Student Engagement Tracking Through Interaction Analysis
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Solution Overview
Problem
Existing classroom management systems lack the ability to accurately assess and enhance student engagement in real-time without placing additional burdens on educators, often relying on manual oversight or simplistic algorithms that fail to account for nuanced behaviors.
Innovation Solution
A computer-implemented method that monitors student interactions with computing devices using machine learning models to determine engagement levels, providing actionable insights and nudges to keep students on track with predefined learning objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual oversight by teachers is used to monitor student engagement, then student engagement can be assessed, but teacher workload increases and time is consumed
Solution Approach 1:
The system enables self-service by having students interact with the learning management system through their devices. The system automatically monitors engagement metrics (time on task, content access patterns, interaction frequency) without requiring teacher intervention. Students engage with the system themselves, and the system collects and analyzes the data autonomously to generate engagement reports.
Solution Approach 2:
The patent replaces the mechanical system of manual teacher monitoring with an automated electronic system. The learning management system uses software algorithms to collect, process, and analyze student interaction data from learning modules, quizzes, and discussions. This electronic substitution eliminates the need for teachers to manually track engagement while providing continuous, objective measurement.
2Productivity
If simplistic algorithms are used to determine student engagement, then processing is faster, but engagement assessment accuracy is insufficient
Solution Approach 1:
The system segments engagement assessment into multiple independent metrics: time-based metrics (duration of task completion), interaction-based metrics (number of contributions to discussions, frequency of question-answering), and performance-based metrics (quiz scores, assignment completion quality). Each metric is processed separately by the system's analytics engine, allowing comprehensive analysis without overwhelming processing complexity.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting engagement thresholds and weighting factors based on course objectives, student performance patterns, and temporal variations. The system adapts engagement criteria over time, learning from historical data to refine its assessment algorithms. This allows the system to maintain high processing speed while continuously improving assessment accuracy through data-driven parameter optimization.
Data Source
AI summary
A computer implemented method includes monitoring, by one or more processors, student interactions with computing devices during a class session to collect student interaction data and obtaining an engagement score representative of a student being on track by interacting with content relevant to a predefined learning objective. A communication is selected to direct the student to interactions to increase the engagement score. Following the communication, student interactions are monitored to determine a post communication engagement score. An effectiveness score of the communication is modified based on a change between the post communication engagement score and the engagement score.