Learning Evaluation Method Based on Artificial Intelligence Technology and Its Application in Education Article Swipe
Artificial intelligence (AI) is a transformative technology that enables machines to perform tasks that typically require human intelligence. Through algorithms and advanced computing systems, AI enables machines to perceive their environment, reason, learn from experience, and make decisions autonomously. From virtual assistants like Siri and Alexa to self-driving cars and advanced medical diagnosis systems, AI applications are reshaping industries and revolutionizing the way we live and work. With its ability to process vast amounts of data and identify complex patterns, AI has the potential to drive innovation, improve efficiency, and solve some of society's most pressing challenges. This paper introduces a novel learning evaluation method based on artificial intelligence technology, specifically leveraging Mamdani Fuzzy Clustering Middle Order Classification (MFCM-OC), and explores its application in education. The proposed method aims to provide a comprehensive assessment of student learning outcomes by analyzing various factors such as academic performance, engagement, and cognitive development. Through simulated experiments and empirical validations, the efficacy of the MFCM-OC-enhanced learning evaluation method is evaluated. Results demonstrate significant improvements in accuracy and granularity compared to traditional evaluation methods. For instance, the MFCM-OC model achieved an average accuracy rate of 85% in predicting student performance, allowing for targeted interventions and personalized learning plans. Additionally, the method enables educators to identify students' strengths and weaknesses more effectively, facilitating data-driven decision-making and continuous improvement in educational practices. These findings underscore the potential of artificial intelligence with MFCM-OC in revolutionizing learning evaluation and enhancing educational outcomes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.52783/jes.1722
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- OA Status
- hybrid
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All OpenAlex metadata
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https://openalex.org/W4394577956Canonical identifier for this work in OpenAlex
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https://doi.org/10.52783/jes.1722Digital Object Identifier
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Learning Evaluation Method Based on Artificial Intelligence Technology and Its Application in EducationWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-31Full publication date if available
- Authors
-
Hua BaoList of authors in order
- Landing page
-
https://doi.org/10.52783/jes.1722Publisher landing page
- PDF URL
-
https://journal.esrgroups.org/jes/article/download/1722/1416Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://journal.esrgroups.org/jes/article/download/1722/1416Direct OA link when available
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Artificial intelligence, Computer science, Machine learningTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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