Understanding the Progression of Educational Topics via Semantic Matching
This work addresses curriculum design challenges for educators and specialists by providing a data-driven tool to enhance learning outcomes, though it is incremental as it applies existing NLP methods to a new domain.
The paper tackled the problem of analyzing educational curriculum progression by using BERT topic modeling to extract topics, identify relationships between subjects, and track conceptual gaps, resulting in reduced redundancy and introduction of new concepts as validated by experts.
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.