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Mock-Data/README.md
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Mock-Data/README.md
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# Mock Pupil-History Dataset — Learning Path Agent Hackathon
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Hand-crafted, internally-consistent JSON dataset for the BoostAI **Learning
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Path Agent** hackathon challenge. Mirrors the production SQLAlchemy schema
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in `elevenplus-backend/src/app/models/` so an agent built on this data
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transfers cleanly to the real database after the hackathon.
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**Reference date:** `2026-05-01` (timestamps in the data are relative to
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this — adjust `TODAY` in `generate.py` to shift them).
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## What's in here
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```
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classroom.json One Year-6 Maths class + tutor + student-classroom links
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students.json 12 students (with _persona annotation per student)
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question_bank.json 50 unique 11+ Maths questions
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assignments.json 8 assignments (5 CLOSED, 2 PUBLISHED, 1 DRAFT)
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assignment_questions.json 64 assignment-question rows (links to question_bank)
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assignment_assignees.json 85 (student × assignment) rows with status/scores
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student_answers.json 593 per-question answer records — THE primary file
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activity_logs.json 593 activity log rows (timestamps, durations, solve_mode)
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dataset.json All of the above bundled into one object
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generate.py Deterministic generator (re-run anytime)
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```
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The agent's main input is `dataset.json` (or `student_answers.json` plus
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`question_bank.json` if you'd rather load files separately).
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## Schema fidelity
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Field names and enum values match the production models exactly:
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| Production model | This dataset |
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|---|---|
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| `users.py` (role=student) | `students.json` |
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| `classrooms.py` + `classroom_student_rs.py` | `classroom.json` |
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| `assignments.py` (status: DRAFT/PUBLISHED/CLOSED) | `assignments.json` |
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| `assignment_questions.py` | `assignment_questions.json` |
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| `assignment_assignees.py` (status: NOT_STARTED/IN_PROGRESS/SUBMITTED) | `assignment_assignees.json` |
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| `question_bank.py` (difficulty: EASY/MEDIUM/HARD; source: BOOST) | `question_bank.json` |
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| `student_assignment_answers.py` (graded_marks, marks_awarded, grading_status=GRADED) | `student_answers.json` |
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| `assignment_activity_logs.py` (activity_type, duration_seconds, extra_data) | `activity_logs.json` |
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| `question_metrics.py` (explanation_type) | embedded as `_solve_mode` on each answer |
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Timestamps are **Unix milliseconds** (BigInteger) per the project convention.
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### Hackathon annotations (not in production schema)
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Fields prefixed with **`_`** are hackathon-only annotations. Strip them if
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you ever seed this data into the real DB.
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| Annotation | Where | Meaning |
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|---|---|---|
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| `_persona` | `students.json` | Engineered behaviour persona (see below) |
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| `_solve_mode` | `student_answers.json` | One of `just_answer`, `step_by_step`, `solve_together`, `handwritten` |
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| `_time_on_task_seconds` | `student_answers.json` | Seconds spent on this question |
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| `_is_correct` | `student_answers.json` | Boolean correctness (already implied by `graded_marks`) |
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| `_misconception_tag` | `student_answers.json` | Set when wrong answer matches a known misconception (e.g. `add_tops_add_bottoms`) |
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| `_question_topic` / `_sub_topic` / `_difficulty` | `student_answers.json` | Denormalised from `question_bank` for convenience |
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| `_answered_at` | `student_answers.json` | Same as `created_at`, just clearer name |
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## The 12 students
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Five students have engineered misconception personas; the other seven are
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realistic noise. The agent should ideally identify the personas from the
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data itself — `_persona` is included only so you can grade the agent.
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| ID | Name | Persona | What you'll see in the data |
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|---|---|---|---|
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| 201 | Aisha Khan | `fraction_inversion` | ~12% on Fractions Add/Subtract/Multiply, ~78% elsewhere. Wrong answers show **add-tops-add-bottoms** pattern (e.g. ½+⅓ → ⅖). |
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| 202 | Ben Carter | `place_value_gaps` | Fails multi-digit subtraction with borrowing & decimal alignment. Strong on single-digit ops. |
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| 203 | Chen Wei | `rushed_careless` | Right method when in `step_by_step`; wrong final answer in `just_answer`. Time-on-task drops week-over-week. **No activity in the last 8 days** — also drives Early Warning. |
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| 204 | Daniela Rossi | `solve_together_dependent` | Solve-Together share rises **21% → 88%** across the period. Independent accuracy degrading. |
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| 205 | Elif Demir | `word_problem_weak` | 0% on word problems, ~90% on bare computation of the same operations. |
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| 206 | Felix Brown | `stable_strong` | ~84% overall — baseline noise. |
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| 207 | Grace Park | `stable_strong` | ~85% overall — baseline noise. |
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| 208–210 | Singh / Nakamura / Williams | `stable_mid` | ~65% overall — baseline noise. |
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| 211–212 | Patel / O'Connor | `stable_weak` | ~50% overall — baseline noise. |
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## The 8 assignments
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| ID | Name | Topic | Due | Status | Why it matters |
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|---|---|---|---|---|---|
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| 3001 | HW1 — Place Value Warmup | Place Value | -28d | CLOSED | Baseline data |
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| 3002 | HW2 — Arithmetic Practice | Arithmetic | -22d | CLOSED | |
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| 3003 | HW3 — Fractions Foundations | Fractions | -16d | CLOSED | First fraction signal |
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| 3004 | HW4 — Negatives & BIDMAS | BIDMAS | -10d | CLOSED | |
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| 3005 | HW5 — Geometry Basics | Geometry | -6d | CLOSED | |
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| 3006 | HW6 — Algebra & Sequences | Algebra | +2d | PUBLISHED | In flight |
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| **3007** | **HW7 — Adding Fractions (test prep)** | **Fractions** | **+5d** | **PUBLISHED** | **Curriculum deadline anchor for the bonus EWS** |
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| 3008 | HW8 — Mixed Revision | Mixed | +12d | DRAFT | No activity yet |
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## Bonus / Early Warning signals embedded in the data
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| Signal | Where it lives | Who exhibits it |
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|---|---|---|
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| Drop in attempt rate (last 7 days) | `student_answers.json` timestamps | Student 203 (no recent activity); 211 reduced volume |
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| Increasing Solve-Together reliance | `_solve_mode` distribution over time | Student 204 (21% → 88%) |
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| Declining score trend on deadline topic | Fractions accuracy across HW3 → HW7 | Student 201 (very weak on Fractions; HW7 is fractions, due in 5 days) |
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| Time since last session | `activity_logs.json` last timestamp | Student 203 (≥ 8 days) |
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**Expected agent output for the bonus monitor on this dataset:**
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| Rank | Student | Topic driving risk | Suggested action |
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|---|---|---|---|
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| 1 | 203 Chen Wei | Engagement collapse | Reach out + light re-entry assignment; check for blockers |
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| 2 | 201 Aisha Khan | Fractions / Add — HW7 in 5 days | 3 targeted fraction-add questions on the add-tops-add-bottoms misconception |
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| 3 | 204 Daniela Rossi | Increasing scaffold dependence (any topic) | Pair with `step_by_step` mode + scheduled `just_answer` checkpoint |
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## Running / re-running the generator
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```bash
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cd boost-ai-eval/mock-data
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python3 generate.py
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```
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The RNG is seeded (`20260501`), so output is byte-stable across runs unless
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you change the source. To shift dates, edit `TODAY` at the top of
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`generate.py`.
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## Quick agent-side recipes
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**Load everything:**
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```python
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import json
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data = json.load(open('boost-ai-eval/mock-data/dataset.json'))
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students = data['students']
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answers = data['student_answers']
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qbank = {q['id']: q for q in data['question_bank']}
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```
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**Pull all answers for a single student:**
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```python
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def answers_for(student_id):
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aa_ids = {aa['id'] for aa in data['assignment_assignees']
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if aa['student_id'] == student_id}
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return [a for a in data['student_answers'] if a['assignee_id'] in aa_ids]
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```
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**Compute topic-level mastery snapshot:**
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```python
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from collections import defaultdict
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topic_acc = defaultdict(lambda: [0, 0]) # [correct, total]
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for a in answers_for(201):
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topic_acc[a['_question_topic']][1] += 1
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topic_acc[a['_question_topic']][0] += int(a['_is_correct'])
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# {'Fractions': [2, 16], 'Place Value': [3, 4], ...}
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```
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