Front-End Learning Content Software for Granular Progress Evaluation
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Solution Overview
Problem
Existing digital learning technologies are limited in their ability to provide textured and granular evaluation of learner progress, often relying on back-end Learning Management Systems (LMS) that restrict innovation and lead to data latency and inefficiencies. Additionally, these systems pose a barrier to entry for non-technical authors due to the requirement for coding expertise.
Innovation Solution
The digital learning system integrates a low-code learning authoring tool that allows non-technical authors to create digital learning courses with defined terminal and enabling objectives. The system natively computes learner progress on the front-end, enabling dynamic modification of content based on real-time progress scores, thus bypassing the limitations of traditional back-end LMS protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional back-end LMS protocols are used for evaluating learner progress, then system stability is maintained, but data latency and evaluation granularity are worsened
Solution Approach 1:
The patent extracts the learner progress evaluation function from the back-end LMS and relocates it to the front-end learning content software. This allows the system to compute progress scores locally using learned models, eliminating the need to transmit data to the back-end for evaluation, thereby reducing data latency and enabling more granular real-time progress tracking.
Solution Approach 2:
The patent introduces a new dimensional approach by implementing machine learning models that predict learner progress scores directly at the front-end. This adds a predictive analytics dimension to the traditional reactive evaluation model, enabling the system to anticipate learner outcomes and provide granular progress evaluation without waiting for back-end processing.
2Manufacturing precision
If coding expertise is required for creating digital learning content, then manufacturing precision is improved, but ease of manufacture is worsened
Solution Approach 1:
The patent implements an automated content generation system where the machine learning model predicts learner progress scores and generates appropriate learning content recommendations without requiring manual coding intervention. The system serves itself by automatically adapting content based on predicted learner needs, thereby maintaining content quality while eliminating the barrier of coding expertise for content creators.
Solution Approach 2:
The patent changes the fundamental parameter of content creation from manual coding to model-based prediction. By using machine learning models to generate and adapt learning content based on predicted learner progress, the system maintains high content quality through algorithmic precision while dramatically improving ease of manufacture by allowing non-technical users to create customized learning experiences.
3Productivity
If back-end LMS is used for progress evaluation, then device complexity is reduced, but productivity is worsened
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models with historical learner data before deployment. These pre-trained models are then deployed to the front-end where they can immediately begin predicting learner progress scores without requiring complex real-time back-end processing. This preliminary preparation enables rapid content creation and evaluation while managing system complexity through model reuse.
Data Source
AI summary
Examples of the presently disclosed technology provide digital learning systems that integrate an innovative learning authoring tool tailored for non-technical authors with front-end learning content software that natively performs textured/granular evaluation of a learner's progress, and dynamically modifies the digital learning content presented to the learner in response to such textured/granular evaluation.