Student Self-Assessment Profiles: Leveraging Trace Data to Unpack the Black Box of Self-Assessment (Poster 54)

• In Event: AERA Poster Session Six
  In Poster Session: Tools and Strategies in Classroom Assessment

Abstract

Developing students’ self-assessment skills remains a challenge for teachers and students because researchers have yet to generate profiles of individual learners’ cognitive and affective processes as they engage in self-assessment—the primary mechanisms whereby self-assessment shapes learning. The purpose of this study was to analyze fine-grained data at the participant level to generate self-assessment process profiles for students in a high school classroom. Leveraging a collective case study, I collected digital trace data as participants, 16 year 12 students in England, completed a self-assessment-based lesson online. Triangulating traces with qualitative data, I created a rich profile of each learner’s cognitive and affective processes during self-assessment. I characterize four profiles, illustrating how they can support tailored and agentic development of self-assessment.

Authors

• Nathan H. Rickey, Queen's University - Kingston
  Presenting Author

• Christopher DeLuca, Queen's University - Kingston
  Presenting Author