高等教育教学中的人工智能技术

IF 0.9 Q3 ENGINEERING, MULTIDISCIPLINARY International Journal of Reliability Quality and Safety Engineering Pub Date : 2022-10-25 DOI:10.1142/s021853932240006x
Qi Chang, Xiajie Pan, N. Manikandan, S. Ramesh
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引用次数: 0

摘要

“人工智能”(AI)一词是指在计算机上模拟人类智能。高等教育可以从人工智能中受益,因为它是一种计算效率很高的范例。适应学生不断变化的需求的学习是人工智能的主要教育优势之一。学生可以调整课程的节奏以提高自己的能力。师资和教学质量差,学生普遍缺乏动力和兴趣是高等教育面临的困难之一。本研究提出了一种人工智能辅助的高等教育一体化教与学框架。多种辅导服务也涉及到课程,这是基于技能。极限学习机(ELM)技术评估设计集成到合适的学生监控模型加权分数(WS)和考试成绩。高等教育领域的人工智能研究,开发出了一种比传统教育更高效、适应性更强、效果更好的教育模式。高等教育对人工智能的使用产生了一种比传统学校更高效、适应性更强、更有效的教育模式。本文提出的AI-ITLF方法具有精度高、性能高、处理成本低、预测率高、错误率低的优点。使用ELM算法评估WS和考试结果,作为适当的学生监控模型的一部分。
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Artificial Intelligence Technologies for Teaching and Learning in Higher Education
The term “Artificial Intelligence” (AI) refers to the simulation of human intelligence on a computer. Higher education can benefit from AI because it is a computationally efficient paradigm. Learning adapted to the changing demands of students is one of the key educational advantages of AI. Students can modify the pace of a course to better competency. Poor faculty and teaching quality and a general lack of motivation and interest among students are among the difficulties facing higher education. An artificial intelligence-assisted integrated teaching–learning framework (AL-ITLF) for higher education is proposed in this research. Multiple tutoring services are also involved in the curriculum, which is skill-based. The extreme learning machine (ELM) technique evaluates designs integrated into the suitable student monitoring model weighted score (WS) and exam results. An educational model that is more efficient, adaptable, and effective than current traditional education has been developed due to AI research in higher education. Higher education’s use of AI has resulted in a more efficient, adaptive, and effective educational model than traditional schooling. High accuracy, higher performance, lower processing costs, and a high prediction and low error rate are advantages of the suggested AI-ITLF approach. The WS and exam results were evaluated using an ELM algorithm as part of a proper student monitoring model.
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来源期刊
CiteScore
1.70
自引率
25.00%
发文量
26
期刊介绍: IJRQSE is a refereed journal focusing on both the theoretical and practical aspects of reliability, quality, and safety in engineering. The journal is intended to cover a broad spectrum of issues in manufacturing, computing, software, aerospace, control, nuclear systems, power systems, communication systems, and electronics. Papers are sought in the theoretical domain as well as in such practical fields as industry and laboratory research. The journal is published quarterly, March, June, September and December. It is intended to bridge the gap between the theoretical experts and practitioners in the academic, scientific, government, and business communities.
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