A Parameter Assessment of Teaching Quality Indicators Based on Data Class Mining Fuzzy K-Mean Type Clustering Article Swipe
Xinhua Huang
,
Yuzudi Tong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.12694/scpe.v25i3.2768
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.12694/scpe.v25i3.2768
This paper proposes a data-based mining and hesitant fuzzy C-canopy-K mean clustering degree algorithm and uses it in the parameter assessment model of teaching quality indicators. Simulation and training are carried out through data class mining, and information input, followed by combining the hesitant fuzzy K-mean classification assessment method, which involves a hesitant fuzzy type evaluation system, a neural network identification and prediction system, and an application system for module identity verification. The simulation results show that the results of the six simulation conditions are consistent with the actual results, with only slight differences in some amplitudes, and a high degree of consistency in the overall trend, the change rule, and the average peak value. Through the prediction model processing in this paper, the teaching quality index parameter assessment has high accuracy and can reach more than 95.0%, in addition, the development of the law also fits very well. a, b, c, d four kinds of teaching quality parameter assessment of the average calculation of the assessment speed increased by 52.5%. In addition, the assessment test after the integrated design of the module shows that the system can effectively identify the four clustering identification processes that can be seen as excellent, good, medium, and poor; at the same time, the test data show that the system class effectively for teaching quality indicator parameter assessment.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.12694/scpe.v25i3.2768
- https://www.scpe.org/index.php/scpe/article/download/2768/997
- OA Status
- diamond
- Cited By
- 1
- References
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394744249
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https://openalex.org/W4394744249Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.12694/scpe.v25i3.2768Digital Object Identifier
- Title
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A Parameter Assessment of Teaching Quality Indicators Based on Data Class Mining Fuzzy K-Mean Type ClusteringWork title
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-12Full publication date if available
- Authors
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Xinhua Huang, Yuzudi TongList of authors in order
- Landing page
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https://doi.org/10.12694/scpe.v25i3.2768Publisher landing page
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https://www.scpe.org/index.php/scpe/article/download/2768/997Direct link to full text PDF
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diamondOpen access status per OpenAlex
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https://www.scpe.org/index.php/scpe/article/download/2768/997Direct OA link when available
- Concepts
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Cluster analysis, Data mining, Identification (biology), Consistency (knowledge bases), Computer science, Fuzzy logic, Class (philosophy), Artificial neural network, Fuzzy clustering, Quality (philosophy), Artificial intelligence, Machine learning, Biology, Philosophy, Botany, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.proposes | 2 |
| abstract_inverted_index.results, | 89 |
| abstract_inverted_index.teaching | 23, 124, 156, 219 |
| abstract_inverted_index.training | 28 |
| abstract_inverted_index.addition, | 139, 172 |
| abstract_inverted_index.algorithm | 13 |
| abstract_inverted_index.combining | 41 |
| abstract_inverted_index.increased | 168 |
| abstract_inverted_index.indicator | 221 |
| abstract_inverted_index.parameter | 19, 127, 158, 222 |
| abstract_inverted_index.processes | 194 |
| abstract_inverted_index.C-canopy-K | 9 |
| abstract_inverted_index.Simulation | 26 |
| abstract_inverted_index.assessment | 20, 47, 128, 159, 166, 174 |
| abstract_inverted_index.clustering | 11, 192 |
| abstract_inverted_index.conditions | 83 |
| abstract_inverted_index.consistent | 85 |
| abstract_inverted_index.data-based | 4 |
| abstract_inverted_index.evaluation | 55 |
| abstract_inverted_index.excellent, | 200 |
| abstract_inverted_index.integrated | 178 |
| abstract_inverted_index.prediction | 62, 117 |
| abstract_inverted_index.processing | 119 |
| abstract_inverted_index.simulation | 73, 82 |
| abstract_inverted_index.amplitudes, | 96 |
| abstract_inverted_index.application | 66 |
| abstract_inverted_index.assessment. | 223 |
| abstract_inverted_index.calculation | 163 |
| abstract_inverted_index.consistency | 102 |
| abstract_inverted_index.development | 141 |
| abstract_inverted_index.differences | 93 |
| abstract_inverted_index.effectively | 188, 217 |
| abstract_inverted_index.indicators. | 25 |
| abstract_inverted_index.information | 37 |
| abstract_inverted_index.verification. | 71 |
| abstract_inverted_index.classification | 46 |
| abstract_inverted_index.identification | 60, 193 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.76868372 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |