15H4NT/Grade-Prophet

Forecasting tomorrow's classrooms today — one learner at a time.

ScholarCast is a forward-looking academic intelligence platform that takes the same spark of inspiration behind GradeGuru and reimagines it as a full classroom forecasting ecosystem. Where simple predictors stop at a single number, ScholarCast paints a portrait — blending attendance rhythms, assignment cadence, engagement signals, and historical performance into a composite forecast that educators can actually act on.

Think of it as a weather station for learning outcomes. Instead of telling you it will rain tomorrow, it tells you which students need an umbrella, which ones are ready to sprint in the sunshine, and which classroom conditions will bring the storm. 🌦️📚

Scholarship is not a lottery — it is a pattern. ScholarCast helps teachers read the pattern before the semester writes its ending.

Traditional grade books are rearview mirrors. They tell you what already happened. By the time the final exam lands, intervention windows have usually closed.

ScholarCast flips the mirror. It uses supervised regression and ensemble learning to produce forward-looking performance bands for each learner, along with confidence intervals and recommended nudges. The goal is not surveillance — it is scaffolding.

Educators deserve tools that respect their time. ScholarCast returns insight in under a second, with no arcane configuration required.

• 🧠 Multi-Model Ensemble Forecasting — blends gradient-boosted trees, ridge regression, and a lightweight neural booster for stable predictions
• 📊 Confidence-Aware Outputs — every prediction ships with a range, not a false-precision single score
• 🧑‍🏫 Cohort & Individual Views — zoom from a single learner to an entire section without reloading
• 📱 Responsive UI — the dashboard reshapes itself gracefully from ultrawide monitors to classroom tablets
• 🌍 Multilingual Support — interface strings available in multiple languages out of the box, with community-extensible translation files
• 🕐 24/7 Customer Support — always-on assistance channel because classrooms do not keep banker's hours
• 🔐 Privacy-First Design — student metadata stays local by default; nothing leaves the device unless you ask it to
• 🧩 Pluggable Data Adapters — bring your own CSV, LMS export, or SIS snapshot
• 📈 Trend Timeline — visualize predicted trajectory across weeks
• 🗂️ Scenario Sandbox — tweak hypothetical inputs (study hours, attendance) and watch the forecast shift in real time
• 🎯 Intervention Recommendations — the model doesn't just say "at risk," it suggests "here is the lever"
• 🧾 Exportable Reports — clean summaries ready for parent conferences or advisory meetings
• 🕶️ Dark & Light Themes — because grading happens at 1 a.m. too
• 🧪 Deterministic Mode — replays predictions bit-for-bit for reproducibility in research settings
• 🛰️ Offline-First Core — the predictor runs without network access; sync is optional

ScholarCast approaches the problem in four movements:

1. Ingestion — raw records flow from CSV, JSON, or LMS exports into a normalized schema.
2. Featurization — attendance ratio, assignment completion cadence, quiz variance, study-hour estimates, prior GPA, and engagement proxies are synthesized.
3. Inference — an ensemble of estimators produces a weighted central forecast plus a dispersion band.
4. Narration — the insights layer translates numbers into human-readable guidance.

Each stage is inspectable. Nothing is a black box that shrugs when asked "why."

If you arrived here while searching for a student performance prediction tool, an academic forecasting dashboard, a classroom analytics engine, or a machine learning grade estimator, you are in the right place. ScholarCast is designed for proactive academic planning, early-warning systems for at-risk learners, and data-informed teaching strategy. It supports multilingual classrooms, responsive dashboards, and round-the-clock help channels for institutions that operate across time zones.

Setting up ScholarCast is intentionally gentle. Rather than a wall of terminal incantations, the flow is:

• Bring your learner dataset into the workspace folder.
• Open the desktop shell or launch the browser-based studio.
• Point the importer at your file; ScholarCast auto-detects the schema.
• Map your columns to the internal fields using the guided wizard.
• Choose a forecast horizon — midterm, semester, or exam-specific.
• Review the ensemble output and export your first report.

No cryptic flags. No kitchen-sink configuration. Just a guided path from data to decision. 🚀

The interface was designed around a simple principle: a teacher should be able to glance at a tablet while walking between desks and still read the signal. Layouts reflow, tables become cards, charts simplify at narrow widths, and color contrast remains accessible under bright classroom lighting.

Strings live in plain translation files, so adding a new language is a matter of copying one template and editing text. Right-to-left layouts are honored automatically. Locale-aware number and date formatting is applied across the dashboard.

Questions do not arrive on a 9-to-5 schedule. The support channel is monitored continuously, with an ever-growing knowledge base of walkthroughs, sample datasets, and troubleshooting recipes. Response times are tracked and published — accountability is part of the product.

• Unit tests cover featurization, model wrappers, and export routines
• Property-based tests stress malformed inputs
• Snapshot tests guard the UI from silent regressions
• A dedicated "golden dataset" ensures predictions remain stable across releases

Predictions about people deserve care. ScholarCast ships with:

• Local-first processing
• Explicit opt-in for any sync
• No third-party telemetry by default
• Clear documentation on bias risks and mitigation strategies
• Practical guidance discouraging use of forecasts as the sole basis for high-stakes decisions

A forecast is a compass, not a verdict. 🧭

ScholarCast is provided as an educational and analytical aid. Predictions are statistical estimates, not deterministic truths, and may be affected by data quality, sampling bias, or model limitations. Do not use output as the sole basis for academic placement, disciplinary action, or any decision with material consequences for a learner. Always pair forecasts with professional judgment, direct observation, and conversation with the student. The maintainers accept no liability for decisions made on the basis of this software. See the license for the full warranty disclaimer.

This project is distributed under the MIT License.

You can read the full text in the repository's LICENSE file: MIT License

Copyright (c) 2026 ScholarCast Contributors

Contributions are welcome. Please open an issue to discuss substantial changes before submitting a pull request. Bug reports, translation improvements, and documentation corrections are especially appreciated. Kindness in reviews is a house rule. 💛

For inquiries about integrations, classroom deployments, or research collaborations, open a discussion thread in the repository. The maintainers read every message, even if replies occasionally take a day or two.

• 2026.1 — Ensemble predictor stabilized; scenario sandbox introduced
• 2026.2 — Multilingual strings refactored; RTL layout support added
• 2026.3 — Deterministic replay mode; export pipeline overhauled
• 2026.4 — Responsive dashboard polish; accessibility audit passed

Thank you for reading. May your forecasts be useful, and your classrooms be kind. 🎓