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Understanding the Progression of Educational Topics via Semantic Matching

Tamador Alkhidir, Edmond Awad, Aamena Alshamsi

TL;DR

B Bidirectional Encoder Representations from Transformers (BERT) topic modeling was used to extract topics from the curriculum and were then used to identify relationships between subjects, track their progression, and identify conceptual gaps.

Abstract

Education systems are dynamically changing to accommodate technological advances, industrial and societal needs, and to enhance students' learning journeys. Curriculum specialists and educators constantly revise taught subjects across educational grades to identify gaps, introduce new learning topics, and enhance the learning outcomes. This process is usually done within the same subjects (e.g. math) or across related subjects (e.g. math and physics) considering the same and different educational levels, leading to massive multi-layer comparisons. Having nuanced data about subjects, topics, and learning outcomes structured within a dataset, empowers us to leverage data science to better understand the progression of various learning topics. In this paper, Bidirectional Encoder Representations from Transformers (BERT) topic modeling was used to extract topics from the curriculum, which were then used to identify relationships between subjects, track their progression, and identify conceptual gaps. We found that grouping learning outcomes by common topics helped specialists reduce redundancy and introduce new concepts in the curriculum. We built a dashboard to avail the methodology to curriculum specials. Finally, we tested the validity of the approach with subject matter experts.

Understanding the Progression of Educational Topics via Semantic Matching

TL;DR

B Bidirectional Encoder Representations from Transformers (BERT) topic modeling was used to extract topics from the curriculum and were then used to identify relationships between subjects, track their progression, and identify conceptual gaps.

Abstract

Education systems are dynamically changing to accommodate technological advances, industrial and societal needs, and to enhance students' learning journeys. Curriculum specialists and educators constantly revise taught subjects across educational grades to identify gaps, introduce new learning topics, and enhance the learning outcomes. This process is usually done within the same subjects (e.g. math) or across related subjects (e.g. math and physics) considering the same and different educational levels, leading to massive multi-layer comparisons. Having nuanced data about subjects, topics, and learning outcomes structured within a dataset, empowers us to leverage data science to better understand the progression of various learning topics. In this paper, Bidirectional Encoder Representations from Transformers (BERT) topic modeling was used to extract topics from the curriculum, which were then used to identify relationships between subjects, track their progression, and identify conceptual gaps. We found that grouping learning outcomes by common topics helped specialists reduce redundancy and introduce new concepts in the curriculum. We built a dashboard to avail the methodology to curriculum specials. Finally, we tested the validity of the approach with subject matter experts.
Paper Structure (13 sections, 7 figures, 1 table)

This paper contains 13 sections, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Dataset and Case Study
  3. Methodology
  4. Results
  5. Conclusion
  6. Acknowledgements

Figures (7)

  • Figure 1: Description of the UAE educational system for 1-12 along with subjects covered by each stream
  • Figure 2: 1-12 UAE curriculum framework for computer science and science subject
  • Figure 3: (a) The percentage of similarity between two subjects can be calculated by dividing the number of learning outcomes that they have in common by the total number of learning outcomes (b) Hierarchical clustering for the different subjects based on the calculated matching percentage
  • Figure 4: Topic modeling is used to identify the main topics that are covered in different learning outcomes. Here are some examples of topics and their corresponding learning outcomes
  • Figure 5: Curriculum framework can be reshaped into three distinct components: core concepts, competencies, and cross-concepts, promoting a more holistic approach to learning
  • ...and 2 more figures