Linguistic Analytics for Student Engagement Detection
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Solution Overview
Problem
In electronic learning systems, it is challenging for instructors to determine student engagement and identify at-risk students without face-to-face interactions, making it difficult to provide timely corrective actions to ensure student success.
Innovation Solution
A system that analyzes student communications using linguistic analysis to predict performance by identifying keywords associated with student metrics such as engagement, life events, and resource needs, and provides guidance for instructors to facilitate responsive interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If electronic learning systems are used to eliminate face-to-face interactions, then geographic limitations are overcome and travel requirements are reduced, but instructor ability to determine student engagement and identify at-risk students deteriorates
Solution Approach 1:
The patent introduces communication analytics as an intermediary mechanism that mediates between the electronic learning environment and student engagement assessment. By analyzing communication patterns, language use, and interaction frequency, the system indirectly measures student engagement without requiring direct face-to-face observation, thus resolving the contradiction between geographic flexibility and engagement detection capability
Solution Approach 2:
The patent replaces the mechanical system of direct visual observation and face-to-face interaction with an automated linguistic analysis system. The system uses natural language processing and communication pattern analysis to substitute for the instructor's direct observational capabilities, enabling engagement detection in electronic learning environments where physical presence is absent
2Productivity
If instructors do not regularly interact with students in person, then electronic learning scalability is improved, but ability to diagnose student problems and provide corrective action deteriorates
Solution Approach 1:
The patent implements a self-service diagnostic system where students' own communications serve as the data source for identifying their problems. The linguistic analysis system automatically extracts information about student difficulties, engagement levels, and risk factors from their natural communications, eliminating the need for instructor-initiated diagnostics while maintaining diagnostic accuracy
Solution Approach 2:
The patent establishes a feedback loop where communication analytics continuously monitor student interactions and provide real-time or near-real-time information about student status to instructors. This automated feedback system enables scalable monitoring of multiple students simultaneously, allowing instructors to identify and address problems promptly without reducing their student load
Data Source
AI summary
The methods, apparatus, and systems described herein facilitate instructor decision-making based on an analysis of communication(s) between an instructor and a student, including to provide predictions of student outcomes. The methods include receiving communication(s) posted by a student, detecting personality types along with keywords and phrases used by the student and the instructor with a psychologically-based linguistic analysis of the communication(s), scoring the student and instructor communications based on the detected keywords and phrases compared to a library of keywords and phrases, aggregating the student scores by personality type and instructor, correlating the student and instructor scores with historical student data, creating an evaluation report to provide guidance to the students for enrollment based on the correlated scores, and displaying the evaluation report on a student device.