Use CT profiles and behavioural metrics to differentiate pre-service teacher instruction and design a learning analytics dashboard¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends using the three empirically identified CT profiles to tailor pre-service teacher education: "Novice learners can benefit from scaffolded coding workshops using tools like Scratch, while Proficient learners can engage in advanced projects". It further proposes a learning analytics dashboard, conceptual at this stage, to visualize CT profiles and behavioural metrics such as time-on-task and perceived difficulty for real-time feedback to teacher educators, and to integrate metacognitive training such as reflective journaling or self-assessment into CT courses.

Design Implications¶

Context¶

Requirements¶

• CT profiles must first be identified via multimodal assessment combining survey measures and behavioural trace data from an interactive CT task

Constraints¶

• The dashboard is conceptual at this stage, not yet built or evaluated
• The study's cross-sectional design precludes causal inferences about coding experience, metacognitive strategies, and CT skills
• Time-on-task metrics may not distinguish active problem-solving from idle time or interface navigation

Target Learners¶

• pre-service teachers in teacher education programs

Target Learning Goals¶

• computational thinking skills
• metacognitive planning strategies
• coding experience and coding comfort

Related Strategies¶

• Use standards-based learner profiles to deliver targeted support and curriculum-level interventions in professional degree programs
• Learning Dashboards
• Adapt e-learning platforms to distinct student engagement profiles with adaptive recommendations and guidance
• Deliver AI-generated visual explanations adaptively based on learner engagement profiles rather than uniformly
• Differentiate AICA support by CT level: open-ended exploration for high CT students, structured SRL scripts for low CT students
• Differentiate CT scaffolding by initial CT level: open-ended challenges for moderate-CT, constraint-based tasks for high-CT, structured pathways for low-CT learners
• Tailor institutional GenAI training to teachers' prior GenAI use experience

Examples¶

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Key Sources¶

• Tanya Chichekian, Maria Cutumisu, Annie Savard and Yi-Mei Zhang. (2026). Profiling Pre-service Teachers' Computational Thinking: The Role of Metacognition and Coding Experience. Journal of Learning Analytics, 13(1), 31–41. https://doi.org/10.18608/jla.2026.9077