Computer Science I · Fall 2026
The Roadmap
How Computer Science I is built and which CSTA 2026 standards it targets. It shows the five assessment buckets and the cull — which standards the course actually claims and, explicitly, which it does not — then how those standards are distributed across the semester’s three modules and its assessments.
This is a map drawn from the standards, not a curriculum taught to them — a design document, not a daily pacing guide.
How to read this
A bucket is not a unit
Buckets are assessment categories, not units or modules. The mapping between buckets and CSTA concepts is many-to-many.
Two bands run through it
The instrument is graduated middle-school → high-school to defeat the floor effect. MS standards are the substrate, not the target.
The cull is deliberate
226 standards exist; this course touches about 70. A roadmap that implied coverage of all 226 would be a lie. Naming ~70 and saying why is the design.
The Cull
Every standard in scope gets exactly one tier. Roughly 31% of the framework is touched at all — the honest number for a one-semester foundational course, and a feature, not a gap.
CORE
Directly taught in CS I. Assessed. Named on the crosswalk.
Instrument role: Scored set.
REACH
Touched, extended into, or available as enrichment. An honest partial claim.
Instrument role: Diagnostic pool.
SCAFFOLD
Middle-school prerequisite. Not a course target — the floor the course stands on, present to locate students on the progression.
Instrument role: Graduated items, MS band.
SKIP
Explicitly out of scope, with a stated reason. Absent from the instrument — but named on this page. See what we are not covering.
The Buckets
Five assessment categories. Each carries items at both bands; each maps to CSTA concepts many-to-many.
1
Systems
What a machine physically is, and what physically limits it.
Covers
RAM, CPU, storage, memory vs. disk, bandwidth, compute vs. capacity, what physically limits a machine, local inference hardware.
CSTA
SYS·HWSYS·IM
CORE3
Differentiate an operating system as a special type of software that manages hardware and other software.
HS-SYS-HW-30←the hardware thread — bandwidth, memory, what limits a machine Demonstrate the capabilities and limitations of a physical or simulated computing device.
Investigate how computing systems and infrastructure impact society and the environment, identifying who is affected.
data centers, local vs. cloud cost
2
Networks & the Web
How machines talk, and where data physically goes.
Covers
Router, DNS, HTTP/HTTPS, client/server, what "the cloud" actually is, where data physically goes, local vs. remote.
CSTA
SYS·NTSYS·SE
CORE2
Diagram a network of computing systems, including hardware and software.
Analyze how the internet functions as a network of networks.
REACH2
Evaluate security trade-offs in a computing system.
security trade-offs
Analyze how breaches and social engineering exploit computing systems.
breaches, social engineering
Buckets 1 and 2 share one CSTA concept. The 2017 Computing Systems / Networks split was merged into Systems & Security in 2026. The buckets stay separate for assessment (different item types, separate diagnostic reads). This is not a mapping error.
3
Data
What data is, how it is shaped, and how it misleads.
Covers
Data types, representation, structured vs. unstructured, distributions, reading a chart, what a mean conceals, metadata, data quality.
CSTA
DAT·DCDAT·DIDAT·IMS1-DSC
CORE6
Create a data dictionary describing name, type, and allowable values for each attribute.
week-one survey instrument
Use a computational tool to clean and organize text-based data.
Evaluate different approaches to verifying consistency and compliance with expected data types, values, and ranges.
source audit
Create a data visualization of a multivariate dataset to answer a question or make a classification or prediction.
Evaluate a data simulation or visualization to answer a data question and identify potential bias.
Evaluate the societal, environmental, and ethical implications of large-scale data collection and processing.
REACH8
Debate approaches to regulating the use of data.
debate data-use regulation
Interpret metadata when using data collected by others.
Apply appropriate analytic and visualization techniques for categorical and quantitative data.
week-one unit, three response types
Interpret the results of a data analysis to explain patterns, anomalies, and trends.
Create a data visualization that communicates key findings to diverse audiences.
Analyze how graphical conventions support accurate interpretation and how breaking conventions misleads.
what the mean conceals
Apply ethical principles to data collection, analysis, and communication.
Assess how data collection and use may impact marginalized and underrepresented groups.
SCAFFOLD4
Distinguish data and metadata.
data and metadata
Sort, filter, group, and summarize data.
sort, filter, group, summarize
How design choices impact the interpretation of data.
design choices impact interpretation
How personal data and metadata are collected.
personal data and metadata collection
4
AI Systems
How a model works, and who is accountable for it.
Covers
Tokenization, embeddings, inference, RAG, prompting, single-pass vs. chat, local vs. cloud, what leaves your machine, auditing output, disclosure, stewardship, who decided.
CSTA
ALG·PSALG·MLALG·IMPRO·RDDAT·IMSYS·IMSOC (all)S1-AIN
CORE10
Describe the differences between deterministic and probabilistic algorithms.
temp 0, fixed seed, single-pass vs. chat
Evaluate AI-generated output to assess bias, accuracy, and potential harms.
Evaluate training data by examining its source, quality, representativeness, potential biases, and privacy implications.
evaluative, not constructive
Design a computing technology using human-centered design principles.
Evaluate the ethical implications, societal impacts, and potential biases of rule-based and data-driven algorithms.
Articulate the values embedded in the design of an algorithmic system.
who decided
Evaluate AI-generated code for accuracy, reliability, and alignment with program requirements.
Evaluate the fundamental technological differences between an emerging technology and established technologies.
Evaluate how human choices in using, designing, deploying, and regulating computing technologies have risks, benefits, and impacts.
Connect computing knowledge and skills to personal goals and career aspirations.
the "keep progressing after this course" exit
REACH10
Justify the selection of an AI algorithm for a given task.
justify AI algorithm selection
Analyze the impacts of an emerging technology.
impacts of emerging tech
Compare human intelligence and artificial intelligence.
human vs. artificial intelligence
Explain the rationale behind laws and policies governing computing.
law/policy rationale
Evaluate whether an AI or non-AI computational solution is appropriate for a real-world problem.
Plan safeguards for AI systems that protect human well-being and privacy while ensuring meaningful human involvement.
Analyze the potential biases and limitations of AI systems.
Analyze the environmental impacts of widespread AI adoption.
Integrate a prebuilt AI agent into an application.
miniRAG, MuggsOfCode
Assess how unauthorized data collection has influenced the practice of training AI models.
SCAFFOLD4
Distinguish procedural, rule-based, and data-driven approaches.
procedural vs. rule-based vs. data-driven
Use an AI tool to generate outputs.
use an AI tool to generate outputs
Analyze AI-generated code.
analyze AI-generated code
Judge when it is appropriate to use AI.
when it is appropriate to use AI
Merge rationale. All 17 Computing & Society standards carry the AI-related tag. SOC is not a sibling of Bucket 4; it is substantially inside it. v0.1 proposed a sixth bucket; the tag data resolved it as a merge.
5
Program Logic
The structure of computation itself — language-agnostic. Java and exam format belong to spring.
Covers
Variables, assignment, control flow, functions, parameters, tracing, decomposition.
CSTA
ALG·PSPRO·PDPRO·VDPRO·RDPRO·TRS1-SWD
CORE8
Optimize the design of an algorithm using procedural abstraction and control structures.
Evaluate algorithms for efficiency, correctness, and clarity, using metrics or test cases.
Use documentation, libraries, APIs, and other tools in program development.
Apply appropriate attribution of intellectual property when developing a computing technology.
Collaborate on a programming project using a defined workflow that includes design documentation and clear task roles.
scrum pods, GitHub Classroom
Analyze how a segment of code works, including the role of parameters, return values, and data structures.
Evaluate a computing technology's alignment with design specifications and responsible design values.
Refine a computing technology based on user feedback, testing results, and responsible design values.
REACH6
Use data structures in the design of an algorithm.
data structures — mostly CSA
Develop modular programs.
modular programs — CSA
Use data structures within programs.
data structures in programs — CSA
Design software that accounts for complexity through abstraction.
Use AI-assisted IDE tools or features to understand unfamiliar code and identify errors during debugging.
Claude Code, CLAUDE.md workflow
Design a test plan that exercises functionality, including edge cases and error conditions.
SDD mechanical gate
SCAFFOLD5
Design an algorithm using variables of multiple data types.
algorithm with variables of multiple data types
Represent an algorithm as a flowchart or pseudocode.
flowchart or pseudocode
Verify the accuracy of an algorithm for given inputs.
verify accuracy for given inputs
Use variables of multiple data types in a program.
variables of multiple data types
Explain the roles of iteration, selection, variables, and procedures.
roles of iteration, selection, variables, procedures
Coverage matrix
Buckets against CSTA concepts. Empty cells are left visibly empty — the gap check only works if the gaps are legible.
CSTA 2026 concept coverage by assessment bucket and tier| Bucket | ALG | PRO | DAT | SYS | SOC | S1-AIN | S1-DSC | S1-SWD |
|---|
| 1Systems | · | · | · | CORESCAFFOLD | · | · | · | · |
|---|
| 2Networks & the Web | · | · | · | COREREACHSCAFFOLD | · | · | · | · |
|---|
| 3Data | · | · | COREREACHSCAFFOLD | · | · | · | REACH | · |
|---|
| 4AI Systems | COREREACHSCAFFOLD | CORESCAFFOLD | · | REACH | COREREACHSCAFFOLD | REACH | · | · |
|---|
| 5Program Logic | COREREACHSCAFFOLD | COREREACHSCAFFOLD | · | · | · | · | · | REACH |
|---|
Practices (ESR / IC / CT / HCD) — mapping pending. The spec calls for a second matrix row-group mapping the four CSTA practice families onto each bucket. That mapping is not yet in the standards data, so it is named here rather than shown — a stated gap, not a silent one. The four families are listed under CSTA 2026 at a glance.
How the course is built
The map above is drawn from the standards; this is the frame the course hangs on — the semester shape, the block rhythm, and the planning basis.
SemesterAug 3 – Dec 15, 2026
Q1Aug 3 – Oct 2 (43 days)
Q2Oct 5 – Dec 15 (42 days)
Blocks5 × 90 minutes per week
Nominal instructional hours~127
Planning basis80% of calendar — ~24 usable blocks per module
Block structure. Two segments per 90-minute block, each running a complete I-do / we-do / you-do loop. The content segment is typically shorter than the lab segment. The I-do models lab-notebook setup — framing the work, not formatting a header.
Module windows & calendar load
1Aug 3 – Sep 11
Light. Labor Day, Sep 8 PD. The cleanest module.
2Sep 14 – Oct 23
Moderate. Q1 benchmark Sep 28 – Oct 2, quarter boundary, Oct 8 half-day PD/Wellness, fall break.
3Oct 26 – Dec 15
Heavy. Thanksgiving Nov 23–27, HS Fall LEAP Dec 1–18, Q2 benchmark Dec 3–10, Dec 11 half-day PD/Wellness. Longer in calendar weeks, shorter in usable blocks.
Module boundaries are provisional — fall break and PD half-days shift the edges, pending final verification against the InspireNOLA 2026–2027 academic calendar.
Modules and their standards
Which standards each six-week module targets, by tier. Identifiers link to the CSTA viewer; the full descriptors appear in The Buckets above.
1Reading the Machine
Weeks 1–6 · Aug 3 – Sep 11
2Authoring the Machine
Weeks 7–12 · Sep 14 – Oct 23
3How Programs Work
Weeks 13–18 · Oct 26 – Dec 15
Assessment
A pre/post growth instrument spanning two bands, unit assessments for grades, and a portfolio in place of a sit-down final.
Assessment spine across the CS I semester| When | What | Purpose |
|---|
| Week 1 (Aug 3–7) | Pretest — graduated MS→HS instrument, five buckets, administered in the closing 30-minute segment across several blocks | Baseline. SLT anchor. Tiering read. |
|---|
| Sep 28 – Oct 2 | Q1 Midterm — subset of pretest items covering taught Module 1 content | Graded. Not LEADS data. Item-level results captured for the scored-set lock. |
|---|
| Early October | Scored set locked — items above the ceiling threshold excluded; set fixed in writing before any post-test | Instrument integrity. |
|---|
| Biweekly | Unit assessments | Grades; diagnostic feed. |
|---|
| Dec 3–10 | Q2 Midterm + Post-test — matched instrument | Growth measure. SLT. |
|---|
| Module 3 | SDD Portfolio — the semester's final deliverable, in place of a sit-down final | Authorship demonstrated through durable artifacts. |
|---|
- Week 1 (Aug 3–7)
- Pretest — graduated MS→HS instrument, five buckets, administered in the closing 30-minute segment across several blocks
- Baseline. SLT anchor. Tiering read.
- Sep 28 – Oct 2
- Q1 Midterm — subset of pretest items covering taught Module 1 content
- Graded. Not LEADS data. Item-level results captured for the scored-set lock.
- Early October
- Scored set locked — items above the ceiling threshold excluded; set fixed in writing before any post-test
- Instrument integrity.
- Biweekly
- Unit assessments
- Grades; diagnostic feed.
- Dec 3–10
- Q2 Midterm + Post-test — matched instrument
- Growth measure. SLT.
- Module 3
- SDD Portfolio — the semester's final deliverable, in place of a sit-down final
- Authorship demonstrated through durable artifacts.
The instrument spans two bands (MS and HS) so that a cohort arriving below grade level lands somewhere real rather than at the floor. Score resolves to highest band answered reliably, per bucket — not percent correct.
The post-test is administered as a window, not a day. During Fall LEAP, students are pulled unpredictably; the closing segment is offered repeatedly until everyone has taken it.
Matched pre/post pairs are the reported figure. Unmatched students are excluded from the growth number, and that count is stated.