Using Gaussian membership functions for improving the reliability and robustness of students’ evaluation systems
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The Gaussian membership function depends on two parameters: the mean of the data and its standard deviation.
“Gaussian membership function (MF) [41] depends on two of its parameters, the mean of the data and its standard deviation.”
From Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media (2020) · cites this paper as [41]
Cites
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An application of fuzzy sets in students' evaluation
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Fuzzy set approach to the assessment of student-centered learning
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Using fuzzy numbers in educational grading system
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New methods for students' evaluation using fuzzy sets
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Evaluating students’ learning achievement using fuzzy membership functions and fuzzy rules
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Evaluating Students' Answerscripts Using Fuzzy Numbers Associated With Degrees of Confidence
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A fuzzy system for evaluating students’ learning achievement
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Self-citationAutomatically constructing grade membership functions of fuzzy rules for students’ evaluation
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Learning achievement evaluation strategy using fuzzy membership function
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Flexible, adaptive, automatic fuzzy-based grade assigning system
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Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106754 · Cited by 375 · see citing works
- Authors
- Hameed, I. A.
- Affiliations
- Menoufia University (EG)
- Corresponding author
- Hameed, Ibrahim A.
- ORCID
- Hameed, Ibrahim A.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2011-06
- Volume
- 38
- Issue
- 6
- Pages
- 7135-7142
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1985947439
- Cited by
- 111
- References
- 14
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 14 · 2023: 8 · 2022: 8 · 2021: 8 · 2020: 3 · 2019: 9 · 2018: 6 · 2017: 7 · 2016: 10 · 2015: 2 · 2014: 10 · 2013: 6 · 2012: 4
- FWCI
- 8.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Keywords
- student assessment, Gaussian membership function, fuzzy inference, test scoring, question difficulty, question complexity, reliability statistics, robustness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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