Interactive Tools
Three working tools, three research questions.
Each tool below operationalizes one idea from the research agenda as a working interactive artifact you can click. The data is synthetic and the scoring is heuristic — validating the underlying constructs against real instructor and student data is the whole point of projects P2 and P4. The tools exist to make the research questions concrete: you can see what is being measured, why the measurement might matter, and exactly where the empirical burden sits.
What these tools are: working interactive artifacts that render a structural claim — a typed curriculum graph, a four-dimension scoring rubric, a greedy set-cover animation — so that a reader can form an independent judgment about whether the underlying measurement is tractable. What they are not: validated instruments. They would become validated instruments only after the studies in P2 and P4 are run against real syllabus corpora, instructor annotations, and student outcomes.
Curriculum Dependency Visualizer
An interactive SVG graph of a synthetic introductory linear algebra curriculum under a typed dependency grammar. Each of 22 nodes is a learning objective; each edge is one of four types — conceptual, procedural, motivational, social. Click any node to focus its neighborhood; live statistics report density, longest path, fan-in/out, motivational ratio, and bottleneck candidates. The empirical question P4 asks is whether structural features of real instructor-annotated graphs predict student outcomes; this tool shows what such a graph looks like and which statistics are worth tracking.
Click anywhere on this card to open the tool.
Pedagogical Debt Analyzer
A text-input tool that scores pasted syllabus language across four dimensions drawn from Harel's necessity principle and the belonging/help-seeking literature: motivational framing, scaffolding visibility, verification structure, and belonging signals. Rules fire on concrete keyword and syntactic patterns and highlight matches inline so the reader can see exactly why each score moved. Three preset excerpts (weed-out, necessity-first, belonging-centered) let you calibrate the rubric without pasting anything. The heuristics are plausible; P2 is the study that would measure whether they correlate with expert annotation and student outcomes.
Click anywhere on this card to open the tool.
Minimum Viable Curriculum Step-Through
A play/pause/step/reset animation of a greedy set-cover approximation for the Minimum Viable Curriculum problem on a small synthetic graph. The theoretical setup: finding the optimal MVC is NP-hard by polynomial reduction from Set Cover, so greedy is a natural baseline with a known ln(n)+1 approximation bound. The tool shows the algorithm picking one objective per step, the coverage set growing, and the approximation gap against a brute-force optimum. The research claim P4 would test is that MVC-distance — how far a real curriculum sits from its minimum viable form — predicts student persistence and grade outcomes.
Click anywhere on this card to open the tool.
Applied Curriculum Designs
Three project designs for an introductory CS course, grounded in Anderson's necessity principle. Each is designed so students experience a real problem before encountering the technical tool that addresses it. These are not research projects — they're instructional designs informed by the research agenda.
Community Resource Aggregator
Students build a searchable map of local support resources for their campus or city. The necessity hook: students first survey three peers about resources they couldn't find. The search problem exists before a line of code is written. Students write a one-page research memo before coding, document design decisions, conduct peer testing with real users, and reflect on what the data revealed versus what they assumed.
Personal Learning System Builder
Students build a scheduling app, habit tracker, or question-capture tool that they actually use. The necessity hook: students audit their current study system in week one, and the gaps they find become the specification for what they build. The meta dimension is deliberate — students simultaneously learn to code and reflect on how they learn.
Neighborhood Data Story
Students pick a local issue, find public data, and build a visualization with a written narrative. Motivated by Anderson's "abuelita test": the final deliverable must include an explanation a non-technical family member could understand. Students first write a one-paragraph argument using only intuition, then find the data — the gap between the two is the learning.
These curriculum designs enact the research principles described on the research page. See the teaching page for the full pedagogical framework.
Platform Design
A longer-form design artifact — ProjectBridge, a project-based learning platform concept for community colleges — lives on the Applied Work page. It operationalizes the same structural equity principles that guide the research: the accountability features are grounded in the help-seeking literature, the data model is designed to produce the behavioral signals P1 would analyze, and the faculty-adoption strategy reflects what the institutional-change literature suggests about how tools actually get used.
Last updated: April 2026