Cloud Workspace Autograding for Hands-On Learning Evaluation
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Solution Overview
Problem
Online learning platforms face challenges in evaluating learners' hands-on skills due to the impracticality of conventional evaluation methods, as they require access to specific software and resources, which can be difficult to standardize and manage for large numbers of learners, and conventional solutions struggle to accurately and timely assess varied hands-on learning experiences.
Innovation Solution
The implementation of cloud workspaces provided by cloud providers, which are pre-configured and managed by online learning platforms, allowing learners to access necessary software and resources, and enabling the recording and evaluation of their interactions through autograding systems that compare learner inputs with solution standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional evaluation methods (quizzes, tests, written assignments) are used for hands-on learning, then evaluation can be performed using standardized techniques, but it is not feasible to accurately evaluate hands-on abilities and solutions vary widely making accurate evaluation difficult
Solution Approach 1:
The patent uses recording to create a copy of the learner's hands-on process and outputs, which can then be evaluated against expected results. This allows accurate measurement of hands-on abilities while accommodating solution variability by comparing actual outputs with expected outputs rather than requiring standardized processes
Solution Approach 2:
The patent implements an automated feedback mechanism that compares recorded learner outputs with expected results and provides evaluation feedback. This enables accurate measurement of hands-on comprehension while handling diverse solutions through systematic comparison and feedback loops
2Ease of operation
If remote access to required software is provided using servers accessible through local browser, then learners can access necessary resources, but the approach is prohibitively expensive and difficult to scale to large numbers of learners
Solution Approach 1:
The patent extracts the evaluation function from the learning platform and implements it as a separate recording and comparison system. This separates the resource-intensive software access component from the evaluation component, allowing the platform to scale to large numbers of learners without proportionally increasing evaluation costs
Solution Approach 2:
Instead of providing actual software instances to all learners through expensive server infrastructure, the patent uses recording to capture learner interactions and creates simplified copies or representations of the hands-on experience that can be evaluated at scale without requiring proportional resource allocation
3Adaptability or versatility
If cloud workspaces with pre-configured software are provided to learners, then hands-on learning can be enabled with standardized resource access, but infrastructure costs and system complexity increase
Solution Approach 1:
The patent creates a universal evaluation system that works across different cloud workspace configurations and software environments. The recording and comparison mechanism is platform-agnostic, allowing the same evaluation infrastructure to handle diverse hands-on learning scenarios without proportionally increasing system complexity
Data Source
AI summary
Described systems and techniques utilize pre-configured cloud workspaces, obtained from cloud providers and configured by/for instructors, that may be deployed to learners with all necessary software and other features included therewith, and that are managed on behalf of the learners and instructors by a hands-on, online learning platform provider. The online learning platform provider may provide many different types of learning content, and may enable a corresponding variety of types of hands-on learning experiences to a large number of learners, and may evaluate such hands-on learning experiences in an accurate, automated, and timely fashion.