Deep Learning Prediction of Thinking Ability From Exercise Results
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing learners' thinking ability are subjective and prone to expert biases, requiring specific interviews or activities, lacking objectivity and convenience.
Innovation Solution
A deep learning-based method using an exercise classification model and a thinking ability prediction model, comprising pre-trained text classification, dropout layers, fully connected layers, and nonlinear activation layers, to objectively predict thinking ability from completed exercises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If interview-based evaluation or activity-based evaluation is used to assess learners' thinking ability, then the assessment can be conducted with expert involvement, but the results are subjective and prone to expert biases
Solution Approach 1:
The patent replaces the mechanical expert evaluation system with an automated deep learning model (Exercise Classification Model and Thinking Ability Prediction Model) that processes exercise text data and prediction data to generate objective thinking ability assessments, eliminating human subjectivity and bias while maintaining measurement precision
Solution Approach 2:
The system enables self-service assessment where the deep learning models automatically process exercise data and generate thinking ability predictions without requiring expert intervention, making the assessment process both objective and scalable
2Measurement precision
If interview-based evaluation or activity-based evaluation is used, then expert judgment can be applied, but the process requires significant time and resources
Solution Approach 1:
The patent performs preliminary action by pre-training the deep learning models on large datasets and pre-processing exercise text data through the Exercise Classification Model before actual assessment, enabling rapid automated predictions that eliminate the time-consuming manual expert evaluation process while maintaining assessment quality
Solution Approach 2:
The automated deep learning-based assessment system replaces the time-intensive manual expert evaluation process, providing rapid thinking ability predictions without sacrificing assessment quality
3Adaptability or versatility
If traditional assessment methods are used, then expert design is possible, but the system lacks adaptability to different learners and contexts
Solution Approach 1:
The patent creates a universal assessment system where the deep learning models can process diverse exercise text data and adapt to different learners automatically, making the system versatile across various contexts without requiring complex custom design for each case
Data Source
AI summary
A deep learning-based method for predicting thinking ability includes: obtaining a practice text set already completed by a user; inputting the exercise text set into an exercise classification model to obtain corresponding exercise categories for each exercise text in the exercise text set; mapping an exercise result set corresponding to the exercise text set to the exercise categories corresponding to each exercise text in the exercise text set, so as to obtain a correspondence set between the exercise categories and the exercise results; constructing an input vector based on the correspondence set between the exercise categories and the exercise results; and inputting the input vector into a thinking ability prediction model to obtain the user's thinking ability prediction result. A thinking ability prediction apparatus, device, and non-transitory computer-readable storage medium based on deep learning are also provided.