Learning platforms · built by an educator
Hands-on tools for learning code and research.
Two browser-based workspaces for students and faculty. Write and run code in six languages, or take a research question from dataset to publishable result. Nothing to install.
hours = {"Ana": 6.8, "Ben": 4.9, "Chen": 8.3} avg = sum(hours.values()) / len(hours) for name, h in hours.items(): print(name, "on track" if h >= avg else "needs a plan") ▶ Ana on track · Ben needs a plan · Chen on track
Why these tools
Built for classrooms where nothing can be assumed installed.
Every student gets the same environment on day one, whether they are on a campus machine, a Chromebook, or a phone.
Six languages, one tab
Python, Oracle SQL and PL/SQL, R, marimo notebooks, C#, and web development, each with its own provisioned workspace.
From question to write-up
A wizard picks the right test, runs it in R or Python, and writes the result in APA, MLA, IEEE, AMA, CSE, Harvard or Chicago.
Live help, inside the tool
Screen sharing and a whiteboard for office hours in the Lab; a research interview room with transcription in StatLab.
Accounts you control
No self-registration. Instructors create accounts, see activity, and deactivate at the end of term.
Quantum, Linux and networking
Qiskit circuits, a Linux terminal and a networking lab sit alongside the classic sandboxes for upper-level courses.
Reproducible by default
Every StatLab analysis exports the exact R or Python script that produced it, tied to the dataset version.
Virtual Computing Lab
Write it, run it, see it. In the browser.
A coding sandbox with practice spaces for every language your courses use, plus live tutoring with screen sharing and a whiteboard. Each student's Oracle schema and workspace is provisioned automatically.
- Python · Oracle SQL / PL/SQL · R · marimo · C# · web dev
- Linux terminal, networking lab, and Qiskit for quantum courses
- Live sessions: students and instructors invite each other
SELECT modality, ROUND(AVG(exam_score), 1) AS avg_score, COUNT(*) AS n FROM students GROUP BY modality ORDER BY avg_score DESC;
StatLab
Research statistics with the reasoning shown.
Upload a dataset, label your variables, and let the wizard choose the test. StatLab runs it in R or Python, checks the assumptions, writes the sentence for your methods section, and exports the script that reproduces it. A qualitative module codes interview transcripts alongside.
- Descriptives through ANOVA, regression, mixed models, CFA and SEM
- Qualitative coding workbench with transcription and reliability
- Learn tab: four tracks and a 15-week course with live sandboxes
| Group | n | M | SD |
|---|---|---|---|
| in_person | 10 | 75.4 | 8.3 |
| online | 10 | 68.9 | 7.1 |
In-person students scored higher than online students, but the difference was not statistically significant, Welch t(17.8) = 1.80, p = .089, d = 0.84, 95% CI [−1.1, 14.1].
How it works
Three steps to a working classroom.
Instructor creates accounts
Import a roster or add students one at a time. Sign-ins follow a predictable pattern, so day one needs no IT ticket.
Students open a browser
Any device, no installs. Each student lands in a provisioned workspace with the course's languages ready.
Learn, run, get help
Guided walkthroughs for the first session, live tutoring when stuck, and results that export cleanly into assignments and theses.
New here? Start with the guided first session.
Sign in, run your first code in the Lab, and your first analysis in StatLab, with checkpoints along the way. About an hour for both tracks.