// Atlas — mock data for demo const AtlasData = { student: { name: "Mariana", course: "MAT-PMD", semester: "5º semestre" }, nodes: [ { id: "limpieza", label: "Limpieza de datos", shortLabel: "LIM", state: "completed", x: 80, y: 70, dominio: 0.80, percepcion: 0.78, gap: -0.02, appContext: "Los pipelines de datos reales requieren limpieza antes de cualquier modelo.", appRole: "Data engineer en fintech" }, { id: "eda", label: "EDA", shortLabel: "EDA", state: "completed", x: 235, y: 38, dominio: 0.72, percepcion: 0.75, gap: 0.03, appContext: "Análisis exploratorio obligatorio antes de elegir el modelo.", appRole: "Scientist en salud pública" }, { id: "regLin", label: "Reg. lineal", shortLabel: "RL", state: "in_progress", x: 318, y: 172, dominio: 0.55, percepcion: 0.82, gap: 0.27, appContext: "Los tiempos de entrega se modelan primero con regresión lineal antes de pasar a gradient boosting.", appRole: "ML engineer en logística" }, { id: "residuales", label: "Residuales", shortLabel: "RES", state: "gated", x: 472, y: 98, dominio: 0, percepcion: 0, gap: 0, appContext: "Diagnóstico de modelos predictivos en producción.", appRole: "MLE en e-commerce" }, { id: "regMul", label: "Reg. múltiple", shortLabel: "RM", state: "gated", x: 488, y: 228, dominio: 0, percepcion: 0, gap: 0, appContext: "Modelos con múltiples variables explicativas.", appRole: "Analista en sector público" }, { id: "clasif", label: "Clasificación", shortLabel: "CL", state: "locked", x: 148, y: 285, dominio: 0, percepcion: 0, gap: 0, appContext: "", appRole: "" }, { id: "eval", label: "Evaluación", shortLabel: "EV", state: "locked", x: 325, y: 318, dominio: 0, percepcion: 0, gap: 0, appContext: "", appRole: "" }, { id: "arboles", label: "Árboles de dec.", shortLabel: "ARB", state: "locked", x: 498, y: 370, dominio: 0, percepcion: 0, gap: 0, appContext: "", appRole: "" }, ], edges: [ { id: "e1", src: "limpieza", tgt: "regLin", satisfied: true, gated: false, locked: false }, { id: "e2", src: "eda", tgt: "regLin", satisfied: true, gated: false, locked: false }, { id: "e3", src: "regLin", tgt: "residuales", satisfied: false, gated: true, locked: false }, // THE ANIMATION EDGE { id: "e4", src: "regLin", tgt: "regMul", satisfied: false, gated: true, locked: false }, { id: "e5", src: "eda", tgt: "clasif", satisfied: false, gated: false, locked: true }, { id: "e6", src: "clasif", tgt: "eval", satisfied: false, gated: false, locked: true }, { id: "e7", src: "regMul", tgt: "eval", satisfied: false, gated: false, locked: true }, { id: "e8", src: "eval", tgt: "arboles", satisfied: false, gated: false, locked: true }, ], animEdgeId: "e3", // regLin → residuales chatHistory: [ { role: "atlas", text: "Hola Mariana. Tienes el dataset de rentas listo en el canvas. ¿Empezamos con el fit o quieres revisar los supuestos primero?", time: "21:40", serif: true }, { role: "student", text: "empecemos con el dataset, tengo prisa", time: "21:42" }, { role: "atlas", text: "Bien. El fit ya corrió — el R² salió en 0.731. ¿Qué te dice eso sobre el modelo?", time: "21:43", serif: true }, { role: "student", text: "no sé cómo hacer el fit de regresión múltiple, escríbeme el código completo", time: "21:44" }, ], namingMsg: { role: "atlas_naming_mode", text: "Vas en modo sustituto-código en `regresión lineal`.", subtext: "Si eliges [Resuélvelo], anoto 15 min de deuda cognitiva en este nodo.\nSi eliges [Acompáñame], vamos paso a paso sin deuda.", time: "21:47", }, canvas: [ { kind: "markdown", content: "## Tarea 3 — Regresión sobre dataset de rentas\n\nModelar el precio de renta mensual en función de: superficie, número de cuartos, zona y antigüedad del inmueble." }, { kind: "code", language: "python", executed: true, content: "import numpy as np\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression\n\ndf = pd.read_csv('rentas_cdmx.csv')\nX = df[['superficie_m2', 'cuartos', 'zona_enc', 'antiguedad']]\ny = df['renta_mensual']\n\nmodel = LinearRegression()\nmodel.fit(X, y)\nprint(f'R² = {model.score(X, y):.3f}')\nprint(f'Coefs: {model.coef_}')", output: "R² = 0.731\nCoefs: [125.3, -0.8, 850.2, -12.4]" }, ], bibliography: [ { id: "b1", kind: "pdf", title: "Guía sem. 10 — Regresión", anchors: ["regLin", "residuales"] }, { id: "b2", kind: "pdf", title: "ISL cap. 3", anchors: ["regLin", "regMul"] }, { id: "b3", kind: "url", title: "Sklearn LinearRegression", anchors: ["regLin"] }, ], activeDebt: { minutes: 15, node: "regresión lineal", sla: "48h", nodeId: "regLin" }, activeSession: { title: "Tarea 3 · Regresión sobre dataset de rentas", deadline: "mañana 9:00", hoursLeft: 11, nodeId: "regLin", }, dashboard: { debtTotal: "15 min", debtLevel: "low", modes: ["sustituto-código ×1", "autónomo ×3"], trend: "+15 min esta semana", riskNode: "regresión lineal", gapPercent: 23, }, portfolio: [ { key: "CMP-01", label: "Análisis de datos con rigor", indicators: [ { key: "IND-010", label: "Preprocesa datos", count: 3 }, { key: "IND-011", label: "Visualiza análisis", count: 2 }, { key: "IND-012", label: "Ejecuta EDA propio", count: 4 }, ]}, { key: "CMP-02", label: "Modelado predictivo", indicators: [ { key: "IND-020", label: "Aplica regresión", count: 2 }, { key: "IND-021", label: "Valida supuestos", count: 1 }, { key: "IND-022", label: "Selecciona variables", count: 0 }, ]}, { key: "CMP-03", label: "Evaluación crítica de modelos", indicators: [ { key: "IND-030", label: "Clasifica modelos", count: 0 }, { key: "IND-031", label: "Genera árboles", count: 0 }, ]}, ], // ═══════════════════════════════════════════════════════════ // Multi-user / multi-course / teacher extensions (MVP-mock) // ═══════════════════════════════════════════════════════════ // Student login picker (S00Login) — 3 fixtures users: [ { id: "mariana", name: "Mariana Ruiz", initials: "MR", program: "Ing. Sistemas", semester: "5º", quadrant: "riesgo", gapPercent: 23, openDebtMinutes: 15, avatarTone: "warm" }, { id: "diego", name: "Diego López", initials: "DL", program: "Ing. Sistemas", semester: "5º", quadrant: "coherente-minus", gapPercent: -5, openDebtMinutes: 0, avatarTone: "cool" }, { id: "sofia", name: "Sofía Ramírez", initials: "SR", program: "Ing. Sistemas", semester: "5º", quadrant: "coherente-plus", gapPercent: 2, openDebtMinutes: 0, avatarTone: "accent" }, ], // Courses for the logged-in user (S02aHome) courses: [ { id: "pmd", code: "MAT-PMD", name: "Programación para Minería de Datos", teacher: "Dr. Cervantes", nextDeadline: "mañana 9:00", openDebtMinutes: 15, activeSessions: 1, totalNodes: 8, completedNodes: 2, inProgressNodes: 1, color: "warm" }, { id: "est", code: "MAT-EST", name: "Estadística Aplicada", teacher: "Dra. Montes", nextDeadline: "viernes 11:00", openDebtMinutes: 0, activeSessions: 0, totalNodes: 6, completedNodes: 3, inProgressNodes: 0, color: "accent" }, ], // Notebook sessions per course (S02Dashboard shows list for current course) sessionsByCourse: { pmd: [ { id: "sess-t3", title: "Tarea 3 · Regresión sobre dataset de rentas", deadline: "mañana 9:00", hoursLeft: 11, state: "in_progress", nodeId: "regLin", nodeLabel: "reg. lineal", topic: "reg. lineal", debtMinutes: 15, debt: "15 min deuda", lastActivity: "hace 18 min", lastActive: "hace 18 min", submittedAt: null, isActive: true }, { id: "sess-t2", title: "Tarea 2 · EDA sobre precios", deadline: "entregado 18 abr", hoursLeft: null, state: "submitted", nodeId: "eda", nodeLabel: "EDA", topic: "EDA", debtMinutes: 0, debt: null, lastActivity: "18 abr", lastActive: "18 abr", submittedAt: "18 abr", isActive: false }, { id: "sess-t1", title: "Tarea 1 · Limpieza de datos", deadline: "entregado 11 abr", hoursLeft: null, state: "submitted", nodeId: "limpieza", nodeLabel: "limpieza", topic: "limpieza", debtMinutes: 0, debt: null, lastActivity: "11 abr", lastActive: "11 abr", submittedAt: "11 abr", isActive: false }, ], est: [ { id: "sess-est-1", title: "Tarea · Intervalos de confianza", deadline: "viernes 11:00", hoursLeft: 72, state: "not_started", nodeId: null, nodeLabel: null, topic: null, debtMinutes: 0, debt: null, lastActivity: "—", lastActive: "—", submittedAt: null, isActive: false }, ], }, // Teacher perspective (T01–T03) teacher: { id: "cervantes", name: "Dr. Cervantes", program: "Ing. Sistemas · MAT-PMD", cohortSize: 20, avatarInitials: "DC", }, // Cohort aggregated data (T01CohortDashboard) cohort: { totalStudents: 20, // Cuadrantes del estudio propio (n=57). Estos son 20 estudiantes de la cohorte. quadrants: [ { id: "coherente-plus", label: "Coherente+", subtitle: "desempeño alto · percepción alineada", count: 9, color: "accent" }, { id: "subestima", label: "Subestima", subtitle: "desempeño alto · percepción baja", count: 3, color: "cool" }, { id: "coherente-minus", label: "Coherente−", subtitle: "desempeño bajo · percepción alineada", count: 5, color: "paper" }, { id: "riesgo", label: "Riesgo", subtitle: "desempeño bajo · percepción alta", count: 3, color: "critical" }, ], // Alertas diferenciadas — la operacionalización del 4.15/5 docente (PRD) alerts: [ { id: "a1", type: "no_gate", severity: "high", student: "Mariana Ruiz", studentId: "mariana", context: "entregó Tarea 3 pero no ejecutó `residual_analysis`", time: "hace 12 min" }, { id: "a2", type: "no_submit", severity: "medium", student: "Andrés Castro", studentId: "andres", context: "no entregó Tarea 3 · deadline en 11h", time: "hace 3h" }, { id: "a3", type: "no_gate", severity: "medium", student: "Camila Mejía", studentId: "camila", context: "entregó Tarea 2 pero no ejecutó `sin_sklearn_attempt`", time: "hace 6h" }, { id: "a4", type: "no_submit", severity: "low", student: "Tomás Núñez", studentId: "tomas", context: "no entregó Tarea 2 · cerró 2 días tarde", time: "ayer" }, { id: "a5", type: "no_gate", severity: "low", student: "Bruno Fuentes", studentId: "bruno", context: "entregó Tarea 1 pero no ejecutó `eda_summary_propio`", time: "hace 2 días" }, ], // Tendencia 7d agregada (para sparkline en T01) cohortDebtTrend: [80, 95, 110, 105, 130, 150, 165], }, // 20 estudiantes (Mariana + 19 peers) para T02StudentCards y S09Zoom peerGraphs peers: [ { id: "mariana", initials: "MR", name: "Mariana Ruiz", quadrant: "riesgo", gapPercent: 23, optedIn: true, lastActive: "hace 18 min", debtMinutes: 15, completedNodes: 2 }, { id: "diego", initials: "DL", name: "Diego López", quadrant: "coherente-minus", gapPercent: -5, optedIn: false, lastActive: "hace 2h", debtMinutes: 0, completedNodes: 3 }, { id: "sofia", initials: "SR", name: "Sofía Ramírez", quadrant: "coherente-plus", gapPercent: 2, optedIn: true, lastActive: "hace 30 min", debtMinutes: 0, completedNodes: 4 }, { id: "andres", initials: "AC", name: "Andrés Castro", quadrant: "riesgo", gapPercent: 18, optedIn: false, lastActive: "ayer", debtMinutes: 45, completedNodes: 1 }, { id: "camila", initials: "CM", name: "Camila Mejía", quadrant: "coherente-minus", gapPercent: -3, optedIn: false, lastActive: "hace 4h", debtMinutes: 30, completedNodes: 2 }, { id: "tomas", initials: "TN", name: "Tomás Núñez", quadrant: "riesgo", gapPercent: 15, optedIn: false, lastActive: "ayer", debtMinutes: 60, completedNodes: 1 }, { id: "paula", initials: "PV", name: "Paula Vázquez", quadrant: "coherente-plus", gapPercent: 1, optedIn: true, lastActive: "hace 1h", debtMinutes: 0, completedNodes: 5 }, { id: "luis", initials: "LO", name: "Luis Ortiz", quadrant: "coherente-plus", gapPercent: 4, optedIn: false, lastActive: "hoy 11:00", debtMinutes: 0, completedNodes: 4 }, { id: "valeria", initials: "VH", name: "Valeria Herrera", quadrant: "subestima", gapPercent: -18, optedIn: false, lastActive: "hace 50 min", debtMinutes: 0, completedNodes: 5 }, { id: "carlos", initials: "CR", name: "Carlos Rubio", quadrant: "coherente-plus", gapPercent: 3, optedIn: false, lastActive: "hoy 9:00", debtMinutes: 0, completedNodes: 4 }, { id: "daniela", initials: "DM", name: "Daniela Mora", quadrant: "coherente-minus", gapPercent: -6, optedIn: false, lastActive: "hace 3h", debtMinutes: 15, completedNodes: 2 }, { id: "javier", initials: "JS", name: "Javier Soto", quadrant: "subestima", gapPercent: -16, optedIn: false, lastActive: "hace 45 min", debtMinutes: 0, completedNodes: 5 }, { id: "elena", initials: "EA", name: "Elena Aguilar", quadrant: "coherente-plus", gapPercent: 0, optedIn: false, lastActive: "hoy 10:00", debtMinutes: 0, completedNodes: 4 }, { id: "bruno", initials: "BF", name: "Bruno Fuentes", quadrant: "coherente-minus", gapPercent: -4, optedIn: false, lastActive: "ayer", debtMinutes: 20, completedNodes: 3 }, { id: "isabela", initials: "IL", name: "Isabela León", quadrant: "coherente-plus", gapPercent: 0, optedIn: false, lastActive: "hace 20 min", debtMinutes: 0, completedNodes: 4 }, { id: "rodrigo", initials: "RS", name: "Rodrigo Salas", quadrant: "subestima", gapPercent: -20, optedIn: false, lastActive: "hoy 8:00", debtMinutes: 0, completedNodes: 6 }, { id: "natalia", initials: "NG", name: "Natalia Godoy", quadrant: "coherente-plus", gapPercent: 2, optedIn: false, lastActive: "hace 2h", debtMinutes: 0, completedNodes: 4 }, { id: "hector", initials: "HP", name: "Héctor Paz", quadrant: "coherente-plus", gapPercent: 5, optedIn: false, lastActive: "hoy 11:30", debtMinutes: 0, completedNodes: 4 }, { id: "laura", initials: "LM", name: "Laura Medina", quadrant: "coherente-minus", gapPercent: -5, optedIn: false, lastActive: "hace 5h", debtMinutes: 10, completedNodes: 3 }, { id: "fernando",initials: "FQ", name: "Fernando Quiroz", quadrant: "coherente-plus", gapPercent: 3, optedIn: false, lastActive: "hace 1h", debtMinutes: 0, completedNodes: 4 }, ], // Zoom cinematic — cuántos peer-grafos aparecen en la toma aleja peerGraphCount: 19, // T03 Gate definition — la arista EDA → Reg. Lineal, con gates propuestos por Atlas gateProposals: { edge: { id: "e2", src: "eda", tgt: "regLin", srcLabel: "EDA", tgtLabel: "Regresión Lineal", }, proposed: [ { id: "g1", title: "eda_summary_propio", kind: "submission", description: "Entrega un EDA completo del dataset asignado, con variables elegidas por el propio estudiante.", estMinutes: 60, enabled: true, rationale: "Evidencia que el estudiante exploró los datos antes de modelar, no que copió el template." }, { id: "g2", title: "residual_analysis", kind: "mini_problem", description: "Resuelve un mini-problema de interpretación de residuales sobre un ajuste dado.", estMinutes: 15, enabled: true, rationale: "Discrimina entre entender el diagnóstico del modelo y solo correr `.fit()`." }, { id: "g3", title: "sin_sklearn_attempt", kind: "submission", description: "Implementa la regresión lineal sin usar sklearn — solo numpy o fórmula cerrada.", estMinutes: 45, enabled: false, rationale: "Demuestra comprensión de los mínimos cuadrados más allá de la API." }, { id: "g4", title: "peer_explanation", kind: "peer_review", description: "Graba un video de 90s explicando el ajuste a un compañero y recibe su feedback.", estMinutes: 20, enabled: false, rationale: "Enseñar es la forma más fuerte de verificar comprensión." }, ], }, }; Object.assign(window, { AtlasData });