Crowdworker Training System for SLA Compliance
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Solution Overview
Problem
Crowdsourcing platforms face challenges in distributing tasks effectively when the bandwidth of skilled crowdworkers is full, leading to potential Service Level Agreement (SLA) violations and business disruptions.
Innovation Solution
A method and system for training crowdworkers by determining expertise gaps and assigning training tasks alongside regular tasks to enhance their skill sets within a predetermined period without violating SLAs, utilizing a computing device and a crowdsourcing platform server with modules for expertise determination and task management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the crowdsourcing platform distributes tasks only to crowdworkers with existing skill sets, then task completion quality is maintained, but the platform cannot handle increased task volume when skilled crowdworkers are at bandwidth capacity
Solution Approach 1:
The system performs preliminary assessment of crowdworker expertise gaps before task assignment, and proactively assigns training tasks in advance to prepare crowdworkers for future task requirements. This allows the platform to build a pipeline of skilled workers before bandwidth constraints become critical, enabling both increased productivity and maintained SLA compliance.
Solution Approach 2:
The system dynamically adjusts the mix of training tasks and regular tasks assigned to crowdworkers based on real-time bandwidth utilization, skill gap analysis, and predicted task influx. This dynamic task allocation allows the platform to flexibly scale its skilled workforce capacity while ensuring current task commitments are met, resolving the contradiction between productivity expansion and reliability maintenance.
2Adaptability or versatility
If training tasks are assigned to crowdworkers, then skill levels improve, but task completion time increases and may violate service level agreements
Solution Approach 1:
The system assigns a controlled portion of training tasks mixed with regular tasks, rather than requiring complete training before task execution. By carefully calibrating the proportion and difficulty of training tasks, the system enables incremental skill development that does not excessively impact task completion timelines, allowing adaptability improvement without prohibitive time loss.
Solution Approach 2:
The system integrates training tasks continuously alongside regular task assignments rather than implementing separate batch training programs. This continuous learning approach allows crowdworkers to develop skills incrementally while maintaining steady task completion flow, ensuring both skill adaptation and timely delivery without interrupting the workflow.
3Productivity
If the platform assigns more tasks to existing skilled crowdworkers, then task volume increases, but the crowdworkers exceed their bandwidth capacity leading to SLA violations
Solution Approach 1:
The system predicts future task volumes and proactively identifies and trains additional crowdworkers in advance of demand spikes. By building a pool of pre-trained workers before bandwidth constraints are reached, the platform can distribute increased task volume across a larger skilled workforce, maintaining both high productivity and SLA compliance without overloading individual workers.
Data Source
AI summary
Disclosed embodiments illustrate methods and systems implementable on a computing device for training a crowdworker on one or more skill sets. The crowdworker attempts a first set of tasks. The method includes determining an expertise gap between a current level of expertise possessed by the crowdworker in the one or more skill sets and a required level of expertise in the one or more skill sets. Further, the method includes determining a number of training tasks that the crowdworker has to complete to achieve the required level of expertise in the one or more skill sets. At least one training task pertaining to the one or more skill sets is assigned along with the first set of tasks such that the crowdworker gets trained on the one or more skill sets in a predetermined period without violating a service level agreement associated with the first set of tasks.