Espinosa Pinos, Carlos Alberto
Preferred name
Espinosa Pinos, Carlos Alberto
Main Affiliation
Ambato
Email
carlosespinosa@uti.edu.ec
ORCID
0000-0002-7841-8090
Scopus Author ID
57982477900
17 results
Now showing 1 - 10 of 17
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Item type:Publication, Applying Classification Techniques in Machine Learning to Predict Job Satisfaction of University Professors: A Sociodemographic and Occupational Perspective(2024); ; Camila Alessandra Valarezo-Calero<jats:p>This article investigates the factors that affect the job satisfaction of university teachers for which 400 teachers from 4 institutions (public and private) in Ecuador were stratified, resulting in a total of 1600 data points collected through online forms. The research was of a cross-sectional design and quantitative and used machine learning techniques of classification and prediction to analyze variables such as ethnic identity, field of knowledge, gender, number of children, job burnout, perceived stress, and occupational risk. The results indicate that the best classification model is neural networks with a precision of 0.7304; the most significant variables for predicting the job satisfaction of university teachers are: the number of children they have, scores related to perceived stress, professional risk, and burnout, province of the university at which the university teacher surveyed works, and city where the teacher works. This is in contrast to marital status, which does not contribute to its prediction. These findings highlight the need for inclusive policies and effective strategies to improve teacher well-being in the university academic environment.</jats:p>25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Beyond Linear Statistics: A Machine Learning Ecosystem for Early Screening of School Bullying(2026); 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification Tools to Assess Critical Thinking in Automotive Engineering Students(2024); ; Noemi Viviana Rodríguez OrtizInadequate conflict resolution skills in automotive engineering students can have negative consequences in the workplace. The development of mathematical logical thinking can help students develop critical analysis skills, improve problem-solving ability, develop reasoning skills, and effective communication, enabling them to deal effectively with conflicts and find creative solutions. This research aims to identify predictors of problem-solving ability using classification algorithms. Methodology: In this study, three classification algo-rithms were applied and the KDD process was used to identify predictors of problem-solving ability. The data set includes 60 records of students from the automotive engineering program at Universidad Equinoccial in Quito, Ecuador, to whom three tools were applied: a sociodemographic card, a Shatnawi test related to mathematical logical thinking, and a Watson Glaser test on conflict resolution ability. Results: The best classification model is the K-nearest neighbors’ algorithm and its predictive ability is very good, with a true positive rate versus false positive rate AUC of 0.75, along with a good performance in classifying negative cases. The model can be improved with increased sampling, cross-validation, or hyper-parameter adjustment. Conclusion: Age and mathematical logical thinking are strongly associated with conflict resolution ability. In future research it is important to consider additional variables such as experience in problem-solving projects, technical knowledge and communication skills; to explore the use of more advanced machine learning algo-rhythms; to design specific educational interventions based on the development of mathematical logical thinking; or to compare conflict resolution ability between different engineering disciplines.22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Unraveling Psycholaboral Risk Factors: Ordinal Prediction of Teacher Well-Being in University Institutions in EcuadorThis article investigates the factors that affect the job satisfaction of university teachers for which 400 teachers from 4 institutions (public and private) were stratifiedly selected, resulting in a total of 1600 data collected through online forms. The research was of cross-sectional design and quantitative type using an ordinal logistic regression model, analyzing variables such as ethnic identity, field of knowledge, gender, number of children, job burnout, perceived stress and occupational risk. The results indicate that teachers belonging to ethnic groups such as Ethnicity White, Mestizo and Montubio have higher job satisfaction probabilities of 96.7%, 92.3% and 115.3%, respectively. In addition, teachers in Humanities and Communication and Information Sciences report higher job satisfaction, with increases of 53.7% and 55.6%. In contrast, those who identify as “Other” in terms of gender experience a 21.2% decrease in satisfaction. Each additional child is associated with a 21.2% decrease in job satisfaction, while an increase in Work-Related Burnout, Perceived Stress, and Occupational Risk is associated with decreases of 27.3%, 16.2%, and 31.4%, respectively. These findings highlight the need for inclusive policies and effective strategies to improve teacher well-being in the academic university environment41 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, From Physical to Digital Storytelling. A Comparative Case in School EducationThe advantages of narratives and stories in school teaching are known, however, nowadays the use of information and communication technologies are consolidated with common practices, opening the way to the use of strategies such as storytelling. The research was carried out at the Indoamerica Educational Unit in the 2022–2023 academic period in Ambato-Ecuador. Its main objective was to analyse the practice of reading comprehension based on storytelling in physical and digital format as a strategy in the teaching process in the subject Language and Communication. The study collects information from two groups of 6-year-old students with the same level of schooling (second year of primary school), group one with 60 students and group two with 61 students. The teacher of group 1 used illustrated pictures based on infographics to tell the stories (physical format). The teacher of group 2, on the other hand, used Tiktok as a digital narrative resource (digital for-mato). The central theme of the stories is respect and protection of animals. On the other hand, and beforehand, the teachers have completed some training stages on both physical and digital storytelling techniques. A mixed quantitative-qualitative methodology and a reading comprehension test were used, in addition to assessing the most striking graphic resources in both formats. The results show that students who have used digital storytelling as a learning tool have shown better reading comprehension and in less time, which is due to the use of movement, sound and light in the digital format. The teacher of group 1, on the other hand, finds that the use of storytelling in digital format captures the children's attention and facilitates concentration. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.41 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bibliometric Analysis of Mental Health Research in Populations Affected by Natural Disasters(2023); ; The number of climate-related disasters has tripled over the past 30 years, culminating in the last two periods during catastrophic climate disasters worldwide such as Cyclone Idai, deadly heat waves in India, Pakistan, and Europe; and floods in Southeast Asia. Because natural disasters can have severe consequences for affected people's mental health, this study aims to identify trends and patterns in scientific production related to the mental health of people affected by natural risks. Five hundred thirty-two relevant articles were initially identified from the Scopus database in February 2023. Based on descriptive results, the number of scientific publications increased steadily from 2019 to 2022, albeit slowly in recent years, with the main publication form being articles, followed by articles, abstracts, and book chapters, conference papers, editorials, memos, books, letters, and short surveys. Among the fields, medicine had the most articles, followed by social sciences, psychology, environmental sciences, earth and planetary sciences, engineering, nursing, computer sciences, arts and humanity, neurosciences, business, economics, energy, chemistry, biology, and health. Co-occurrence analysis of terms of titles and abstracts identified four themes: 1) impact of natural disasters on mental health and COVID-19 and risk management; 2) the effect of forest fires on the mental health of the affected population; 3) earthquakes and tsunamis affect the mental health of the affected population; and 4) resilience and social support in psychological adjustment during pregnancy. A longitudinal analysis based on titles and abstracts showed how the focus shifted from initial associations between natural disasters and the physical and mental health of survivors (in 2018) to the association between natural disasters and cardiovascular disease and traumatic experiences and postpartum depressive symptoms (at the beginning 2023). This study concludes by discussing the theoretical and practical implications of the findings and showing how an area of particular interest for future research is the study of community mental health resilience as an intervention strategy to mitigate the negative effects of natural disasters in disaster-affected communities. © 2023 IEEE.41 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Use of Gamification in Mathematics Education: Enhancing Geometry Comprehension with High School Students(2024); 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Impact of Gamification on Reading Comprehension: Transforming Literacy Skills Among High School Students in Puyo, EcuadorThis research investigates the impact of gamification on reading comprehension among high school students in Puyo and Pastaza, Ecuador, where traditional methods face challenges compounded by limited technology access, COVID-19 disruptions, and specific community needs. Addressing the inadequacy of traditional approaches, the study explores gamification's potential to improve literacy skills. Employing a quantitative approach, the study involved 100 students divided into experimental and control groups. The experimental group received a gamified intervention using digital tools like Genially, while the control group followed traditional teaching methods. Reading comprehension was assessed using the PROLEC test, evaluating multiple dimensions of reading ability. The experimental group demonstrated significant improvements in reading comprehension, particularly in lexical selection, semantic categorization, and expository comprehension (p < 0.001). Student surveys confirmed increased engagement and motivation through gamification. The findings highlight the positive influence of digital gamification on reading development and critical thinking skills. Gamification positively impacts student engagement and academic performance in reading comprehension, offering an effective and replicable strategy for similar educational settings. However, the study underscores the necessity of robust technological infrastructure and adequate training for educators and students to fully leverage these strategies, ensuring improved literacy skills within challenging environments and addressing existing gaps in public education.78 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Predicting Academic Performance in Mathematics Using Machine Learning AlgorithmsSeveral factors, directly and indirectly, influence students’ performance in their various activities. Children and adolescents in the education process generate enormous data that could be analyzed to promote changes in current educational models. Therefore, this study proposes using machine learning algorithms to evaluate the variables influencing mathematics achievement. Three models were developed to identify behavioral patterns such as passing or failing achievement. On the one hand, numerical variables such as grades in exams of other subjects or entrance to higher education and categorical variables such as institution financing, student’s ethnicity, and gender, among others, are analyzed. The methodology applied was based on CRISP-DM, starting with the debugging of the database with the support of the Python library, Sklearn. The algorithms used are Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF), the last one being the best, with 92% accuracy, 98% recall, and 97% recovery. As mentioned above, the attributes that best contribute to the model are the entrance exam score for higher education, grade exam, and achievement scores in linguistic, scientific, and social studies domains. This confirms the existence of data that help to develop models that can be used to improve curricula and regional education regulations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Augmented Reality as a Promoter of Visualization for the Learning of Mathematics in Ninth-Year of Basic Education(2023); ; Quinatoa-Casicana J.This research focused on developing a mobile application in Meta-verse augmented reality to improve the learning of notable products, factoring, and linear equations in ninth-grade students of the intercultural bilingual educational unit of the millennium “Pueblo Kisapincha” based on the notional method. The methodology applied was quasi-experimental and longitudinal, where related samples were compared using a diagnostic test versus a subsequent evaluation of knowledge. The sampling technique was by non-probabilistic convenience comprising 25 students and 15 teachers to whom a structured questionnaire was applied to determine the predisposition to work in the classroom with augmented reality, which was validated with Cronbach's alpha statistic (α = 0.844). The students’ scores improved significantly after participating in both evaluations, with the post-evaluation being the one that showed the highest score according to the Bayesian T-test applied to related samples. A proposal of activities was designed to motivate the study of algebraic expressions; this product was implemented considering the ADDIE instructional model, guiding each movement with its respective resolution process as a form of feedback. The proposal was evaluated by two experts in technology and two experts in education with more than ten years of experience. In conclusion, developing an augmented reality mobile application in Metaverse to improve the learning of introductory algebra proved to be a valuable and effective tool to contribute to student learning. The mobile application provided an interactive and engaging learning experience, so it is recommended to incorporate this mobile application in the curriculum of the Kisapincha educational unit and its possible implementation in other similar educational institutions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.43