Naaz Sibia, Jessica Wen, Amber Richardson, Yashika Jain, Khushi Malik, Bogdan Simion, Carolina Nobre, Angela Zavaleta Bernuy, Andrew Petersen, Michael Liut (2026). ICER 2026 ๐ Full text (arXiv)
Overview
Students spent ~47% of gaze time on code despite visual scaffolds. Three factors shape selective engagement with multi-representational tools: Agency (students want control over cognitive effort), Representational Fit (same design feels helpful to some, overwhelming to others), and Legitimacy (metaphorical scaffolds perceived as childish at university level).
Key Contributions
Paper 1: Code as Anchor, Memory and Metaphor as Support: Learner Experiences with Multi-View Visualizations
- Problem: Students often ignore well-designed program visualizations; existing cognitive design principles don't explain learner engagement/disengagement.
- Method: Within-subjects study (N=19 undergraduates, post-CS1/CS2) using think-aloud, interviews, and webcam gaze tracking with a multi-representational probe.
- Gaze finding: ~47% of time on code despite visual scaffolds; students without prior experience anchored more in code and ignored metaphor views.
- Three engagement themes: Agency (control over cognitive effort), Representational Fit (wide individual variation), Legitimacy (metaphors seen as childish at university level).
- Implication: multi-representational-tools need attention to affective and social factors, not just cognitive design.
Paper 2: Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring
- Problem: Static-prompt LLM tutoring systems fail to adapt across diverse academic disciplines.
- Solution: Subject-aware prompt routing using 14 pedagogical features extracted from transcripts; contextual bandit formulation with 20 pedagogical prompts.
- Simulation: Router achieves 0.694 vs 0.647/0.64 static baselines (p<0.001).
- Real-world A/B test: N=656 conversations, 359 Dutch high-school students. Stochastic router achieves 28.1% exercise conversion rate vs 19.6% baseline.
- Implication: adaptive-prompt-routing with stochastic sampling improves both efficiency and engagement in real-world tutoring.
Paper 3: Confidence-Aware Automated Assessment of Student-Drawn Scientific Models
- Problem: Automated scoring of student-drawn scientific models lacks reliability indicators, leaving teachers unable to decide when to trust scores.
- Method: Vision Transformer (ViT) with LoRA + confidence-aware framework using test-time perturbations.
- Dataset: Six NGSS-aligned middle school assessment items (477-816 responses each, scored Beginning/Developing/Proficient).
- Key innovation: Response-level confidence enables selective automation โ high-confidence auto-scored, uncertain cases deferred for human review.
- Implication: confidence-aware-ai-assessment enables practical triage between automation and human oversight in educational assessment.
Paper 4: PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback
- Problem: AES systems treat scoring and feedback as separate; neural scoring lacks interpretability; LLM feedback is ability-agnostic.
- Solution: Unified psychometric latent space (ฮธ) via Neural GPCM Trait-Adaptive Scorer.
- Scoring performance: QWK 0.747 (besting prior SOTA 0.722); 1st in 6/8 ASAP++ prompts and 10/11 trait dimensions.
- Feedback quality: ZPD-based strategy mapping (Explicit Correction for ฮธ<-1, Scaffolding for -1<ฮธ<1, Socratic for ฮธ>1). Multi-agent fusion from Llama-4-Scout, Qwen3-235B, GPT-4o.
- Evaluation: >90% win rate in Actionability vs GPT-4o, Llama-4-Scout, Qwen3.
- Implication: psychometrically-aware-ai can unify assessment and instruction, transforming automated essay scoring from summative to formative.
Related Pages
Citation
APA: Naaz Sibia, Jessica Wen, Amber Richardson, Yashika Jain, Khushi Malik, Bogdan Simion, Carolina Nobre, Angela Zavaleta Bernuy, Andrew Petersen, Michael Liut (2026). Code as Anchor, Memory and Metaphor as Support: Learner Experiences with Multi-View Visualizations. arXiv:2606.19570. ICER 2026.