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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Dora Arce-Santillan
Universidad Nacional Tecnológica de Lima Sur

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Sentiment analysis through twitter as a mechanism for assessing university satisfaction Omar Chamorro-Atalaya; Dora Arce-Santillan; Guillermo Morales-Romero; César León-Velarde; Primitiva Ramos-Salaza; Elizabeth Auqui-Ramos; Miguel Levano-Stella
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp430-440

Abstract

Currently, the data generated in the university environment related to the perception of satisfaction is generated through surveys with categorical response questions defined on a Likert scale, with factors already defined to be evaluated, applied once per academic semester, which generates very biased information. This leads us to wonder why this survey is applied only once and why it only asks about some factors. The objective of the article is to demonstrate the feasibility of a proposal to determine the degree of perception of student satisfaction through the use of data science and natural language processing (NLP), supported by the social network twitter, as an element of data collection. As a result of the application of this proposal based on data science, it was possible to determine the level of student satisfaction, being 57.27%, through sentiment analysis using the Python library "NLTK"; Thus, it was also possible to extract texts linked to the relevant factors of teaching performance to achieve student satisfaction, through the term frequency and inverse document frequency (TF-IDF) approach, these being those linked to the use of tools of simulation in the virtual learning process.
Supervised learning using support vector machine applied to sentiment analysis of teacher performance satisfaction Omar Chamorro-Atalaya; Dora Arce-Santillan; José Antonio Arévalo-Tuesta; Lilia Rodas-Camacho; Ronald Fernando Dávila-Laguna; Rufino Alejos-Ipanaque; Lilly Rocío Moreno-Chinchay
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp516-524

Abstract

Satisfaction with teaching performance is an important measurement process in higher education institutions, for this reason, applying sentiment analysis to the opinions of university students through the support vector machine (SVM) Fine Gaussian supervised learning algorithm represents an important contribution to the academic literature. This article identifies the best classification algorithm according to performance parameters for predicting student satisfaction with teaching performance through sentiment analysis; the subsequent implementation of the research has the purpose of strengthening teaching practices, in addition to allowing continuous training of teaching for the benefit of student learning. This article has provided a compact predictive model, with literature review based on SVM and sentiment analysis techniques. Through the machine learning classification learner technique, it is identified that the SVM algorithm: Fine Gaussian SVM is the one with the best accuracy equal to 98.3%. Likewise, the performance metrics for the four classes of the model were identified, which have a sensitivity equal to 88.89%, a specificity of 98.04%, a precision of 99.21% and an accuracy of 98.85%.
Text mining and sentiment analysis of teacher performance satisfaction in the virtual learning environment Omar Chamorro-Atalaya; Dora Arce-Santillan; José Antonio Arévalo-Tuesta; Lilia Rodas-Camacho; Genaro Sandoval-Nizama; Rosa Valle-Chavez; Yadit Rocca-Carvajal
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp525-534

Abstract

Although it is true that artificial intelligence and data science have become key tools that contribute to the improvement of many processes, identifying patterns and contributing to decision making, however, there are environments in which they are not yet being using it relevantly and effectively. The objective of this study is to identify the relevant factors, based on the opinions expressed by the students through the social network Twitter regarding the perception of satisfaction with the teaching performance during the virtual learning environment. For which sentiment analysis and text mining are used under the Python programming language environment, through JupyterLab. As results, it was determined that a predominance of 57.27% of positive polarity, identifying that the relevant factors of student satisfaction with teaching performance, are related to the development of the teacher in the class sessions that contributes to the learning of the process control subject through the use of simulation tools such as simulink and tools linked to proportional integral derivative (PID) controllers; on the other hand, there is a percentage of negative polarity of 15.45% that belongs to the factors linked to the laboratory sessions in which graphic representation and block diagrams were used to explain the class session.
Evaluation of the functionality of the virtual platform in the teaching process: analysis based on the usability factor Omar Chamorro-Atalaya; Dora Arce-Santillan; Guillermo Morales-Romero; Beatriz Caycho-Salas; Teresa Guía-Altamirano; César León-Velarde; Risley Rengifo-Tello
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp583-590

Abstract

The use of virtual platforms has been increasing exponentially during the context of distance education, however, there are still limitations to innovate in pedagogical proposals. This can hinder the assurance of student learning, either due to the little planning that occurred in its incorporation, the little knowledge of teachers and students in the educational use or the lack of use of the functionalities that they have incorporated for communication. The purpose of the research is to evaluate the operability of the virtual platform in the teaching-learning process through analysis based on the usability factor, the results will allow us to continue improving the tools linked to distance higher education. At the development of the investigation, a reliability value of 0.985 was obtained by means of Cronbach's Alpha. It was found as findings that 73.8% perceive an improvement in communication and in the exchange of information. Regarding the usability factor, 73.9% fully agree with the information available on the virtual platform and its accessibility. From what was determined, it is concluded that 65.98% of students consider that the functionality of the virtual platform with respect to the usability factor positively influences the teaching-learning process.
Co-Authors Beatriz Caycho-Salas César León-Velarde César León-Velarde Elizabeth Auqui-Ramos Genaro Sandoval-Nizama Guillermo Morales-Romero José Antonio Arévalo-Tuesta Lilia Rodas-Camacho Lilly Rocío Moreno-Chinchay Miguel Levano-Stella Omar Chamorro-Atalaya Primitiva Ramos-Salaza Risley Rengifo-Tello Ronald Fernando Dávila-Laguna Rosa Valle-Chavez Rufino Alejos-Ipanaque Teresa Guía-Altamirano Yadit Rocca-Carvajal