Treffer: Development of a Forecasting Model of Teaching Effectiveness.

This research project aims to utilize Python programming and machine learning algorithms to design a predictive model for assessing faculty effectiveness. The model considers various factors such as teaching effectiveness, course management, course materials, class openness, and course management. By analyzing these factors and testing the various model's performance against standard metrics, the collected data is processed and analyzed using regression analysis and decision trees, enabling the development of a predictive model. This model may provide estimates of future performance, allowing for the identification of high-performing faculty members, areas for improvement, and optimal resource allocation. The study results demonstrate that Naive Bayes, Random Forest, and Decision Tree algorithms are particularly effective in predicting faculty performance based on the provided data. These findings promise to inform the development of strategies and policies that enhance faculty effectiveness and contribute to institutional excellence. By employing a data-driven approach, this study offers valuable insights into the utility of different machine learning algorithms and their predictive capabilities in assessing faculty performance within the context of higher education. [ABSTRACT FROM AUTHOR]

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