Strategic Identification and Evaluation Using Machine Learning and Fuzzy Logic in Teacher Training

Abstract

Classroom teachers influence the learning environment, are responsible for designing appropriate teaching modes and making appropriate changes in order to meet current education standards. The training of teaching staff requires complex assessments of the teacher’s skills and the ability of the student, as well as the quality of teaching and the current educational standards. Therefore, in this paper, the researcher propose a Development focused Training Strategy by assimilating conventional machine learning and fuzzy logic for maximum-fit outcomes. First, the training process is based on the current educational standards and staff skills. The ability of the staff to learn and adapt to the new standards is analyzed using a recurrent neural network (RNN).This learning network trains its computing layer based on the previous adaptability level and the range of current standards. The minor difference between the standards is overcome by recommending additional training sessions for the teaching staff. Fuzzy logic is applied to identify the maximum amount of improvement achieved through the training. The complex processing part is filtered using the maximum fuzzy derivative on the achievement obtained from the previous session. Based on the results achieved, the computing layer’s adaptability is flexibly adjusted. This improves to determine a more effective strategy without interrupting any training session.

Keywords: Fuzzy Logic; Machine Learning; Pedagogical Staff; Staff Training

Cite As

J. Zhang and B. Hou, " Strategic Identification and Evaluation Using Machine Learning and Fuzzy Logic in Teacher Training", Engineering Intelligent Systems, vol. 33 no. 1, pp. 5-18, 2025.