A Fuzzy-Logic-Based Student Learning Assessment System for Outcome-Based Education

IF 2 2区 工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES
Abdul Aziz;Md. Asaf-uddowla Golap;M. M. A. Hashem
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引用次数: 0

Abstract

Contribution: This research designs a student evaluation framework integrating the fuzzy-logic system that assesses the student’s performances in the soft boundary system for outcome-based education (OBE), measuring the course learning outcome (CLO) and program learning outcome (PLO). The framework fills the gap between conventional grading methods and offers insights into learning for course assessment and continuous development. Background: A well-established evaluation technique is a requirement to deliver a productive, skilled, worthy, and compatible student and faculty. Moreover, OBE, with a documented and structured academic curriculum, has to ensure the accreditation of an academic program. Research Questions: What are the drawbacks of traditional student evaluation techniques? Does the proposed system work as a better, more reliable, and meaningful student evaluation method? Methodology: To assess, it considers the final examination paper containing several questions and continuous assessment comprising a few items like class tests, quizzes, viva voce, homework, etc., where the course teachers and moderators assign marks on these questions and items considering the CLOs, learning methods, and Bloom’s taxonomy. Then, the framework records and tracks the ratio of earned marks to assigned marks for the fuzzification, while the defuzzification computes the values indicating the CLOs and PLOs earned by a student. Findings: The results study cases for 40 courses of a particular student and analyze statistics for 100 students from the consecutive eight semesters. This fuzzy-logic-based evaluation technique is fairer, reliable, and unbiased to the learners and greatly helps to get accreditation and recognition for the degree worldwide.
基于模糊逻辑的结果型教育学生学习评价系统
贡献:本研究设计了一个整合模糊逻辑系统的学生评价框架,以评估结果为基础的软边界教育系统(OBE)中学生的表现,衡量课程学习成果(CLO)和项目学习成果(PLO)。该框架填补了传统评分方法之间的空白,并为课程评估和持续发展提供了对学习的见解。背景:一个完善的评估技术是提供一个富有成效的,熟练的,有价值的,兼容的学生和教师的要求。此外,OBE,与文件和结构化的学术课程,必须确保学术课程的认证。研究问题:传统的学生评价技术有哪些缺点?所提出的系统是否作为一种更好、更可靠、更有意义的学生评价方法?方法:为了评估,它考虑包含几个问题的期末考试试卷和持续的评估,包括一些项目,如课堂测试,测验,语音,家庭作业等,其中课程教师和主持人根据CLOs,学习方法和布鲁姆的分类法对这些问题和项目分配分数。然后,框架记录并跟踪获得分数与分配分数的比例,用于模糊化,而去模糊化计算指示学生获得的CLOs和PLOs的值。研究结果:对某学生的40门课程进行案例分析,并对连续8个学期的100名学生进行统计分析。这种基于模糊逻辑的评价方法对学习者来说更加公平、可靠、无偏见,极大地帮助了学位在世界范围内获得认可和认可。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Education
IEEE Transactions on Education 工程技术-工程:电子与电气
CiteScore
5.80
自引率
7.70%
发文量
90
审稿时长
1 months
期刊介绍: The IEEE Transactions on Education (ToE) publishes significant and original scholarly contributions to education in electrical and electronics engineering, computer engineering, computer science, and other fields within the scope of interest of IEEE. Contributions must address discovery, integration, and/or application of knowledge in education in these fields. Articles must support contributions and assertions with compelling evidence and provide explicit, transparent descriptions of the processes through which the evidence is collected, analyzed, and interpreted. While characteristics of compelling evidence cannot be described to address every conceivable situation, generally assessment of the work being reported must go beyond student self-report and attitudinal data.
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