isExactEqual

An evaluation function that checks exact equality between a student response and the correct answer. Both inputs are cast to a specified type before comparison using Python's == operator. The function is deployed on the lambda-feedback platform.

Table of Contents

• isExactEqual
  • Table of Contents
  • Repository Structure
  • Usage
    • Parameters
    • Examples
  • How it works
    • Docker & Amazon Web Services (AWS)
    • Middleware Functions
    • GitHub Actions
  • Documentation
  • Pre-requisites
  • Contact

Repository Structure

app/
    evaluation.py           # Main evaluation_function
    evaluation_tests.py     # Unit tests
    schema.json             # JSON schema for input validation
    requirements.txt        # Python dependencies
    Dockerfile              # Container image for AWS Lambda
    docs/
        user.md             # End-user documentation
        dev.md              # Developer reference

.github/
    workflows/
        test-lint.yml           # Run tests and linting on pull requests
        staging-deploy.yml      # Deploy to staging on push to main
        production-deploy.yml   # Deploy to production

config.json     # Evaluation function name
.gitignore

Usage

Send a POST request to the deployed function endpoint with the following JSON body.

Parameters

Field	Type	Required	Description
response	any	Yes	The student's submitted response
answer	any	Yes	The correct answer
params.type	string	Yes	Cast type for both inputs before comparison. One of: "int", "float", "str", "dict"
params.display_submission_count	boolean	No	If true, includes a submission count message in the feedback
params.submission_context.submissions_per_student_per_response_area	integer	No	Number of previously processed submissions for this student and response area

Examples

Simple string comparison:

{
  "response": "hydrophobic",
  "answer": "hydrophobic",
  "params": {
    "type": "str"
  }
}

{
  "is_correct": true
}

Integer comparison:

{
  "response": "45",
  "answer": "45",
  "params": {
    "type": "int"
  }
}

{
  "is_correct": true
}

With submission count feedback:

{
  "response": "1",
  "answer": "1",
  "params": {
    "type": "int",
    "display_submission_count": true,
    "submission_context": {
      "submissions_per_student_per_response_area": 2
    }
  }
}

{
  "is_correct": true,
  "feedback": "You have submitted 3 responses."
}

---

How it works

The function is built on top of a custom base layer, BaseEvaluationFunctionLayer, which provides tooling, testing helpers, and schema checking common to all evaluation functions.

Docker & Amazon Web Services (AWS)

The evaluation function is hosted on AWS Lambda and packaged as a Docker container. Docker bundles the function and all its dependencies into a single image, which AWS runs on demand inside a container. For background reading, see this introduction to containerisation and the AWS Lambda documentation.

Middleware Functions

Middleware provided by BaseEvaluationFunctionLayer handles request validation, error formatting, and response serialisation. The evaluation.py file only needs to implement the core comparison logic.

GitHub Actions

Three pipelines are configured in .github/workflows/:

• test-lint.yml — runs on every pull request: executes the unit test suite and linting via flake8.
• staging-deploy.yml — runs on every push to main: re-runs tests, then builds the Docker image, pushes it to the shared ECR repository, and deploys to the staging environment.
• production-deploy.yml — promotes a tested build to the production environment.

Documentation

• app/docs/user.md — end-user guide explaining inputs and parameters
• app/docs/dev.md — developer reference with detailed input/output specs and examples

Pre-requisites

To develop and test locally:

• Python 3.8 or higher
• Docker
• git CLI or GitHub Desktop
• A code editor (VS Code, PyCharm, etc.)

Contact

For questions or issues, open a GitHub issue or visit the lambda-feedback organisation.