Toward a Framework for Teaching Artificial Intelligence to a Higher Education Audience
Toward a Framework for Teaching Artificial Intelligence to a Higher Education Audience
复制标题
建立向高等教育受众教授人工智能的框架
DOI：
10.1145/3485062
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发表时间：
2021
影响因子：
2.4
通讯作者：
Allen B
中科院分区：
文献类型：
--
作者：
Allen B
人工智能及其子学科在学术界和工业界的众多领域中变得越来越重要，现在可以被视为计算机科学的核心领域。高等教育部门提供的机器学习和人工智能课程比以往任何时候都多。然而，缺乏与这个严重依赖计算和数学知识的复杂领域教学最佳实践相关的研究。我们对来自一系列教育机构的学生和高等教育讲师进行了文献综述和定性研究，旨在确定高等教育这一领域的最佳实践。我们假设信心、数学焦虑和学生教育背景的差异是这里的关键因素。然后，我们调查了围绕这些问题以及它们是否抑制了与人工智能和机器学习理论基础相关的知识和技能的获取。本文分享了在人工智能和机器学习教育领域拥有丰富经验的学生和讲师的见解，旨在为该领域的未来教学法和研究提供信息，并朝着这些主题的教学和学习的最佳实践框架迈进。
Artificial Intelligence and its sub-disciplines are becoming increasingly relevant in numerous areas of academia as well as industry and can now be considered a core area of Computer Science . The Higher Education sector are offering more courses in Machine Learning and Artificial Intelligence than ever before. However, there is a lack of research pertaining to best practices for teaching in this complex domain that heavily relies on both computing and mathematical knowledge. We conducted a literature review and qualitative study with students and Higher Education lecturers from a range of educational institutions, with an aim to determine what might constitute best practices in this area in Higher Education. We hypothesised that confidence, mathematics anxiety, and differences in student educational background were key factors here. We then investigated the issues surrounding these and whether they inhibit the acquisition of knowledge and skills pertaining to the theoretical basis of artificial intelligence and machine learning. This article shares the insights from both students and lecturers with experience in the field of AI and machine learning education, with the aim to inform prospective pedagogies and studies within this domain and move toward a framework for best practice in teaching and learning of these topics.