How Data Mining Can Transform School Learning: Insights from AI

Data Mining in School Learning

One of today’s three hot scientific topics is artificial intelligence (AI). AI aims to give artificial objects decision‑making abilities similar to humans, relying on computers, the internet, programming, and mathematics to process massive data—e.g., distinguishing cats from dogs in photos or identifying individuals.

The purpose of this article is not to discuss AI itself, but to imagine how the data‑mining ideas that AI depends on can inspire the exploration of learning patterns in education.

Concepts such as deep learning, machine learning, and expert systems share a common approach: using computers to perform tasks traditionally done by humans. Early machines were clumsy, but modern technology enables them to discover patterns in huge datasets—something difficult for humans but straightforward for computers.

Applying machine learning to school learning begins with data. Traditional educational testing evaluates scores, item difficulty, discrimination, reliability, and validity, but these external measures reveal little about students’ thought processes. Direct teacher‑student interaction can provide insight, yet it is impractical to interview every learner, and sampling inevitably loses information.

Today, vast amounts of data are collected automatically by machines tracking user behavior, but most Chinese schools still rely on blackboards and PPTs, which offer no interaction or data capture. To enable data mining, a new framework for data collection is required.

The first step is to build a framework that defines indicators such as learning time, classroom participation, response time, and accuracy, then quantifies these metrics into a data table.

The second step is to analyze the collected data using established mathematical, statistical, or computer‑science models—decision trees, clustering, and other techniques provide numerous analytical options.

The third step involves interpreting the analysis results.

By adopting a rigorous statistical research perspective, novices can accelerate their path to expertise, gaining not only known expert conclusions but also uncovering previously unknown relationships that can improve educational practice.