A pattern mining method for teaching practices

Abstract

When integrating digital technology into teaching, many teachers experience similar challenges. Nevertheless, sharing experiences is difficult as it is usually not possible to transfer teaching scenarios directly fromone subject to another because subject-specific characteristicsmake it difficult to reuse them. To address this problem, instructional scenarios can be described as patterns,which has already been applied in educational contexts. Patterns capture proven teaching strategies and describe teaching scenarios in a unified structure that can be reused. Since priorities for content,methods, and tools are different in each subject,we showan approach to develop a domain-independent graph database to collect digital teaching practices froma taxonomic structure via the intermediate step of an ontology. Furthermore,we outline amethod to identify effective teaching practices frominterdisciplinary data as patterns fromthe graph database using an association rule algorithm. The results showthat an association-based analysis approach can derive initial indications of effective teaching scenarios.