Extraction of characteristics for data-informed guidance and counseling using trace data

Abstract

Guidance and Counseling (G&C) in schools is a crucial activity that helps learners acquire academic knowledge and skills while fostering comprehensive psychological and social development. The digitization of the educational environment means that trace data related to students’ learning and lives are being accumulated; it is expected that integrating this with other data sources will enable teachers to better understand learners and enhance G&C strategies. However, the utilization of trace data for G&C has not been examined. One of the reasons is the difficulty of interpreting granular data without considering the subject characteristics and activity contexts. Therefore, to enable homeroom teachers to utilize trace data from a G&C perspective, this study aimed to extract characteristics from trace data to support teachers’ understanding of learners in the G&C process. Based on the “Multi-Tiered Support” model adopted in G&C practice, we extracted characteristics that capture the learners’ state and interviewed teachers to investigate how these characteristics can be used in actual settings. The results suggest that these characteristics would help teachers to understand learners' situations both in and outside of schools. The contribution of this study is to aggregate and standardize trace data, transforming it into a level of granularity that homeroom teachers can interpret from a G&C perspective.