Student Input Classification for Attitude Recognition
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Solution Overview
Problem
Teachers face challenges in recognizing a student's tackling attitude towards class, as existing techniques lack effective methods to monitor and analyze student input behavior in real-time.
Innovation Solution
A processing apparatus that acquires input words from student terminals, classifies them into categories such as NG words, interesting words, high frequency words, and low frequency words, and displays a list with classification results, allowing teachers to intuitively recognize trends in student input behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If teachers manually monitor student behavior in class, then they can recognize student tackling attitudes, but it requires significant time and labor from teachers
Solution Approach 1:
The system enables automated self-monitoring of student input behavior through the input word acquisition unit that automatically collects data from student terminals without teacher intervention. The classification unit then automatically categorizes the collected input words, allowing the system to serve itself in monitoring and analyzing student attitudes without requiring teacher time and labor.
Solution Approach 2:
The patent replaces the mechanical manual monitoring system with an automated information processing system. The input word acquisition unit, classification unit, and output unit work together to automatically collect, categorize, and display student input data, substituting teacher manual observation with automated computational analysis of student terminal inputs.
2Measurement precision
If comprehensive student behavior data is collected to accurately assess tackling attitude, then recognition accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts only the essential feature - input words from student terminals - that directly reflects tackling attitude. The input word acquisition unit specifically targets and collects this relevant data, while the classification unit extracts meaningful categories from the collected words. This selective extraction avoids collecting unnecessary comprehensive behavior data, thereby reducing system complexity while maintaining recognition accuracy.
Solution Approach 2:
The classification unit applies different classification criteria to different categories of input words, treating each category with appropriate quality standards. By categorizing words into different types and applying specific analysis methods to each category, the system achieves precise attitude recognition without requiring a single complex unified system, thereby reducing overall complexity.
3Speed
If real-time monitoring of student input is implemented, then tackling attitude can be recognized during class, but processing load increases
Solution Approach 1:
The output unit displays a list of at least some of the input words rather than processing and displaying all collected data in real-time. This partial action approach allows the system to provide timely monitoring feedback while reducing the processing load and energy consumption by selectively displaying representative samples or key categories of input words.
Solution Approach 2:
The classification unit pre-processes and categorizes input words as they are collected, organizing data into meaningful groups before final display. This preliminary classification reduces the complexity of real-time processing by preparing structured data in advance, enabling faster response speed while minimizing the energy required for immediate analysis and display.
Data Source
AI summary
To provide a new technique for a teacher to recognize a tackling attitude of a student toward class is provided, the present invention provides a processing apparatus 100 including: an input word acquisition unit 101 that acquires an input word being a word input to each of a plurality of student terminals; a classification unit 102 that classifies the input words into a plurality of categories; and a first output unit 103 that displays a list of at least some of the input words, and also outputs, via an output terminal, a screen indicating a result of the classification of each of the input words displayed in the list.