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<title>PLRS — Personalized Learning Recommendation System</title>
<meta name="description" content="Constraint-aware personalized learning recommendations. Plug in your curriculum, get intelligent recommendations out." />
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</style>
</head>
<body>
<!-- ── Nav ── -->
<nav>
<a href="#" class="nav-logo">PL<span>RS</span></a>
<div class="nav-links">
<a href="#how-it-works">How it works</a>
<a href="#results">Results</a>
<a href="#quickstart">Quickstart</a>
<a href="https://github.com/clementina-tom/plrs" target="_blank">GitHub</a>
<a href="https://huggingface.co/spaces/Clementio/PLRS" class="btn btn-primary btn-hero" target="_blank">Live Demo →</a>
</div>
</nav>
<!-- ── Hero ── -->
<div class="hero">
<div class="hero-grid"></div>
<div class="hero-eyebrow">Knowledge Tracing · Constraint-Aware · Open Source</div>
<h1>Recommendations that<br/><em>respect</em> how learning works.</h1>
<p class="hero-sub">
PLRS combines Self-Attentive Knowledge Tracing with a DAG prerequisite constraint layer
to generate personalized learning recommendations that are pedagogically sound —
not just statistically optimal.
</p>
<div class="hero-ctas">
<a href="https://huggingface.co/spaces/Clementio/PLRS" target="_blank" class="btn btn-primary btn-hero">
Try the live demo
</a>
<a href="https://github.com/clementina-tom/plrs" target="_blank" class="btn btn-outline btn-hero">
View on GitHub
</a>
<a href="#quickstart" class="btn btn-outline btn-hero">
Quickstart
</a>
</div>
<div class="stat-strip">
<div class="stat-item">
<div class="stat-num"><span>0.0</span>%</div>
<div class="stat-label">Prerequisite violation rate</div>
</div>
<div class="stat-item">
<div class="stat-num">0.7692</div>
<div class="stat-label">SAKT Val AUC (OULAD)</div>
</div>
<div class="stat-item">
<div class="stat-num">69</div>
<div class="stat-label">Curriculum topics (2 domains)</div>
</div>
<div class="stat-item">
<div class="stat-num">52</div>
<div class="stat-label">Tests passing</div>
</div>
</div>
</div>
<!-- ── How it works ── -->
<section id="how-it-works">
<div class="section-label">Architecture</div>
<h2 class="section-title">Three layers. One guarantee.</h2>
<p class="section-body">
Standard recommendation systems optimise for engagement or accuracy —
they will happily recommend Calculus to a student who hasn't mastered Algebra.
PLRS adds a constraint layer that makes this <em>structurally impossible</em>.
</p>
<div class="arch-flow">
<div class="arch-node">Student History</div>
<div class="arch-arrow">→</div>
<div class="arch-node highlight">SAKT Model</div>
<div class="arch-arrow">→</div>
<div class="arch-node">Mastery Vector</div>
<div class="arch-arrow">→</div>
<div class="arch-node highlight">DAG Constraints</div>
<div class="arch-arrow">→</div>
<div class="arch-node">Multi-Objective Ranker</div>
<div class="arch-arrow">→</div>
<div class="arch-node highlight">Recommendations</div>
</div>
<div class="tier-grid">
<div class="tier-card green">
<div class="tier-icon">✅</div>
<div class="tier-name">Approved</div>
<div class="tier-desc">All prerequisites met above the mastery threshold. Student is ready to learn this topic now.</div>
</div>
<div class="tier-card amber">
<div class="tier-icon">⚠️</div>
<div class="tier-name">Challenging</div>
<div class="tier-desc">Prerequisites partially met — above the soft threshold but below full mastery. Proceed with awareness.</div>
</div>
<div class="tier-card red">
<div class="tier-icon">❌</div>
<div class="tier-name">Vetoed</div>
<div class="tier-desc">One or more prerequisites not met. Structurally blocked until foundations are solid.</div>
</div>
</div>
</section>
<!-- ── Results ── -->
<section id="results" style="border-top: 1px solid var(--border);">
<div class="section-label">Evaluation</div>
<h2 class="section-title">0% violation rate. Not a tuning choice.</h2>
<p class="section-body">
Evaluated on the Open University Learning Analytics Dataset (OULAD) with
Nigerian secondary school curriculum knowledge maps. The 0% violation rate
is a structural guarantee from the DAG constraint layer — not a hyperparameter.
</p>
<table class="results-table">
<thead>
<tr>
<th>Model</th>
<th>Val AUC</th>
<th>Prerequisite Violation Rate</th>
<th>Coverage</th>
</tr>
</thead>
<tbody>
<tr class="highlight-row">
<td><strong>PLRS (SAKT + DAG)</strong></td>
<td><strong>0.7692</strong></td>
<td><span class="badge-green">0.0%</span></td>
<td>Full curriculum</td>
</tr>
<tr>
<td>Collaborative Filtering</td>
<td>—</td>
<td><span class="badge-red">81.3%</span></td>
<td>Partial</td>
</tr>
<tr>
<td>Matrix Factorization</td>
<td>—</td>
<td><span class="badge-red">83.7%</span></td>
<td>Partial</td>
</tr>
<tr>
<td>BKT (baseline)</td>
<td>~0.67</td>
<td><span class="badge-red">No constraint layer</span></td>
<td>Partial</td>
</tr>
</tbody>
</table>
</section>
<!-- ── Quickstart ── -->
<section id="quickstart" style="border-top: 1px solid var(--border);">
<div class="section-label">Quickstart</div>
<h2 class="section-title">Plug in your curriculum.</h2>
<p class="section-body">
PLRS is curriculum-agnostic. Define your knowledge graph in a simple JSON format
and get recommendations immediately. No retraining required for new domains.
</p>
<div class="code-wrap">
<div class="code-header">
<div class="code-dots"><span></span><span></span><span></span></div>
<div class="code-lang">PYTHON</div>
</div>
<pre><span class="ck">from</span> plrs <span class="ck">import</span> PLRSPipeline
<span class="ck">from</span> plrs.curriculum <span class="ck">import</span> load_dag
<span class="cm"># Load your curriculum (JSON knowledge graph)</span>
curriculum = <span class="cf">load_dag</span>(<span class="cs">"math_dag.json"</span>)
<span class="cm"># Create pipeline — no model needed for mastery-dict mode</span>
pipeline = <span class="cf">PLRSPipeline</span>(curriculum)
<span class="cm"># Get recommendations from student mastery scores</span>
results = pipeline.<span class="cf">recommend_from_mastery</span>({
<span class="cs">"whole_numbers"</span>: <span class="cn">0.90</span>,
<span class="cs">"algebraic_expressions"</span>: <span class="cn">0.75</span>,
<span class="cs">"quadratic_equations"</span>: <span class="cn">0.40</span>,
})
<span class="ck">for</span> rec <span class="ck">in</span> results[<span class="cs">"approved"</span>]:
<span class="cf">print</span>(<span class="cs">f"✅ {rec['topic_label']} (score={rec['score']})"</span>)
<span class="cf">print</span>(<span class="cs">f" {rec['reasoning']}"</span>)
<span class="cm"># What-if: what does mastering this topic unlock?</span>
wi = pipeline.<span class="cf">what_if</span>(<span class="cs">"algebraic_expressions"</span>)
<span class="cf">print</span>(<span class="cs">f"Unlocks {wi['total_unlocked']} downstream topics"</span>)</pre>
</div>
<div class="code-wrap" style="margin-top: 1rem;">
<div class="code-header">
<div class="code-dots"><span></span><span></span><span></span></div>
<div class="code-lang">REST API</div>
</div>
<pre><span class="cm"># Start the server</span>
$ python scripts/serve.py
<span class="cm"># → http://127.0.0.1:8000/docs</span>
<span class="cm"># Get recommendations</span>
$ curl -X POST http://localhost:<span class="cn">8000</span>/recommend \
-H <span class="cs">"Content-Type: application/json"</span> \
-d <span class="cs">'{"domain":"math","mastery_scores":{"whole_numbers":0.9}}'</span></pre>
</div>
</section>
<!-- ── Features ── -->
<section style="border-top: 1px solid var(--border);">
<div class="section-label">Features</div>
<h2 class="section-title">Built for real deployment.</h2>
<div class="feature-grid">
<div class="feature-cell">
<div class="feature-icon">🔌</div>
<div class="feature-title">Curriculum-agnostic</div>
<div class="feature-desc">Define any knowledge graph in a simple JSON format. Ships with Nigerian secondary school Maths and CS Fundamentals (NERDC JSS3–SS2).</div>
</div>
<div class="feature-cell">
<div class="feature-icon">⚡</div>
<div class="feature-title">FastAPI REST backend</div>
<div class="feature-desc">Production-ready API with <code>/recommend</code>, <code>/what-if</code>, and <code>/curriculum</code> endpoints. Auto-generated OpenAPI docs.</div>
</div>
<div class="feature-cell">
<div class="feature-icon">🧠</div>
<div class="feature-title">SAKT + Forgetting Curve</div>
<div class="feature-desc">Self-Attentive Knowledge Tracing with optional Ebbinghaus decay attention — older interactions contribute less to current mastery estimates.</div>
</div>
<div class="feature-cell">
<div class="feature-icon">🔍</div>
<div class="feature-title">What-If Simulator</div>
<div class="feature-desc">"If I master Trigonometry now, what unlocks?" — live DAG traversal shows direct and transitive downstream topics.</div>
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<div class="feature-icon">📦</div>
<div class="feature-title">PyPI-ready package</div>
<div class="feature-desc"><code>pip install plrs</code> — modular architecture with clean public API. Full type annotations throughout.</div>
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<div class="feature-icon">🧪</div>
<div class="feature-title">52 tests, CI on 3 Python versions</div>
<div class="feature-desc">Unit tests, API integration tests, and evaluator tests. GitHub Actions runs on Python 3.10, 3.11, and 3.12.</div>
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<div class="cta-title">Try it now — no setup required.</div>
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The live demo runs the full pipeline in your browser.
Adjust mastery sliders, simulate student sequences, explore the curriculum graph.
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<a href="https://huggingface.co/spaces/Clementio/PLRS" target="_blank" class="btn btn-primary btn-hero">
Open live demo →
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PLRS — Personalized Learning Recommendation System<br/>
MIT License · Built by <a href="https://github.com/clementina-tom" style="color:var(--blue);text-decoration:none;">Clementina Tom</a>
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