Volume 20 No 22 (2022)
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Fair Appraisal and Academic Growth: Enhancing Professors' Performance in Indian Universities through Machine Learning
Prithviraj Singh SOLANKI
In Indian institutes and universities, fair and effective evaluation of professors is essential for fostering academic advancement and guaranteeing a high standard of instruction. Traditional appraisal techniques frequently have subjectivity, biases, and little transparency, which can result in assessment process inequalities. This study suggests a fresh method for fostering academics' academic development by incorporating machine learning techniques into the evaluation procedure. This strategy seeks to assure impartial and unbiased decision-making while supporting academic achievement and a welcoming environment for professors by utilising large-scale data, sophisticated analytics, and fairness-aware algorithms [13]. The study looks at how machine learning methods like clustering, classification, and regression can be used to analyse a variety of performance metrics, such as student feedback, research productivity, teaching efficacy, and professional growth. The results of this study have the potential to revolutionise the evaluation procedure in educational institutions and universities in India by promoting a climate of justice, transparency, and ongoing improvement[8].
fair appraisal, academic growth, fairness-aware algorithms, professional development, effective evaluation.
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