Sequential Pattern Mining in Educational Data: The Application Context, Potential, Strengths, and Limitations

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