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Question classification based on Bloom's taxonomy cognitive domain using modified TF-IDF and word2vec.

Manal Mohammed1,2, Nazlia Omar1

  • 1CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.

Plos One
|March 20, 2020
PubMed
Summary

This study introduces an automated method for classifying exam questions across multiple domains using Bloom's taxonomy. The novel approach combines TFPOS-IDF and word2vec features, achieving high accuracy in classifying educational assessments.

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Area of Science:

  • Educational Technology
  • Natural Language Processing
  • Artificial Intelligence in Education

Background:

  • Effective student assessment is vital in educational settings, with examinations being a primary evaluation tool.
  • Bloom's taxonomy is a widely adopted framework for categorizing cognitive skills in educational questions.
  • Existing automated question classification methods often focus on single domains, lacking multi-domain applicability.

Purpose of the Study:

  • To develop and present a novel classification model for automatically categorizing exam questions based on Bloom's taxonomy across multiple domains.
  • To address the limitation of existing methods that primarily classify questions within specific subject areas.

Main Methods:

  • Proposed a hybrid feature extraction technique combining Term Frequency-Part of Speech-Inverse Document Frequency (TFPOS-IDF) and pre-trained word2vec embeddings.

Related Experiment Videos

  • TFPOS-IDF was utilized to assign appropriate weights to significant terms within questions based on their part of speech.
  • The combined features were input into three distinct classifiers: K-Nearest Neighbour, Logistic Regression, and Support Vector Machine.

    • Main Results:

    • The model demonstrated strong performance on two datasets (141 and 600 questions).
    • Weighted F1-measures achieved were 71.1%, 82.3%, and 83.7% for the first dataset.
    • Weighted F1-measures for the second dataset reached 85.4%, 89.4%, and 89.7% respectively.

    Conclusions:

    • The proposed method effectively classifies multi-domain exam questions according to Bloom's taxonomy.
    • The combination of TFPOS-IDF and word2vec features significantly enhances classification accuracy.
    • This approach offers a valuable tool for improving the quality and balance of educational assessments.