Adaptive Learning Scaffold for Autism Language Training
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Solution Overview
Problem
Existing language barrier training games for children with autism lack intelligence in providing adaptive training tasks and questions, failing to meet the requirements of adaptive intervention and personalized training, and often result in negative effects due to mismatched learning abilities.
Innovation Solution
A training method and system based on adaptive learning scaffold that analyzes user state before training, generates an initialized training path, predicts question-answering correct rates, constructs a proximal development zone, and provides adaptive prompt information to ensure personalized and adaptive training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing language barrier training games are used for children with autism, then training can be provided, but the training lacks intelligence and adaptability to individual learning abilities
Solution Approach 1:
The system dynamically adjusts training parameters including question difficulty, prompt information level, and task complexity based on real-time assessment of the child's learning ability. The adaptive learning scaffold modifies these parameters automatically to match the child's proximal development zone, transforming a static training game into an adaptive intervention system without requiring complex hardware changes
Solution Approach 2:
The training system transitions from a fixed, static structure to a dynamic one that continuously adapts to the child's performance. The system monitors learning ability in real-time and adjusts the training path, question selection, and prompt provision dynamically, allowing the training to evolve with the child's developing capabilities
2Reliability
If training tasks are provided without adaptive intervention, then training can be conducted, but negative effects occur due to mismatched learning abilities
Solution Approach 1:
The system implements continuous feedback loops where the child's responses to training questions are immediately assessed, and the results feed back into adjusting the next set of training parameters. This feedback mechanism ensures that training remains appropriately challenging and avoids both frustration from overly difficult tasks and boredom from overly easy ones, thereby improving reliability
Solution Approach 2:
The system performs preliminary assessment of the child's learning ability before beginning formal training. This preliminary action includes evaluating the child's current language skills and cognitive level to establish an initial training path that is appropriately matched to their abilities, preventing negative effects from mismatched difficulty levels
3Productivity
If personalized training is implemented for each child with autism, then training effectiveness improves, but the system requires high intelligence and complex adaptation mechanisms
Solution Approach 1:
The system enables the training program to self-adjust and self-optimize based on automated assessment of child performance. The adaptive learning scaffold automatically modifies training parameters without requiring manual intervention from therapists, allowing the system to serve itself in adapting to each child's unique learning curve while maintaining high productivity
Data Source
AI summary
Disclosed are training method and system for autism language barrier based on adaptive learning scaffold, and the method includes the following steps: analyzing and assessing the state of the user before training to obtain an analysis result, and generating an initialized training path based on the analysis result; obtaining training question information, predicting a question-answering correct rate of the user based on user information and training question information, constructing a proximal development zone, and adding training questions that meet the accuracy requirements to the proximal development zone; updating the initialized training path, classifying the training questions in the proximal development zone, adding the classified training questions to the main training task or branch training task, and the user will perform learning and training according to the training task. The disclosure may recommend a suitable training path, and formulate training tasks that are suitable for the user's ability level.