TRACE Dataset Card (v1)

Name: TRACE — Taxonomy-Referenced ABA Clinical Examples Version: v1 (dataset hash pinned to commit 945248d) Date: 2026-04-24 Primary language: English License: Synthetic data, released under [TBD — recommend CC BY-NC 4.0 for research use; no clinical deployment]. Total examples: 2,999 Tasks: 2 — (1) ABA teaching program generation, (2) behavioral session interpretation. Maintained by: Festus Kahunla (Drexel University).

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1. TL;DR

TRACE is a synthetic instruction-tuning dataset for two clinical tasks in Applied Behavior Analysis (ABA):

1. Teaching program generation — given a learner profile and a skill target, produce a structured teaching program (DTT, NET, or Task Analysis/chaining) covering stimulus control, prompt hierarchy, reinforcement schedule, error correction, mastery criterion, and generalization plan.
2. Behavioral session interpretation — given a multi-session behavioral log (accuracies, target behaviors with measurements, optional ABC and IOA data), produce clinical concerns, a pattern classification, a function hypothesis, programming recommendations, and (when applicable) a crisis plan.

The dataset was produced by a taxonomy-driven generator whose controlled vocabulary is grounded in the canonical ABA literature (Cooper, Heron, & Heward 2020; VB-MAPP; AFLS; key JABA papers). Every example carries full provenance metadata — the exact taxonomy cells that were sampled to produce it. Clinical accuracy was iterated via practitioner-in-the-loop ad-hoc review.

Intended use. Fine-tune small language models (4-bit Gemma 4 E2B with QLoRA) for on-device decision support drafting. Not for autonomous clinical decisions. A Board Certified Behavior Analyst (BCBA) remains responsible for every program and every interpretation.

Not for: training on real client data; replacing a BCBA; writing final Behavior Intervention Plans without clinical review; clinical use without facility approval.

1.1 Inputs, outputs, and evaluation

TRACE is a supervised fine-tuning corpus. For both tasks, the model input is messages.user, the supervised target is messages.assistant, and meta.gold_labels holds extractable categorical labels used for automated scoring. The system prompt (messages.system) is identical across all examples within a task and is not part of the input the model has to interpret.

Task 1 — Teaching program generation

Input	A BCBA-style request specifying skill target, curriculum reference, learner profile, and current mastery state.
Output	A structured Markdown teaching program with sections for overview, end goal / trial procedure, prompt strategy, error correction, reinforcement, mastery criteria, data collection, and generalization.
Evaluation	(a) Categorical accuracy on meta.gold_labels.method (dtt / net / task_analysis) and meta.gold_labels.program_type (Task Analysis only: independence / toleration); (b) BERTScore F1 between the generated assistant message and the gold assistant message.

Worked example (trimmed):

INPUT (messages.user):
  Skill Target: tolerating tooth brushing by caregiver
  Curriculum: AFLS Basic Living Skills
  Learner: School-Age Learner
  Mastery Status: Generalization phase
  Please include the toleration end goal, shaping progression,
  antecedent strategies, reinforcement plan, refusal / safety
  procedures, and mastery criteria.

OUTPUT (messages.assistant, abbreviated):
  ## Program Overview
  - Target skill: tolerating tooth brushing by caregiver
  - Approach: Toleration / systematic desensitization
  ## Shaping Progression
  1. 1-second toothbrush contact with teeth
  2. 3 seconds of brushing
  ...
  ## Reinforcement
  Deliver high-magnitude, high-preference reinforcement contingent
  on successful toleration of the target duration.
  ## Mastery Criteria
  End-goal activity tolerated with 2 different caregivers across
  2 consecutive weeks.

GOLD LABELS (meta.gold_labels):
  method:          "task_analysis"
  program_type:    "toleration"
  domain:          "AFLS.basic_living"
  learner_profile: "school_age"
  mastery_state:   "generalization"

Task 2 — Behavioral session interpretation

Input	A multi-session behavioral log (5–12 sessions): learner profile, acceleration programs with per-session accuracy / latency / prompt-distribution data, deceleration targets with measurement traces, and (in ~30% of logs) ABC entries and IOA agreement values.
Output	A structured Markdown interpretation with sections for clinical concerns, pattern classification, behavior function hypothesis, programming recommendations (antecedent / replacement / consequence / crisis), escalation level, confidence, and data-supported rationale.
Evaluation	(a) Categorical accuracy on pattern_class (12-way), behavior_functions (5-way per behavior), escalation_level (1–5; exact-match accuracy and MAE), confidence (3-way), and crisis_plan_required (binary F1); (b) BERTScore F1 against the gold assistant message.

Worked example (trimmed):

INPUT (messages.user, abbreviated):
  LEARNER PROFILE: Early Learner, 4 acceleration programs,
                   1 deceleration target
  Session 1 (2026-06-10): mand 4/10 (38%); listener 5/12 (42%); ...
  Session 2 (2026-06-12): mand 6/14 (44%); listener 4/9 (47%); ...
  ...
  Session 8 (2026-06-24): mand 9/12 (72%); listener 14/15 (92%); ...
  Vocal stereotypy: ranged freq 0–1 across sessions

OUTPUT (messages.assistant, abbreviated):
  ## Clinical Concerns
  Steady improvement: accuracy rose from 38% to 72% across 8 sessions.
  ## Pattern Classification
  mastery_progression
  ## Behavior Function Hypothesis
  Vocal stereotypy: automatic
  ## Escalation Level
  1 — Continue monitoring
  ## Confidence
  moderate

GOLD LABELS (meta.gold_labels):
  pattern_class:        "mastery_progression"
  behavior_functions:   { "Vocal stereotypy": "automatic" }
  escalation_level:     1
  confidence:           "moderate"
  crisis_plan_required: false

Why two scoring channels

The assistant message is free-form clinical prose but the underlying clinical decision is categorical (which method, which pattern, what escalation level). We score both layers because a generation that hits the labels but produces unsafe prose is a failure, and so is eloquent prose with the wrong pattern classification. The categorical labels capture clinical correctness; BERTScore captures prose alignment with the taxonomy-grounded reference.

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2. Dataset Composition

2.1 Splits

Split	Examples	Fraction	Purpose
train.jsonl	2,549	85.0%	LoRA fine-tuning
valid.jsonl	149	5.0%	Periodic validation loss during training
test.jsonl	281	9.4%	Held-out evaluation (headline metrics)
sanity.jsonl	20	0.7%	Training smoke-test (tiny stratified subset)
Total	2,999	100%

Splits are stratified by task × category (method for teaching programs; pattern_class for session interpretation) so each split mirrors the corpus distribution. The test set is the full curation pool minus a 20-example stratified sanity carveout.

2.2 Per-area breakdown

Area	Count	Task type	Primary source
DTT	800	teaching_program	VB-MAPP (array-based discrete-response domains)
NET	500	teaching_program	VB-MAPP (mand, social, spontaneous vocal, intraverbal)
Task Analysis	499	teaching_program	AFLS (basic_living, home, community, vocational, independent_living)
Session Interpretation	1,200	session_interpretation	12 clinical trajectory patterns

2.3 DTT skill-domain distribution (800 total)

VB-MAPP domain	Count
Reading	131
LRFFC (Listener Responding by Feature/Function/Class)	126
Math	120
Visual Perceptual / Matching-to-Sample	115
Listener Responding	111
Tact	106
Writing	91

Sampled across VB-MAPP Levels 1 (≈45%), 2 (≈40%), and 3 (≈15%).

2.4 NET skill-domain distribution (500 total)

VB-MAPP domain	Count
Spontaneous Vocal	143
Mand (including bathroom, break, all-done)	141
Social Behavior & Play	131
Intraverbal	85

Each NET program carries a Motivating Operation arrangement matched to the skill (deprivation, missing-item, break opportunity, completion opportunity, bathroom opportunity, peer presence, reciprocal conversation, routine lead-in) and is embedded in a natural context (snack, free play, transition, arrival, etc.).

2.5 Task Analysis distribution (499 total)

AFLS module	Count		Program type	Count
Basic Living	172		Independence	413
Home	96		Toleration (systematic desensitization)	87
Community	94
Vocational	76
Independent Living	62

Toleration programs are a distinct program type for learners whose clinical goal is to allow a caregiver-delivered routine (tooth brushing, hair washing, nail clipping, haircuts, showering, medical exam) rather than to perform it independently. They use a shaping progression with duration targets rather than a step chain.

2.6 Session Interpretation distribution (1,200 total)

Twelve trajectory patterns, roughly uniform (84–113 each):

Pattern	Count
regression	113
prompt_dependency	109
setting_event_trigger	107
rapid_acquisition	106
motivating_operation_shift	104
generalization_failure	103
mastery_progression	100
skill_loss_after_break	100
plateau	96
frustration_pattern	91
variable_performance	87
extinction_burst	84

Target behaviors sampled into logs (presence per log; a log carries 0–3 behaviors):

Behavior	Logs	Measurement shape
Aggression	95	freq
Toileting (urine + BM, in-toilet + accidents)	89	urine: X in-toilet / Y accidents; BM: P in-toilet / Q accidents
Pica	81	attempts N (X unsuccessful; Y successful)
Self-Injurious Behavior (SIB)	79	freq
Verbal Aggression	79	freq
Fecal Smearing (scatolia)	78	attempts N (X intercepted; Y completed)
Property Destruction	75	freq
Elopement	75	freq
Non-compliance	70	freq
Tantrum	66	freq N, duration Mm total
Mouthing	56	freq N; PIR P%
Motor Stereotypy	50	freq N; PIR P%
Vocal Stereotypy	48	freq N; PIR P%

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3. Data Format

3.1 Wire format

Each example is one JSONL line:

{
  "messages": [
    {"role": "system",    "content": "<ABA clinical-assistant system prompt>"},
    {"role": "user",      "content": "<task-specific prompt>"},
    {"role": "assistant", "content": "<structured clinical response>"}
  ],
  "meta": {
    "task_type":    "teaching_program" | "session_interpretation",
    "example_id":   "<deterministic hash, 16-hex>",
    "gold_labels":  { ... task-specific labels ... },
    "provenance":   {
      "layer": 1,
      "area":  "dtt" | "net" | "task_analysis" | "session_interpretation",
      "template_id": "...",
      "taxonomy_cells": { ... exact sampled values ... },
      "teacher_model": null,
      "seed_tag": "...",
      "generated_at": "..."
    }
  }
}

Only messages is used for training (mask_prompt: true). meta is preserved for evaluation and provenance tracking.

3.2 Gold labels per task

Teaching program:

{
  "method":          "dtt" | "net" | "task_analysis",
  "domain":          "VB-MAPP.<domain>" | "AFLS.<module>",
  "level":           "L1" | "L2" | "L3" | ...,     # VB-MAPP only
  "learner_profile": "early" | "school_age" | "adolescent" | "adult",
  "mastery_state":   "emerging" | "developing" | "approaching" | "near" | "mastered" | "generalization",
  "program_type":    "independence" | "toleration",  # Task Analysis only
  "chain_type":      "forward" | "backward" | "total_task"  # Task Analysis only
}

Session interpretation:

{
  "pattern_class":        "<one of 12 patterns>",
  "behavior_functions":   { "<behavior_name>": "escape"|"attention"|"tangible"|"automatic"|"unknown" },
  "escalation_level":     1 | 2 | 3 | 4,
  "confidence":           "high" | "moderate" | "low",
  "crisis_plan_required": true | false
}

3.3 Provenance

Every sampled choice is recorded in meta.provenance.taxonomy_cells. For teaching programs this includes the skill target, prompt hierarchy, reinforcement schedule, error correction, and mastery criterion. For session interpretation it includes the pattern, number of sessions, behaviors sampled, functions inferred, and whether IOA and ABC data are present. This enables:

• Traceability: any clinical issue in a generated example can be traced back to the exact taxonomy cell that produced it.
• Reproducibility: the dataset can be regenerated deterministically from the configs and seed.
• Stratification: splits can be regenerated with different stratification keys without regenerating the corpus.
• Ablation: subsets can be constructed by filtering on provenance cells.

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4. How It Was Built

4.1 Architecture

configs/
├── shared/                          # cross-area primitives (learner profiles, mastery states, prompt types)
├── dtt/                             # DTT area: taxonomy + template + compatibility
├── net/                             # NET area
├── task_analysis/                   # Chaining area (independence + toleration templates)
└── session_interpretation/          # Session interp area:
                                     #   taxonomy + compatibility + trajectory_rules
                                     #   + recommendations (per-pattern bullets) + template
src/generators/
├── aba_dtt.py                       # per-area generator
├── aba_net.py
├── aba_task_analysis.py
└── aba_session_interp.py
src/generate.py                      # orchestrator

4.2 Generation loop

For each example:

1. Sample a clinical configuration from the taxonomy, weighted by realistic clinical frequency (level weights, module weights, pattern weights).
2. Apply compatibility rules (e.g., DTT errorless error correction pairs only with most-to-least prompting; certain patterns have bias toward certain behavior functions).
3. Compute template slots — current-prompt-level guidance from mastery state; MO arrangement from skill keywords; mastery criterion; reinforcement schedule.
4. Render user_content from a random user-variant.
5. Render assistant_content by filling {slots} in the assistant template.
6. Stamp meta.gold_labels and meta.provenance.
7. Write as one JSONL line.

4.3 Grounding

• Skill curricula — VB-MAPP milestones (Sundberg 2008) for verbal-behavior domains; AFLS (Partington & Mueller) for adaptive-living domains.
• Teaching methods — DTT (Lovaas 1987; Smith 2001); NET (Hart & Risley 1975 in CHH Ch. 18); Task Analysis / chaining (CHH Ch. 20).
• Operational definitions of target behaviors — Cooper/Heron/Heward Ch. 3, 27; key JABA papers (Iwata et al. 1994 for SIB and functional analysis; Carr & Durand 1985 for FCT; Hanley, Iwata, & McCord 2003 for FBA).
• Session patterns — derived from CHH Ch. 6–7 (analyzing behavior change) and Stokes & Baer 1977 (generalization).
• Crisis plans — BACB Ethics Code (2020) §3.05; ABAI Position Statement on Restraint and Seclusion (2010). Physical-intervention procedures are left vague on purpose because they vary by training program (Safety-Care, CPI, PMT, TCI) and jurisdiction; the dataset emphasizes verbal de-escalation, environmental safety, BIP authorization, and contraindications.

4.4 Clinical-accuracy pipeline

Initial generation produced the structural skeleton. The corpus was then iterated via practitioner-in-the-loop review: a full-text render of every held-out candidate was browsed by a reviewer with ABA practitioner exposure, flags were raised for clinically inaccurate topographies, and each flag triggered a taxonomy or rendering fix plus a regeneration and a commit. Examples of iterations shipped:

Issue flagged	Fix
"Context: R/D" tag leaking a specific facility's tool	Removed from all generators and templates
"Raises hand to request" wrong topography for mand	Replaced with AAC, vocal, or full-sentence requesting; added Tolerating Denied Access as a replacement-behavior target
"Whining during trials" in frustration indicators	Removed; replaced with property destruction, aggression, peer aggression, SIB onset
Pica measured as simple frequency	Split into attempts / successful / unsuccessful
Toleration programs missing entirely	Added as a distinct program type for hygiene routines
Crisis plans too generic	Rewritten with tiered framework, BIP-authorization requirement, contraindications, BACB Ethics citations
maintenance mastery state (post-mastery retention) appeared as a current teaching state	Removed; maintenance is retained only as a post-mastery probe concept in generalization plans
Only one dishwashing variant in Home Skills	Added by-hand, operating dishwasher, unloading dishwasher
Token-economy backups abstract	Made concrete for children (candy, cookies, leisure, screen time, sensory item)
Fecal smearing and toileting accidents absent	Added as target behaviors with attempts/intercepted/completed and urine/BM in-toilet/accidents rendering
Bathroom requesting absent from mand curriculum	Added to NET L1 mands with bathroom_opportunity MO

Each iteration is one commit — the provenance chain is preserved end-to-end.

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5. Intended Uses

5.1 Direct use

• Instruction-tuning a small language model (recommended: Gemma 4 E2B 4-bit with QLoRA) to draft ABA teaching programs and interpret session logs.
• Evaluation of task-specific competencies using the held-out test.jsonl (279 examples).
• Research on taxonomy-driven synthetic data generation for clinical decision support.

5.2 Downstream use

• Pilot drafting assistants for BCBAs, always requiring clinician review before use with learners.
• Comparison baselines for future ABA-specific LLM work (Kumar et al. 2024 "Personalized-ABA" is the closest direct predecessor; the present dataset extends to structured program generation and session-log interpretation).

5.3 Out-of-scope (do NOT use for)

• Autonomous clinical decisions.
• Writing final Behavior Intervention Plans without BCBA review.
• Training models on or combined with real client records (the pipeline and schema are explicitly designed to avoid this).
• Legal or insurance documentation.
• Medical diagnosis.

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6. Ethics & Risks

6.1 Provenance

Every example is synthetic. No real client data, no real session notes, no real identifiers were used at any step. Learner references use synthetic IDs (SYN-####); dates fall in the range 2026-01-01 to 2026-12-31.

6.2 Clinical-risk framing

The dataset is framed — in system prompts, in the dataset card, and in the paper — as producing drafts for clinician review. Assistant responses are structured so that a BCBA can quickly see the method, the stimulus arrangement, the prompt hierarchy, the reinforcement plan, the error-correction procedure, the mastery criterion, and the generalization plan; approve what is clinically appropriate; and revise what is not. Session-interpretation responses surface a confidence level (high / moderate / low) and an escalation level (1–4) explicitly so the clinician can calibrate action.

6.3 Crisis-plan sensitivity

Crisis plans were written against the ABAI 2010 Position Statement on Restraint and Seclusion and BACB Ethics Code 2020 §3.05. The dataset references facility crisis-prevention frameworks (Safety-Care, CPI, PMT, TCI) only as examples and deliberately avoids specifying restraint procedures because those procedures are (a) jurisdiction-dependent, (b) training-certification-gated, and (c) learner-specific (many learners have contraindications). The dataset embeds explicit text in every crisis-plan bullet that physical intervention is used only when specifically authorized in the learner's BIP and only by staff currently certified in the facility's training program.

6.4 Population representation

Learner profiles are intentionally abstract (early / school-age / adolescent / adult). The dataset does not encode demographic categories (race, socioeconomic status, gender identity) and does not attempt to characterize clinical presentations by such categories. This is a deliberate choice for a first release; future versions may add representation if grounded in published demographic work.

6.5 Known limitations

• English only. Teaching method terminology, curriculum targets, and session-log conventions are rendered in English.
• Synthetic distributions. Pattern frequencies are approximately uniform for learnability; real clinical practice has different frequencies (mastery-progression is far more common than frustration-pattern in a healthy caseload). The dataset is explicitly a teaching set, not an epidemiological sample.
• VB-MAPP + AFLS only. Other curricula (ABLLS-R, Essential for Living, PEAK) are not covered. Practitioners using those curricula should adapt.
• No longitudinal data. Sessions within a log are temporally ordered but the pipeline does not model real continuity over months or years.
• Toleration covers hygiene only. Other toleration programs (e.g., wearing glasses, riding in a car seat) are not represented.
• Toilet-training acquisition is out of scope. Accidents are tracked in session logs and bathroom-requesting is taught as a NET mand, but the full Azrin & Foxx 1971 rapid toilet-training acquisition protocol is not included as a task-analysis program. This is a planned v2 addition.

6.6 License

TBD — recommend CC BY-NC 4.0 (research use, non-commercial) pending the hackathon submission requirements. Clinical use of the dataset or any model fine-tuned on it requires facility approval and BCBA oversight.

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7. Reproducibility

The corpus is regenerated deterministically from:

• configs/ (all YAMLs)
• configs/generation.yaml (seed and per-area counts)
• src/generators/*.py (generator code)

uv run python src/generate.py --all                  # regenerate the 2999-example corpus
uv run python src/split_data.py                      # stratified split
uv run python src/prepare_curation.py                # browseable review.md
uv run python src/compile_curation.py                # test.jsonl + sanity.jsonl

The repo commit pinned for this dataset version is 945248d.

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8. Citation

Please cite as:

Kahunla, F. (2026). TRACE: Taxonomy-Grounded Synthetic Data for Teaching Program Generation and Session Interpretation in Applied Behavior Analysis. Drexel University CS 614 final project / Gemma 4 Good Hackathon submission.

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9. Appendices

• Datasheet (Gebru et al. 2021 format): docs/dataset/datasheet.md
• Data statement (Bender & Friedman 2018 format): docs/dataset/data-statement.md
• Taxonomy reference (operational definitions + citations): docs/dataset/taxonomy-v1.md
• Schema reference (wire format + slot specifications): docs/dataset/schema-v1.md
• Research paper draft: docs/paper/paper-draft.md