Pub Date : 2025-10-13DOI: 10.1109/RITA.2025.3620839
María de Los Ángeles Martínez-Mercado;Gisela Elízabeth López-Bustamante;Azucena Minerva García-León;Elva Patricia Puente-Aguilar;Daniela del Carmen Bacre-Guzmán
This study analyzes the perception and level of learning in artificial intelligence (AI) topics among Industrial Engineering students at a university in northern Mexico. Using a quantitative approach, a survey was administered to 64 students, focusing on dimensions such as perceived learning, academic and professional use of AI, and the perceived importance of its curricular integration. The findings reveal a limited perception of AI learning among Industrial Engineering students, with the Internet of Things and Data Security and Protection emerging as the highest-rated topics. In contrast, low levels of learning were reported in Predictive Maintenance, Deep Learning, and Quality Control. While 85% of participants consider the inclusion of AI in the curriculum to be essential, only 50% report using these tools in workplace settings. A strong association was identified between Predictive Maintenance and Quality Control, suggesting thematically relevant links for the discipline. These results highlight a gap between theoretical training and practical application of AI, indicating clear opportunities to strengthen its curricular integration.
{"title":"Artificial Intelligence Learning: Perceptions and Challenges in the Profile of Industrial Engineering Students","authors":"María de Los Ángeles Martínez-Mercado;Gisela Elízabeth López-Bustamante;Azucena Minerva García-León;Elva Patricia Puente-Aguilar;Daniela del Carmen Bacre-Guzmán","doi":"10.1109/RITA.2025.3620839","DOIUrl":"https://doi.org/10.1109/RITA.2025.3620839","url":null,"abstract":"This study analyzes the perception and level of learning in artificial intelligence (AI) topics among Industrial Engineering students at a university in northern Mexico. Using a quantitative approach, a survey was administered to 64 students, focusing on dimensions such as perceived learning, academic and professional use of AI, and the perceived importance of its curricular integration. The findings reveal a limited perception of AI learning among Industrial Engineering students, with the Internet of Things and Data Security and Protection emerging as the highest-rated topics. In contrast, low levels of learning were reported in Predictive Maintenance, Deep Learning, and Quality Control. While 85% of participants consider the inclusion of AI in the curriculum to be essential, only 50% report using these tools in workplace settings. A strong association was identified between Predictive Maintenance and Quality Control, suggesting thematically relevant links for the discipline. These results highlight a gap between theoretical training and practical application of AI, indicating clear opportunities to strengthen its curricular integration.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"338-346"},"PeriodicalIF":1.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-08DOI: 10.1109/RITA.2025.3613135
Andrea Lazarte-Aguirre;Rafael Fernández-Concha;Nicolás Núñez
In a highly competitive context where generative artificial intelligence (GAI) tools are gaining increasing relevance in educational learning environments, it is essential to understand the motivations and factors driving graduate students to adopt these technologies. This study systematically identifies the factors influencing graduate students’ intentions to use GAI tools. Students and alumni from a graduate business school in Peru were surveyed to assess their intentions regarding GAI technology usage. The study builds on the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating GAI literacy as a variable. In late 2024, 251 participants from diverse backgrounds completed a questionnaire, which was then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4.1.0.2. This analysis aimed to uncover key factors influencing GAI adoption in higher education. The findings reveal that performance expectancy (PE), effort expectancy (EE), and perceived risk (PR) significantly influence the intention to use GAI, whereas facilitating conditions (FC) and social influence (SI) do not. Furthermore, prior experience with GAI moderates the relationships between FC, SI, and the intention to use GAI. These insights into the factors shaping GAI adoption intentions are vital for informing strategies to ethically leverage artificial intelligence (AI) in business and academia. By understanding user motivations, organizations can develop targeted policies and training programs to ensure responsible AI integration and maximize its potential benefits.
{"title":"Determinants of Generative AI Adoption Through the UTAUT Model: Insights From Postgraduate Business Students","authors":"Andrea Lazarte-Aguirre;Rafael Fernández-Concha;Nicolás Núñez","doi":"10.1109/RITA.2025.3613135","DOIUrl":"https://doi.org/10.1109/RITA.2025.3613135","url":null,"abstract":"In a highly competitive context where generative artificial intelligence (GAI) tools are gaining increasing relevance in educational learning environments, it is essential to understand the motivations and factors driving graduate students to adopt these technologies. This study systematically identifies the factors influencing graduate students’ intentions to use GAI tools. Students and alumni from a graduate business school in Peru were surveyed to assess their intentions regarding GAI technology usage. The study builds on the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating GAI literacy as a variable. In late 2024, 251 participants from diverse backgrounds completed a questionnaire, which was then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4.1.0.2. This analysis aimed to uncover key factors influencing GAI adoption in higher education. The findings reveal that performance expectancy (PE), effort expectancy (EE), and perceived risk (PR) significantly influence the intention to use GAI, whereas facilitating conditions (FC) and social influence (SI) do not. Furthermore, prior experience with GAI moderates the relationships between FC, SI, and the intention to use GAI. These insights into the factors shaping GAI adoption intentions are vital for informing strategies to ethically leverage artificial intelligence (AI) in business and academia. By understanding user motivations, organizations can develop targeted policies and training programs to ensure responsible AI integration and maximize its potential benefits.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"391-400"},"PeriodicalIF":1.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-06DOI: 10.1109/RITA.2025.3616857
Rômulo Afonso L. V. de Omena;John Vitor T. da Silva;Manoel Messias de O. Rodrigues;Maurício Freitas Dos Santos Filho;Sarah Kauane L. Silva;Heshelley Roberta M. L. Costa;Débora Ruthe N. Moraes;Arthur da Rocha Albuquerque;Johnny Guilherme da Silva;Victor Gabriel de Jesus Oliveira;Marina Marra M. de Oliveira;Heloise Rayane da R. Santos;Ana Luisa de P. S. Melo;Hewerton Nascimento da Silva;Mariana Paulino Dos Santos;José Kawê S. M. da Silva;Allisson Luiz N. da Silva;Jacksiel José de Abreu
An education model with pillars on science, technology, engineering, and mathematics (STEM) is increasingly necessary to prepare our students for future jobs. A didactic tool that can engage students in STEM is robotics. A study area of robotics, mobile robotics is a rich tool that generates enthusiasm in students and involves diverse disciplines. While commercial robotic platforms for education exist, their high cost and limited customizability often pose challenges, particularly within the Brazilian public education system. This paper presents an experience with ten technical secondary school students focused on developing two distinct low-cost mobile robots: a differential drive and an omnidirectional one, sponsored by a research foundation. The primary objectives were to investigate the impact of a maker-approach environment on the enhancement of STEM skills and to provide accessible robotic platforms for future educational projects. Students, divided into pairs, worked collaboratively on various aspects of robot development, including chassis design, power supply, motor drive, data acquisition, and simulation and coding, utilizing computational tools like Tinkercad, AutoCAD, Arduino IDE, and ROS 2. This project aimed to answer how such an initiative could foster specific STEM competencies and what challenges and perceptions arise from the students’ perspective. The experience demonstrated that students not only developed STEM skills but also contributed valuable robotic platforms to the academic community. The initiative underscores the importance of foundational support in equipping the Brazilian education system to prepare students with skills vital for future professions.
{"title":"Enhancing STEM Skills With the Design of Mobile Robots: An Experience With Technical Secondary School Students","authors":"Rômulo Afonso L. V. de Omena;John Vitor T. da Silva;Manoel Messias de O. Rodrigues;Maurício Freitas Dos Santos Filho;Sarah Kauane L. Silva;Heshelley Roberta M. L. Costa;Débora Ruthe N. Moraes;Arthur da Rocha Albuquerque;Johnny Guilherme da Silva;Victor Gabriel de Jesus Oliveira;Marina Marra M. de Oliveira;Heloise Rayane da R. Santos;Ana Luisa de P. S. Melo;Hewerton Nascimento da Silva;Mariana Paulino Dos Santos;José Kawê S. M. da Silva;Allisson Luiz N. da Silva;Jacksiel José de Abreu","doi":"10.1109/RITA.2025.3616857","DOIUrl":"https://doi.org/10.1109/RITA.2025.3616857","url":null,"abstract":"An education model with pillars on science, technology, engineering, and mathematics (STEM) is increasingly necessary to prepare our students for future jobs. A didactic tool that can engage students in STEM is robotics. A study area of robotics, mobile robotics is a rich tool that generates enthusiasm in students and involves diverse disciplines. While commercial robotic platforms for education exist, their high cost and limited customizability often pose challenges, particularly within the Brazilian public education system. This paper presents an experience with ten technical secondary school students focused on developing two distinct low-cost mobile robots: a differential drive and an omnidirectional one, sponsored by a research foundation. The primary objectives were to investigate the impact of a maker-approach environment on the enhancement of STEM skills and to provide accessible robotic platforms for future educational projects. Students, divided into pairs, worked collaboratively on various aspects of robot development, including chassis design, power supply, motor drive, data acquisition, and simulation and coding, utilizing computational tools like Tinkercad, AutoCAD, Arduino IDE, and ROS 2. This project aimed to answer how such an initiative could foster specific STEM competencies and what challenges and perceptions arise from the students’ perspective. The experience demonstrated that students not only developed STEM skills but also contributed valuable robotic platforms to the academic community. The initiative underscores the importance of foundational support in equipping the Brazilian education system to prepare students with skills vital for future professions.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"290-301"},"PeriodicalIF":1.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-06DOI: 10.1109/RITA.2025.3616509
Sebastián Martín-Gómez;Carlos J. González Ruiz
This study was made possible by the University of La Laguna. The Educational Technology courses within the Bachelor’s degrees in Early Childhood Education and in Physical Activity and Sports Sciences (n = 103) were redesigned, integrating AI agents such as ChatGPT, Gemini and Perplexity into six phases for the critical use of these tools—questioning, comparison, critical dialogue, verification, re-elaboration and reflection—which guided students’ reflective interaction with AI. After the intervention, a mixed 23-item questionnaire was administered; descriptive statistics and thematic analysis of the “Evaluation and use of AI” dimension revealed that 88% of students believe AI should be didactically promoted in higher education and 42% identified ChatGPT as the agent providing the best answers. The most developed competencies were comparing outputs from different AI systems (66%), designing effective prompts (63%) and critically analysing responses (52%). The main potentialities highlighted were rapid access to information and time saving, while perceived risks centred on plagiarism and cognitive dependence. The findings corroborate the pedagogical validity of the model for strengthening prompt-engineering skills and critical thinking, yet underscore the need to deepen ethical training and rigorous verification frameworks to address concerns about academic integrity and AI reliability. It is concluded that a reflective and regulated integration of these technologies can significantly enhance teacher education in higher education.
{"title":"AI in Higher Education: Initial Teacher Training in the Critical and Didactic Use of Artificial Intelligence","authors":"Sebastián Martín-Gómez;Carlos J. González Ruiz","doi":"10.1109/RITA.2025.3616509","DOIUrl":"https://doi.org/10.1109/RITA.2025.3616509","url":null,"abstract":"This study was made possible by the University of La Laguna. The Educational Technology courses within the Bachelor’s degrees in Early Childhood Education and in Physical Activity and Sports Sciences (n = 103) were redesigned, integrating AI agents such as ChatGPT, Gemini and Perplexity into six phases for the critical use of these tools—questioning, comparison, critical dialogue, verification, re-elaboration and reflection—which guided students’ reflective interaction with AI. After the intervention, a mixed 23-item questionnaire was administered; descriptive statistics and thematic analysis of the “Evaluation and use of AI” dimension revealed that 88% of students believe AI should be didactically promoted in higher education and 42% identified ChatGPT as the agent providing the best answers. The most developed competencies were comparing outputs from different AI systems (66%), designing effective prompts (63%) and critically analysing responses (52%). The main potentialities highlighted were rapid access to information and time saving, while perceived risks centred on plagiarism and cognitive dependence. The findings corroborate the pedagogical validity of the model for strengthening prompt-engineering skills and critical thinking, yet underscore the need to deepen ethical training and rigorous verification frameworks to address concerns about academic integrity and AI reliability. It is concluded that a reflective and regulated integration of these technologies can significantly enhance teacher education in higher education.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"302-309"},"PeriodicalIF":1.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11186800","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1109/RITA.2025.3616004
Manuel Domínguez-Dorado;Francisco J. Rodríguez-Pérez;David Cortés-Polo;Jesús Galeano-Brajones;Jesús Calle-Cancho
Situational awareness is crucial in cybersecurity to identify, understand, and anticipate cyber threats, and to make effective decisions during cyber crises. This study evaluates the effectiveness of a hands-on approach based on interactive scenarios using FLECO Studio software, integrated into the holistic management model CyberTOMP, compared to a traditional lecture-based approach framed within the same model. An experiment was designed with 200 participants, divided into a treatment group (70%) and a control group (30%). Both groups received training aimed at increasing their level of situational awareness in cybersecurity, with assessments conducted before and after the training. The treatment group employed the interactive scenario-based approach modeled with FLECO Studio, while the control group received traditional lecture-based training. To mitigate potential biases, preventive measures were implemented during the design and analysis of the study. The statistically analyzed results illustrated a significant improvement (up to 54%) in the situational awareness level of the treatment group, along with a strongly positive perception of effectiveness among the participants. These findings suggest that the interactive scenario-based approach, using tools embedded within a comprehensive holistic framework, significantly enhances the acquisition of situational awareness capabilities in cybersecurity compared to a traditional lecture-based approach.
{"title":"Integration of Scenario-Based Learning in CyberTOMP With FLECO Studio: Enhancing Situational Awareness Training in Cybersecurity","authors":"Manuel Domínguez-Dorado;Francisco J. Rodríguez-Pérez;David Cortés-Polo;Jesús Galeano-Brajones;Jesús Calle-Cancho","doi":"10.1109/RITA.2025.3616004","DOIUrl":"https://doi.org/10.1109/RITA.2025.3616004","url":null,"abstract":"Situational awareness is crucial in cybersecurity to identify, understand, and anticipate cyber threats, and to make effective decisions during cyber crises. This study evaluates the effectiveness of a hands-on approach based on interactive scenarios using FLECO Studio software, integrated into the holistic management model CyberTOMP, compared to a traditional lecture-based approach framed within the same model. An experiment was designed with 200 participants, divided into a treatment group (70%) and a control group (30%). Both groups received training aimed at increasing their level of situational awareness in cybersecurity, with assessments conducted before and after the training. The treatment group employed the interactive scenario-based approach modeled with FLECO Studio, while the control group received traditional lecture-based training. To mitigate potential biases, preventive measures were implemented during the design and analysis of the study. The statistically analyzed results illustrated a significant improvement (up to 54%) in the situational awareness level of the treatment group, along with a strongly positive perception of effectiveness among the participants. These findings suggest that the interactive scenario-based approach, using tools embedded within a comprehensive holistic framework, significantly enhances the acquisition of situational awareness capabilities in cybersecurity compared to a traditional lecture-based approach.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"310-320"},"PeriodicalIF":1.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-29DOI: 10.1109/RITA.2025.3615512
Laura Icela González-Pérez;Francisco José García-Peñalvo;Amadeo José Argüelles-Cruz
The responsible integration of Artificial Intelligence in Education (AIED) offers a strategic opportunity to align learning environments with the principles of Society 5.0, fostering human–technology synergy in support of quality education and social well-being. This study presents a systematic review of 36 peer-reviewed articles (2021–2025) focused on educational applications that employ learning analytics (LA) through data-driven approaches and integrate machine learning (ML) models as part of their empirical evidence. Each study was analyzed according to three key dimensions: the context of AIED application, the data-driven approach adopted, and the ML model implemented. The findings reveal a persistent disconnect between the AI models employed and the available educational data, which in many cases are limited to access logs or manually recorded grades that fail to capture deeper cognitive processes. This limitation constrains both the effective training of ML models and their pedagogical utility for delivering meaningful interventions such as personalized learning pathways, real-time feedback, early detection of learning difficulties, and monitoring and visualization tools. Another significant finding is the absence of psychopedagogical frameworks integrated with quality standards and data governance, which are essential for advancing prescriptive and ethical approaches aligned with learning goals. It is therefore recommended that educational leaders foster AIED applications grounded in data governance and ethics frameworks, ensuring valid and reliable metrics that can drive a more equitable and inclusive education.
{"title":"Data-Driven Learning Analytics and Artificial Intelligence in Higher Education: A Systematic Review","authors":"Laura Icela González-Pérez;Francisco José García-Peñalvo;Amadeo José Argüelles-Cruz","doi":"10.1109/RITA.2025.3615512","DOIUrl":"https://doi.org/10.1109/RITA.2025.3615512","url":null,"abstract":"The responsible integration of Artificial Intelligence in Education (AIED) offers a strategic opportunity to align learning environments with the principles of Society 5.0, fostering human–technology synergy in support of quality education and social well-being. This study presents a systematic review of 36 peer-reviewed articles (2021–2025) focused on educational applications that employ learning analytics (LA) through data-driven approaches and integrate machine learning (ML) models as part of their empirical evidence. Each study was analyzed according to three key dimensions: the context of AIED application, the data-driven approach adopted, and the ML model implemented. The findings reveal a persistent disconnect between the AI models employed and the available educational data, which in many cases are limited to access logs or manually recorded grades that fail to capture deeper cognitive processes. This limitation constrains both the effective training of ML models and their pedagogical utility for delivering meaningful interventions such as personalized learning pathways, real-time feedback, early detection of learning difficulties, and monitoring and visualization tools. Another significant finding is the absence of psychopedagogical frameworks integrated with quality standards and data governance, which are essential for advancing prescriptive and ethical approaches aligned with learning goals. It is therefore recommended that educational leaders foster AIED applications grounded in data governance and ethics frameworks, ensuring valid and reliable metrics that can drive a more equitable and inclusive education.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"440-451"},"PeriodicalIF":1.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1109/RITA.2025.3612280
Rasikh Tariq;Diego Gutiérrez Vargas;Farhan Ali;Miguel Gonzalez-Mendoza;Cristina Sofia Torres-Castillo
Employability is vital for graduates to succeed in competitive job markets and reflects higher education institutions’ effectiveness. It is essential to investigate which specific traits contribute to a higher success rate of employability, as understanding these factors can help optimize targeted interventions and improve employment outcomes. The objective of this research is to identify and analyze the key traits that influence student employability using educational data mining techniques integrated with machine learning and deep learning models while providing an explainable framework to inform targeted interventions and enhance job market readiness among graduates. Addressing gaps in existing research, this study integrates a wide range of variables and employs advanced Artificial Intelligence (AI) techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to develop a predictive framework for understanding employability over time. Using data from mock job interviews, the study applies Shapley Additive exPlanations (SHAP) values to assess the impact of traits like Self-Confidence and Ability to Present Ideas. Hyperparameter tuning through Grid Search and k-fold cross-validation is employed to optimize model performance. The LSTM model, configured with three layers, achieved an accuracy of 91.46%, and demonstrated the highest performance among the evaluated models. Its robustness was further supported by a 90.48% accuracy obtained through 3-fold cross-validation. The current findings highlight the importance of soft skills, such as Self-Confidence, Ability to Present Ideas, and General Appearance, identified by SHAP analysis as critical predictors of employability, emphasizing the need for educational institutions to actively integrate soft skills development into their curricula to ensure students are both academically prepared and professionally equipped.
{"title":"What Determines Student Employability? Educational Data Mining Through Machine and Deep Learning Approach","authors":"Rasikh Tariq;Diego Gutiérrez Vargas;Farhan Ali;Miguel Gonzalez-Mendoza;Cristina Sofia Torres-Castillo","doi":"10.1109/RITA.2025.3612280","DOIUrl":"https://doi.org/10.1109/RITA.2025.3612280","url":null,"abstract":"Employability is vital for graduates to succeed in competitive job markets and reflects higher education institutions’ effectiveness. It is essential to investigate which specific traits contribute to a higher success rate of employability, as understanding these factors can help optimize targeted interventions and improve employment outcomes. The objective of this research is to identify and analyze the key traits that influence student employability using educational data mining techniques integrated with machine learning and deep learning models while providing an explainable framework to inform targeted interventions and enhance job market readiness among graduates. Addressing gaps in existing research, this study integrates a wide range of variables and employs advanced Artificial Intelligence (AI) techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to develop a predictive framework for understanding employability over time. Using data from mock job interviews, the study applies Shapley Additive exPlanations (SHAP) values to assess the impact of traits like Self-Confidence and Ability to Present Ideas. Hyperparameter tuning through Grid Search and k-fold cross-validation is employed to optimize model performance. The LSTM model, configured with three layers, achieved an accuracy of 91.46%, and demonstrated the highest performance among the evaluated models. Its robustness was further supported by a 90.48% accuracy obtained through 3-fold cross-validation. The current findings highlight the importance of soft skills, such as Self-Confidence, Ability to Present Ideas, and General Appearance, identified by SHAP analysis as critical predictors of employability, emphasizing the need for educational institutions to actively integrate soft skills development into their curricula to ensure students are both academically prepared and professionally equipped.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"271-289"},"PeriodicalIF":1.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-08DOI: 10.1109/RITA.2025.3607479
Mihaela I. Chidean;Ana Arboleya;Maria Cerezo-Magaña;Antonio J. Caamaño;Eduardo del Arco;David Cortés-Polo
Actual and future society requires more and more technology-related knowledge. One of the goals of the educational system is to prepare current students and future workers for the different challenges and jobs that they might encounter, even if most future jobs are largely unknown. Although administrations gradually modify education policies that will affect future generations, nowadays, there are students enrolled in non-STEM degrees who require the same opportunities. There are multiple approaches to this issue, such as double major art-engineering degrees or specific technological courses offered for students enrolled in non-STEM degrees. In this work, we present a case study conducted in a mandatory course for an undergraduate design degree in the art and humanities field. The course objective is to teach students basic electronic design and programming with the Arduino platform. To evaluate the previous knowledge and attitude of the students with regard to technology, initial tests were conducted. To evaluate their acquired knowledge, the students’ final projects developed during the course were assessed. The present study analyzes the benefits for this student profile, showing that besides acquiring new expertise they have also broadened their options and opportunities in the labour market.
{"title":"Technology Courses for Non-STEM Degrees: A Project-Based Learning Case Study","authors":"Mihaela I. Chidean;Ana Arboleya;Maria Cerezo-Magaña;Antonio J. Caamaño;Eduardo del Arco;David Cortés-Polo","doi":"10.1109/RITA.2025.3607479","DOIUrl":"https://doi.org/10.1109/RITA.2025.3607479","url":null,"abstract":"Actual and future society requires more and more technology-related knowledge. One of the goals of the educational system is to prepare current students and future workers for the different challenges and jobs that they might encounter, even if most future jobs are largely unknown. Although administrations gradually modify education policies that will affect future generations, nowadays, there are students enrolled in non-STEM degrees who require the same opportunities. There are multiple approaches to this issue, such as double major art-engineering degrees or specific technological courses offered for students enrolled in non-STEM degrees. In this work, we present a case study conducted in a mandatory course for an undergraduate design degree in the art and humanities field. The course objective is to teach students basic electronic design and programming with the Arduino platform. To evaluate the previous knowledge and attitude of the students with regard to technology, initial tests were conducted. To evaluate their acquired knowledge, the students’ final projects developed during the course were assessed. The present study analyzes the benefits for this student profile, showing that besides acquiring new expertise they have also broadened their options and opportunities in the labour market.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"262-270"},"PeriodicalIF":1.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11153631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-02DOI: 10.1109/RITA.2025.3605317
Williamson Silva;Renato de Souza Garcia;Rodrigo Cargnelutti;Maicon Bernardino
Requirements Engineering (RE) represents a fundamental activity in the software development process. When executed correctly, RE can have a beneficial impact on the final software quality. Given the increasing demand for competent professionals in the software industry, adopting pedagogical strategies that effectively align theory and practice in teaching Requirements Engineering is imperative. This ensures the training of qualified professionals capable of successfully executing software projects. This paper reports our experience designing and enhancing an innovative proposal pedagogy to teach RE through active blended learning. We grounded our proposal on the Problem-Based Learning (PBL) methodology, which enables external community stakeholders to present real-world problems within the classroom environment. Students take on the role of requirements engineers and participate in various RE activities as they design their software solutions. Our pedagogical proposal combines PBL with other methodologies, e.g., Flipped Classroom, Diaries, and Gamification. We also provide evidence from a case study conducted in our course, in which we assess students’ perceptions of our approach. The results indicate increased student engagement, motivation, and performance, as well as improved understanding of RE concepts and their application to real-world problems. Additionally, we improved the active blended learning proposal based on our lessons learned and students’ perceptions. This work concludes that an active blended learning approach can significantly enhance RE education, offering a practical and adaptable strategy to foster both technical and soft skills among software engineering students. The main contributions of this study are (i) the design of a structured and replicable pedagogical framework for teaching RE using blended learning, (ii) the empirical evaluation of this framework through its implementation in two undergraduate cohorts, and (iii) the refinement of the framework based on lessons learned and student feedback.
{"title":"Innovative Active Blended Learning Pedagogy in Software Requirements Engineering Education","authors":"Williamson Silva;Renato de Souza Garcia;Rodrigo Cargnelutti;Maicon Bernardino","doi":"10.1109/RITA.2025.3605317","DOIUrl":"https://doi.org/10.1109/RITA.2025.3605317","url":null,"abstract":"Requirements Engineering (RE) represents a fundamental activity in the software development process. When executed correctly, RE can have a beneficial impact on the final software quality. Given the increasing demand for competent professionals in the software industry, adopting pedagogical strategies that effectively align theory and practice in teaching Requirements Engineering is imperative. This ensures the training of qualified professionals capable of successfully executing software projects. This paper reports our experience designing and enhancing an innovative proposal pedagogy to teach RE through active blended learning. We grounded our proposal on the Problem-Based Learning (PBL) methodology, which enables external community stakeholders to present real-world problems within the classroom environment. Students take on the role of requirements engineers and participate in various RE activities as they design their software solutions. Our pedagogical proposal combines PBL with other methodologies, e.g., Flipped Classroom, Diaries, and Gamification. We also provide evidence from a case study conducted in our course, in which we assess students’ perceptions of our approach. The results indicate increased student engagement, motivation, and performance, as well as improved understanding of RE concepts and their application to real-world problems. Additionally, we improved the active blended learning proposal based on our lessons learned and students’ perceptions. This work concludes that an active blended learning approach can significantly enhance RE education, offering a practical and adaptable strategy to foster both technical and soft skills among software engineering students. The main contributions of this study are (i) the design of a structured and replicable pedagogical framework for teaching RE using blended learning, (ii) the empirical evaluation of this framework through its implementation in two undergraduate cohorts, and (iii) the refinement of the framework based on lessons learned and student feedback.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"252-261"},"PeriodicalIF":1.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-22DOI: 10.1109/RITA.2025.3601613
Alejandro Moreno-Cruz;Jaime Muñoz-Arteaga;Julio C. Ponce-Gallegos;Francisco L. Gutiérrez-Vela
Students with disabilities at the upper secondary level often encounter significant barriers to accessing and engaging with education. This study examines the use of the Design-Based Research (DBR) methodology to develop a virtual reality (VR) application prototype designed to address these challenges. Conducted within the Care Centers for Students with Disabilities (CAED) in Aguascalientes, Mexico, the project involved 18 students with different types of disabilities and their educators in an iterative design process. By utilizing VR technologies, the study aimed to enhance accessibility, student engagement, and academic outcomes for students with disabilities. The DBR approach facilitated continuous refinement of the prototype through iterative feedback cycles, promoting collaboration among researchers, educators, and students to ensure alignment with user needs. Results indicated increased motivation and academic performance for most participants, although significant visual impairments limited the tool’s effectiveness for two students. This paper details the design, implementation, and evaluation of the VR application, emphasizing the integration principles of inclusive education with advanced technologies. The findings highlight the potential of VR to create immersive, adaptive, and inclusive learning environments, providing valuable guidance for future advancements in educational technology.
{"title":"Agile Production of Inclusive Learning Environments With Virtual Reality to Support Bachelor Students With Disabilities","authors":"Alejandro Moreno-Cruz;Jaime Muñoz-Arteaga;Julio C. Ponce-Gallegos;Francisco L. Gutiérrez-Vela","doi":"10.1109/RITA.2025.3601613","DOIUrl":"https://doi.org/10.1109/RITA.2025.3601613","url":null,"abstract":"Students with disabilities at the upper secondary level often encounter significant barriers to accessing and engaging with education. This study examines the use of the Design-Based Research (DBR) methodology to develop a virtual reality (VR) application prototype designed to address these challenges. Conducted within the Care Centers for Students with Disabilities (CAED) in Aguascalientes, Mexico, the project involved 18 students with different types of disabilities and their educators in an iterative design process. By utilizing VR technologies, the study aimed to enhance accessibility, student engagement, and academic outcomes for students with disabilities. The DBR approach facilitated continuous refinement of the prototype through iterative feedback cycles, promoting collaboration among researchers, educators, and students to ensure alignment with user needs. Results indicated increased motivation and academic performance for most participants, although significant visual impairments limited the tool’s effectiveness for two students. This paper details the design, implementation, and evaluation of the VR application, emphasizing the integration principles of inclusive education with advanced technologies. The findings highlight the potential of VR to create immersive, adaptive, and inclusive learning environments, providing valuable guidance for future advancements in educational technology.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"227-236"},"PeriodicalIF":1.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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