體驗區
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| 篇名 |
人工智能教育评估应用的潜力和局限
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| 並列篇名 |
The Potentials and Limitations of Artificial Intelligent in Education Assessment
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| 作者 |
袁莉(YUAN Li)
、曹梦莹(CAO Mengying)
、约翰．加德纳(GARDNER John)
、迈克尔．奥利里(O'LEARY Michael)
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| 中文摘要 |
随着机器学习和大数据的发展，如何利用人工智能技术优化教学过程和改进教学评估，已成为教育主管部门、科研人员、教育科技公司和教育工作者共同关注的话题。近年来，数十亿学习者在各种学习平台随时随地进行正式和非正式学习，形成了特定的活动轨迹和大量学习数据，应用人工智能技术对数字化学习环境中海量学习数据进行分析，给学生提供自动反馈和评估得到了广泛认可。因此，运用智能技术和大数据分析提高教育评估的效率和有效性也引起研究者越来越多的关注。但人工智能在高风险考试中应用的合理性和有效性备受质疑，其在形成性评估中应用的潜力和局限仍有待探讨。本文通过文献研究，从计算机测评领域相对成熟的两个自动测评系统:作文自动评分系统（AES)和计算机化自适应测验（CAT)的应用以及学术界对其存在问题的争论入手，对人工智能应用于教育评估的前景进行分析，并对人工智能和机器学习在形成性评估中的应用潜力和局限开展讨论。本研究认为，尽管人工智能的算法和大数据分析提高了自动测评系统的反馈速度和准确性，但其对学生深度学习和能力发展评价的应用价值仍然有限，教育评估中应用人工智能要掌握和了解计算机在总结性评估（如AES和CAT等）中的特征和局限，充分利用学习分析在形成性评估中的潜力，促进学生在数字化学习环境下创造力和自主学习能力的发展和培养。
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| 英文摘要 |
With the development of machine learning and big data, how to use Artificial Intelligence (AI) to optimize teaching and learning processes and improve the quality of educational assessment has become a hot topic among educational policymakers, educational researchers, technology developers, and educators. The latest emergence of MOOCs platforms has provided opportunities for learners worldwide to learn anytime and anywhere, which has generated a large amount of learning data to help identify patterns of various learning activities conducted by learners. It is widely recognized that AI can capture data in the learning process in a digital learning environment. It can be analyzed to provide students with instant feedback and evaluate their learning. Therefore, the use of AI and big data to improve the efficiency and effectiveness of educational assessment have attracted great attention. However，in practice， the rationality and effectiveness of the application of artificial intelligence in high-stakes tests are challenged, and the potential and limitations of AI in formative assessment need to be further explored. In evaluating the state of play of Artificial Intelligence informative and summative educational assessment, this paper offers a critical perspective on the two core applications ： Automated Essay Scoring systems and Computerized Adaptive Tests. It also, along with the Big Data analysis approaches to machine learning that underpin them. In this regard，this paper showed that AI had improved the efficiency, speed，and sophistication of summative assessment, especially in analyzing large-scale assessment process data. However，their application value for deep learning of life and evaluation of capacity development is still limited. Therefore, when applying AI in educational assessment, it is important to understand the characteristics and limitations of computerized summative assessment applications (e. g. , AES and CAT) and explore the potential of appropriate, and purposeful learning analytics for formative assessment to support learners in developing their ability and skills on creativity and self-regulated learning in a digital world.
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| 頁次 |
004-014
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| 關鍵詞 |
人工智能
、机器学习
、作文自动评分
、计算机自适应测验
、学习分析
、形成性评估
、artificial intelligence
、machine learning automated essay scoring
、computerized adaptive tests
、learning analytics
、formative assessment
、CSSCI
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| 卷期 |
27:5
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| 日期 |
202110
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| 刊名 |
開放教育研究
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| 出版單位 |
上海遠程教育集團、上海電視大學
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| DOI |
10.13966/j.cnki.kfjyyj.2021.05.001
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