Analyzing Process Data from Problem-Solving Items with N-Grams

Abstract

This chapter draws on process data recorded in a computer-based large-scale program, the Programme for International Assessment of Adult Competencies (PIAAC), to address how sequences of actions recorded in problem-solving tasks are related to task performance. The purpose of this study is twofold: first, to extract and detect robust sequential action patterns that are associated with success or failure on a problem-solving item, and second, to compare the extracted sequence patterns among selected countries. Motivated by the methodologies of natural language processing and text mining, we utilized feature selection models in analyzing the process data at a variety of aggregate levels and evaluated the different methodologies in terms of predictive power of the evidence extracted from process data. It was found that action sequence patterns significantly differed by performance groups and were consistent across countries. This study also demonstrated that the process data were useful in detecting missing data and potential mistakes in item development.