Prometheus-X Components & Services

Learning Records Converter (LRC)

Overview

Learning Records are available in many formats, either standardized (xAPI, SCORM, IMS Caliper, cmi5) or proprietary (Google Classroom, MS Teams, csv, etc). This wide variety of formats is a barrier to many use cases of learning records as it prevents the easy combination and sharing of learning records datasets from multiple sources or organizations.

As a result, Inokufu was tasked, within the Prometheus-X ecosystem, to develop a Learning Records Converter which is a parser translating datasets of learning traces according to common xAPI profiles.

Approach

LRC facilitates a streamlined conversion process through a two-phase operation, which ensures that the input data is correctly interpreted, transformed, augmented, and validated to produce compliant JSON outputs. The first phase converts a Learning Record from various input formats, into a single xAPI format. The second phase converts the xAPI learning records to ensure that they comply with the xAPI DASES Profiles.

Here is an architecture diagram illustrating the approach of the LRC to parse an input Learning Record in various standard, custom or unknown formats into an output Learning Record according to DASES xAPI profile.

LRC_Phase1.png

Phase 1: Learning Records to xAPI

The aim of this first phase is to convert a Learning Record to xAPI. In order to do this, we will set up two consecutive processes: Input Data Validation, and Data Transformation.

Input Data Validation

This component’s role is to identify the format of the input Learning Record, and to validate that the records are valid. Each dataset of Learning Records will have a metadata attribute stating the input-format of the learning record.

If the input-format is known, the corresponding data descriptor will be loaded to validate Learning Records are compliant. Otherwise, every data descriptors will be loaded, and try to interpret learning records.

Data Transformation

This component’s role is to convert the validated input data into the xAPI format.

Depending on the input-format of the learning records dataset, the processing will differ as follows:

The first phase of the LRC is built with community collaboration in mind. It allows for easy contributions and extensions to both the input and output formats. The community can develop and share their own data descriptors and converters, which can be seamlessly integrated into the LRC’s ecosystem, thereby enhancing the application’s versatility to handle various input and output formats.

Here is a detailed architecture diagram illustrating the first phase of the LRC, to parse an input Learning Record in various standard, custom or unknown formats into an output Learning Record according to the xAPI standard.

LRC_Phase1.png

These two consecutive processes can be summarized by this flow chart.

LRC_Phase1_FlowChart.png

Phase 2: xAPI to DASES

The aim of this second phase is to transform the xAPI Learning Record according to DASES xAPI profiles.

Each profile is defined in a JSON-LD file and includes specific concepts, extensions, and statement templates that guide the transformation and validation of xAPI statements. The DASES profiles in JSON-LD format are automatically downloaded and updated from their respective GitHub repositories as defined in the .env file.

The LRC enriches xAPI statements with profile-specific data, validates statements against profile rules, and give recommendations for improving compliance with the profiles.

This ensures that the converted learning records are not just in xAPI format, but also adhere to the specific DASES profile standards, enhancing interoperability and consistency across different learning systems.

Enriched Fields

DASES Profiles in Detail

The LRC currently supports theses main profiles :

LMS Profile
Forum Profile
Assessment Profile

Setup and installation

Getting Started

Prerequisites

Installation

  1. Clone the repository: 
    git clone [repository_url]
cd [project_directory]
  2. Install pipenv if you haven’t already: 
    pip install pipenv
  3. Install the project dependencies: 
    pipenv install
  4. Set up environment variables: Create a .env file in the project root by copying .env.default: 
    cp .env.default .env

    You can then modify the variables in .env as needed.

Usage

Running the Application

Start the FastAPI server using the script defined in Pipfile:

pipenv run start

The API will be available at http://localhost:8000.

Converting Traces

To convert a trace, send a POST request to the /convert endpoint:

POST /convert
Content-Type: application/json

{
  "input_trace": {
    // Your input trace data here
  },
  "input_format": "<input_format>"
}

Supported input formats:

Response format:

{
  "output_trace": {
    // Converted xAPI trace data
  },
  "meta": {
    "input_format": "<input_format>",
    "recommendations": [
      {
        "rule": "presence",
        "path": "$.result.completion",
        "expected": "included",
        "actual": "missing"
      }
    ]
  }
}

Validating Traces

This endpoint validates the input trace and returns the confirmed input format. If the validation fails, an appropriate error response will be returned instead. Send a POST request to the /validate endpoint:

POST /validate
Content-Type: application/json

{
  "input_trace": {
    // Your input trace data here
  },
  "input_format": "<input_format>"
}

Response format:

{
  "input_format": "<input_format>"
}

API Documentation

Once the server is running, you can access the interactive API documentation:

These interfaces provide detailed information about all available endpoints, request/response schemas, and allow you to test the API directly from your browser.

Development

Code Formatting and Linting

The project uses Black for code formatting, Flake8 for linting, and isort for import sorting. You can run these tools using their respective commands in the pipenv environment.

Mapping

To understand how mapping works or to create your own mapping, a document is available here.

Project Architecture

An explanation of how the project is organised is available here.

Environment Variables

The following table details the environment variables used in the project:

Variable Description Required Default Value Possible Values
LOG_LEVEL Minimum logging level for the application No info debug, info, warning, error, critical
DOWNLOAD_TIMEOUT Timeout for downloading profiles (in seconds) No 10 Any positive integer
CORS_ALLOWED_ORIGINS Allowed origins for CORS No * Comma-separated list of origins or * for all
PROFILES_BASE_PATH Base path for storing profile files Yes data/dases_profiles Any valid directory path
PROFILES_NAMES Names of the profiles to be used Yes lms,forum,assessment Comma-separated list of profile names
PROFILE_LMS_URL URL for the LMS profile JSON-LD file Yes https://raw.githubusercontent.com/gaia-x-dases/xapi-lms/master/profile/profile.jsonld Any valid URL
PROFILE_FORUM_URL URL for the Forum profile JSON-LD file Yes https://raw.githubusercontent.com/gaia-x-dases/xapi-forum/master/profile/base.jsonld Any valid URL
PROFILE_ASSESSMENT_URL URL for the Assessment profile JSON-LD file Yes https://raw.githubusercontent.com/gaia-x-dases/xapi-assessment/add-mandatory-statements/profile/profile.jsonld Any valid URL

Note: The URLs for the profiles are examples and may change. Always use the most up-to-date URLs for your project.

Refer to .env.default for a complete list of configurable environment variables and their default values.

Contribution guidelines

We welcome and appreciate contributions from the community! There are two ways to contribute to this project:

Before submitting your pull request, please ensure that your code follows our coding and documentation standards. Don’t forget to include tests for your changes!

Project status

Please note this project is work in progress.

Interoperability of Learning Records: State-of-the-Art in 2023

As a preparatory work for the development of the Learning Records Converter, Inokufu has conducted an exhaustive state of the art and quantitative study about the interoperability of Learning records.

This study is available here

References

https://gaia-x.eu/gaia-x-framework/

https://prometheus-x.org/

https://dataspace.prometheus-x.org/building-blocks/interoperability/learning-records