QuizBanao isn't just another study tool—it's grounded in decades of research on how the human brain learns and retains information.
In 1885, German psychologist Hermann Ebbinghaus discovered that we forget information exponentially over time—a phenomenon now known as the "forgetting curve." However, he also found that reviewing information at spaced intervals dramatically slows this forgetting process.
QuizBanao uses sophisticated algorithms to determine the optimal spacing between reviews. Each time you successfully recall information, the next review interval increases, ensuring that knowledge moves from short-term to long-term memory.
Red: Without review | Green: With spaced repetition
Retention: ~70-80%
Retention: ~10-20%
Research by cognitive scientists like Robert Bjork shows that actively retrieving information from memory (active recall) strengthens neural pathways far more effectively than passively re-reading notes. When you quiz yourself, you're not just reviewing—you're rebuilding the memory.
QuizBanao turns every study session into an active recall exercise. Instead of mindlessly scrolling through notes, you're constantly retrieving and reinforcing knowledge, making it stick for the long term.
Not everyone learns at the same speed. Some concepts click immediately, while others need more time. QuizBanao's AI analyzes your performance patterns and adapts in real-time:
Hermann Ebbinghaus's pioneering research on memory and forgetting laid the foundation for spaced repetition. His work demonstrated that memory retention follows a predictable exponential decay curve, which can be counteracted through strategic review.
Robert and Elizabeth Bjork's research on "desirable difficulties" shows that learning strategies that feel harder in the short term (like active recall and spaced practice) lead to better long-term retention. QuizBanao incorporates these principles to maximize learning effectiveness.
QuizBanao uses algorithms inspired by SuperMemo and Anki, refined with modern machine learning techniques. These algorithms optimize review timing based on individual performance data, ensuring maximum retention with minimum effort.
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