How learners produce data from text in classifying clickbait

TL;DR

This study investigates how undergraduate students understand and produce data from text when classifying headlines as clickbait or news, revealing insights into their reasoning processes and feature recognition.

Contribution

It introduces a task-based interview method to explore students' reasoning with text data and identifies key feature types used in classification tasks.

Findings

Students recognize function, content, and form features in text.

Most features were identified in the initial scenario.

Participants engaged in both perception and computational reasoning.

Abstract

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.