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Related Experiment Video

Updated: Apr 8, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Enhancing educational assessment through automated question classification using a RoBERTa-based ensemble model.

Muhammad Hamid¹, Saadia Malik², Muhammad Saleem³

¹Department of Computer Science, Government College Women University, Sialkot, Pakistan. mhamid@gcwus.edu.pk.

Scientific Reports

|March 24, 2026

Summary

This summary is machine-generated.

This study introduces a Deep Learning (DL) system for classifying exam questions using Bloom's Taxonomy, achieving 92.37% accuracy. The automated system significantly reduces educator workload and enhances educational assessment accuracy.

Keywords:

Automated Question Classification Bloom’s Taxonomy Educational Assessment Ensemble Learning RoBERTa

Related Experiment Videos

Last Updated: Apr 8, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Area of Science:

Educational Technology
Artificial Intelligence
Natural Language Processing

Background:

Manual classification of exam questions using Bloom's Taxonomy is time-consuming and subjective.
Developing automated methods is crucial for efficient and objective educational assessment.

Purpose of the Study:

To develop and evaluate a Deep Learning (DL) system for automatic classification of exam questions based on Bloom's Taxonomy.
To compare the performance of individual DL models (GRU, LSTM, CNN) and a weighted ensemble model.

Main Methods:

Utilized RoBERTa for text feature extraction.
Employed Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models.
Implemented a Weighted Ensemble model to combine predictions from individual models.

Main Results:

All DL models demonstrated high efficacy in classifying questions.
The weighted ensemble model achieved superior performance with 92.37% accuracy, 0.923 macro F1-Score, and 0.992 AUC.
Statistical significance (McNemar's Test, P=0.048) confirmed the ensemble model's improvement over the best single model.

Conclusions:

The proposed DL system offers a scalable and accurate solution for automatic educational assessment.
This approach can reduce educator workload, minimize bias, and improve the overall quality of educational practices.