Ordoñez-Avila Ricardo, Salgado Reyes Nelson, Meza Jaime, Ventura Sebastián
Facultad de Ciencias Informáticas, Universidad Técnica de Manabí (UTM), Portoviejo, 130105, Ecuador.
Facultad de Ingeniería, Escuela Sistemas de Información, Pontificia Universidad Católica del Ecuador (PUCE), Quito, 170129, Ecuador.
Heliyon. 2023 Feb 21;9(3):e13939. doi: 10.1016/j.heliyon.2023.e13939. eCollection 2023 Mar.
Teacher evaluation is presented as an object of study of great interest, where multiple efforts converge to establish models from the association of heterogeneous data from academic actors, one of these is the students' community, who stands out for their contribution with rich data information for the establishment of teacher evaluation in higher education. This study aims to present the search results for references on the prediction of teacher evaluation based on the associated data provided by the performance of university students. For this purpose, a systematic literature review was carried out, established by the phases of planning (search objective, research questions, inclusion and exclusion criteria), search and selection (literature control group and keywords, the definition of the search string, results filtering), and extraction (synthesis of the contributions). As a result, a set of references on the application of predictions is obtained, focused on educational data mining techniques, such as Fuzzy logic, Fuzzy clustering, Fuzzy Neural Network (FNN), Neural networks, multilayer perceptron (MLP), Decision Trees, Logistic Regression, Random Forest Classifier, Naïve Bayes Classifier, Support Vector Machine (SVM), K-Nearest-Neighbor (KNN), and Associative classification model. In conclusion, prediction and mining techniques have been widely explored; however, teacher evaluation is in the process of growth with particular emphasis on fuzzy principles, considering that human decision-making is developed with uncertainty, which is strongly related to human behavior.
教师评价作为一个备受关注的研究对象，众多努力都聚焦于从学术参与者的异构数据关联中建立模型，其中学生群体便是其中之一，他们以丰富的数据信息为高等教育中教师评价的建立做出贡献而脱颖而出。本研究旨在展示基于大学生表现所提供的关联数据对教师评价预测的参考文献搜索结果。为此，进行了一项系统的文献综述，该综述由规划阶段（搜索目标、研究问题、纳入和排除标准）、搜索与筛选阶段（文献对照组和关键词、搜索字符串的定义、结果筛选）以及提取阶段（贡献的综合）组成。结果，获得了一组关于预测应用的参考文献，这些文献聚焦于教育数据挖掘技术，如模糊逻辑、模糊聚类、模糊神经网络（FNN）、神经网络、多层感知器（MLP）、决策树、逻辑回归、随机森林分类器、朴素贝叶斯分类器、支持向量机（SVM）、K近邻（KNN）和关联分类模型。总之，预测和挖掘技术已得到广泛探索；然而，教师评价仍处于发展阶段，尤其强调模糊原则，因为人类决策是在不确定性中进行的，这与人类行为密切相关。
