To use ASYST with universal BERT model based on German language just Run these commands at CLI.
This plugin, along with the relevant Docker infrastructure, is designed to facilitate teachers’ activities in evaluating students’ short answers.
The plugin uses the ASYST grading [script](https://transfer.hft-stuttgart.de/gitlab/ulrike.pado/ASYST) modified to function as a web endpoint.
The plugin requires the ASYST ML Backend to be isolated in a standalone Docker container accessible via the local network or the Internet.
ASYSTGRADE Moodle Plugin runs each time the teacher reaches some manual grading page (a part of [Essay auto-grade plugin](https://moodle.org/plugins/qtype_essayautograde) maintained by Gordon Bateson)!
This solution is part of the Master’s Thesis titled “Integration of a Machine Learning Backend into Assessment Processes in a PHP-based Educational Environment” at the University of Applied Sciences Stuttgart, within the Software Technology course, by Artem Baranovskyi.
## Plugin and ASYST ML Backend Interaction Concept
```mermaid
flowchart TD
A[Moodle LMS] --> B[PHP Plugin]
B -->|HTTP Request| C[Flask API Python Backend]
C --> D[Pre-trained BERT Model]
C --> E[Logistic Regression Model]
C --> F[Sentence Transformers]
D --> C
E --> C
F --> C
C -->|Response| B
B -->|Processed Results| A
subgraph Backend-Services
C
D
E
F
end
subgraph Docker Containers
Backend-Services
end
B --> G{cURL}
```
## Description of Use Case processes at Sequence Diagram
```mermaid
sequenceDiagram
participant Student
participant Teacher
participant Moodle
participant PHP Plugin
participant Flask API
participant BERT Model
Student->>Moodle: Submits Answer
Teacher->>PHP Plugin: Visit Manual Grading Page
Moodle->>PHP Plugin: Answers GET Params ($qid, $slot)
PHP Plugin->>Flask API: Sends HTTP POST Request
Flask API->>BERT Model: Processes Data (Embeddings)
BERT Model-->>Flask API: Returns Processed Result
Flask API-->>PHP Plugin: Sends Grading Response
PHP Plugin-->>Moodle: Displays Predicted Grade
Moodle-->>Teacher: Displays Predicted Grade
Teacher->>Moodle: Grade (Regrade) Answer
Moodle-->>Student: Displays Final Result
```
## Plugin components' Diagram
```mermaid
classDiagram
class local_asystgrade_lib {
+void local_asystgrade_before_footer()
+void pasteGradedMarks(array grades, array inputNames, float maxmark) // DOM Manipulation
### Full Solution with Moodle Server (demo with all Moodle+MariaDb+Flask):
To use ASYST with a universal BERT model based on the German language, run these commands in the CLI.
They will build and run all 3 containers (Moodle+MariaDb+Flask) for demo, development, and testing purposes along with the Moodle environment installation.
~~~bash
~~~bash
docker-compose up -d--build&& ./install_moodle.sh
docker-compose up -d--build&& ./install_moodle.sh
~~~
~~~
Infrastructure rolls up a Brand New Moodle instance. If you already have Moodle LMS, you could use it's DB backup at this project. Just paste it at root folder and rename it to moodle_backup.sql
This will set up a brand new Moodle instance with ready to go PHPUnit test environment.
If you already have a Moodle LMS, you can use its database backup in this project.
Just place it in the /moodle folder and rename it to moodle_backup.sql.
#### Use these credentials to access the Moodle admin page:
**admin**:*rootpassword*
These credentials can be easily changed along with other environment variables in the [.env](https://github.com/ArtemBaranovsky/moodle-asyst-sync/blob/master/.env) file.
After installation, the database will have all the necessary entities to check the plugin’s functionality (Courses, Tests, Students, Quiz Attempts, etc.).
For a demo, simply visit the link: https://www.moodle.loc/mod/quiz/report.php?id=2&mode=grading&slot=1&qid=1&grade=needsgrading and wait for the auto answer evaluation.
Current plugin implementation starts automatically for a batch of answers right at **Essay (auto-grade)** page.
Teacher can accept propose evaluated mark or regrade it.
ASYSTGRADE Plugin didn't save anything to DB on his own!
## Using only ASYST API on Flask
It is not necessary to build full solution if you want just use the plugin at your existing Moodle LMS.
Use these creds to access Moodle admin page
To build only the Flask ASYST microservice, run:
admin:rootpassword
These creds could be easily changed as other environmental variables at .env
After installation the Database will have all necessary entities to check plugins functionality (Cource / Test / Students / QuizAttempts ...).
~~~bash
docker-compose up flask -d
~~~
If a standalone Flask image is ready, it can be run with (-p 5000:5000 can be omitted):
~~~bash
docker run -p 5000:5000 asyst-flask
~~~
For demo, it's quite enough to get the link https://www.moodle.loc/mod/quiz/report.php?id=2&mode=grading&slot=1&qid=1&grade=needsgrading and wait for auto answer valuation.
If ASYST ML Backend is being used alone, ASYSTGRADE plugin should be copied from /asystgrade with the folder to ../moodle/local/ folder for local plugins.
## Development tips
## Development tips
To facilitate DB monitoring at IDE set such a Database connection URL:
To facilitate DB monitoring in your IDE, set the following database connection URL:
~~~bash
~~~bash
jdbc:mariadb://localhost:3306/moodle
jdbc:mariadb://localhost:3306/moodle
~~~
~~~
It is suggested to use our moodle plugin to communicate with Flask-based ASYST script using such a
It is suggested to use our Moodle plugin to communicate with the Flask-based ASYST script using this route: http://127.0.0.1:5000/api/autograde
route http://127.0.0.1:5000/api/autograde
Now the preinstalled MOODLE LMS is available at https://www.moodle.loc
Now the preinstalled Moodle LMS is available at https://www.moodle.loc
**Note**: Bind https://www.moodle.loc to your localhost at **hosts** file depending on your OS.
**Note**: Bind https://www.moodle.loc to your localhost at **hosts** file depending on your OS.
## Running Unit Tests
## Running Unit Tests
To run only Plugin's Test please run at project's CLI (inside container):
To run only the plugin’s tests, execute in the project’s CLI (inside the container):
