# Learning Program Embeddings to Propagate Feedback on Student Code

arXiv: https://arxiv.org/abs/1505.05969 · HTML: https://mlsanity.com/paper/1505.05969/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2015.

## Built on by (most cited first, 19 of 19)

- [Multi-Factors Aware Dual-Attentional Knowledge Tracing](https://mlsanity.com/paper/2108.04741/) (2021)
- [Modeling Student Learning with 3.8 Million Program Traces](https://mlsanity.com/paper/2510.05056/) (2025)
- [Toward Semi-Automatic Misconception Discovery Using Code Embeddings](https://mlsanity.com/paper/2103.04448/) (2021)
- [Math Operation Embeddings for Open-ended Solution Analysis and Feedback](https://mlsanity.com/paper/2104.12047/) (2021)
- [From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks](https://mlsanity.com/paper/2205.01265/) (2022)
- [Explainable Few-shot Knowledge Tracing](https://mlsanity.com/paper/2405.14391/) (2024)
- [Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection](https://mlsanity.com/paper/2202.12481/) (2022)
- [Giving Feedback on Interactive Student Programs with Meta-Exploration](https://mlsanity.com/paper/2211.08802/) (2022)
- [Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming](https://mlsanity.com/paper/2312.11567/) (2023)
- [Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review](https://mlsanity.com/paper/2301.10231/) (2023)
- [A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models](https://mlsanity.com/paper/2403.07322/) (2024)
- [deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search](https://mlsanity.com/paper/2103.13020/) (2021)
- [Play to Grade: Testing Coding Games as Classifying Markov Decision Process](https://mlsanity.com/paper/2110.14615/) (2021)
- [Generating Feedback-Ladders for Logical Errors in Programming using Large Language Models](https://mlsanity.com/paper/2405.00302/) (2024)
- [Automatic Assessment of the Design Quality of Python Programs with Personalized Feedback](https://mlsanity.com/paper/2106.01399/) (2021)
- [Learning Generalizable Program and Architecture Representations for Performance Modeling](https://mlsanity.com/paper/2310.16792/) (2023)
- [Learning Code-Edit Embedding to Model Student Debugging Behavior](https://mlsanity.com/paper/2502.19407/) (2025)
- [ast2vec: Utilizing Recursive Neural Encodings of Python Programs](https://mlsanity.com/paper/2103.11614/) (2021)
- [Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Model](https://mlsanity.com/paper/2602.19620/) (2026)

## Abstract (arXiv)

Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students. We introduce a neural network method to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features. We apply our algorithm to assessments from the Code.org Hour of Code and Stanford University's CS1 course, where we propagate human comments on student assignments to orders of magnitude more submissions.