Application of fuzzy logic for the assessment of engineering students

Σαμαράκου, Μαρία; Prentakis, Pantelis; Mitsoudis, Dimitrios A.; Karolidis, Dimitrios; Athinaios, Spyros

doi:10.1109/educon.2017.7942914

Cited by 15 publications

(5 citation statements)

References 8 publications

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“…Yang et al 11 proposed a two-tier test-based learning method for enhancing learning outcomes in computer-programming courses in a web-based learning environment. Samarakou et al 12 presented a fuzzy-logic-based model for the diagnosis of the so-called students’ learning profile. The fuzzy logic module was coupled with an interactive open learning environment that incorporated the text comprehension theory by Denhière and Baudet, the dialogue theory of Collins and Beranek, and the learning styles theory of Felder and Silverman.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic and Gagné learning hierarchy for assessing mathematics skills

2021

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This study developed a fuzzy logic and Gagné learning hierarchy (FL-GLH) for assessing mathematics skills and identifying learning barrier points. Fuzzy logic was used to model the human reasoning process in linguistic terms. Specifically, fuzzy logic was used to build relationships between skill level concepts as inputs and learning achievement as an output. Gagné learning hierarchy was used to develop a learning hierarchy diagram, which included learning paths and test questions for assessing mathematics skills. First, the Gagné learning hierarchy was used to generate learning path diagrams and test questions. In the second step, skill level concepts were grouped, and their membership functions were established to fuzzify the input parameters and to build membership functions of learning achievement as an output. Third, the inference engine generated fuzzy values by applying fuzzy rules based on fuzzy reasoning. Finally, the defuzzifier converted fuzzy values to crisp output values for learning achievement. Practical applications of the FL-GLH confirmed its effectiveness for evaluating student learning achievement, for finding student learning barrier points, and for providing teachers with guidelines for improving learning efficiency in students.

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Section: Introductionmentioning

confidence: 99%

Fuzzy logic and Gagné learning hierarchy for assessing mathematics skills

2021

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“…Ideally, assessments should consider individual student strengths and provide valuable information on learning outcomes. Among the recent developments in this area is Assess AI meant to provide a more comprehensive evaluation, considering evidence and student progress over time (Samarakou et al, 2014). These assessment tools use machine learning techniques such as semantic analysis, voice recognition, and reinforcement learning to improve their evaluations and reduce the workload of instructors.…”

Section: Revolutionising Assessmentmentioning

confidence: 99%

Generative AI and Higher Education Assessments: A Competency-Based Analysis

Tenakwah,

Boadu,

Tenakwah

et al. 2023

Preprint

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The development and introduction of AI language models have transformed the way humans and institutions interact with technology, enabling natural and intuitive communication between humans and machines. This paper conducts a competence-based analysis of an emerging AI language model’s task response to provide insight into its language proficiency, critical analysis and reasoning ability, and structure and relevance of the response. A multidisciplinary approach is adopted, drawing from fields such as Accounting, Education, Management, Social Work and Law, to evaluate the responses generated by the AI to higher education assignments. This paper offers insights into the strengths and limitations of language-based AI responses and identifies implications for the design and implementation of higher education assessments.

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“…Knowledge component stored the knowledge about each topic, the analytics component analyzed students' interactions and the student model tracked students' progress on a particular topic. Similarly, Samarakou et al [57] have developed an AI assessment tool that also does qualitative evaluation of students to reduce the workload of instructors who would otherwise spend hours evaluating every exercise. Such tools can be further empowered by machine learning techniques such as semantic analysis, voice recognition, natural language processing and reinforcement learning to improve the quality of assessments.…”

Section: Revolutionizing Assessmentsmentioning

confidence: 99%