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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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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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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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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The Use of Gamification in Mathematics Education: Enhancing Geometry Comprehension with High School Students

2024 , Espinosa Pinos, Carlos Alberto , Alex Mauricio Mazaquiza-Paucar , Clara Augusta Sánchez Benítez

The non-existence or non-application of strategies, methodologies, innovative techniques such as gamification, which offers a more attractive and dynamic way of approaching the mathematical topic about the equation of the line, a concept that is often abstract and difficult to understand for many students. The present study aims to analyze the effectiveness of gamification in the pedagogical process between educators and learners in relation to the understanding of the equation of the line in students of the tenth year of higher EGB. This analysis was carried out through a quantitative approach that included the administration of a knowledge test before and after applying gamification to 92 GBS tenth grade students. These students were divided into two groups of 46 students each, one experimental group (EG) worked on gamification activities and the other control group (CG) employed traditional techniques. The purpose of this division was to determine if there were significant differences in performance means between the two groups. The gamified proposal incorporates elements of games, challenges, rewards and competitions, taking into account the learners in a dynamic way, providing them with a sense of purpose and achievement. After the T-Student application, it was concluded that the proposal not only significantly increases the academic performance of the students, but also develops logical thinking and reasoning, and favors autonomous and collaborative work.

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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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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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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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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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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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