RubricTracing-9B-Item

Rubric Tracing is knowledge tracing over item-specific rubrics. This model performs item-level prediction: given a student's past interactions and the rubric of the next problem, it predicts whether the student will satisfy every criterion of that problem.

It is Qwen3.5-9B, fully fine-tuned on KT-PSP-25 (Korean first-year high-school mathematics). Its criterion-level counterpart is RubricTracing-9B-Criterion.

Input and output

The student's history is rendered as a full rubric history. For each past problem, it contains the problem, its criteria, the student's Pass/Fail verdict on each criterion together with the grader's rationale, and the student's written problem-solving process. The target problem follows, with its reference solution and rubric. The prompts are in Korean, and the system prompt the model was trained with is system_prompt.txt in this repository.

The model answers with a single JSON object:

{"correct": false}

true means the student is predicted to satisfy every criterion of the target problem. This rubric-derived label disagrees with the platform's own correct/incorrect flag on 24.5% of interactions, because grading reads the written solution rather than only the final answer.

Results

Results are on the held-out test cohort: 268 students and 4,165 target interactions. Pass denotes all criteria satisfied. Neural KT and fine-tuned rows are the mean ± standard deviation over five trainings.

Revisions

main holds the fold-0 model. The branches fold0 to fold4 hold all five trainings. Each was trained on a different train/validation split and evaluated on the same test cohort, and the table above averages over them.

Usage

from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "sjin4861/RubricTracing-9B-Item"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto")
system = open(hf_hub_download(repo, "system_prompt.txt"), encoding="utf-8").read()

messages = [{"role": "system", "content": system},
            {"role": "user", "content": rubric_history_and_target}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False,
                              return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=32, do_sample=False, eos_token_id=tok.eos_token_id)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))

• Pass enable_thinking=False. The model was trained and evaluated without the thinking block.
• Pass eos_token_id=tok.eos_token_id (<|im_end|>). The bundled generation_config.json also stops on it. Without it, generation can continue past the answer.
• The code that renders a rubric history in the trained format is released with the paper.

Training

• Full fine-tuning of Qwen3.5-9B.
• Learning rate 1e-5 with cosine decay and 3% warmup, no weight decay, gradient clipping at 1.0, gradient accumulation of 4, seed 0, four epochs.
• Checkpoint selection: the epoch with the highest Macro-F1 on freely generated answers over a fixed sample of 1,000 validation interactions.
• Supervision: the item-level label (every criterion satisfied) from rubric label set 22289-be26167af4e4. It was produced by a Generator–Grader–Auditor pipeline over Qwen3.5-27B, which writes one rubric per problem and grades each student's written solution against it.

Limitations

• The model was trained and evaluated on one dataset: Korean first-year high-school mathematics.
• Its supervision is model-graded, not human-graded.
• KT-PSP-25 has no recoverable interaction order, so the history is treated as a set. Nothing here is a claim about sequence modelling.
• The model forecasts outcomes for research on knowledge tracing. It is not validated for grading or for high-stakes decisions about individual students.