The Science Behind LRNR

Our concept-centric learning approach is grounded in cognitive science and educational research

Evidence-Based Learning

LRNR isn't just another learning platform. Our approach is built on decades of cognitive science research about how the brain learns most effectively. We've designed every feature to align with proven learning principles that maximize retention and understanding.

Traditional learning methods often rely on passive review and cramming, which research has shown to be ineffective for long-term retention. Instead, LRNR implements active learning strategies that have been validated through rigorous scientific studies.

Our concept-centric approach focuses on building a network of knowledge rather than isolated facts. This aligns with research on how the brain forms and strengthens neural connections, leading to deeper understanding and better application of knowledge.

Key Learning Principles

Spaced Repetition

Reviewing information at optimal intervals to enhance long-term retention. Our algorithm schedules concept reviews based on your mastery level.

Research: Research by Ebbinghaus (1885) and later by Bjork & Bjork (1992) shows that spacing out learning sessions leads to better long-term retention than cramming.

Retrieval Practice

Actively recalling information strengthens memory more effectively than passive review. Our quizzes and self-assessments leverage this principle.

Research: Karpicke & Roediger (2008) demonstrated that testing yourself on material is more effective for long-term retention than simply restudying it.

Interleaving

Mixing different topics during study sessions improves discrimination and transfer of knowledge. Our recommendation system interleaves related concepts.

Research: Rohrer & Taylor (2007) found that interleaving practice of different problem types leads to better performance than blocked practice.

Elaboration

Connecting new information to existing knowledge creates stronger neural pathways. Our concept-centric approach encourages making these connections.

Research: Craik & Lockhart's (1972) levels of processing theory shows that deeper processing through elaboration leads to better memory retention.

Peer Learning

Explaining concepts to others and collaborative learning enhances understanding. Our platform encourages sharing and discussing concepts.

Research: Chi et al. (1994) demonstrated that explaining material to oneself or others (self-explanation effect) improves understanding and retention.

Metacognition

Awareness and understanding of one's own thought processes improves learning efficiency. Our analytics help you understand your learning patterns.

Research: Research by Flavell (1979) and later by Dunlosky & Metcalfe (2009) shows that metacognitive awareness improves learning outcomes.

Scientific References

Making it stick: The science of successful learning

Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014)

Harvard University Press

DOI: 10.4159/9780674419377

Comprehensive overview of evidence-based learning techniques including spaced repetition and retrieval practice.

The spacing effect: A case study in the failure to apply the results of psychological research

Dempster, F. N. (1988)

American Psychologist, 43(8), 627-634

DOI: 10.1037/0003-066X.43.8.627

Classic paper on the spacing effect and its applications to education.

Improving Students' Learning With Effective Learning Techniques

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013)

Psychological Science in the Public Interest, 14(1), 4-58

DOI: 10.1177/1529100612453266

Comprehensive review of learning techniques and their effectiveness.

The critical importance of retrieval for learning

Karpicke, J. D., & Roediger, H. L. (2008)

Science, 319(5865), 966-968

DOI: 10.1126/science.1152408

Seminal study demonstrating the power of retrieval practice for long-term retention.

Strengthening the Student Toolbox: Study Strategies to Boost Learning

Dunlosky, J. (2013)

American Educator, 37(3), 12-21

Practical applications of cognitive science research to educational settings.

The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning

Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012)

Cognitive Science, 36(5), 757-798

DOI: 10.1111/j.1551-6709.2012.01245.x

Framework connecting cognitive science to educational practice.

Our Concept-Centric Approach

Traditional learning platforms organize content by topics or courses. LRNR takes a different approach by focusing on concepts as the fundamental unit of knowledge. This approach is inspired by research on knowledge graphs and semantic networks in cognitive science.

When you learn with LRNR, you're not just memorizing isolated facts. You're building a network of interconnected concepts that mirror how knowledge is organized in the brain. This leads to deeper understanding and better transfer of knowledge to new situations.

Our adaptive algorithm tracks your mastery of each concept and uses spaced repetition principles to schedule reviews at optimal intervals. This ensures you're reviewing concepts just as you're about to forget them, strengthening your memory with each review.

The platform also implements retrieval practice through quizzes and self-assessments, which research has shown to be one of the most effective learning techniques. By actively recalling information rather than passively reviewing it, you strengthen neural pathways and improve long-term retention.

Experience Evidence-Based Learning

Join thousands of learners who are using scientifically-proven methods to learn more effectively