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7  <title>EDM-ARS &amp; LSAR &mdash; Educational Data Mining Research Tools</title>
8  <meta name="description" content="EDM-ARS automates end-to-end educational data mining research — prediction, causal inference and psychometrics — from raw data to a formatted manuscript. LSAR provides automated peer review across 8 dimensions for 12 learning science venues, with thresholds calibrated on published papers." />
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16  <!-- ==================== NAV ==================== -->
17  <nav>
18    <a class="nav-logo" href="#hero">EDM-ARS</a>
19    <ul class="nav-links">
20      <li><a href="#about">Overview</a></li>
21      <li><a href="#pipeline">Architecture</a></li>
22      <li><a href="#capabilities">Features</a></li>
23      <li><a href="#demo">Demo Stats</a></li>
24      <li><a href="#lsar">LSAR</a></li>
25      <li><a href="#roadmap">Roadmap</a></li>
26      <li><a href="#papers">Papers</a></li>
27    </ul>
28  </nav>
29
30  <!-- ==================== HERO ==================== -->
31  <section id="hero">
32    <canvas id="mars-canvas" aria-hidden="true"></canvas>
33    <div class="hero-vignette"></div>
34    <div class="hero-content">
35      <p class="hero-badge">🪐 EDM-ARS &nbsp;·&nbsp; v5 · Open Source</p>
36      <h1>Educational Data Mining<br><span>Automated</span> Research System</h1>
37      <p class="tagline">A multi-agent LLM pipeline that turns a dataset and a research prompt into a complete, reviewer-ready academic paper — across prediction, causal inference and psychometrics — then scores it against thresholds calibrated on papers those venues actually published.</p>
38      <div class="hero-buttons">
39        <a class="btn btn-primary" href="#pipeline">System Architecture</a>
40        <a class="btn btn-outline" href="#capabilities">Key Features</a>
41        <a class="btn btn-outline" href="https://github.com/cgpan/edm-ars-public" target="_blank" rel="noopener">Source Code</a>
42        <a class="btn btn-outline" href="#lsar">Meet LSAR</a>
43      </div>
44
45      <div class="hero-next">
46        <span class="hero-next-flag"><span class="hero-next-dot" aria-hidden="true"></span>Coming Soon</span>
47        <span class="hero-next-body"><strong>QM-ARS</strong> &mdash; the Quantitative Methods Automated Research System. The next generation of EDM-ARS, widening the pipeline to broader quantitative methods and research topics.</span>
48      </div>
49    </div>
50    <div class="hero-stats">
51      <div class="hero-stat">
52        <div class="hero-stat-val">5</div>
53        <div class="hero-stat-label">Study Types Supported</div>
54      </div>
55      <div class="hero-stat">
56        <div class="hero-stat-val">12</div>
57        <div class="hero-stat-label">Review Venues (LSAR)</div>
58      </div>
59      <div class="hero-stat">
60        <div class="hero-stat-val">30–60 min</div>
61        <div class="hero-stat-label">End-to-End Runtime</div>
62      </div>
63      <div class="hero-stat">
64        <div class="hero-stat-val">$0.53</div>
65        <div class="hero-stat-label">Measured Cost Per Paper</div>
66      </div>
67    </div>
68  </section>
69
70  <!-- ==================== ABOUT ==================== -->
71  <section class="section reveal" id="about">
72    <p class="section-label">Overview</p>
73    <h3 class="section-title">What It Does</h3>
74    <p class="section-body">
75      Inspired by <a href="https://analemma.ai/blog/introducing-fars/" target="_blank" rel="noopener noreferrer">FARS</a>, EDM-ARS is an open-source, domain-specific multi-agent LLM pipeline that automates the complete workflow of educational data mining research. Given a curated dataset and a research prompt, it formulates a research question, engineers the analytic sample, runs the analysis, retrieves real citations via the <strong>Semantic Scholar API</strong> with three-layer verification (exact title match, Jaccard similarity, CrossRef cross-validation), and produces a complete <strong>LaTeX manuscript</strong> in ACM <em>sigconf</em> or APA-7 journal format — with a built-in Critic agent that enforces methodological rigor through automated peer review and targeted revision loops.
76    </p>
77    <p class="section-body">
78      v5 covers <strong>five study types</strong>: predictive modeling, three causal-inference designs (selection-on-observables, individualized treatment regimes, difference-in-differences), and psychometrics (IRT, measurement invariance, DIF, cognitive diagnosis). Ten estimation methods are certified against synthetic data with known ground truth before they are allowed near a real dataset. Four datasets are curated: <strong>HSLS:09</strong>, <strong>ELS:2002</strong>, <strong>
78ASSISTments 2009&ndash;10</strong>, and a constructed ELS&times;HSLS cross-cohort panel.
79    </p>
80
81    <div class="about-grid">
82      <div class="about-item">
83        <div class="about-item-icon">🎓</div>
84        <h4>Domain-Specific Design</h4>
85        <p>Each dataset carries a <strong>three-tier variable registry</strong>: hand-curated Tier 1 variables with educational annotations, auto-generated Tier 2 variables, and Tier 3 exclusions (weights, IDs) enforced programmatically. Survey-design realities — school clustering, sentinel missing codes, legitimate skips, suppressed identifiers — are encoded per dataset rather than left for the model to infer.</p>
86      </div>
87      <div class="about-item">
88        <div class="about-item-icon">🧩</div>
89        <h4>Skill-Based Architecture</h4>
90        <p><strong>70 composable skills</strong> across four layers — task type, dataset, methodology and writing — are matched at runtime and injected into each agent's prompt. Adding a capability means adding a skill, not enlarging a prompt. Skills marked <em>mandatory</em> bypass the size cap, because a rule whose violation corrupts output silently must always reach the agent.</p>
91      </div>
92      <div class="about-item">
93        <div class="about-item-icon">🔬</div>
94        <h4>Certified Before Live Use</h4>
95        <p>Every estimator faces a <strong>synthetic-data gate</strong> before it touches real data: a data-generating process with a known true effect, and the method must recover it. Difference-in-differences was held back until it hit 0.006 bias and detected pre-trend violations at a rate of 1.0.</p>
96      </div>
97      <div class="about-item">
98        <div class="about-item-icon">🛑</div>
99        <h4>Deterministic Honesty Guards</h4>
100        <p>Prompting a model not to invent numbers does not stop it. Every figure in the manuscript is <strong>reconciled against the analysis artifacts</strong>, unverifiable claims are flagged, and a paper whose numbers cannot be traced is blocked from passing the gate — regardless of how well it reads.</p>
101      </div>
102    </div>
103  </section>
104
105  <hr class="divider" />
106
107  <!-- ==================== PIPELINE ==================== -->
108  <section id="pipeline">
109    <div class="inner reveal">
110      <p class="section-label">Architecture</p>
111      <h3 class="section-title">Six-Agent Pipeline</h3>
112      <p class="section-body">The dynamic graph below demonstrates our architecture and its information flow. It replays a real EDM-ARS run.</p>
113
114      <div class="rp" id="replay">
115
116        <div class="rp-transport">
117          <div class="rp-controls">
118            <button class="btn btn-primary rp-btn" id="rp-replay" type="button">
119              <svg viewBox="0 0 12 12" aria-hidden="true"><path d="M9.6 3.1A4.3 4.3 0 1 0 10.3 6.9" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"/><path d="M10.6 0.9v3.4H7.2z" fill="currentColor"/></svg>
120              <span>Replay run</span>
121            </button>
122            <button class="btn btn-outline rp-btn" id="rp-toggle" type="button" disabled>
123              <svg viewBox="0 0 12 12" aria-hidden="true"><path d="M2.5 1.2v9.6L10.6 6z" fill="currentColor"/></svg>
124              <span>Resume</span>
125            </button>
126            <button class="btn btn-outline rp-btn" id="rp-end" type="button">
127              <svg viewBox="0 0 12 12" aria-hidden="true"><path d="M1.4 1.2v9.6L7.7 6zM8.6 1.2h2.2v9.6H8.6z" fill="currentColor"/></svg>
128              <span>Jump to end</span>
129            </button>
130          </div>
131          <div class="rp-speed">
132            <label class="rp-speed-lab" for="rp-speed">Speed</label>
133            <input type="range" id="rp-speed" min="0" max="4" step="1" value="1" />
134            <output id="rp-speed-out" for="rp-speed">30× · ≈ 1 min</output>
135          </div>
136          <div class="rp-status">
137            <span class="rp-pill" id="rp-pill" data-kind="done">Completed</span>
138            <span class="rp-clock" title="Wall-clock time of the run, UTC"><span id="rp-clock">18:22:41</span><span class="rp-elapsed" id="rp-elapsed">+29:33</span></span>
139          </div>
140        </div>
141
142        <div class="rp-panel">
143          <header>
144            <span class="rp-eyebrow">Agents, orchestrator and the resources they reach</span>
145            <span class="rp-eyebrow rp-dim" id="rp-stage">run complete</span>
146          </header>
147          <div class="rp-graph-scroll">
148            <svg id="rp-graph" viewBox="0 0 966 384" role="img"
149                 aria-label="Diagram of the EDM-ARS pipeline: five agents above a central orchestrator rail, with the external resources they reach above them and orchestrator-only steps below."></svg>
150          </div>
151          <p class="rp-pan-hint">Swipe sideways to see the whole diagram.</p>
152          <div class="rp-caption">
153            <div class="rp-cap-hd"><span class="rp-cap-art" id="rp-cap-art">run complete</span><span class="rp-cap-route" id="rp-cap-route"></span></div>
154            <p class="rp-cap-note" id="rp-cap-note">Press Replay run to watch the 29-minute run compressed into about a minute, or drag the trace below to scrub.</p>
155          </div>
156        </div>
157
158        <div class="rp-panel">
159          <header>
160            <span class="rp-eyebrow">Wall-clock trace · 2026-08-29 17:53:08 → 18:22:41 UTC</span>
161            <span class="rp-eyebrow rp-dim">click or drag to scrub</span>
162          </header>
163          <div class="rp-tl" id="rp-tl" role="slider" tabindex="0"
164               aria-label="Replay position" aria-valuemin="0" aria-valuemax="1773" aria-valuenow="1773">
165            <svg id="rp-timeline" aria-hidden="true"></svg>
166          </div>
167          <div class="rp-legend">
168            <span class="rp-key"><i class="k-work"></i>agent working</span>
169            <span class="rp-key"><i class="k-revise"></i>Critic &rarr; REVISE</span>
170            <span class="rp-key"><i class="k-pass"></i>Critic &rarr; PASS</span>
171            <span class="rp-key"><i class="k-mech"></i>LaTeX compile</span>
172            <span class="rp-key"><i class="k-tick"></i>LLM call returned &middot; 21 calls, bar height = tokens</span>
173          </div>
174          <p class="rp-foot">Hand-offs recorded in the same second are staggered by a few seconds so the packets can be told apart; every clock time shown is as recorded.</p>
175        </div>
176
177      </div>
178    </div>
179  </section>
180
181  <hr class="divider" />
182
183  <!-- ==================== CAPABILITIES ==================== -->
184  <section class="section reveal" id="capabilities">
185    <p class="section-label">Features</p>
186    <h3 class="section-title">Key Capabilities</h3>
187
188    <div class="caps-grid">
189      <div class="cap-card">
190        <div class="cap-icon">🤖</div>
191        <p class="cap-stat">6 Specialized Agents</p>
192        <p class="cap-desc">Coordinated by a state-machine orchestrator. Each agent has its own system prompt, temperature and model routing, configurable per stage. The reference configuration runs the whole pipeline on <strong>DeepSeek-v4-pro</strong>; Anthropic, OpenAI and MiniMax backends are supported through the same interface.</p>
193      </div>
194      <div class="cap-card">
195        <div class="cap-icon">📜</div>
196        <p class="cap-stat">End-to-End Automation</p>
197        <p class="cap-desc">From a raw CSV and a research prompt to a compiled ACM LaTeX paper — with real citations, methodology validation, and SHAP explainability figures.</p>
198      </div>
199      <div class="cap-card">
200        <div class="cap-icon">🛡</div>
201        <p class="cap-stat">Self-Healing Pipeline</p>
202        <p class="cap-desc">Contract validation at every stage boundary. Auto-patching for classifiable errors (SHAP failure, dtype mismatch, missing column) before falling back to LLM repair.</p>
203      </div>
204      <div class="cap-card">
205        <div class="cap-icon">📚</div>
206        <p class="cap-stat">Live Academic Citations</p>
207        <p class="cap-desc">The ProblemFormulator queries the Semantic Scholar API with exponential-backoff retry logic to retrieve and validate real, current citations.</p>
208      </div>
209      <div class="cap-card">
210        <div class="cap-icon">⚗️</div>
211        <p class="cap-stat">6-Model Battery</p>
212        <p class="cap-desc">Logistic Regression, Random Forest, XGBoost, ElasticNet, MLP, and a Stacking Ensemble are trained, compared, and reported with SHAP explainability. A <strong>Model Quality Gate</strong> (AUC &ge; 0.60 for classification, R&sup2; &ge; 0.05 for regression) runs before SHAP, so a model that has learned nothing is not given an interpretation.</p>
213      </div>
214      <div class="cap-card">
215        <div class="cap-icon">🐳</div>
216        <p class="cap-stat">Docker Sandboxing</p>
217        <p class="cap-desc">LLM-generated analysis code executes inside a Docker sandbox (network-disabled). Gracefully falls back to subprocess when Docker is unavailable.</p>
218      </div>
219      <div class="cap-card">
220        <div class="cap-icon">🎯</div>
221        <p class="cap-stat">Causal Inference <span class="cap-new">New</span></p>
222        <p class="cap-desc">Selection-on-observables (matching, IPW, regression adjustment, AIPW/TMLE), c
222ausal forests for heterogeneous effects, individualized treatment regimes, and difference-in-differences. Estimands are declared before estimation; balance, overlap and unmeasured-confounding sensitivity are reported as a matter of course.</p>
223      </div>
224      <div class="cap-card">
225        <div class="cap-icon">📐</div>
226        <p class="cap-stat">Psychometrics <span class="cap-new">New</span></p>
227        <p class="cap-desc">Item response theory, graded response models, measurement invariance testing, differential item functioning, and cognitive diagnosis (DINA / G-DINA) — executed in R through a bridge, with degeneracy reported rather than hidden.</p>
228      </div>
229      <div class="cap-card">
230        <div class="cap-icon">🧾</div>
231        <p class="cap-stat">Numeric Reconciliation <span class="cap-new">New</span></p>
232        <p class="cap-desc">Every number in the manuscript is matched back to the analysis outputs that produced it. An audit once found a paper that scored well while reporting fabricated values for fields the analysis left empty; that class of failure is now blocked deterministically rather than discouraged by prompt.</p>
233      </div>
234      <div class="cap-card">
235        <div class="cap-icon">📏</div>
236        <p class="cap-stat">Calibrated Review Gate <span class="cap-new">New</span></p>
237        <p class="cap-desc">Papers are scored by LSAR against the 25th percentile of papers the target venue actually published — not an arbitrary threshold. Borderline verdicts trigger median-of-three sampling, because a single review carries real measurement noise.</p>
238      </div>
239      <div class="cap-card">
240        <div class="cap-icon">💵</div>
241        <p class="cap-stat">Measured Token Accounting <span class="cap-new">New</span></p>
242        <p class="cap-desc">Every run records prompt, completion and cached-input tokens per call. Rates live in configuration, so a price change re-prices past runs without re-running them, and an unpriced model yields <code>null</code> rather than a silent zero.</p>
243      </div>
244    </div>
245  </section>
246
247  <!-- ==================== DEMO STATS ==================== -->
248  <section id="demo">
249    <div class="inner reveal">
250      <p class="section-label">Measured Results</p>
251      <h3 class="section-title">What It Actually Produces</h3>
252      <p class="section-body">Fifteen complete papers have been generated and scored through the calibrated review gate. Every study type has at least one paper that clears its venue's threshold. Scores span <strong>3.7 to 7.5</strong> — the low end is reported here for the same reason it is reported in the papers themselves.</p>
253
254      <div class="demo-grid">
255        <div class="demo-stat-card">
256          <div class="demo-stat-val">15</div>
257          <div class="demo-stat-label">Papers Generated &amp; Scored</div>
258        </div>
259        <div class="demo-stat-card">
260          <div class="demo-stat-val">7.5</div>
261          <div class="demo-stat-label">Best LSAR Score (of 10)</div>
262        </div>
263        <div class="demo-stat-card">
264          <div class="demo-stat-val">30–60 min</div>
265          <div class="demo-stat-label">Runtime Per Paper</div>
266        </div>
267        <div class="demo-stat-card">
268          <div class="demo-stat-val">$0.53</div>
269          <div class="demo-stat-label">Measured Cost Per Paper</div>
270        </div>
271      </div>
272
273      <p class="section-body" style="margin-top:28px;">
274        That cost is measured, not estimated: one fully instrumented run made <strong>84 LLM calls</strong>. <strong>81% of input tokens were prompt-cache hits</strong>, billed roughly ten times lower, which is the dominant lever and one that summed token counts cannot see. An earlier figure on this page quoted several dollars per paper; it came from a hardcoded rate constant rather than a measurement, and it was wrong by nearly an order of magnitude.
275      </p>
276    </div>
277  </section>
278
279  <!-- ==================== LSAR ==================== -->
280  <section id="lsar">
281    <div class="inner reveal">
282      <p class="section-label">Companion Tool &nbsp;·&nbsp; v0.4</p>
283      <h3 class="section-title">LSAR &mdash; Learning Science Auto-Reviewer</h3>
284      <p class="section-body">Inspired by <a href="https://paperreview.ai/tech-overview" target="_blank" rel="noopener noreferrer">PaperReview.ai</a>, LSAR is an automated, agentic paper reviewer for learning science venues. A six-stage pipeline ingests a PDF, identifies the venue, retrieves related work, and produces a structured review across <strong>8 quality dimensions</strong> for <strong>12 venues</strong> — four conferences and eight journals.</p>
285
286      <div class="lsar-dims-grid">
287        <div class="lsar-dim-card">
288          <div class="lsar-dim-icon">🎯</div>
289          <strong>Relevance</strong>
290          <p>Alignment with venue scope and topic fit</p>
291        </div>
292        <div class="lsar-dim-card">
293          <div class="lsar-dim-icon">💡</div>
294          <strong>Novelty</strong>
295          <p>Originality and contribution beyond prior work</p>
296        </div>
297        <div class="lsar-dim-card">
298          <div class="lsar-dim-icon">📖</div>
299          <strong>Theoretical Grounding</strong>
300          <p>Strength of conceptual framework and motivation</p>
301        </div>
302        <div class="lsar-dim-card">
303          <div class="lsar-dim-icon">🔧</div>
304          <strong>Methodological Rigor</strong>
305          <p>Soundness of research design and analysis</p>
306        </div>
307        <div class="lsar-dim-card">
308          <div class="lsar-dim-icon">📊</div>
309          <strong>Empirical Support</strong>
310          <p>Quality and interpretation of evidence</p>
311        </div>
312        <div class="lsar-dim-card">
313          <div class="lsar-dim-icon">🌟</div>
314          <strong>Significance</strong>
315          <p>
315Impact on the field and practical implications</p>
316        </div>
317        <div class="lsar-dim-card">
318          <div class="lsar-dim-icon">⚖️</div>
319          <strong>Ethics &amp; Fairness</strong>
320          <p>Ethical considerations and bias awareness</p>
321        </div>
322        <div class="lsar-dim-card">
323          <div class="lsar-dim-icon">✏️</div>
324          <strong>Communication</strong>
325          <p>Clarity, structure, and readability of writing</p>
326        </div>
327      </div>
328
329      <div class="lsar-venues">
330        <span class="lsar-venue-tag">AIED</span>
331        <span class="lsar-venue-tag">EDM</span>
332        <span class="lsar-venue-tag">L@S</span>
333        <span class="lsar-venue-tag">LAK</span>
334        <span class="lsar-venue-tag">AERA Open</span>
335        <span class="lsar-venue-tag">JEDM</span>
336        <span class="lsar-venue-tag">JLA</span>
337        <span class="lsar-venue-tag">JEBS</span>
338        <span class="lsar-venue-tag">JEM</span>
339        <span class="lsar-venue-tag">JREE</span>
340        <span class="lsar-venue-tag">MBR</span>
341        <span class="lsar-venue-tag">Psychometrika</span>
342      </div>
343
344      <div class="lsar-loop-callout" style="margin-top:32px;">
345        <div class="lsar-loop-icon">📏</div>
346        <p><strong>Calibrated thresholds.</strong> A raw review score means little on its own — nothing anchors &ldquo;6.5&rdquo; to any real standard. LSAR reviews papers a venue <em>actually published</em> and takes the 25th percentile of their scores. A submission below that sits below the weakest quartile of what the venue accepted. Current gates: <strong>EDM 6.3</strong> (15 anchors), <strong>AERA Open 6.6</strong> (11), <strong>JEDM 5.3</strong> (10), <strong>JLA 5.4</strong> (9).</p>
347      </div>
348
349      <div class="lsar-loop-callout" style="margin-top:16px;">
350        <div class="lsar-loop-icon">⚠️</div>
351        <p><strong>And what those numbers cannot do.</strong> Measured test&ndash;retest deviation is <strong>1.9 points</strong>, so a single review is a noisy instrument and borderline cases are sampled three times and taken at the median. Anchor corpora run 9&ndash;15 papers per venue: these are working thresholds, not population parameters. A gate is a triage signal for authors, never an acceptance decision.</p>
352      </div>
353
354      <div class="lsar-features">
355        <div class="lsar-feature">
356          <div class="lsar-feature-icon">🔎</div>
357          <p><strong>Venue Detection</strong> — automatically classifies papers for their intended conference or allows manual specification</p>
358        </div>
359        <div class="lsar-feature">
360          <div class="lsar-feature-icon">📦</div>
361          <p><strong>Batch Processing</strong> — review multiple papers in a single run from directories or CSV manifests</p>
362        </div>
363        <div class="lsar-feature">
364          <div class="lsar-feature-icon">📄</div>
365          <p><strong>Multiple Formats</strong> — generates reviews in Markdown, JSON, and PDF reports with modular pipeline stages</p>
366        </div>
367        <div class="lsar-feature">
368          <div class="lsar-feature-icon">🩺</div>
369          <p><strong>Review Health Checks</strong> — a truncated review is rejected rather than scored. 21% of reviews were once cut off between Strengths and Weaknesses, keeping the praise and losing the criticism, and scored <strong>+0.83 too high</strong> as a result. Truncation is now a hard error at three layers.</p>
370        </div>
371      </div>
372
373      <div class="lsar-loop-callout">
374        <div class="lsar-loop-icon">🔄</div>
375        <p><strong>EDM-ARS</strong> generates a paper &rarr; <strong>LSAR</strong> reviews it across 8 dimensions &rarr; feedback drives targeted revisions — a closed-loop generate-then-review cycle for automated research refinement.</p>
376      </div>
377
378      <div class="lsar-buttons">
379        <a class="btn btn-primary" href="https://github.com/cgpan/LSAR-public" target="_blank" rel="noopener">View LSAR on GitHub</a>
380      </div>
381    </div>
382  </section>
383
384  <hr class="divider" />
385
386  <!-- ==================== PAPERS ==================== -->
387  <section class="section reveal" id="papers">
388    <p class="section-label">Publications</p>
389    <h3 class="section-title">Papers &amp; Reports</h3>
390    <p class="section-body">Technical reports and demo papers generated by EDM-ARS. Demo papers are full submission-formatted manuscripts produced by the pipeline on HSLS:09 prediction tasks &mdash; APA-7 journal format for the JEDM submissions, ACM <em>sigconf</em> for the EDM submissions.</p>
391
392    <div class="papers-grid">
393
394      <div class="paper-card">
395        <div class="paper-meta">
396          <span class="paper-badge paper-badge-report">Technical Report</span>
397          <span class="paper-date">March 2026</span>
398        </div>
399        <h4 class="paper-title">EDM-ARS: A Domain-Specific Multi-Agent System for Automated Educational Data Mining Research</h4>
400        <p class="paper-authors">Chenguang Pan, Zhou Zhang, Weixuan Xiao, Chengyuan Yao</p>
401        <a class="paper-link" href="papers/edmars_tech_report_v2.pdf" target="_blank" rel="noopener">PDF &rarr;</a>
402      </div>
403
404      <div class="paper-card">
405        <div class="paper-meta">
406          <span class="paper-badge paper-badge-demo">Demo Paper</span>
407          <span class="paper-badge paper-badge-review">LSAR Reviewed</span>
408          <span class="paper-date">September 2026</span>
409        </div>
410        <h4 class="paper-title">Engagement and Belonging Add Predictive Signal for High School Dropout Episodes Beyond Achievement and SES: Evidence from a Nationally Representative U.S. Cohort</h4>
411        <p class="paper-authors">EDM-ARS (Automated) &middot; Target Venue: JEDM</p>
412        <div class="paper-links">
413          <a class="paper-link" href="papers/demo_paper_03.pdf" target="_blank" rel="noopener">Paper PDF &rarr;</a>
414          <a class="paper-link paper-link-review" href="papers/demo_paper_03_LSAR_review.pdf" target="_blank" rel="noopener">LSAR Review &rarr;</a>
415        </div>
416      </div>
417
418      <div class="paper-card">
419        <div class="paper-meta">
420          <span class="paper-badge paper-badge-demo">Demo Paper</span>
421          <span class="paper-badge paper-badge-review">LSAR Reviewed</span>
422          <span class="paper-date">September 2026</span>
423        </div>
424        <h4 class="paper-title">Predicting Two-Year Public Versus Four-Year Postsecondary Enrollment from Ninth-Grade Measures and the High School Transcript: Calibration and Subgroup Fairness in HSLS:09</h4>
425        <p class="paper-authors">EDM-ARS (Automated) &middot; Target Venue: JEDM</p>
426        <div class="paper-links">
427          <a class="paper-link" href="papers/demo_paper_04.pdf" target="_blank" rel="noopener">Paper PDF &rarr;</a>
428          <a class="paper-link paper-link-review" href="papers/demo_paper_04_LSAR_review.pdf" target="_blank" rel="noopener">LSAR Review &rarr;</a>
429        </div>
430      </div>
431
432      <div class="paper-card">
433        <div class="paper-meta">
434          <span class="paper-badge paper-badge-demo">Demo Paper</span>
435          <span class="paper-badge paper-badge-review">LSAR Reviewed</span>
436          <span class="paper-date">September 2026</span>
437        </div>
438        <h4 class="paper-title">
438High School Preparation, Not Background, Predicts Postsecondary Persistence: An Access&ndash;Persistence Contrast Using Machine Learning on HSLS:09</h4>
439        <p class="paper-authors">EDM-ARS (Automated) &middot; Target Venue: JEDM</p>
440        <div class="paper-links">
441          <a class="paper-link" href="papers/demo_paper_05.pdf" target="_blank" rel="noopener">Paper PDF &rarr;</a>
442          <a class="paper-link paper-link-review" href="papers/demo_paper_05_LSAR_review.pdf" target="_blank" rel="noopener">LSAR Review &rarr;</a>
443        </div>
444      </div>
445
446      <div class="paper-card">
447        <div class="paper-meta">
448          <span class="paper-badge paper-badge-demo">Demo Paper</span>
449          <span class="paper-badge paper-badge-review">LSAR Reviewed</span>
450          <span class="paper-date">March 2026</span>
451        </div>
452        <h4 class="paper-title">Fairness-Through-Unawareness Does Not Produce Equitable Predictions: Evidence from HSLS:09 on Subgroup Disparities in College Attendance Prediction</h4>
453        <p class="paper-authors">EDM-ARS (Automated) &middot; Target Venue: EDM</p>
454        <div class="paper-links">
455          <a class="paper-link" href="papers/demo_paper_01.pdf" target="_blank" rel="noopener">Paper PDF &rarr;</a>
456          <a class="paper-link paper-link-review" href="papers/demo_paper_01_LSAR_review.pdf" target="_blank" rel="noopener">LSAR Review &rarr;</a>
457        </div>
458      </div>
459
460      <div class="paper-card">
461        <div class="paper-meta">
462          <span class="paper-badge paper-badge-demo">Demo Paper</span>
463          <span class="paper-badge paper-badge-review">LSAR Reviewed</span>
464          <span class="paper-date">March 2026</span>
465        </div>
466        <h4 class="paper-title">Do Ninth-Grade Math and Science Identity Predict STEM Degree Non-Completion? Evidence from HSLS:09 and Machine Learning</h4>
467        <p class="paper-authors">EDM-ARS (Automated) &middot; Target Venue: EDM</p>
468        <div class="paper-links">
469          <a class="paper-link" href="papers/demo_paper_02.pdf" target="_blank" rel="noopener">Paper PDF &rarr;</a>
470          <a class="paper-link paper-link-review" href="papers/demo_paper_02_LSAR_review.pdf" target="_blank" rel="noopener">LSAR Review &rarr;</a>
471        </div>
472      </div>
473
474    </div>
475  </section>
476
477  <!-- ==================== TECH STACK ==================== -->
478  <section class="section reveal" id="tech">
479    <p class="section-label">Built With</p>
480    <h3 class="section-title">Tech Stack</h3>
481
482    <div class="tech-groups">
483      <div>
484        <div class="tech-group-title">Core Pipeline</div>
485        <div class="tech-tags">
486          <span class="tech-tag accent">Python 3.11</span>
487          <span class="tech-tag accent">DeepSeek-v4-pro</span>
488          <span class="tech-tag">Anthropic / OpenAI / MiniMax</span>
489          <span class="tech-tag">Docker Sandbox</span>
490          <span class="tech-tag">PyYAML</span>
491        </div>
492      </div>
493      <div>
494        <div class="tech-group-title">Prediction &amp; Causal</div>
495        <div class="tech-tags">
496          <span class="tech-tag">scikit-learn</span>
497          <span class="tech-tag">XGBoost</span>
498          <span class="tech-tag">SHAP</span>
499          <span class="tech-tag">causal forests</span>
500          <span class="tech-tag">AIPW / TMLE</span>
501          <span class="tech-tag">pandas</span>
502        </div>
503      </div>
504      <div>
505        <div class="tech-group-title">Psychometrics</div>
506        <div class="tech-tags">
507          <span class="tech-tag accent">R bridge</span>
508          <span class="tech-tag">IRT / GRM</span>
509          <span class="tech-tag">measurement invariance</span>
510          <span class="tech-tag">DIF</span>
511          <span class="tech-tag">DINA / G-DINA</span>
512        </div>
513      </div>
514      <div>
515        <div class="tech-group-title">Data &amp; Literature</div>
516        <div class="tech-tags">
517          <span class="tech-tag accent">HSLS:09</span>
518          <span class="tech-tag accent">ELS:2002</span>
519          <span class="tech-tag accent">ASSISTments</span>
520          <span class="tech-tag">ELS&times;HSLS panel</span>
521          <span class="tech-tag">Semantic Scholar + arXiv</span>
522        </div>
523      </div>
524      <div>
525        <div class="tech-group-title">Output &amp; Verification</div>
526        <div class="tech-tags">
527          <span class="tech-tag accent">ACM sigconf LaTeX</span>
528          <span class="tech-tag accent">APA-7 journal</span>
529          <span class="tech-tag">numeric reconciliation</span>
530          <span class="tech-tag">calibrated LSAR gate</span>
531          <span class="tech-tag">token metering</span>
532        </div>
533      </div>
534    </div>
535  </section>
536
537  <!-- ==================== ROADMAP ==================== -->
538  <section id="roadmap">
539    <div class="inner reveal">
540      <p class="section-label">Scope & Roadmap</p>
541      <h3 class="section-title">v5 &amp; Beyond</h3>
542      <p class="section-body">Every phase on the original roadmap — polymorphism, findings memory, causal inference, multi-dataset support — has shipped. The current release spans five study types across four datasets, with the controlled human evaluation now in progress.</p>
543
544      <div class="roadmap-grid">
545        <div>
546          <div class="roadmap-col-label done">✅ &nbsp;v5 &mdash; Delivered</div>
547          <div class="roadmap-item">
548            <div class="roadmap-dot done"></div>
549            <div class="roadmap-text"><strong>Five Study Types</strong> &mdash; predictive modeling, three causal-inference designs (selection-on-observables, individualized treatment regimes, difference-in-differences) and psychometrics</div>
550          </div>
551          <div class="roadmap-item">
552            <div class="roadmap-dot done"></div>
553            <div class="roadmap-text"><strong>Ten Certified Estimators</strong> &mdash; each validated against synthetic data with a known true effect before being allowed near a real dataset</div>
554          </div>
555          <div class="roadmap-item">
556            <div class="roadmap-dot done"></div>
557            <div class="roadmap-text"><strong>Four Curated Datasets</strong> &mdash; HSLS:09, ELS:2002, ASSISTments 2009&ndash;10 and a constructed ELS&times;HSLS cross-cohort panel, each with its own registry and quirks</div>
558          </div>
559          <div class="roadmap-item">
560            <div class="roadmap-dot done"></div>
561            <div class="roadmap-text"><strong>Skill-Based Architecture</strong> &mdash; 70 composable skills across four layers, matched at runtime and injected per agent &mdash; capabilities are added as skills, not as longer prompts</div>
562          </div>
563          <div class="roadmap-item">
564            <div class="roadmap-dot done"></div>
565            <div class="roadmap-text"><strong>Calibrated Review Gate</strong> &mdash; thresholds set at the 25th percentile of papers each venue actually published, with median-of-three sampling for borderline verdicts</div>
566          </div>
567          <div class="roadmap-item">
568            <div class="roadmap-dot done"></div>
569            <div class="roadmap-text"><strong>Deterministic Honesty Guards</strong> &mdash; numeric reconciliation against analysis artifacts, unverified-claim flagging, and review-health checks that reject a truncated review rather than scoring it</div>
570          </div>
571          <div class="roadmap-item">
572            <div class="roadmap-dot done"></div>
573            <div class="roadmap-text"><strong>Measured Token Accounting</strong> &mdash; per-call prompt / completion / cached token counts with rates in configuration, so past runs re-price without re-running</div>
574          </div>
575          <div class="roadmap-item">
576            <div class="roadmap-dot done"></div>
577            <div class="roadmap-text"><strong>Open Source Release</strong> &mdash; EDM-ARS and LSAR both published, runnable from a clean checkout, with their test suites green</div>
578          </div>
579        </div>
580
581        <div>
582          <div class="roadmap-col-label next">🔭 &nbsp;Next</div>
583          <div class="roadmap-item">
584            <div class="roadmap-dot next"></div>
585            <div class="roadmap-text"><strong>Controlled Evaluation</strong> &mdash; blind human review comparing EDM-ARS output against other automated research systems on matched topics &mdash; in progress</div>
586          </div>
587          <div class="roadmap-item">
588            <div class="roadmap-dot next"></div>
589            <div class="roadmap-text"><strong>
589Wider Venue Calibration</strong> &mdash; anchor corpora for the eight venues that currently have review criteria but no calibrated threshold</div>
590          </div>
591          <div class="roadmap-item">
592            <div class="roadmap-dot next"></div>
593            <div class="roadmap-text"><strong>Additional Datasets</strong> &mdash; PISA 2022 (plausible values) and ECLS-K, extending the registry approach to new survey designs</div>
594          </div>
595          <div class="roadmap-item">
596            <div class="roadmap-dot next"></div>
597            <div class="roadmap-text"><strong>Reproducibility Packaging</strong> &mdash; release the locked specifications alongside published results so any run can be reproduced end to end</div>
598          </div>
599        </div>
600      </div>
601    </div>
602  </section>
603
604  <!-- ==================== FOOTER ==================== -->
605  <footer>
606    <div class="footer-left">
607      <p><span>EDM-ARS</span> &nbsp;·&nbsp; <span>LSAR</span> &nbsp;·&nbsp; Educational Data Mining Research Tools</p>
608      <p style="font-size:12px; margin-top:4px;">Built by Chenguang Pan and his friends</p>
609    </div>
610    <div class="footer-links">
611      <a href="https://github.com/cgpan/edm-ars-public" aria-label="View EDM-ARS on GitHub">EDM-ARS GitHub</a>
612      <a href="https://github.com/cgpan/LSAR-public" aria-label="View LSAR on GitHub">LSAR GitHub</a>
613      <a href="papers/edmars_tech_report_v2.pdf" aria-label="Read the technical report">Paper</a>
614    </div>
615  </footer>
616
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