Annotated
Reading List
Every citation below is annotated with its connection to this curriculum. These are not readings assigned to students — they are the research that shaped how every course on this site is designed and assessed.
Seymour, E. & Hunter, A.-B. (2019). Talking About Leaving Revisited. Springer.
The definitive longitudinal study of why students leave STEM. Identifies seven categories of departure factors. The framework for P3 (Why They Left) interviews. Key insight: most departure is caused by institutional structure, not student ability.
Margolis, J. & Fisher, A. (2002). Unlocking the Clubhouse: Women in Computing. MIT Press.
Ethnographic study of women in CS at Carnegie Mellon. Identifies 'the experience of computing' as a cultural system that excludes. Directly informs the equity-as-design principle.
Walton, G. M. & Brady, S. T. (2017). The many questions of belonging. In Handbook of Competence and Motivation.
Belonging uncertainty as a mechanism: students from stigmatized groups are more vigilant to belonging cues. The theoretical foundation for P5 (BelongingSignals).
Lewis, C. M. et al. (2017). Building community in CS. ACM Inroads.
Community-building practices that reduce isolation in CS courses. Practical interventions that connect to the social dependency type in CurriculumGraph.
Karabenick, S. A. & Berger, J.-L. (2013). Help seeking as a self-regulated learning strategy. In Applications of Self-Regulated Learning.
Help-seeking as a strategic, adaptive behavior — not a sign of weakness. The theoretical foundation for P1 (HelpMap). Key: help-seeking suppression is a measurable, structural phenomenon.
Wise, A. F. & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics.
Learning analytics without theory is pattern-matching without meaning. Guides the feature family design in HelpMap: features must map to theoretical constructs, not just correlate with outcomes.
Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. Basic Books.
The foundational text for constructionism: learning happens through building artifacts in the world. The philosophical root of 'Build Before Import' and the Build a Computer project.
Harel, I. & Papert, S. (1991). Constructionism. Ablex Publishing.
Extends Papert's constructionism with empirical studies. Children who build instructional software learn more deeply than those who receive instruction. The research basis for project-based curriculum.
Blikstein, P. (2013). Digital fabrication and 'making' in education. In FabLabs: Of Machines, Makers, and Inventors.
Making as pedagogy. Physical computing closes the gap between abstraction and experience. Directly informs the Build a Computer project's design rationale.
Harel, G. (2013). Intellectual need. In Vital Directions for Mathematics Education Research. Springer.
The intellectual need principle: concepts should be introduced only when students experience the inadequacy of their current tools. The theoretical foundation for 'Derive Before Compute' and the SyllabusAudit project.
Ko, A. J. et al. (2020). Critically Conscious Computing. University of Washington.
Equitable CS education that centers justice. Read-before-write sequencing. Student agency. The pedagogical framework underlying every course in this curriculum.
Anderson, J. (2023). Strategic Deep Learning. Foothill College.
Antiracist learning science applied to classroom practice. Ungrading, portfolio assessment, the five anti-racist learner-centered objectives. The invisible architecture of this entire curriculum.
Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning. Prentice-Hall.
The experiential learning cycle: concrete experience → reflective observation → abstract conceptualization → active experimentation. The Build a Computer project follows this cycle in every module.
Kapur, M. (2008). Productive failure in mathematical problem solving. Instructional Science.
Students who struggle with problems before receiving instruction learn more deeply than those who receive instruction first. The research basis for presenting the 'headache' before the 'aspirin.'
Perkins, D. N. & Salomon, G. (1992). Transfer of learning. In International Encyclopedia of Education.
Transfer requires explicit bridging. Knowledge doesn't automatically move between contexts. The Build a Computer project's 'STEM Bridge Moments' are a direct implementation of this research.
Deci, E. L. & Ryan, R. M. (2000). Self-determination theory. In Handbook of Self-Determination Research.
Intrinsic motivation requires autonomy, competence, and relatedness. The three-track system provides autonomy (choose your depth), competence (every track is a serious outcome), and relatedness (community projects).
Ambrose, S. A. et al. (2010). How Learning Works: Seven Research-Based Principles. Jossey-Bass.
Seven principles of learning synthesized from cognitive science. Prior knowledge, motivation, practice, feedback, climate, self-directed learning, mastery. The practical design handbook for every course.
Bailey, T. R., Jaggars, S. S., & Jenkins, D. (2015). Redesigning America's Community Colleges. Harvard University Press.
The guided pathways framework. Community colleges fail students through complexity, not rigor. The structural argument for clear learning pathways and the course pathway visualization.
Fincher, S. & Robins, A. (Eds.) (2019). The Cambridge Handbook of Computing Education Research. Cambridge University Press.
The methodological reference for CS education research. Chapters on qualitative methods, learning analytics, and assessment inform the multi-method approach across all five projects.
Guzdial, M. (2015). Learner-Centered Design of Computing Education. Morgan & Claypool.
Computing education designed around how students actually learn, not how experts think. The research-practice bridge that connects learning science to CS curriculum design.
Porter, L. & Zingaro, D. (2024). Learn AI-Assisted Python Programming. Manning.
The first serious textbook on teaching programming with AI assistance. Reframes the instructor's role from code demonstrator to learning designer. Informs how AI tools are integrated in courses.
Freire, P. (1968/2000). Pedagogy of the Oppressed. Continuum.
Students are not empty vessels. The banking model reproduces passivity. Knowledge is a liberatory act. The philosophical foundation for student-proposed grading and course co-evaluation.
hooks, b. (1994). Teaching to Transgress: Education as the Practice of Freedom. Routledge.
The classroom as a site of freedom. The instructor's vulnerability and intellectual engagement as pedagogical tools. The foundation for reflective writing requirements alongside every project.
Washington, A. N. (2020). When twice as good isn't enough. Communications of the ACM.
Structural racism in CS education. The gap between stated values and experienced reality. The motivation for equity-as-design as a core curriculum principle, not an add-on.