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 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsSequential Pattern MiningSimilar Papers 제목 키워드 기반
Building a Classification Model for Enrollment In Higher Educational Courses using Data Mining Techniques
Data Mining is the process of extracting useful patterns from the huge amount of database and many data mining techniques are used for mining these patterns. Recently, one of the remarkable facts in higher educational in…
General ClassificationManagementTowards Mining Effective Pedagogical Strategies from Learner-LLM Educational Dialogues
Dialogue plays a crucial role in educational settings, yet existing evaluation methods for educational applications of large language models (LLMs) primarily focus on technical performance or learning outcomes, often neg…
TaSPM: Targeted Sequential Pattern Mining
Sequential pattern mining (SPM) is an important technique of pattern mining, which has many applications in reality. Although many efficient sequential pattern mining algorithms have been proposed, there are few studies …
Sequential Pattern MiningTowards Mining Creative Thinking Patterns from Educational Data
Creativity, i.e., the process of generating and developing fresh and original ideas or products that are useful or effective, is a valuable skill in a variety of domains. Creativity is called an essential 21st-century sk…
Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room
Despite significant advancements in AI-driven educational systems and ongoing calls for responsible AI for education, several critical issues remain unresolved -- acting as the elephant in the room within AI in education…
BenchmarkingFairness