A Diagnosis Model for College Teachers' Teaching Ability Based on Big Data and its Evaluation

Abstract

The mining of big data provides new ideas, methods, and technical support for the evaluation of college teachers’ teaching ability. Existing studies generally over-emphasize outcome evaluations and the evaluation methods are not scientific or objective enough, thus the evaluation results are often trapped in large errors and single pattern of manifestation. To overcome such defects, this paper took college English teaching as an example to develop a diagnosis model for college teachers’ teaching ability based on big data and evaluate its feasibility. At first, the evaluation indexes of college teachers' teaching ability were determined and the entropy weight method was adopted to assign weight values to the evaluation indexes. Then, based on the Gradient Boosted Decision Tree (GBDT), the diagnosis model was constructed and the steps were detailed. After that, an improved Particle Swarm Optimization (PSO) algorithm was adopted to optimize the proposed model. At last, experimental results proved the feasibility of the proposed diagnosis model.