Evaluating Handwritten and Multimodal, Free-Style Responses in Algorithms and Data Structures: A RAG-LLM-Based Feedback Framework
Bibliographic record
Abstract
This innovative practice full paper presents a retrieval-augmented large language model (RAG-LLM) framework for evaluating handwritten and multimodal, freeform student responses. In the age of AI, open-ended questions play a vital role in computer science and engineering education, aligning with the ICAP framework to promote deeper cognitive engagement. However, large enrollments pose significant challenges in assessing such responses and delivering highquality, personalized feedback at scale while minimizing attentional errors. To address this issue, we introduce a tool that leverages RAG-LLMs to enable scalable, automated assessment and feedback generation with interpretable reasoning. By incorporating domain-specific content, vector-based context retrieval, and evaluation validation, our approach aims to reduce hallucinations, errors, and biases in generative AI outputs—ultimately enhancing both feedback accuracy and instructional value. We applied the framework to 816 student submissions from a graduate-level Algorithms course (Fall 2023), focusing on responses identifying the Big O notation of recurrence relations, which were manually graded with curated feedback. Using five LLMs and three embedding models, we conducted prompt engineering and evaluated the pipeline across four quality metrics. Our results show that while LLM choice had minimal impact, the selection of sentence encodings significantly influenced evaluation outcomes. We also applied the pipeline to auto-assess 770 responses from the Spring 2025 offering of the same course, with positive and promising results based on student perception data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".