YouLeQD: Decoding the Cognitive Complexity of Questions and Engagement in Online Educational Videos from Learners' Perspectives

TL;DR

This paper introduces YouLeQD, a dataset of learner questions from YouTube educational videos, and develops models to classify question types and analyze their cognitive complexity, aiding AI-driven educational tools.

Contribution

The study presents a new dataset of learner questions and models for classifying question types and assessing cognitive complexity based on Bloom's Taxonomy.

Findings

Learner questions vary in cognitive complexity.

Question types correlate with engagement metrics.

Models effectively classify question types and complexity.

Abstract

Questioning is a fundamental aspect of education, as it helps assess students' understanding, promotes critical thinking, and encourages active engagement. With the rise of artificial intelligence in education, there is a growing interest in developing intelligent systems that can automatically generate and answer questions and facilitate interactions in both virtual and in-person education settings. However, to develop effective AI models for education, it is essential to have a fundamental understanding of questioning. In this study, we created the YouTube Learners' Questions on Bloom's Taxonomy Dataset (YouLeQD), which contains learner-posed questions from YouTube lecture video comments. Along with the dataset, we developed two RoBERTa-based classification models leveraging Large Language Models to detect questions and analyze their cognitive complexity using Bloom's Taxonomy. This dataset and our findings provide valuable insights into the cognitive complexity of learner-posed questions in educational videos and their relationship with interaction metrics. This can aid in the development of more effective AI models for education and improve the overall learning experience for students.