Adaptive Learning System with Hierarchical Knowledge Map
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Solution Overview
Problem
Current e-learning technologies fail to provide true mastery learning by not adequately mapping learning objectives hierarchically, lacking effective checks for learner understanding, and not dynamically adapting learning activities to a learner's level of mastery, leading to inadequate scaffolding and feedback.
Innovation Solution
A computer-implemented system with a knowledge map that hierarchically represents learning objectives and their prerequisite relationships, dynamically selects and modifies learning activities based on learner performance, incorporating adaptive scaffolding and feedback to ensure mastery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current e-learning systems test only for surface results, then the testing process is simple and quick, but the system fails to discover and address deficiencies in learners' fundamental skill sets
Solution Approach 1:
The assessment system is segmented into multiple hierarchical levels corresponding to different nodes in the knowledge map. Each node represents a specific learning objective with its own assessment criteria, allowing the system to evaluate fundamental skills at granular levels rather than only surface results. This segmentation enables comprehensive discovery of skill deficiencies while maintaining manageable complexity through modular assessment design.
Solution Approach 2:
The system adds a hierarchical dimension to assessment by organizing learning objectives into prerequisite relationships across multiple levels. Instead of flat surface-level testing, the assessment operates across vertical dimensions of the knowledge map, evaluating both foundational and advanced skills simultaneously. This multi-dimensional approach enables deep assessment of fundamental skills without overwhelming system complexity.
2Adaptability or versatility
If learning activities are static and not dynamically adapted, then the system is simpler to implement, but the system fails to address each learner's immediate needs and level of mastery
Solution Approach 1:
Learning activities are made dynamic through real-time adaptation to learner performance. The system continuously monitors learner interactions and automatically adjusts activity selection, scaffolding levels, and feedback mechanisms based on current mastery levels. This dynamic behavior enables the system to address each learner's immediate needs while the underlying knowledge map structure maintains organizational simplicity.
Solution Approach 2:
The system implements continuous feedback loops where learner performance on activities feeds back into the selection of subsequent activities and adjustment of scaffolding. This feedback mechanism enables automatic adaptation to learner needs without requiring complex manual intervention. The feedback-driven adaptation occurs within the structured framework of the knowledge map, balancing adaptability with manageable system complexity.
3Reliability
If the system provides comprehensive checks for learner understanding at each prerequisite concept, then mastery learning is achieved, but the learning process becomes longer and more time-consuming
Solution Approach 1:
The system performs preliminary assessment actions by evaluating learner knowledge at each node before allowing progression to subsequent concepts. This preliminary checking ensures mastery of prerequisite skills is verified in advance, preventing gaps in understanding that would require remediation later. By catching deficiencies early through systematic node-based assessment, the system achieves reliable mastery without excessive time loss, as issues are addressed immediately rather than accumulating.
Solution Approach 2:
The system applies assessment checks selectively at critical nodes in the knowledge map rather than uniformly at every single concept. This partial action approach focuses comprehensive checks on prerequisite relationships that are essential for mastery, while allowing more efficient progression through less critical areas. This selective assessment strategy maintains high reliability of mastery achievement while optimizing learning time by avoiding redundant checks.
4Ease of operation
If learning objectives are arranged hierarchically with prerequisite relationships, then the learning structure is more logical and effective, but the system complexity increases
Solution Approach 1:
The knowledge map is segmented into discrete nodes, each representing a specific learning objective with clear prerequisites and relationships. This segmentation creates a modular structure that is logically organized and easy to navigate, while the standardized node format keeps the overall system manageable. Each node can be independently defined and assessed, simplifying the complexity of the hierarchical structure.
Solution Approach 2:
The knowledge map structure serves multiple functions simultaneously: it organizes learning content hierarchically, determines activity selection, guides progression paths, and enables assessment routing. This universal structure handles diverse learning scenarios through a single coherent framework, reducing the need for separate complex systems for each function while maintaining clarity of learning paths.
Data Source
AI summary
Disclosed herein are systems, media, methods, and platforms for providing personalized mastery learning utilizing: a knowledge map; a plurality of learning activities, each learning activity comprising dynamically variable scaffolding, feedback, and content; a learning activity selection module selecting one or more learning activities for the learner; and a learning activity modification module dynamically varying one or more of the scaffolding, feedback, and content of at least one selected learning activity based on performance of the learner.