A fully synthetic textual dataset of student learning habits and preferences generated using a large language model

TL;DR

This paper introduces a synthetic dataset of student learning habits and preferences generated using a large language model to support educational research without privacy concerns.

Contribution

The novelty lies in creating a fully synthetic, privacy-preserving dataset of student learning data using a large language model.

Findings

The dataset contains 10,000 records with attributes like education level, study habits, and learning preferences.

It is designed for benchmarking educational NLP pipelines and evaluating synthetic data generation techniques.

The dataset uses controlled distributions and avoids real personal information to ensure privacy.

Abstract

Educational data mining and learning analytics have become important research areas for supporting pedagogical analysis, algorithm development, and privacy-preserving educational research. The advancement of natural language processing (NLP) methods in educational contexts depends on the availability of structured and well-documented textual datasets; however, access to real student data is often restricted due to ethical, legal, and privacy concerns. This article presents a fully synthetic textual dataset of student learning habits and preferences generated using a large language model (LLM). The dataset contains 10,000 CSV-formatted records representing fictional students and includes attributes such as education level, study hours, preferred learning methods, learning challenges, motivation levels, opinions on online learning, and primary devices used for study. Data generation was performed using structured prompting strategies with explicitly defined controlled vocabularies to ensure internal consistency and reproducibility while avoiding the use of any real personal information. The resulting dataset follows intentionally controlled and near-uniform distributions, with variables generated under independent constraints. This design limits its suitability for modelling real-world stochastic behaviour or discovering natural correlations but makes it appropriate for benchmarking educational NLP pipelines, evaluating synthetic data generation techniques, and conducting privacy-preserving survey and machine learning experiments.

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