Adaptive Learning Overview
Our adaptive learning feature is designed to personalize the educational experience for each learner by intelligently adjusting difficulty, pacing, and review schedules based on the individual's performance and learning style. By leveraging sophisticated AI algorithms, this system creates customized learning paths and ensures that users stay engaged, challenged, and continually progressing toward mastery.
- Personalized Content: Tailored to match your unique style, pace, and goals.
- Optimized Review Schedules: Uses spaced repetition for better long-term retention.
- Adaptive Difficulty: Questions adjust based on your performance.
- Progress Tracking: Detailed analytics to measure improvement and stay motivated.
Immediate insights on correct or incorrect answers let learners pinpoint areas to reinforce, ensuring efficient and effective learning.
Spaced Repetition and the Ease Factor
A cornerstone of our adaptive learning system is the Spaced Repetition algorithm, which ensures that learners review concepts at optimal intervals. This maximizes retention and prevents both over- and under-reviewing. Central to our spaced repetition model is the ease factor (EF), a value that changes according to the learner's performance.
- Definition: The ease factor (EF) represents how "easy" or "hard" a particular concept or flashcard is for the learner.
- Starting Value: We typically start new items at an EF of 2.5.
- Updates: After each review, the EF is updated based on whether the learner answered correctly or incorrectly and the intensity of success or error.
- Minimum EF: We enforce a minimum EF of 1.3 to prevent items from becoming so "hard" that the system fails to reintroduce them in a helpful manner.
The Ease Factor directly influences how often you'll review a concept:
- A higher EF means less frequent reviews, assuming you grasp the concept well.
- A lower EF triggers more frequent reviews to reinforce challenging topics.
- This dynamic system ensures you spend more time on difficult concepts and less on those you've mastered.
Ease Factor Formulas
Where:
= New ease factor
= Previous ease factor
= Correct answer factor (set to 0.1)
= A score reflecting how confidently the learner demonstrated knowledge
Where:
= New ease factor
= Previous ease factor
= Incorrect answer factor (set to 0.2)
= A score reflecting how seriously the learner struggled
Additional Features
After introducing new content, short quizzes gauge comprehension and immediately adjust the EF.
A dashboard displays the learner's average EF over time, highlighting improvement and areas still needing review.
Advanced users or educators can tune a, b, and the default EF to suit different learning scenarios or subject complexities.
The system prevents EF from dropping too low (below 1.3) so learners aren't endlessly drilled on a single topic without additional guidance or varied approaches.