Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach
In the process of self-assessment and accreditation examination, assessment is carried out according to a scale that covers four levels of compliance with the quality criteria of the educational program and educational activities. Assessing the quality of education is complicated by the fact that t...
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| Format: | Article |
| Language: | English |
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Academy of Cognitive and Natural Sciences
2022-03-01
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| Series: | CTE Workshop Proceedings |
| Subjects: | |
| Online Access: | https://acnsci.org/journal/index.php/cte/article/view/112 |
| _version_ | 1797794447310716928 |
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| author | Andriy V. Ryabko Oksana V. Zaika Roman P. Kukharchuk Tetiana A. Vakaliuk Viacheslav V. Osadchyi |
| author_facet | Andriy V. Ryabko Oksana V. Zaika Roman P. Kukharchuk Tetiana A. Vakaliuk Viacheslav V. Osadchyi |
| author_sort | Andriy V. Ryabko |
| collection | DOAJ |
| description |
In the process of self-assessment and accreditation examination, assessment is carried out according to a scale that covers four levels of compliance with the quality criteria of the educational program and educational activities. Assessing the quality of education is complicated by the fact that the value of quality criteria is due to a large number of factors, possibly with an unknown nature of influence, as well as the fact that when conducting pedagogical measurements it is necessary to work with non-numerical information. To solve these problems, the authors proposed a method for assessing the quality of educational programs and educational activities based on the adaptive neuro-fuzzy input system (ANFIS), implemented in the package Fuzzy Logic Toolbox system MATLAB and artificial neural network direct propagation with one output and multiple inputs. As input variables of the system ANFIS used criteria for evaluating the educational program. The initial variable of the system formed a total indicator of the quality of the curriculum and educational activities according to a certain criterion or group of criteria. The article considers a neural network that can provide a forecast for assessing the quality of educational programs and educational activities by experts. The training of the artificial neural network was carried out based on survey data of students and graduates of higher education institutions. Before the accreditation examination, students were offered questionnaires with a proposal to assess the quality of the educational program and educational activities of the specialty on an assessment scale covering four levels. Student assessments were used to form the vector of artificial neural network inputs. It was assumed that if the assessments of students and graduates are sorted by increasing the rating based on determining the average grade point average, the artificial neural network, which was taught based on this organized data set, can provide effective forecasts of accreditation examinations. As a result of comparing the initial data of the neural network with the estimates of experts, it was found that the neural network does make predictions quite close to reality.
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| first_indexed | 2024-03-13T03:02:00Z |
| format | Article |
| id | doaj.art-c35fc792352a4d388c3746e47a704824 |
| institution | Directory Open Access Journal |
| issn | 2833-5473 |
| language | English |
| last_indexed | 2024-03-13T03:02:00Z |
| publishDate | 2022-03-01 |
| publisher | Academy of Cognitive and Natural Sciences |
| record_format | Article |
| series | CTE Workshop Proceedings |
| spelling | doaj.art-c35fc792352a4d388c3746e47a7048242023-06-27T11:09:04ZengAcademy of Cognitive and Natural SciencesCTE Workshop Proceedings2833-54732022-03-01910.55056/cte.112Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approachAndriy V. Ryabko0Oksana V. Zaika1Roman P. Kukharchuk2Tetiana A. Vakaliuk3Viacheslav V. Osadchyi4Oleksandr Dovzhenko Hlukhiv National Pedagogical University Oleksandr Dovzhenko Hlukhiv National Pedagogical University Oleksandr Dovzhenko Hlukhiv National Pedagogical University Zhytomyr Polytechnic State University Bogdan Khmelnitsky Melitopol State Pedagogical University In the process of self-assessment and accreditation examination, assessment is carried out according to a scale that covers four levels of compliance with the quality criteria of the educational program and educational activities. Assessing the quality of education is complicated by the fact that the value of quality criteria is due to a large number of factors, possibly with an unknown nature of influence, as well as the fact that when conducting pedagogical measurements it is necessary to work with non-numerical information. To solve these problems, the authors proposed a method for assessing the quality of educational programs and educational activities based on the adaptive neuro-fuzzy input system (ANFIS), implemented in the package Fuzzy Logic Toolbox system MATLAB and artificial neural network direct propagation with one output and multiple inputs. As input variables of the system ANFIS used criteria for evaluating the educational program. The initial variable of the system formed a total indicator of the quality of the curriculum and educational activities according to a certain criterion or group of criteria. The article considers a neural network that can provide a forecast for assessing the quality of educational programs and educational activities by experts. The training of the artificial neural network was carried out based on survey data of students and graduates of higher education institutions. Before the accreditation examination, students were offered questionnaires with a proposal to assess the quality of the educational program and educational activities of the specialty on an assessment scale covering four levels. Student assessments were used to form the vector of artificial neural network inputs. It was assumed that if the assessments of students and graduates are sorted by increasing the rating based on determining the average grade point average, the artificial neural network, which was taught based on this organized data set, can provide effective forecasts of accreditation examinations. As a result of comparing the initial data of the neural network with the estimates of experts, it was found that the neural network does make predictions quite close to reality. https://acnsci.org/journal/index.php/cte/article/view/112evaluation criteriaeducational programeducational activitiesprognosticationratingANFIS |
| spellingShingle | Andriy V. Ryabko Oksana V. Zaika Roman P. Kukharchuk Tetiana A. Vakaliuk Viacheslav V. Osadchyi Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach CTE Workshop Proceedings evaluation criteria educational program educational activities prognostication rating ANFIS |
| title | Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach |
| title_full | Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach |
| title_fullStr | Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach |
| title_full_unstemmed | Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach |
| title_short | Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach |
| title_sort | methods for predicting the assessment of the quality of educational programs and educational activities using a neuro fuzzy approach |
| topic | evaluation criteria educational program educational activities prognostication rating ANFIS |
| url | https://acnsci.org/journal/index.php/cte/article/view/112 |
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