Task Time Estimation Using Participant Data for Course Scheduling
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Solution Overview
Problem
Planning the framework of electronic learning courses is challenging due to varying participant progress rates and review orders, requiring accurate estimation of time spent on tasks to customize course structures and materials.
Innovation Solution
A computer-implemented system that utilizes participant data, historical time data, and metadata attributes to estimate the time required for completing tasks, allowing for customized course scheduling based on individual participant calendars.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual task time estimation is used, then course framework planning can be simplified, but accuracy of time estimation deteriorates due to varying participant progress rates
Solution Approach 1:
The system automatically estimates task completion times by having participants interact with the electronic learning system. The system monitors actual completion times and uses this data to calculate average rates for different task types, eliminating the need for manual estimation while maintaining high accuracy through data-driven approaches.
Solution Approach 2:
The system continuously collects feedback on actual participant completion times and uses this information to refine and update the average completion rate calculations. This feedback loop ensures that time estimates remain accurate as participant progress patterns evolve, allowing the system to adapt to varying progress rates automatically.
2Adaptability or versatility
If customized course scheduling is implemented, then participant engagement is improved, but system complexity increases due to individual calendar integration
Solution Approach 1:
The scheduling system segments the course into discrete tasks with standardized time estimates. Each task can be independently scheduled based on participant calendars, allowing customization without requiring complex holistic rescheduling. This segmentation enables the system to handle individual calendar variations through modular task assignment rather than complex overall coordination.
3Productivity
If automatic time estimation based on historical data is used, then productivity of course planning is improved, but reliability deteriorates when historical data is insufficient for new task types
Solution Approach 1:
The system performs preliminary analysis of historical completion data to establish average completion rates for various task types before actual scheduling occurs. By pre-calculating these averages from accumulated historical data, the system prepares reliable estimation benchmarks that can be quickly applied to new scheduling scenarios without needing to collect fresh data for each case.
Data Source
AI summary
A method and system for automatic task time estimation and scheduling comprising the steps of: (1) storing a plurality of media items; (2) defining an aggregate task; (3) storing participant data and historical time data; (4) determining a plurality of metadata attributes; and (5) determining a final time estimate.