Designing automated assessment activity that stimulates metacognitive actions: a case study on fractions

Feedback is most effective when learners actively engage in self-reflection on their learning processes. A key challenge for formative assessment platforms is developing tasks that, through automated assessment, stimulate such self-reflection. This study presents a design pattern featuring complementary example-eliciting tasks (EETs), developed using multiple modes of automated feedback, interactive diagrams, and inquiry logic principles. Focused on equivalent fractions, the study involved 75 7^th grade students. Using a design-based research approach, the study aimed to refine the platform and stimulate metacognitive actions (MCAs). We concentrated on one student to assess the platform's effectiveness in encouraging metacognitive actions during interaction with its elements, with a focus on how these tasks and assessments could enrich the student's personal example space (PES). Data was collected through triangulation of observation methods, including a task-based protocol and automated assessment reports generated by the platform. A qualitative analysis, grounded in metacognitive action theory, was employed to examine the students' metacognitive actions, while descriptive analysis explored changes in PES. The results revealed that engaging with the platform's elements successfully stimulated the student's metacognitive actions, leading to enhanced self-reflection and learning. This research contributes to understanding how automated feedback within inquiry EETs can improve learning outcomes.