Fuzzy logic computational model for performance evaluation of Sudanese Universities and academic staff

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

“…We perform experiments with three baseline methods, namely: (i) lexiconbased SA for student feedback analysis [2], (ii) fuzzy-based student feedback analysis [5], and (iii) Supervised machine learning approach for teacher's performance evaluation [3]. The first study [2] has used lexicon for sentiment scoring of opinion words only, whereas, we have proposed improved sentiment scoring technique for both opinion words and modifiers used in student feedback.…”

“…The first study [2] has used lexicon for sentiment scoring of opinion words only, whereas, we have proposed improved sentiment scoring technique for both opinion words and modifiers used in student feedback. The 2 nd baseline study [5] has used traditional questionnaire-based data collection and analysis, whereas our proposed work uses sentiment-based approach in which aggregated sentiment score is made input to fuzzy logic module for analyzing student feedback and satisfaction. Finally, a machine learning technique are applied by [3] for student feedback analysis based on classical feature sets.…”

“…Yousif et al [5] proposed a fuzzy-based computational model for measuring and classifying performance of teaching staff on the basis student feedback collected using questionnaires and surveys. Experimental results are encouraging showing performance improvement over the similar methods.…”

“…In this work, we propose to compute sentiment scores of opinion words and modifiers, and then use the aggregated sentiment score as an input to the fuzzy-logic module for analyzing and measuring the student feedback and satisfaction. As baselines, we compare with student feedback evaluation systems proposed by [2,3,5], based on the lexicon entries and supervised learning, which lacks in providing evaluation of student feedback at fine-grained level. In this work, we answer following research questions:…”

Predicción del rendimiento académico por medio de técnicas de inteligencia artificial

“…Por su parte, Vedel et al (2015), analizaron los rasgos de personalidad, entre estudiantes de diferentes pregrados, frente al rendimiento académico de los mismos, encontrando diferencias significativas en cada uno de los grupos, con lo cual se pudo establecer una predicción acorde a tales rasgos. En general, las técnicas Inteligentes han sido empleadas para predecir una amplia variedad de eventos, entre los que se destacan: caracterización y predicción de descargas académicas (Li y Rijke, 2019); mejoramiento del rendimiento de equipos de trabajo en el aula (Alberola et al, 2016); predicción del rendimiento con base en factores cognitivos y no cognitivos (Fonteyne et al, 2017); sistemas basados en lógica difusa para medir el rendimiento de las universidades y su personal (Yousif y Shaout, 2018); empleo de sistemas de información geográfica para establecer la influencia de las zonas verdes en el rendimiento académico de un estudiante (Kweon et al, 2017); análisis del pensamiento crítico en el rendimiento académico de estudiantes de maestría (D'Alessio et al, 2019); ansiedad (Crişan y Copaci, 2015). Igualmente, se encontraron muy buenos estudios sobre el pronóstico de las acciones en la bolsa, realizados por personas, con un alto rendimiento académico (Zhu et al, 2018), aspecto que justifica más las investigaciones que se hagan en este campo.…”