2017/05/31 by Nirmal Patel, Patel, Nirmal, Collin Sellman +3
Business, Management and Accounting · Computer Science · #Business Process Modeling and Analysis #Computers and Society (cs.CY) #FOS: Computer and information sciences #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services
paper · pdf · doi:10.48550/arxiv.1705.11125
openalex publication_date 2017/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we describe data mining techniques used to extract frequent learning pathways from a large educational dataset. These pathways were extracted as a directed graph that encoded student learning processes. Our dataset contains more than 800 million interactions of over 3 million anonymized students in an online learning platform. Performing process mining on large and complex datasets regularly yields incomprehensible process models. Although, if we cluster data and obtain groups following similar processes, we can greatly improve process mining results. To this end, we developed a sequence clustering algorithm that let us group students who followed similar learning pathways. To extract frequent learning pathways from these clusters of data, we developed a graph-based process discovery algorithm that revealed to us the sequences of learning activities that many students followed. These sequences represented highways of student learning.