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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Suicide Risk and Social Support in Young Ecuadorian Women Victims of Violence: A Psychosocial and Educational Analysis

2025 , Lascano Arias, Giovanni , Evelyn Cuesta-Andaluz , Espinosa Pinos, Carlos Alberto

This study is part of the project entitled "Multidimensional Assessment and Intervention in Mental and Physical Health throughout the Life Cycle in the Ecuadorian Population" and its main objective was to identify the complex relationships between perceived social support, the various types of violence experienced and the sociodemographic characteristics in the context of suicide risk of women victims of violence in Ecuador. The sample consisted of 106 women victims of violence, aged between 12 and 44 years (M = 21.49, SD = 9.01). For data analysis, statistical and correlation statistics, tests of differences for independent samples, as well as cross tables (X², Cramer's V and contingency coefficient) were used. Among the most relevant findings, it was highlighted that sexual violence was the most predominant form of violence in the population studied. Additionally, a significant negative correlation was observed between perceived social support and suicide risk, positioning social support as a protective factor in this context. However, no evidence was found of a significant influence of sociodemographic factors on social support or suicide risk, so the need for additional research to delve deeper into these dynamics is discussed.

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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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Unraveling Psycholaboral Risk Factors: Ordinal Prediction of Teacher Well-Being in University Institutions in Ecuador

2025 , Espinosa Pinos, Carlos Alberto , Acosta Pérez, Paul Bladimir , Valarezo Calero, Camila

This 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 environment

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Classification Tools to Assess Critical Thinking in Automotive Engineering Students

2024 , Espinosa Pinos, Carlos Alberto , Amaluisa Rendón, Paulina Magally , Noemi Viviana Rodríguez Ortiz

Inadequate 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.

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The Impact of Gamification on Reading Comprehension: Transforming Literacy Skills Among High School Students in Puyo, Ecuador

2025 , Moyano Barahona, Álvaro , Espinosa Pinos, Carlos Alberto , Amaluisa Rendón, Paulina Magally

This 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.

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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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Applying Classification Techniques in Machine Learning to Predict Job Satisfaction of University Professors: A Sociodemographic and Occupational Perspective

2024 , Espinosa Pinos, Carlos Alberto , Acosta Pérez, Paul Bladimir , Camila Alessandra Valarezo-Calero

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.

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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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Active Learning Strategies in Geometry: Effectiveness of a Gamified Digital Application at the Upper Basic Level

2025 , Ruiz Vega, Andrés , Espinosa Pinos, Carlos Alberto , Núñez Torres, María Giovanna

This research addresses the low effectiveness of traditional methodologies in teaching geometry to students of higher basic education, which has contributed to the low performance in mathematical competencies. In view of this situation, the implementation of a gamified digital application is proposed to promote a more participatory and meaningful learning. The type of sampling is non-probabilistic by convenience, so 99 students were considered. The research technique used is the survey addressed to students. The instruments used were: a questionnaire to determine the methodological strategies currently used by teachers; and a written test to measure the level of geometric reasoning according to Van Hiele. Both instruments were validated in content and construct by means of the expert judgment technique. The results obtained show that the use of traditional strategies predominates in the process of learning geometry in the classroom, and that 92% of the students, regarding their level of geometric reasoning, are at level 2 (low) out of 6 (complete); This can be corroborated by the significant differences between the pre-test and post-test after the application of the gamified tool; therefore, it is concluded that this proposal is valid to be implemented in traditional educational scenarios and thus ensure meaningful learning of geometry at the upper elementary level.

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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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