Schema: A Quantified Learning Solution to Augment, Assess, and Analyze Learning in Medicine

Area of Science:

• Educational Technology
• Medical Education
• Digital Learning

Background:

• Quantified learning, integrating digital tools for personalized education, is crucial for the shift to competency-based learning.
• Technology's role in education is expanding, yet medical education lags in adopting these advancements.
• Existing technology use in medical schools is often limited to simulations.

Purpose of the Study:

• To introduce Schema, an app-based e-learning solution for undergraduate medical students.
• To promote quantified, integrative, and self-directed learning with feedback-based self-assessment.
• To provide insights into medical learning patterns through technology.

Main Methods:

• Schema links to a database of multiple-choice questions (MCQs) organized by granular subtopics.
• The app monitors learner progress and performance on MCQs.
• Quantifiable visual feedback is generated to guide learning and competency assessment.

Main Results:

• Schema provides personalized, measurable educational experiences.
• It enables targeted revision and competency assessment for medical students.
• The app facilitates self-directed learning and progress monitoring.

Conclusions:

• Schema supports quantified and integrative learning in medical education.
• It addresses the need for technology-driven, self-assessment tools for modern medical students.
• Schema offers a platform for deeper insights into how medicine is learned.