Ashraf Alam2026-03-082026-03-082026-02-1397983373654669798337365480https://doi.org/10.4018/979-8-3373-6546-6.ch004https://gnanaganga.alliance.edu.in/handle/123456789/9912This chapter proposes a pedagogy first architecture for computing assessment that couples formative routines with minimalist analytics and equity by design. It argues for alignment to disciplinary big ideas, visible success criteria, short revision cycles, and multimodal evidence including code histories, test rationales, diagrams, and oral defenses. Peer and self-judgment are cultivated as disciplined practices, supported by universal design and culturally sustaining tasks. Analytics remain small, interpretable, and privacy preserving, answering concrete instructional questions within hours. Capacity is built through coaching, lesson study, micro credentials, and calibrated observation, with change managed through protected schedules and toolkits. Comparative cases from K to 8, secondary, undergraduate, and reskilling contexts show feasibility and impact. An improvement science agenda links logic models to measurable gains in competence, fairness, and credible external signaling.enComputer Science EducationComputer EducationSociotechnical PedagogyMetacognitionPedagogical Innovationsbook-chapter