Is ChatGPT a Threat to Formative Assessment in College-Level Science? An Analysis of Linguistic and Content-Level Features to Classify Response Types
Abstract
The impact of OpenAI’s ChatGPT on education has led to a reexamination of traditional pedagogical methods and assessments. However, ChatGPT’s performance capabilities on a wide range of assessments remain to be determined. This study aims to classify ChatGPT-generated and student constructed responses to a college-level environmental science question and explore the linguistic- and content-level features that can be used to address the differential use of language. Coh-Metrix textual analytic tool was implemented to identify and extract linguistic and textual feature. Then we employed random forest feature selection method to determine the best representative and nonredundant text-based features. We also employed TF-IDF metrics to represent the content of written responses. The true performance of classification models for the responses was evaluated and compared in three scenarios: (a) using content-level features alone, (b) using linguistic-level features alone, (c) using the combination of two. The results demonstrated that the accuracy, specificity, sensitivity, and F1-score all increased when we used the combination of two-level features. The results of this study hold promise to provide valuable insights for instructors to detect student responses and integrate ChatGPT into their course development. This study also highlights the significance of linguistic- and content-level features in AI education research.