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All Journal International Journal of Evaluation and Research in Education (IJERE) Indonesian Journal of Electrical Engineering and Computer Science
Claudia Poma-Garcia
Universidad César Vallejo
Education

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Supervised learning through k-nearest neighbor, used in the prediction of university teaching performance Omar Chamorro-Atalaya; Nestor Alvarado-Bravo; Florcita Aldana-Trejo; Claudia Poma-Garcia; Carlos Aliaga-Valdez; Gutember Peralta-Eugenio; Abel Tasayco-Jala
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1625-1634

Abstract

This study initially seeks to identify the most optimal supervised learning algorithm to be used in predicting the perception of teacher performance, and then to evaluate its performance indicators that validate its predictive capacity. For this, the Matlab R2021a software is used; the experimental results determine that the supervised learning algorithm K-Nearest Neighbor Weighted (Weighted KNN) will be correct in 98.10% in predicting the perception of teaching performance, this has been validated by carrying out two evaluations through its performance indicators obtained in the confusion matrix and the receiver operating characteristic (ROC) curve, in the first evaluation an average sensitivity of 97.9%, a specificity of 99.1%, an accuracy of 98.8% and a precision of 96.7% are observed, thus validating the ability of the Weighted KNN model to correctly predict the perception of teacher performance; while in the receiver operating characteristic (ROC) curve, values of the area under the curve (AUC) equal to 0.99 and 1 are obtained, with this it is possible to validate the capacity that the model will have to distinguish between the 4 classes of the perception of the university teaching performance.
Automatic learning algorithm for troubleshooting in hydraulic machinery Jose Castro-Puma; Miguel Castro-Puma; Verónica More-Sánchez; Juana Marcos-Romero; Elio Huamán-Flores; Claudia Poma-Garcia; Rufino Alejos-Ipanaque
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.pp535-544

Abstract

In Peru, there are many companies linked to the category of heavy machinery maintenance, in which, on the one hand, although it is true they generate a record of events linked to equipment maintenance indicators, on the other hand they do not make efficient use of these data generating operational patterns, through machine learning, that contribute to the improvement of processes linked to the service. In this sense, the objective of this article is to generate a tool based on automatic learning algorithms that allows predicting the location of faults in hydraulic excavators, in order to improve the management of the maintenance service. When developing the research, it was obtained that the algorithm that assembles bagged trees presents an accuracy of 97.15%, showing a level of specificity of 99.04%, an accuracy of 98.56% and a sensitivity of 97.12%. Therefore, the predictive model using the ensemble bagged trees algorithm shows significant performance in locating the system where failures occur in hydraulic excavator fleets. It is concluded then that it was possible to improve aspects associated with the planning and availability of supplies or components of the maintenance service, also optimizing the continuity and response capacity in the maintenance process.
Student satisfaction in the context of hybrid learning through sentiment analysis Omar Chamorro-Atalaya; Lisle Sobrino-Chunga; Rosemary Guerrero-Carranza; Ademar Vargas-Díaz; Claudia Poma-Garcia
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i2.26717

Abstract

With the incursion of data science into the academic field and the massification of social networks, it is possible to extract information on student satisfaction that contributes to feedback on teacher teaching strategies and methods. This article aims to determine student satisfaction with teaching performance, through sentiment analysis. Methodologically, the research is of a non-experimental longitudinal design, with a quantitative approach. Data collection was carried out through the social network Twitter, and data analysis was carried out through the sentiment analysis technique. As a result, it was identified that in the first week of class, the highest level of satisfaction was obtained, reaching 96.3% of the total number of students. Meanwhile, in the evaluation weeks, the highest level of dissatisfaction was reaching 29.17%. It is concluded that when going from totally virtual learning to hybrid learning, students express a certain level of dissatisfaction typical of a process of progressive adaptation. Therefore, teachers should take advantage of these findings to redesign assessment rubrics in the context of hybrid teaching. Aspects such as collecting opinions through social networks and extracting a degree of satisfaction through them apply in a crossed way to other professional fields.
Co-Authors Abel Tasayco-Jala Ademar Vargas-Díaz Carlos Aliaga-Valdez Elio Huamán-Flores Florcita Aldana-Trejo Gutember Peralta-Eugenio Jose Castro-Puma Juana Marcos-Romero Lisle Sobrino-Chunga Miguel Castro-Puma Nestor Alvarado-Bravo Omar Chamorro-Atalaya Rosemary Guerrero-Carranza Rufino Alejos-Ipanaque Verónica More-Sánchez