A Unified Framework for Computer Literacy Pedagogy in STEM Education

Thesis Abstract

This study addresses the persistent gap in computer literacy among STEM students and the uneven effectiveness of existing pedagogical approaches in preparing learners for data-driven science and engineering tasks. Motivated by the need for a cohesive pedagogical framework that integrates computational thinking, digital fluency, and disciplinary problem-solving, the research aims to develop and validate a Unified Framework for Computer Literacy Pedagogy in STEM Education. The specific objectives are to (1) diagnose current curricula and instructional practices across undergraduate STEM courses, (2) identify core competencies constituting computer literacy within STEM contexts, (3) design a unified pedagogical framework that specifies learning objectives, instructional strategies, assessment rubrics, and professional development requirements for instructors, (4) empirically evaluate the framework’s impact on student outcomes including computational thinking, coding proficiency, and STEM problem-solving performance, and (5) generate evidence-based guidelines for scalable implementation in higher education settings. The study employs a mixed-methods design grounded in the sociocultural and situated learning theories, drawing on Vygotsky’s zone of proximal development and Kolb’s experiential learning cycle to justify instructional design decisions. The population comprises 24 undergraduate STEM programs across three public universities, with a stratified sample of 24 courses and 1,200 students enrolled in introductory to intermediate computer literacy–intensive modules. For qualitative insight, semi-structured interviews are conducted with 48 instructors and instructional designers, complemented by classroom observations in 12 sections. For quantitative analysis, data are collected from 1,200 students using a validated Computer Literacy Assessment Instrument (CLAI) and course performance metrics, alongside instructor surveys assessing pedagogical practices and preparedness. Instrument validity is established through expert content review (n=6), pilot testing (n=120 students), and reliability analyses (Cronbach’s alpha > 0. Eight). Data collection spans two academic semesters, with a follow-up cohort in the subsequent term to examine longitudinal effects. Analytical procedures include thematic analysis of interview transcripts and observation notes to derive core components of computer literacy pedagogy, while structural equation modeling (SEM) tests the proposed framework’s causal relationships among instructional practices, student engagement, and learning outcomes. Multilevel modeling accounts for nested data (students within courses) to estimate the framework’s influence on CLAI scores, coding proficiency, and STEM problem-solving performance, controlling for prior achievement, demographics, and course type. Regression analyses identify the most predictive instructional strategies, and ANOVA assesses differences across STEM disciplines and delivery modes (online, hybrid, in-person). The expected findings indicate that a coherent framework integrating explicit learning objectives, scaffolded coding activities, contextualized projects, and standardized assessment rubrics significantly enhances computational thinking, coding fluency, and disciplinary problem-solving performance, with effect sizes ranging from medium to large (Cohen’s d = 0.40–0.75) and SEM path coefficients demonstrating robust mediating roles for student engagement and instructor professional development. The study contributes to knowledge by operationalizing a transferable, evidence-based framework that unifies disparate computer literacy efforts into a cohesive pedagogy for STEM education, clarifying the alignment between curricular design, instructional practice, and assessment. It offers a validated model of instructional architecture, performance rubrics, and teacher professional development modules that can be adapted to varied institutional contexts, thereby informing policy and program design for higher education. The main conclusion posits that computer literacy is best achieved when curricula explicitly embed computational thinking within authentic STEM problems, supported by structured guidance for instructors and consistent, objective assessment. Recommendations include scaling the framework through institutional change initiatives, developing sector-wide professional development programs, integrating cross-disciplinary projects, and extending the research to examine long-term retention and workforce relevance in post-graduate STEM pathways.