Affiliation:
1. Department of Mathematics and Statistics Amherst College Amherst Massachusetts USA
2. Concord Consortium Concord Massachusetts USA
Abstract
AbstractText 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.
Funder
National Science Foundation
Subject
Education,Statistics and Probability
Reference27 articles.
1. AI4K12.org.Big idea progression chart.2022https://ai4k12.org.
2. Modern Data Science with R
3. Research on data science education;Biehler R.;Stat. Educ. Res. J.,2022
4. C.Bishop.Pattern recognition and machine learning Springer.2006https://www.microsoft.com/en‐us/research/uploads/prod/2006/01/Bishop‐Pattern‐Recognition‐and‐Machine‐Learning‐2006.pdf.
5. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献