Understanding the Progression of Educational Topics via Semantic
  Matching
Tamador Alkhidir Edmond Awad Aamena Alshamsi
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.