Student Engagement Score Prediction via Wireless Network Data
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Solution Overview
Problem
Higher education institutions face challenges in accurately and efficiently identifying students at risk of dropping out due to lack of resources and effective technological tools to track on-campus activities and measure student engagement objectively, leading to inadequate preventative measures.
Innovation Solution
A system and method utilizing machine learning to analyze passive student activity data from wireless local area networking, generating a student engagement score to predict at-risk students, and providing real-time reports and dashboards for counselors to intervene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual attendance tracking by teachers is used, then student engagement can be monitored, but it requires excessive time and resources from every teacher to take accurate attendance records for every class
Solution Approach 1:
The patent replaces the mechanical manual attendance tracking system with an automated electronic system using wireless local area networking data. The system automatically captures student presence information through network connections in classrooms, eliminating the need for teachers to manually record attendance while maintaining accurate tracking of student engagement.
Solution Approach 2:
The system enables self-service attendance tracking by utilizing the student's own wireless device and network connection to automatically report presence information. The infrastructure serves itself by using existing network data to monitor engagement without requiring additional human intervention for data collection.
2Loss of information
If historical data correlation methods are used to identify at-risk students, then early insights can be provided, but the data sources are prejudiced, delayed, or subjective and cannot reflect current at-risk profiles
Solution Approach 1:
The patent replaces subjective historical data collection methods with objective electronic monitoring systems. Instead of relying on prejudiced or delayed human-reported data, the system uses automated wireless network data to objectively track current student behaviors and engagement levels, providing reliable real-time information about at-risk profiles.
Solution Approach 2:
The system performs preliminary analysis of wireless networking patterns to identify emerging engagement issues before they become critical problems. By continuously monitoring network usage data, the system can detect changes in student behavior patterns and alert counselors proactively, enabling early intervention based on objective current data rather than delayed historical information.
Data Source
AI summary
Embodiments leverage wireless data passively collected by access points of a school to correlate a student's behavior with respect to time and place. This data-driven approach can quantify student behaviors and objectively measure and track how students move and interact on campus. For example, if a student accesses a wireless router that is in the same physical location as a class on the student's schedule at the time the class takes place, the correlated time and place quantifies how the student behaves with respect to class attendance. The invention takes the sum of such interactions (e.g., attending classes, studying in the library, etc.) and produces a student engagement score (SES) for each student. The SES is evaluated to determine how student behaviors change over time (e.g., throughout a semester) and whether the student is trending to a low engagement score and thus “at-risk” of dropping out.