A Pedagogical-Computational Alignment Framework for K-12 Coding Education

Thesis Abstract

Despite widespread integration of coding within K-12 curricula, significant gaps persist between pedagogical practices and computational thinking outcomes, resulting in inconsistent student achievement and uneven engagement across classrooms. This study develops a Pedagogical-Computational Alignment Framework (PCAF) to systematically integrate instructional design principles with core computational concepts, aiming to enhance learning outcomes, teacher efficacy, and equity in access to coding education. The objectives are to (i) identify the key pedagogical dimensions that reliably predict gains in computational thinking and programming proficiency, (ii) map these dimensions to central computational constructs such as sequencing, conditionals, loops, and abstraction, (iii) design and validate an alignment framework that guides curriculum materials, instructional strategies, assessment, and professional development, and (iv) empirically test the framework’s impact on student achievement, engagement, and teacher confidence in a multi-site trial. A mixed-methods research design is employed, combining sequential explanatory and instrumental case study components. The study will be conducted in three urban and three rural K-12 schools across two states, involving a total target population of approximately 1,200 students in grades 5–9 and 60 teachers. A stratified random sampling approach will select 12 classrooms (about 1,000 students) for the core intervention and 6 classrooms (about 200 students) as a control group. Data collection instruments include standardized measures of computational thinking (CTx), coding proficiency tests aligned with the framework, classroom observation protocols, teacher self-efficacy scales, student engagement surveys, and a structured rubric for evaluating alignment quality in curricula and assessments. Instrument validity and reliability will be established through expert review (n=6 coding education specialists) and pilot testing (n=120 students), with Cronbach’s alpha targets of at least 0.80 for internal consistency. Quantitative data will be analyzed using multilevel linear modeling to account for hierarchical data structures (students nested within classrooms, classrooms within schools), with pre- and post-test scores as primary outcomes. Mediation analyses will examine whether alignment quality mediates the relationship between instructional interventions and CTx growth. The thematic component will involve semi-structured interviews with 24 teachers and focus groups with 120 students, analyzed via thematic analysis to extract core patterns related to perceived alignment, instructional experience, and barriers. NVivo and R will be used for qualitative and quantitative analyses, respectively. Model specification will include fixed effects for time and intervention, random effects for schools and classrooms, and interaction terms to explore context-specific effects. A process evaluation will document fidelity, dosage, and adaptation, using a convergent parallel design to triangulate quantitative and qualitative findings. Expected findings include (i) a robust positive effect of PCAF-aligned instruction on CTx and coding proficiency (anticipated effect size d ? 0.40–0.70, p < .05), with greater gains in schools demonstrating higher alignment fidelity; (ii) enhanced student engagement and intrinsic motivation toward computational tasks, mediated by perceived coherence between pedagogy and computation; (iii) improved teacher self-efficacy and instructional practices, evidenced by higher observable quality scores and self-report gains; and (iv) contextual moderators such as class size, resource availability, and prior student CTx levels shaping framework effectiveness. The study will also identify practical design patterns for aligning learning objectives, instructional activities, and assessment rubrics with computational constructs, and provide a scalable professional development protocol. The contribution to knowledge lies in (a) operationalizing a theory-driven alignment framework that integrates pedagogical design with computational content, (b) providing empirical evidence of its impact on student learning and teacher capability in diverse K-12 settings, and (c) offering a validated measurement toolkit and implementation guide for policymakers, curriculum developers, and school leaders. The findings are expected to advance theories of computational education by clarifying how alignment among pedagogy, assessment, and programming concepts translates into measurable learning gains, and to inform scalable models for equitable coding education across varied educational contexts. Based on the results, the study recommends (1) embedding alignment checklists into curriculum development cycles, (2) adopting professional development modules focused on bridging pedagogy and computation, and (3) establishing ongoing formative assessment practices that reflect both procedural fluency and conceptual understanding in coding education.