Comprehensive System for Assessing and Enhancing Faculty Retention in Engineering Colleges

Abstract

The proposed system is designed to assess and enhance faculty retention in engineering colleges by monitoring various factors that influence job satisfaction, teaching quality, and overall well-being. The system evaluates faculty members’ mental status, financial situation, teaching quality, research output, and professional growth to predict attrition risks and suggest retention strategies. By integrating data from student feedback, peer evaluations, financial records, and mental health assessments, the system generates personalized recommendations for faculty improvement and support. This helps management make informed decisions about interventions, thereby improving faculty satisfaction and retention.

Specification

Description:FIELD OF INVENTION
[001] This invention relates to the field of faculty retention management systems, specifically within engineering colleges. The invention focuses on improving retention by evaluating teaching quality, mental well-being, financial status, and professional development of faculty members.
BACKGROUND AND PRIOR ART
[002] Faculty retention is a significant challenge in higher education, particularly in engineering colleges where job stress, financial dissatisfaction, and limited professional growth often lead to high turnover rates. While individual efforts have been made to assess faculty teaching quality or well-being, there is no comprehensive system that addresses all major factors contributing to faculty attrition. This invention provides a unified approach by monitoring teaching performance, mental health, financial well-being, and professional growth to create tailored retention strategies.
SUMMARY OF THE INVENTION
[003] The invention proposes a comprehensive system for assessing and enhancing faculty retention through data collection, predictive analytics, and personalized recommendations. The system monitors faculty members' teaching quality, mental health, financial status, and research output, generating data-driven suggestions to mitigate attrition risks. The integration of machine learning models provides predictive insights, allowing management to make proactive decisions regarding faculty retention.
BRIEF DESCRIPTION OF DRAWINGS
[004] Figure 1 illustrates Proposed System Architecture of Faculty Retention System
[005] Figure 2 illustrates Flow Chart
DETAILED DESCRIPTION OF THE INVENTION
[006] The system consists of the following key components: Mental Health and Well-being Monitoring: Regular assessments of faculty mental health through surveys and counseling services.
[007] Financial Well-being Evaluation: Data collection on salary, financial stress, and access to research funding.
[008] Teaching Quality Assessment: Collection of student feedback on communication, adaptability, and engagement.
[009] Professional Development Tracking: Analysis of faculty research output, participation in seminars, and professional growth opportunities.
[010] Attrition Risk Prediction: A machine learning model that predicts the likelihood of faculty attrition based on the aforementioned factors.
[011] Retention Strategies: Personalized recommendations for improving teaching quality, financial planning, mental health support, and research opportunities. , C , Claims:[012] 1. A system for assessing faculty retention by analyzing teaching quality, mental health, financial status, and professional development through an automated data collection process.
[013] 2. The system of claim 1, wherein a machine learning model is applied to predict faculty attrition risk based on collected data.
[014] 3. The system of claim 1, further comprising a feedback loop providing faculty members with personalized suggestions for improvement in teaching and financial well-being.
[015] 4. The system of claim 1, wherein data from student feedback and peer reviews is integrated to evaluate faculty performance in teaching.

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