Project Info
Inspiration
With a great amount of experience teaching and tutoring at the university level, we knew there was a lot to be desired in the grading experience for both students and instructors. We wished that there was a way students could receive feedback quickly and overworked instructors could focus their attention on more impactful things than grading. As a result, we decided to build a tool that would auto grade short answer response while allowing a high degree of accuracy and customization.
What it does
Given a student response, our program analyzes the similarity to teacher provided answers. Furthermore, it uses GPT to provide quick feedback for students.
How we built it
We used ChromaDB to handle our vector database operations and GPT4 to provide feedback for students. For our front-end, we used Reflex as our full-stack solution. pls Demo https://youtu.be/S7EiVUkjzv4
AutoScore
AutoScore aims to simplify the free response grading pipeline faced by instructors by utilizing ML methodologies to predict whether or not a student's answer is deemed correct.
Inspiration
With a great amount of experience teaching and tutoring at the university level, we knew there was a lot to be desired in the grading experience for both students and instructors. We wished that there was a way students could receive feedback quickly and overworked instructors could focus their attention on more impactful things than grading. As a result, we decided to build a tool that would auto grade short answer response while allowing a high degree of accuracy and customization.
What it does
Given a student response, our program analyzes the similarity to teacher provided answers. Furthermore, it uses GPT to provide quick feedback for students.
How we built it
We used ChromaDB to handle our vector database operations and GPT4 to provide feedback for students. For our front-end, we used Reflex as our full-stack solution.
Demo
- Click for demo video!
- Click for slides!
Built With
- chroma
- gpt-4
- python
- reflex
- scikit-learn
Analysis
View
Metric
- 18
- 16
- 11
- 6
- 2
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- PythonIn code
1 of 1 appear in the indexed code.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
16 KB
Source files
7
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
prabina-p/AutoScore
44 files · 21.4 MB · @ 4a45a21
Structure
Application logic
19 files · 43%Domain rules, services and shared utilities.
+2 moreData & schema
4 files · 9%Schema definitions, migrations and data access.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- Python91%
- Markdown9%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
autoscore/requirements.txt
pypi · 1- reflex
requirements.txt
pypi · 1- reflex
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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