Identifying Best Practices for Teaching Machine Learning

目前，机器学习技术人员明显短缺，无法满足就业市场日益增长的需求。为了解决这个问题，高等教育（HE）部门正在提供更多的机器学习和人工智能（AI）课程。然而，在这一复杂领域缺乏有关教学最佳做法的研究。这项研究旨在确定机器学习和人工智能主题中的阈值概念，沿着确定数学焦虑和低自我效能感这两个认知障碍是否成立。我们通过使用问卷调查和观察等定性研究方法来实现这一目标。在质性研究的基础上，提出了应对策略。这项研究的成果将是创建一个在线学习工具，包括减轻学生在进行机器学习或人工智能课程时面临的障碍的方法。

There is currently a distinct shortage of individuals skilled in machine learning to fill the increasing demand within the job market. To counteract this issue the Higher Education (HE) sector are offering more courses in machine learning and Artificial Intelligence (AI). However, there is a lack of research pertaining to best practices for teaching in this complex domain. This research aims to identify the threshold concepts within the topic of machine learning and AI along with identifying if two perceived cognitive barriers of maths anxiety and low self-efficacy hold true. We achieve this through the use of qualitative research methods such as questionnaires and observations. Counteracting strategies will be developed based on the analysis of the qualitative research. The outcome of this study will be creation of an online learning tool encompassing methods to alleviate the barriers students face when undertaking a machine learning or AI course.