19 research outputs found
Development and evaluation of a didactic tool with augmented reality for Quechua language learning in preschoolers
"It is important to preserve our cultural identity through the preservation of our
mother tongue, contributing to its dissemination. Augmented reality (AR) is a
great ally of education that provides efficiency, and productivity and increases
the interest of students in their academic activities. An AR application was
developed for learning Quechua in preschool children, thus improving their
learning, satisfaction, and preference compared to traditional teaching.
Previously, learning styles were identified for better coverage of the
application; the design thinking methodology was applied for the
development of the application, then the respective tests were conducted
where it was obtained that the children's performance improved by 28.3%
more compared to traditional teaching, with an average satisfaction of 89% of
the classrooms, and 81% of students' preference. It was concluded that the
proposed application considerably favors the written and audiovisual learning
of the Quechua language in preschool students.
Changing Mathematical Paradigms at the University Level: Feedback from a Flipped Classroom at a Peruvian University
The university-level mathematics teaching adopted by many professors is still a traditional classroom, and many students’ perception of mathematics is that it is a complicated subject. The operationality of the flipped classroom proposal implemented at a university has a poten tial that can be used to change the perception that university students and teachers have towards the mathematics course, as well as to change the methodology of many teachers on how they teach their courses in the classroom. This research is the result of the implementa tion of the flipped classroom methodology in the basic mathematics course that is part of the professional careers of the engineering faculty of a Peruvian university. The aim of this study was to analyze the impact of applying the flipped classroom on academic results and atti tudes towards mathematics, with an experimental group of 227 students and a control group of 215 students. The academic results were measured at each of the stages indicated in the course syllabus, T1, partial exam, T2 and final exam; attitudes towards mathematics were also assessed at cognitive, procedural and affective levels at the end of the university semester. The Kolmogorov-Smirnov normality test was applied and yielded a value of p = 0.00, indicating that the grades obtained by the students did not follow a normal distribution. With the data obtained, the Mann-Whitney U test was performed, obtaining a p = 0.00 value (α = 0,052 tails). p < α makes us conclude that there are statistically significant differences between the scores of the experimental group compared to the control group. The results show a significant improvement in the academic performance and positive attitudes of students who took the course using the flipped classroom compared to those who did not use this methodolog
We Can Rely on ChatGPT as an Educational Tutor: A Cross-Sectional Study of its Performance, Accuracy, and Limitations in University Admission Tests
The aim of this research was to evaluate the performance of ChatGPT in answering multiple-choice questions without images in the entrance exams to the National University of Engineering (UNI) and the Universidad Nacional Mayor de San Marcos (UNMSM) over the past five years. In this prospective exploratory study, a total of 1182 questions were gathered from the UNMSM exams and 559 questions from the UNI exams, encompassing a wide range of topics including academic aptitude, reading comprehension, humanities, and scientific knowledge. The results indicate a significant (p < 0.001) and higher proportion of correct answers for UNMSM, with 72% (853/1182) of questions answered correctly. In contrast, there is no significant difference (p = 0.168) in the proportion of correct and incorrect answers for UNI, with 52% (317/552) of questions answered correctly. Similarly, in the World History course (p = 0.037), ChatGPT achieved its highest performance at a general level, with an accuracy of 91%. However, this was not the case in the language course (p = 0.172), where it achieved the lowest score of 55%. In conclusion, to fully harness the potential of ChatGPT in the educational setting, continuous evaluation of its performance, ongoing feedback to enhance its accuracy and minimize biases, and tailored adaptations for its use in educational settings are essential
Convolutional Neural Networks with Transfer Learning for Pneumonia Detection
"Pneumonia is a type of acute respiratory infection
caused by microbes, and viruses that affect the lungs. Pneumonia
is the leading cause of infant mortality in the world, accounting
for 81% of deaths in children under five years of age. There are
approximately 1.2 million cases of pneumonia in children under
five years of age and 180 000 died in 2016. Early detection of
pneumonia can help reduce mortality rates. Therefore, this paper
presents four convolutional neural network (CNN) models to
detect pneumonia from chest X-ray images. CNNs were trained
to classify X-ray images into two types: normal and pneumonia,
using several convolutional layers. The four models used in this
work are pre-trained: VGG16, VGG19, ResNet50, and
InceptionV3. The measures that were used for the evaluation of
the results are Accuracy, recall, and F1-Score. The models were
trained and validated with the dataset. The results showed that
the Inceptionv3 model achieved the best performance with 72.9%
accuracy, recall 93.7%, and F1-Score 82%. This indicates that
CNN models are suitable for detecting pneumonia with high
accuracy.
Text prediction recurrent neural networks using long shortterm memory-dropout
"Unit short-term memory (LSTM) is a type of recurrent neural network (RNN)
whose sequence-based models are being used in text generation and/or
prediction tasks, question answering, and classification systems due to their
ability to learn long-term dependencies. The present research integrates the
LSTM network and dropout technique to generate a text from a corpus as
input, a model is developed to find the best way to extract the words from the
context. For training the model, the poem ""La Ciudad y los perros"" which is
composed of 128,600 words is used as input data. The poem was divided into
two data sets, 38.88% for training and the remaining 61.12% for testing the
model. The proposed model was tested in two variants: word importance and
context. The results were evaluated in terms of the semantic proximity of the
generated text to the given context.
Augmented reality for innovation: Education and analysis of the glacial retreat of the Peruvian Andean snow-capped mountains
Mountain glaciers are considered great reservoirs of water, and their importance lies in the fact that many of our ecosystems and numerous communities depend on them; Peru has one of the largest extensions of Andean snow-capped mountains, which have been affected by the decline in their glacier coverage and that is warned, will disappear due to environmental conditions and alterations in the current global temperature. This problem has increased due to ignorance, misinformation, indifference, and lack of solidarity on the part of the population who favors this discouraging situation. Taking advantage of the current technological immersion, in which we live, the development of a mobile application was proposed as a pedagogical resource to raise awareness among educational institutions about the glacial retreat of the Peruvian Andean snow-capped mountains, showing the current situation of some of the snow-capped mountains of the Andes that have suffered a greater impact, implementing augmented reality technology to obtain an interactive link. To provide greater detail of the situation, previous studies were carried out on glacial retreats in two Peruvian snow-capped mountains over the last 40 years, where it was found that, of the snow-capped mountains considered, Chicon had a decrease of 32.5% of its glacier cover, and Pumahuanca had a decrease of 56.9%. Such results are exposed within the application to provide realistic data on the glacial conditions of both Peruvian snow-capped mountains, as well as the consequences and conservation techniques to mitigate and cope with deglaciation. Taking into consideration that environmental education from an early age turns out to be key to forming an informed and participatory society about climate change
Techniques and algorithms to predict the outcome of soccer matches using data mining, a review of the literature
El resultado de un deporte se ha convertido en una necesidad para los competidores, asà como para los fanáticos que siguen a sus equipos favoritos. Sin embargo, la predicción de los resultados de un partido de fútbol (PSMR) es muy variada debido a los diversos modelos existentes. La investigación es una revisión sistemática de la literatura (SLR) basada en manuscritos publicados en IEEE Xplore, Scopus, Science Direct y Springer. Se utilizó la metodologÃa Prisma para el análisis y sistematización. El objetivo de esta investigación es ofrecer una guÃa para haciendo uso de técnicas de machine learning (ML). Los resultados mostraron que las técnicas de ML más utilizadas son el aprendizaje supervisado (SL) y el aprendizaje no supervisado (UL) y el algoritmo de ML más frecuente para predecir el resultado de un partido de fútbol es Random Forest (RF), teniendo en cuenta su gran contribución en la precisión de la predicción. Además, tras el estudio se propone un modelo novedoso y eficiente para predecir el resultado de los partidos de fútbol, apoyado con Data Mining (DM) y centrado en ML
"Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education"
"With the onset of the COVID-19 pandemic, online
education has become one of the most important options
available to students around the world. Although online
education has been widely accepted in recent years, the sudden
shift from face-to-face education has resulted in several obstacles
for students. This paper, aims to predict the level of adaptability
that students have towards online education by using predictive
machine learning (ML) models such as Random Forest (RF), KNearest-Neighbor (KNN), Support vector machine (SVM),
Logistic Regression (LR) and XGBClassifier (XGB).The dataset
used in this paper was obtained from Kaggle, which is composed
of a population of 1205 high school to college students. Various
stages in data analysis have been performed, including data
understanding and cleaning, exploratory analysis, training,
testing, and validation. Multiple parameters, such as accuracy,
specificity, sensitivity, F1 count and precision, have been used to
evaluate the performance of each model. The results have shown
that all five models can provide optimal results in terms of
prediction. For example, the RF and XGB models presented the
best performance with an accuracy rate of 92%, outperforming
the other models. In consequence, it is suggested to use these two
models RF and XGB for prediction of students' adaptability level
in online education due to their higher prediction efficiency. Also,
KNN, SVM and LR models, achieved a performance of 85%,
76%, 67%, respectively. In conclusion, the results show that the
RF and XGB models have a clear advantage in achieving higher
prediction accuracy. These results are in line with other similar
works that used ML techniques to predict adaptability levels.
"The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model"
"Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered
eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have
seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies.
As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most
monkeypox patients have been discharged, we cannot neglect the monitoring of the population
with respect to the monkeypox virus. Lately, the population has started to express their feelings
and opinions through social media, specifically Twitter, as it is the most used social medium and is
an ideal space to gather what people think about the monkeypox virus. The information imparted
through this medium can be in different formats, such as text, videos, images, audio, etc. The objective
of this work is to analyze the positive, negative, and neutral feelings of people who publish their
opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease,
a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction
accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other
performance metrics were also used to evaluate the model, such as specificity, recall level, and F1
score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings
through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor
negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease.
The results of this work contribute to raising public awareness about the monkeypox virus.
Search and classify topics in a corpus of text using the latent dirichlet allocation model
This work aims at discovering topics in a text corpus and classifying the most relevant terms for each of the discovered topics. The process was performed in four steps: first, document extraction and data processing; second, labeling and training of the data; third, labeling of the unseen data; and fourth,
evaluation of the model performance. For processing, a total of 10,322 "curriculum" documents related to data science were collected from the web during 2018-2022. The latent dirichlet allocation (LDA) model was used for the analysis and structure of the subjects. After processing, 12 themes were
generated, which allowed ranking the most relevant terms to identify the skills of each of the candidates. This work concludes that candidates interested in data science must have skills in the following topics: first, they must be technical, they must have mastery of structured query language, mastery of programming languages such as R, Python, java, and data management, among other tools associated with the technology.Campus Lima Centr