Overall, this review provides an appropriate reference for further research by identifying research needs in aspects of student performance assessment.

In this paper we present a method for early identification of students who would have serious difficulties in studying a particular subject due to lack of preliminary knowledge. Methods from formal concept analysis are applied for finding relationships between results from a test on students' preliminary knowledge and the outcome of learning new knowledge within the same subject. Fuzzy logic rules are further incorporated into the process of establishing level of difficulties a student would have while learning new concepts and skills within a particular topic in that subject.

2014

We apply a Triangular Fuzzy Model (TFM) for assessing students' problem solving skills. The TFM is a variation of a special form of the Centre of Gravity (COG) defuzzification technique that we have used in earlier papers for assessing students' performance in several mathematical tasks. The main idea of the TFM is the replacement of the rectangles appearing in the graph of the COG technique by isosceles triangles sharing common parts. In this way we cover the ambiguous cases of students' scores being in the limits between two successive grades (e.g. between A and B). A classroom experiment is also presented illustrating our results in practice.

American Journal of Educational Research, 2014

Fuzzy logic due to its nature of characterizing each case with multiple values offers a rich field of resources covering the assessment of situations characterized by a degree of vagueness and/or uncertainty. In this paper, we apply a new trapezoidal fuzzy assessment model (TRFAM) for measuring students' mathematical modeling (MM) skills. TRFAM is actually a variation of the commonly used in fuzzy logic centre of gravity (COG) defuzzification technique, which we have properly adapted and utilized in earlier papers as an assessment model. Our COG and TRFAM models focus on students' quality performance by assigning greater coefficients to the higher scores. A classroom experiment is also presented, illustrating our results in practice. Through this experiment the above two models are compared to each other and also with two traditional assessment methods based on principles of bivalent logic (the calculation of means and of the GPA index) and some useful conclusions are drawn.

The quality of teaching and learning mathematics have been one of the major challenges of the educators. Mathematics is the subject that has the lot of skills, concepts with variety of topics. This paper presents a mathematical model to analyze the students' mathematical learning skill using fuzzy logic. In this analyses the degree of satisfaction is already defined by experts with respect to levels of performance. From this, the degree of satisfaction of a mathematical topic is calculated and the result is calculated based on all the topics in mathematics. The obtained results from the proposed approach are compared with the conventional non fuzzy approach and the comparative results are given.

International Journal of Advanced Computer Science and Applications

Computational Thinking is a phrase employed to explain the developing concentration on students' knowledge development regarding designing computational clarifications to problems, algorithmic Thinking, and coding. The difficulty of learning computer programming is a challenge for students and teachers. Students' ability in programming is closely related to their problem-solving skills and their cognitive abilities. Even though computational thinking is a problem-solving skill in the 21st century, its use for programming needs to be planned systematically taken into account the appropriate components and elements. Therefore, this study aims to validate the main components and elements of computational thinking for solving problems in programming. At the beginning of the study, researchers conducted a literature review to determine the components and the elements of computational thinking that could be used in teaching and learning programming. This validation involved the consensus of a group of experts using the Fuzzy Delphi method. The data were analysed using the Fuzzy Delphi technique, where the experts individually evaluated the components and elements agreed upon prior discussion. A group of experts consisting of 15 people validated 14 components and 35 elements. The results showed that all components and elements reached a threshold (d) value of less than 0.2, a percentage of agreement exceeded 75%, and the Fuzzy score (A) exceeded 0.5. The finding indicates that the main components and elements of the proposed computational thinking are suitable for problemsolving approaches in programming.

If information and communication technologies (ICT) are to bring about a transformational change to a sustainable society, then we need to transform our thinking. Computer professionals already have a conceptual toolkit for problem solving, sometimes known as computational thinking. However, computational thinking tends to see the world in terms of a series of problems (or problem types) that have computational solutions (or solution types). Sustainability, on the other hand, demands a more systemic approach, to avoid technological solutions, and to acknowledge that technology, human behavior and environmental impacts are tightly interrelated. This paper concerns training about computational thinking in discrete mathematics teaching. Firstly, four main components of computational thinking are given, which are abstract thinking, logical thinking, modeling thinking and constructive thinking. Secondly, some content of discrete mathematics, which have close relationship with computational thinking, are described by corresponding application example. Finally, we give a mapping from knowledge unit of discrete mathematics onto corresponding of details of computational thinking.

2019

Learning assessments of students are very vital in assessing the overall learning performance of students. Knowing the learners capabilities are always part of a teacher’s job to fully understand the learner’s need. Hence, the researchers were motivated to conduct this study on the purpose of evaluating and identifying the performance of students using the fuzzy inference. The research is focusedonthe private university students.Results of examinations from both the lecture and laboratory classes are evaluated to determine the how the student performs in a class. The exam scores of lecture and laboratory class serves as input to the fuzzification technique to determine which among the two class types would the students perform well. In this paper, fuzzy logic basic concepts are applied on theexam results. The authors then concluded that students are performing better during laboratory examinations.

Journal of Information System and Technology Management, 2022

The education system in Malaysia employs classical method in evaluation students' progress performances during their foundation level. The result was obtained by adding all the assessments scored and was graded based on grade A, A-, B+, B, …, and F. Somehow, non-classical methods may be applied such as fuzzy logic, where it can be applied to many forms of decision making. This paper proposed evaluation on students' performances by using fuzzy logic approach. The assessment score obtained based on their assignment, midterm examination and final term examination of 26 Agriscience students, in Preparatory Centre for Science and Technology (PPST), University Malaysia Sabah, UMS was carried out with fuzzy logic. Their results are calculated based on classical method and fuzzy logic approach, which is then compared and analysed. The comparison shows variation between classical and fuzzy logic approach in terms of their overall results.