Fuzzy Quality Evaluation Algorithm for Higher Engineering Education Quality via Quasi neural network Framework
Quality evaluation for higher engineering education has important guiding significance and feedback role on cultivating engineering talents. Combining with the educational core concept of outcomes based education (OBE) and the educational process data, a fuzzy quality evaluation algorithm is developed for engineering education deriving from a constructed Quasi Neural Network (QNN) framework. More specifically, considering the logical relationships among basic components in the whole process of engineering education, a four layers QNN framework is designed first to underly and implement the educational concept of OBE reasonably, which includes the training objectives layer, requirement capability for graduation layer, requirement sub capability for graduation layer, and course layer. After that, by employing the educational process data under the proposed QNN framework, a fuzzy comprehensive evaluation algorithm is designed to describe the achievement scale of target capability for engineering education. Finally, focusing on the research capability for computer science with related four courses, the experiments based on the process educational data sets show the superiority and efficiency of the proposed framework and algorithm.
2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)