本地运行的自动评分系统，保护学生数据且提升成绩与信心
LaTA: A Drop-in, FERPA-Compliant Local-LLM Autograder for Upper-Division STEM Coursework
- 用本地部署的大模型比对答案与评分标准，全程不上传数据
- 每份作业1-3分钟完成评分，错误率仅0.02%-0.04%，支持重评
- 适合注重数据隐私、希望减轻教师负担的高年级理工课程
大语言模型自动评分可缓解高年级理工课程的批改压力，但多数方案需将学生作业发送至第三方接口，违反FERPA规定并带来数据风险，还需大幅修改作业形式。我们提出LaTA（LaTeX Teaching Assistant），一个即插即用的开源自动评分系统，可在普通本地硬件上运行，适配已使用LaTeX的教学流程。LaTA采用四阶段流水线（接收、分割、评分、报告），使用本地部署的开源思维链大模型（gpt-oss:120b）比对学生作答与教师参考答案，并依据YAML评分标准进行二元评分。我们在俄勒冈州立大学2026年冬季学期的机械工程方法课（ME 373）中部署了LaTA，为约200名学生在单台Mac Studio上以零边际成本完成了每周作业评分，每份提交耗时1-3分钟，支持修正后重新评分，显著增加了助教答疑时间。教师确认的评分错误率稳定在每项0.02%–0.04%之间。相比以往传统评分班级，该班中期考试平均提升11%，期末考试提升8%，自评学习目标信心显著提高（N=159，Δ≥+1.49李克特点，所有比较p<10⁻²⁷）。代码已按AGPLv3开源。
原文摘要 · Abstract (English)
Large-language-model (LLM) graders promise to relieve the grading burden of upper-division STEM courses, but most deployments to date send student work to third-party APIs, violating FERPA and exposing institutions to data risk while requiring substantial assignment modification. We present $\textbf{LaTA}\ (\textit{LaTeX Teaching Assistant})$, a drop-in, open-source autograder that runs entirely on commodity on-premises hardware and assumes a LaTeX-native workflow already adopted by many engineering and physics courses. LaTA implements a four-stage pipeline (ingest, segment, grade, report) using a locally hosted open-weight chain-of-thought LLM grader (gpt-oss:120b) that compares student work to an instructor-authored reference solution and applies a YAML rubric with binary per-item scoring. We deployed LaTA in Winter~2026 in ME 373 (Mechanical Engineering Methods) at Oregon State University, grading every weekly assignment for approximately 200 students on a single Mac Studio at \$0 marginal cost per assignment and 1--3 minutes of wall-clock time per submission, enabling regrading of corrected assignments and greatly expanded TA office hour offerings. The instructor-confirmed grading-error rate held at roughly $0.02$--$0.04\%$ per rubric line item across the term. Relative to the same instructor's previous traditionally-graded cohort, the LaTA-graded cohort outperformed by approximately $11\%$ on the midterm exam and $8\%$ on the final exam, and reported large gains in self-assessed confidence on every stated learning objective ($N = 159$ survey responses, $Δ\geq +1.49$ Likert points, $p < 10^{-27}$ on every comparison). We release the code under AGPLv3.
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