This is a web-based, artificially intelligent learning and assessment system that uses knowledge space theory to enhance the understanding of students. The system is designed to help students deepen their understanding of the concepts, retain the material, and be confident in their learning ability. It uses adaptive questioning and customised data visualization to provide students with a personalized learning experience.
Learning route implementation using knowledge space theory in Python and R.
The system creates states, content and illustrations for each topic, and uses a question app to determine the student's understanding. The questions are designed to assess the student's knowledge in a particular subject and to determine their current state.
The adaptive questioning system is designed to quickly and accurately determine what a student knows and does not know. It provides students with a tailored learning experience by instructing them on the topics they are most ready to learn.
As a student works through the course, the system periodically reassesses the student to ensure that they have retained the knowledge they have learned. If the student feels difficulty, they can work through the concepts they don’t understand using the system's thorough explanations.
To encourage students and reward their progress, the system introduces badges and rewards. As students master the concept and move on, they will feel a sense of accomplishment, which will further motivate them in their learning journey.
The system displays the data of each student in the form of a customised pie chart. The chart shows what the student knows and what they still need to learn. As the pie chart changes and the mastered portion grows, students can feel a sense of accomplishment in their progress and motivation in their learning.
The system implements knowledge space theory using Python and R. It uses the power of these programming languages to create a system that is efficient and effective in helping students understand the concepts and retain the material.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
- Python 3
- Django
- Virtual Environment
- Clone the repository
- Activate the virtual env
python -m venv env
source env/bin/activate
python manage.py runserver
First creating states, content + illustration and question app.
- Associating user with his current state --> Assessment
Why we need any such system with us?
Our main objective is to prepare students to succeed in regular classes by helping them to deepen their understanding of the concepts, retain the material, and be confident in their learning ability.
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Make students now more engaged because they should not feel that they are so far behind in a lesson. Make the system run with them.
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A system and a platform which is web-based, artificially intelligent learning and assessment system, uses adaptive questioning to quickly and accurately determine exactly what a student knows and doesn’t know within a course and within a chapter.
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It then instructs the student on the topics they are most ready to learn. As a student works through a course, it periodically reassesses the student to ensure that topics learned are also retained.
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If students feels difficulty then: work through concepts they don’t understand using the system thorough explanations. And once they master the concept and move on, there is a great sense of accomplishment. Introducing Badges and reward at this point of time.
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Display the data of each student: A customised pie chart shows what the student knows and what the student has yet to learn. As the pie chart changes and the mastered portion grows, students can feel a sense of accomplishment in their progress and motivation in their learning.
- Python
- Django
- R