Espinosa Pinos, Carlos Alberto
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Espinosa Pinos, Carlos Alberto
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Ambato
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carlosespinosa@uti.edu.ec
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57982477900

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Occupational Risks: A Comparative Study of the Most Common Indicators in Uruguay, Cuba and Ecuador

2023 , Acosta Pérez, Paul Bladimir , Espinosa Pinos, Carlos Alberto , Acuña Mayorga, José Miguel , Lascano Arias, Giovanni

Efficiency and effectiveness in daily work activities demand the control of processes, those elements that can affect the health of employees known as occupational risks. The objective of this study was to identify indicators of more frequent labor risks in the countries of Uruguay, Cuba and Ecuador, for which a bibliographic compilation was carried out, as well as a descriptive analysis of the indicators of occupational risk. The results show that the countries analyzed coincide as the highest index of risks to manufacturing industries. It is concluded that the international regulations and conventions that govern safety have been accepted by the different countries that make them up, mainly in the statistical registry of accidents, reports, affiliates among others. Finally, policies aimed at the prevention, detection, monitoring and eradication of occupational risks in the workplace must be established. © 2023 IEEE.

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From Physical to Digital Storytelling. A Comparative Case in School Education

2023 , Núñez Torres, María Giovanna , Amaluisa Rendón, Paulina Magally , Espinosa Pinos, Carlos Alberto

The 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.

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Bibliometric Analysis of Mental Health Research in Populations Affected by Natural Disasters

2023 , Espinosa Pinos, Carlos Alberto , Lascano-Arias G.S. , Acosta Pérez, Paul Bladimir , Acuña Mayorga, José Miguel

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.

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Augmented Reality as a Promoter of Visualization for the Learning of Mathematics in Ninth-Year of Basic Education

2023 , Espinosa Pinos, Carlos Alberto , Amaluisa Rendón P.M. , Núñez Torres, María Giovanna , 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.

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Predicting Academic Performance in Mathematics Using Machine Learning Algorithms

2022 , Espinosa Pinos, Carlos Alberto , Ayala-Chauvin, Manuel Ignacio , Buele, Jorge

Several 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.

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Ordinal Logistic Regression Model for Predicting Employee Satisfaction from Organizational Climate

2023 , Espinosa Pinos, Carlos Alberto , Acuña-Mayorga J.M. , Acosta-Pérez P.B. , Lara-Álvarez P.

The article introduces ordinal logistic regression as an alternative method for modelling the relationship between predictor variables and job satisfaction. It emphasizes the importance of comprehending job satisfaction factors to enhance organizational performance. The study employs a quantitative approach to predict job satisfaction levels among operational staff in the textile industry, using a test devised by Sonia Palma, consisting of 7 dimensions and 36 items for job satisfaction assessment. Additionally, a 50-items test measures the work climate. By applying a logistic model, the study categorizes job satisfaction into "low - medium"or "high"levels. The dataset encompasses socio-demographic variables and questions from the work climate test CL-SPC (Work Climate - Satisfaction, Productivity and Commitment), which includes five dimensions. Significant factors for the logistic regression model are identified through exploratory factor analysis. These include commitment, autonomy at work, leadership, interpersonal relationships, learning and personal development, clarity of job expectations, motivation, and performance. The analysis unveils associations between these factors and the likelihood of predicting job satisfaction levels. Motivation, job performance and clarity of job expectations emerge as influential predictors. The article recommends fostering a culture of commitment, empowering decision-making, and clearly defining job responsibilities to improve job satisfaction in the textile industry. In conclusion, ordinal logistic regression analysis deepens our understanding of job satisfaction factors in the textile industry, enabling organizations to implement strategies to increase job satisfaction and overall performance. The results of the study enrich our knowledge of job satisfaction and work climate in the textile industry, offering practical guidance to professionals responsible for talent management. © 2023 IEEE.

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