  Increasingly, researchers have suggested the benefits of temporal analysis to
improve our understanding of the learning process. Sequential pattern mining
(SPM), as a pattern recognition technique, has the potential to reveal the
temporal aspects of learning and can be a valuable tool in educational data
science. However, its potential is not well understood and exploited. This
chapter addresses this gap by reviewing work that utilizes sequential pattern
mining in educational contexts. We identify that SPM is suitable for mining
learning behaviors, analyzing and enriching educational theories, evaluating
the efficacy of instructional interventions, generating features for prediction
models, and building educational recommender systems. SPM can contribute to
these purposes by discovering similarities and differences in learners'
activities and revealing the temporal change in learning behaviors. As a
sequential analysis method, SPM can reveal unique insights about learning
processes and be powerful for self-regulated learning research. It is more
flexible in capturing the relative arrangement of learning events than the
other sequential analysis methods. Future research may improve its utility in
educational data science by developing tools for counting pattern occurrences
as well as identifying and removing unreliable patterns. Future work needs to
establish a systematic guideline for data preprocessing, parameter setting, and
interpreting sequential patterns.
