Module 42: Introduction to Analyzing Unstructured Data in Education

Module Overview

Unstructured data, e.g., text, audio, handwriting and drawings, video, and interaction logs, are increasingly abundant in education. However, they lack the predefined schema that conventional psychometric tools assume. This module introduces a principled, end-to-end workflow for turning such data into defensible measures. In this instructional module, we first define unstructured data (Section 1) and then proceed to four analytic stages: preprocessing raw unstructured data across modalities (Section 2), data representation (Section 3), making inferences (Section 4), and evaluating the resulting measures against psychometric standards of reliability, validity, and fairness (Section 5). The final Section 6 gives an overview of data privacy, consent, and governance as integral to measurement quality rather than as an afterthought. We demonstrate the methodologies using a rich dataset collected from a collaborative problem-solving task. By the end, learners will have a conceptual map of the analysis pipeline from raw signals to valid inference, a solid understanding of modern representation methods including large language model (LLM) embeddings, and a framework for evaluating and governing measures built from unstructured data in the field of education.