README.md

---
license: apache-2.0
language: en
base_model: microsoft/deberta-v3-base
pipeline_tag: text-classification
tags:
- education
- tutoring
- misconception-detection
- k-12
- math
---

# qlm-map-classifier

**A DeBERTa-v3 misconception-detection classifier for K-12 mathematics tutoring, in production at Quantum Learning Machines since April 2026.** It classifies student utterances and worked steps against a taxonomy of documented math misconceptions, and serves as an *observation model* feeding a Bayesian student-state layer — not as a standalone judge of student thinking. We publish it, with its honest numbers, because the field's progress depends on knowing what this class of model can and cannot do.

## What it does in production

Given a student turn (utterance, answer, or worked step) in a math tutoring context, the model emits misconception-class probabilities over 34 production classes drawn from QLM's 481-construct misconception ontology (423 constructs public under CC-BY-4.0 via our export API). Its outputs are **not** used to label students directly. They enter a persistent Bayesian posterior alongside triage signals that distinguish misconception-driven errors from slips and disengagement; instructional decisions condition on the accumulated state, not on any single classifier call.

## Evaluation — read the limitations first

Evaluated on an internal MathTutorBench-derived error-detection probe (unseen, GSM8K-based). All figures below are from versioned result files.

| Variant | Detection recall | Mistake-location accuracy | Notes |
|---|---|---|---|
| **MAP-34 (this model, deployed)** | **25.1%** | **19.0%** | conservative by design; low over-flagging |
| Combined-39 (research retrain, not deployed) | 86.2% | 16.3% | 8,207 mixed examples (EEDI public + expert-pattern-derived synthetic); **49.5% solution-correctness accuracy — severe over-flagging** |
| EEDI-only retrain (not deployed) | 72.1% | 15.7% | same over-flagging pattern |
| Bare LLM baseline (no classifier) | 78.3% | 14.7% (F1 micro) | flags nearly everything |

**The pattern that matters:** every high-recall variant achieves its recall by over-flagging — calling correct or merely-slipped work "misconception." The training corpora available to this task (misconception-labeled datasets, expert-authored trigger patterns) contain, by construction, **no negative space**: no slips, no disengagement, no correct-but-unconventional work. A classifier trained only on what misconceptions look like cannot learn what they *don't* look like. This is a data-ecology finding about the field, not an implementation artifact — and it is why the deployed configuration is conservative and why the architecture treats the classifier as one observation source among several.

## The architecture lesson (ablation)

In controlled ablation, injecting this classifier's outputs directly into a tutor model's per-turn prompt **reduced** tutoring quality (solution-correctness evaluation: 60% → 55%). Classifier signal helps when integrated as evidence into persistent student state; it hurts when bolted onto generation. Build state layers, not prompt decorations.

## Training data

Production K-12 tutoring interaction data (private; de-identified), labeled against the misconception ontology. We do not publish interaction counts or corpus composition. The ontology's public tier — 423 constructs with descriptions, diagnostic questions, and research citations — is available CC-BY-4.0 via the QLM export API.

## Intended use

- Research on misconception detection, error-type triage, and tutoring-system evaluation
- As an observation component inside stateful student-modeling systems
- Reproducing and extending the negative-space findings above

## Out-of-scope use

- Grading, placement, or any consequential decision about an individual student
- Standalone "misconception detector" products without state-level integration and human oversight
- Non-mathematics domains; languages other than English (untested)

## Bias, risks, and limitations

Detection behavior has not been separately validated across demographic subgroups; measurement-invariance testing on authentic classroom data is planned, funded work. Low recall means many real misconceptions pass undetected; downstream systems must not treat absence of a flag as evidence of understanding. Trained on production interactions from QLM platforms; distribution shift to other tutoring contexts is untested.

## Citation

@software{qlm_map_classifier_2026,
author = {Quantum Learning Machines},
title = {qlm-map-classifier: misconception detection as an observation model},
year = {2026},
url = {https://huggingface.co/QuantumLearningMachines/qlm-map-classifier},
license = {Apache-2.0}
}

## Contact: hello@quantumlearningmachines.com · quantumlearningmachines.com