What Is Hard about Teaching Machine Learning to Non-Majors? Insights from Classifying Instructors’ Learning Goals

Abstract

Given its societal impacts and applications to numerous fields, machine learning (ML) is an important topic to understand for many students outside of computer science and statistics. However, machine-learning education research is nascent, and research on this subject for non-majors thus far has only focused on curricula and courseware. We interviewed 10 instructors of ML courses for non-majors, inquiring as to what their students find both easy and difficult about machine learning. While ML has a reputation for having algorithms that are difficult to understand, in practice our participating instructors reported that it was not the algorithms that were difficult to teach, but the higher-level design decisions. We found that the learning goals that participants described as hard to teach were consistent with higher levels of the Structure of Observed Learning Outcomes (SOLO) taxonomy, such as making design decisions and comparing/contrasting models. We also found that the learning goals that were described as easy to teach, such as following the steps of particular algorithms, were consistent with the lower levels of the SOLO taxonomy. Realizing that higher-SOLO learning goals are more difficult to teach is useful for informing course design, public outreach, and the design of educational tools for teaching ML.

Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks

“…However, teaching it still is a challenge. As Sulmont et al [20] found for non‐majors at the university level, it is more difficult to teach reasoning about machine learning models than it is to teach the algorithms, yet it is possible [19]. Regarding teacher education, Zieffler et al [22] recently noted that, analogously for mathematics teachers, decision tree algorithms can be taught well in in‐service training, but the evaluation of decision trees is a greater challenge.…”

DataStory™: an interactive sequential art approach for data science and artificial intelligence learning experiences

“…Currently, there is an increasing need for wider access to A.I. learning experiences, including the development of reusable training modules and teaching A.I to non-majors (Sulmont et al, 2019;Way et al, 2016Way et al, , 2017. Even so, pedagogical challenges remain and can benefit from structured efforts such as workflow tools (WINGS) to create A.I.…”

“…Even so, pedagogical challenges remain and can benefit from structured efforts such as workflow tools (WINGS) to create A.I. processes (Gil, 2016;Sulmont et al, 2019). Further, early work indicates positive motivational effects when gaming strategies are integrated into the machine learning educational experience (Wallace et al, 2010).…”

Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning

“…In order to make it easier for students to learn the knowledge they want to learn, the system should have a full understanding of the three objects of teachers, students and courses, and then carry out the service in line with users' expectations. However, traditional algorithms extract specific data based on surveys and other methods, and then use fixed algorithms to analyze users and projects [1,2]. However, this method may not be able to reasonably process part of the data under certain circumstances, resulting in a large proportion of some data and affecting the service quality.…”