Search for: All records

This experience report shares lessons learned when expanding demographic options on an undergraduate survey. The study is designed to better understand the relationship between pre-college computing experiences and the choice to major in computing, particularly focusing on Black women’s experiences. Expansive options for gender (5 plus an openended), race (18 non-mutually-exclusive options), and disability (8) gave respondents more opportunity for specificity. Yet we faced unexpected challenges in analysis and interpretation as we hadn’t considered the implications of being so expansive ahead of time. This paper presents our lessons learned, analysis choices and plans for future iterations of the survey. 

Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order to observe how students reason with text data in scenarios designed to elicit certain aspects of the domain, we employed a task‐based interview method using a structured protocol with six pairs of undergraduate students. Our goal was to shed light on students' understanding of text as data using a motivating task to classify headlines as “clickbait” or “news.” Three types of features (function, content, and form) surfaced, the majority from the first scenario. Our analysis of the interviews indicates that this sequence of activities engaged the participants in thinking at both the human‐perception level and the computer‐extraction level and conceptualizing connections between them.