Data science education provides tremendous opportunities but remains inaccessible to many communities. Increasing the accessibility of data science to these communities not only benefits the individuals entering data science, but also increases the field's innovation and potential impact as a whole. Education is the most scalable solution to meet these needs, but many data science educators lack formal training in education. Our group has led education efforts for a variety of audiences: from professional scientists to high school students to lay audiences. These experiences have helped form our teaching philosophy which we have summarized into three main ideals: 1) motivation, 2) inclusivity, and 3) realism. To put these ideals better into practice, we also aim to iteratively update our teaching approaches and curriculum as we find ways to better reach these ideals. In this manuscript we discuss these ideals as well practical ideas for how to implement these philosophies in the classroom.

pdf/motivation-inclusivity-and-realism-should-drive-data-science-1dlaiwn1.pdf

Motivation, inclusivity, and realism should drive data science education

Instructors of programming courses must manage a variety of pedagogical dependencies in their teaching materials. For instance, updating the code used in a single lesson can require cascading changes to other lessons in the course. Currently, they must manually maintain these dependencies across many files, which is tedious and error-prone. To help instructors track pedagogical code dependencies, we created a system called Codehound that uses static analysis to automatically detect where functions are introduced and reused through an entire course. To show how Codehound can be used, we present three usage scenarios inspired by our own experiences teaching large data science courses. These scenarios demonstrate how Codehound can help instructors create new content, collaborate with staff to refactor existing content, and estimate the cost of future course changes.

Codehound: Helping Instructors Track Pedagogical Code Dependencies in Course Materials

Computer science instructors have one of the most crucial roles in training and making educational materials. However, they face many challenges everyday that make it difficult to provide a high-quality learning experience to their students. Additionally, demand for computer science training is rapidly increasing, and to meet this demand, classrooms need to run on a larger scale, which may exacerbate instructor pain points further. While many of the previous studies in the computer science education community have focused on improving the students' learning experience, in this study we investigate computer science instructors. It is paramount to understand how instructors can be supported more effectively while continuing to improve the material they use in their courses and allow them to focus on student needs. To understand these instructor challenges, we conducted semi-structured interviews with 32 computer science instructors at universities and community colleges to ask about their experiences in preparing course material, lecturing, grading, providing feedback to students, and what they wished they could change. In this paper, we summarize our findings as themes of challenges and pain points for instructors, the consequences of not solving them, and suggested guidelines that may help resolve or reduce these pain points.

https://dl.acm.org/doi/pdf/10.1145/3545945.3569816

What Is Your Biggest Pain Point?: An Investigation of CS Instructor Obstacles, Workarounds, and Desires

Table transformations are a critical skill to master in order to fluently work with data. In introductory data science courses, however, students have found these transformations particularly challenging to learn. One complex transformation is the pivot transformation, which reorganizes a table based on aggregation and summarizing along selected columns and rows. Current assessments test student understanding in static scenarios. Thus, there is an opportunity to help students explicitly work through the steps and variables needed to express a pivot transformation in a randomizable manner. As such, we explore whether a dynamic digital assessment for the pivot transformation can effectively achieve mastery learning towards this skill. Our design is inspired by Parsons problems, in which answer components (pivot table output labels and values) can be composed into the output of a pivot transformation. A question can be derived from a small randomized dataset, and randomized Pandas code that operates upon the dataset and can be autograded. We plan to conduct a pilot study with data science students to investigate whether 1) using a programmable online platform to practice pivot tables helps improve performance on exams, and 2) randomization and instant feedback on the online platform contribute to improved student learning.

Data Science Mastery Learning Using Parsons Problems-Inspired Table Transformations

Instructors who teach large-scale technical courses, especially on data science and programming, must do a large amount of logistical work when updating their courses. All of this behind-the-scenes labor takes time away from the pedagogically-meaningful work of teaching students. Over the past five years, the authors of this paper have created and updated eight courses for an undergraduate data science program that serves over 2,000 students per year. We present four case studies from our teaching experiences that highlight major challenges in maintaining and updating technical courses: 1) There were intricate dependencies between course materials, so making updates to one part of the course would require updating many other parts. 2) We needed to maintain several variants of course materials such as assignments. 3) We wrote large amounts of ad-hoc custom software infrastructure to manage logistics. 4) We could not easily reuse software written by others. Our case studies point to design ideas for instructor-oriented tools that can reduce the logistical complexities of teaching at scale, thus letting instructors focus on the substance of teaching rather than on mundane logistics.

The Challenges of Evolving Technical Courses at Scale: Four Case Studies of Updating Large Data Science Courses

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The Challenges of Evolving Technical Courses at Scale: Four Case Studies of Updating Large Data Science Courses