Assessment-via-Teaching: Exploring an Alternative Assessment Strategy in Undergraduate Introductory Data Science Course

Learning-by-teaching is an active learning method that has the promise of engaging students and enhancing their learning. These benefits include the improvement of the metacognitive process, increased motivation and self-efficacy, and the opportunity to refine communication skills. These benefits are particularly valuable in data science education. Though not as widely studied, teaching assignments, where students demonstrate competency through teaching others can be used as a formative assessment tool. This paper describes an "alternative midterm" experiment conducted in an undergraduate introductory data science class. In this pilot, students were asked to demonstrate their newly learned data science skills by teaching them to individuals without data science backgrounds. The evidence of learning will be illustrated through an in-depth analysis of the students' teaching products. These products consist of two parts: (1) "Teaching Preparation" materials, such as Python Notebooks containing problems and solutions crafted by the students, and (2) "Teaching Implementation", video recordings of real-time interactions between the students and their "tutees." The paper will also present qualitative feedback from the students, highlighting promising signs of engagement and acceptance. We will conclude by discussing the challenges and opportunities of implementing this assessment strategy on a larger scale.