Hybrid OBT+MBT framework for automatic MCQ stem generation

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Evidence · 2 claims (2 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description

A hybrid question-generation system that combines ontology-based Wh-question generation with machine-learning-based Cloze generation for technical-domain multiple-choice assessment.

Components

• Hybrid OBT+MBT framework combining ontology-based and machine-learning-based generation of heterogeneous MCQ stems: The article proposes a hybrid automatic question generation framework in which an Ontology-Based Technique (OBT) generates Wh-type question stems from DL and SWRL rules built over a domain ontology, while a Machine-Learning Based Technique (MBT) generates Cloze stems from unstructured textbook text. As the article states, it "describes a technique for combining semantic-based and machine-learning-based techniques to generate grammatically correct MCQ stems of various types for a technical domain." The two pipelines run simultaneously on a preprocessed text file, and the resulting stems are evaluated against Bloom's Taxonomy cognitive levels. (Praveen Kumar et al. (2024))

Claims

• Prior AQG work shows ML yields grammatically correct Cloze questions while ontology approaches struggle with Cloze but produce meaningful Wh-type questions [+M]
• Existing AQG approaches concentrate on non-technical domains, and technical-domain Wh-type generation has been limited to Bloom's cognitive Level I [+M]

Related Products and Programmes

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Key Sources

• Praveen Kumar, A., Nayak, A., Shenoy K., M., Chaitanya, & Ghosh, K. (2024). A Novel Framework for the Generation of Multiple Choice Question Stems Using Semantic and Machine-Learning Techniques. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00333-6

Page metadata

Field	Value
type	product
id	hybrid-obtmbt-framework-for-automatic-mcq-stem-generation
title	Hybrid OBT+MBT framework for automatic MCQ stem generation
description	A hybrid question-generation system that combines ontology-based Wh-question generation with machine-learning-based Cloze generation for technical-domain multiple-choice assessment.
status	review
product_kind	software
generated	by process:settle-candidates on 2026-10-27
trust	unverified — no one has confirmed this page's content

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