Predicting in advance the likelihood of students failing a course or withdrawing from a degree program has emerged as one of the widely embraced applications of Learning Analytics. While the literature extensively addresses the identification of at-risk students, it often doesn’t evolve into actual interventions, focusing more on reporting experimental outcomes than on translating them into real-world impact. The goal of early identification is straightforward, empowering educators to intervene before actual failure or dropout, but not enough attention is paid to what happens after the students are flagged as at risk. Interventions like personalized feedback, automated alerts, and targeted support can be game-changers, reducing failure and dropout rates. However, as this paper shows, few studies actually dig into the effectiveness of these strategies or measure their impact on student outcomes. Even more striking is the lack of research targeting stakeholders beyond students, like educators, administrators, and curriculum designers, who play a key role in driving meaningful interventions. The paper explores recent literature on automated academic risk prediction, focusing on interventions in selected papers. Our findings highlight that only about 14% of studies propose actionable interventions, and even fewer implement them. Despite these challenges, we can see that a global momentum is building around Learning Analytics, and institutions are starting to tap into the potential of these tools. However, academic databases, loaded with valuable insights, remain massively underused. To move the field forward, we propose actionable strategies, like developing intervention frameworks that engage multiple stakeholders, creating standardized metrics for measuring success and expanding data sources to include both traditional academic systems and alternative datasets. By tackling these issues, this paper doesn’t just highlight what is missing; it offers a roadmap for researchers and practitioners alike, aiming to close the gap between prediction and action. It’s time to go beyond identifying risks and start making a real difference where it matters most.
{"title":"Analyzing Intervention Strategies Employed in Response to Automated Academic-Risk Identification: A Systematic Review","authors":"Augusto Schmidt;Cristian Cechinel;Emanuel Marques Queiroga;Tiago Primo;Vinicius Ramos;Andréa Sabedra Bordin;Rafael Ferreira Mello;Roberto Muñoz","doi":"10.1109/RITA.2025.3540161","DOIUrl":"https://doi.org/10.1109/RITA.2025.3540161","url":null,"abstract":"Predicting in advance the likelihood of students failing a course or withdrawing from a degree program has emerged as one of the widely embraced applications of Learning Analytics. While the literature extensively addresses the identification of at-risk students, it often doesn’t evolve into actual interventions, focusing more on reporting experimental outcomes than on translating them into real-world impact. The goal of early identification is straightforward, empowering educators to intervene before actual failure or dropout, but not enough attention is paid to what happens after the students are flagged as at risk. Interventions like personalized feedback, automated alerts, and targeted support can be game-changers, reducing failure and dropout rates. However, as this paper shows, few studies actually dig into the effectiveness of these strategies or measure their impact on student outcomes. Even more striking is the lack of research targeting stakeholders beyond students, like educators, administrators, and curriculum designers, who play a key role in driving meaningful interventions. The paper explores recent literature on automated academic risk prediction, focusing on interventions in selected papers. Our findings highlight that only about 14% of studies propose actionable interventions, and even fewer implement them. Despite these challenges, we can see that a global momentum is building around Learning Analytics, and institutions are starting to tap into the potential of these tools. However, academic databases, loaded with valuable insights, remain massively underused. To move the field forward, we propose actionable strategies, like developing intervention frameworks that engage multiple stakeholders, creating standardized metrics for measuring success and expanding data sources to include both traditional academic systems and alternative datasets. By tackling these issues, this paper doesn’t just highlight what is missing; it offers a roadmap for researchers and practitioners alike, aiming to close the gap between prediction and action. It’s time to go beyond identifying risks and start making a real difference where it matters most.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"77-85"},"PeriodicalIF":1.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865300","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 : 2025-01-22DOI: 10.1109/RITA.2025.3532879
Aurelio Lopez-Fernandez;Federico Divina;Francisco A. Gomez-Vela;Miguel García-Torres
This work introduces an innovative teaching methodology based on microcompetences applied in a higher education context. The intervention involved creating a repository of practical case studies in the form of quizzes and integrating microcompetences into each course activity. The digital tool Sapiens was used to identify learning deficiencies and provide both collective and individualized feedback. The results indicate a significant increase in student participation and academic performance compared to previous years. Furthermore, students voluntarily used virtual teaching modalities to reinforce their knowledge, particularly in more complex areas. Data mining techniques identified performance patterns among students, highlighting the methodology’s effectiveness in improving both transversal and specific competences. The study’s findings underscore the importance of implementing microcompetency-based methodologies in higher education to enhance the quality of learning and continuous assessment. This approach not only facilitated a deeper understanding of course content but also promoted critical thinking, abstract reasoning, and interpersonal skills, preparing students for future academic and professional challenges. Additionally, the flexibility and adaptability of the digital tools used provided a seamless transition across different teaching modalities, such as in-person, hybrid, and online formats. Thus, the implementation of this innovative methodology has demonstrated its potential to significantly improve student engagement, participation, and academic success, thereby contributing to a more effective and comprehensive educational experience in higher education.
{"title":"Data Mining for Enhancing Learning and Assessment to a Microcompetence-Based Methodology in Higher Education","authors":"Aurelio Lopez-Fernandez;Federico Divina;Francisco A. Gomez-Vela;Miguel García-Torres","doi":"10.1109/RITA.2025.3532879","DOIUrl":"https://doi.org/10.1109/RITA.2025.3532879","url":null,"abstract":"This work introduces an innovative teaching methodology based on microcompetences applied in a higher education context. The intervention involved creating a repository of practical case studies in the form of quizzes and integrating microcompetences into each course activity. The digital tool Sapiens was used to identify learning deficiencies and provide both collective and individualized feedback. The results indicate a significant increase in student participation and academic performance compared to previous years. Furthermore, students voluntarily used virtual teaching modalities to reinforce their knowledge, particularly in more complex areas. Data mining techniques identified performance patterns among students, highlighting the methodology’s effectiveness in improving both transversal and specific competences. The study’s findings underscore the importance of implementing microcompetency-based methodologies in higher education to enhance the quality of learning and continuous assessment. This approach not only facilitated a deeper understanding of course content but also promoted critical thinking, abstract reasoning, and interpersonal skills, preparing students for future academic and professional challenges. Additionally, the flexibility and adaptability of the digital tools used provided a seamless transition across different teaching modalities, such as in-person, hybrid, and online formats. Thus, the implementation of this innovative methodology has demonstrated its potential to significantly improve student engagement, participation, and academic success, thereby contributing to a more effective and comprehensive educational experience in higher education.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"22-31"},"PeriodicalIF":1.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361192","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 : 2025-01-20DOI: 10.1109/RITA.2025.3531978
{"title":"2024 Index IEEE Revista Iberoamericana de Tecnologias del Aprendizaje Vol. 19","authors":"","doi":"10.1109/RITA.2025.3531978","DOIUrl":"https://doi.org/10.1109/RITA.2025.3531978","url":null,"abstract":"","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"19 ","pages":"418-430"},"PeriodicalIF":1.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10846973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184083","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 : 2025-01-13DOI: 10.1109/RITA.2025.3528369
Patricia Mariotto Mozzaquatro Chicon;Leo Natan Paschoal;Sandro Sawicki;Fabricia Roos-Frantz;Rafael Z. Frantz
Technology development has led to increased data generated in education, sparking interest in information extraction to support educational management through automated data analysis. Using such data to create models identifying students likely to drop out has drawn research interest. A crucial factor in reducing dropout rates is the systematic and early identification of the level of student engagement, especially by detecting the students’ behavior profile in the virtual environment, such as grades in assessments. There are predictive models based on data mining processes that identify students prone to dropping out. Unfortunately, the predictive models do not characterize the profiles of these students or the specific trends associated with these profiles. This article aims to fill a gap by presenting a study that identifies and tracks the profiles of undergraduate students likely to drop out, starting with an analysis of academic performance. We propose a predictive model beyond classification by combining data mining techniques such as decision trees, clustering, and frequent pattern analysis. Decision trees, a data mining technique that uses a tree-like graph to represent decisions and their possible consequences, identify students at risk of failure from the entire dataset. Clustering analysis, a data mining technique that groups similar data points together, groups students based on similar characteristics (e.g., students who scored between 0 and 30 points on a specific activity). Frequent pattern analysis, a data mining technique that identifies patterns that occur frequently in a dataset, uncovers the underlying factors contributing to low performance (e.g., identify which activities had the most significant influence on a specific group’s low performance). This integrated approach predicts dropout risk with 93.9% precision and provides a deeper understanding of student profiles and the trends associated with academic failure. The model’s practical application is demonstrated through a study.
{"title":"A Predictive Model for the Early Identification of Student Dropout Using Data Classification, Clustering, and Association Methods","authors":"Patricia Mariotto Mozzaquatro Chicon;Leo Natan Paschoal;Sandro Sawicki;Fabricia Roos-Frantz;Rafael Z. Frantz","doi":"10.1109/RITA.2025.3528369","DOIUrl":"https://doi.org/10.1109/RITA.2025.3528369","url":null,"abstract":"Technology development has led to increased data generated in education, sparking interest in information extraction to support educational management through automated data analysis. Using such data to create models identifying students likely to drop out has drawn research interest. A crucial factor in reducing dropout rates is the systematic and early identification of the level of student engagement, especially by detecting the students’ behavior profile in the virtual environment, such as grades in assessments. There are predictive models based on data mining processes that identify students prone to dropping out. Unfortunately, the predictive models do not characterize the profiles of these students or the specific trends associated with these profiles. This article aims to fill a gap by presenting a study that identifies and tracks the profiles of undergraduate students likely to drop out, starting with an analysis of academic performance. We propose a predictive model beyond classification by combining data mining techniques such as decision trees, clustering, and frequent pattern analysis. Decision trees, a data mining technique that uses a tree-like graph to represent decisions and their possible consequences, identify students at risk of failure from the entire dataset. Clustering analysis, a data mining technique that groups similar data points together, groups students based on similar characteristics (e.g., students who scored between 0 and 30 points on a specific activity). Frequent pattern analysis, a data mining technique that identifies patterns that occur frequently in a dataset, uncovers the underlying factors contributing to low performance (e.g., identify which activities had the most significant influence on a specific group’s low performance). This integrated approach predicts dropout risk with 93.9% precision and provides a deeper understanding of student profiles and the trends associated with academic failure. The model’s practical application is demonstrated through a study.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"12-21"},"PeriodicalIF":1.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361194","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 : 2024-12-25DOI: 10.1109/RITA.2024.3522255
Ana Belén González-Rogado;Ana Belén Ramos-Gavilán;María Ascensión Rodríguez-Esteban;Alicia García-Holgado
The gender gap in engineering is one of the problems in achieving gender equality in society. In this context, the “Engineering with a Gender Perspective” project, an equality initiative developed at the Higher Polytechnic School of Zamora (HPSZ), University of Salamanca, aims to understand the university community’s perception of the gender gap and gender equality and to promote interest in STEAM (Science, Technology, Engineering, Arts, Mathematics) subjects, particularly among female students. To achieve these objectives, a questionnaire tailored to Engineering and Architecture, which had previously been validated for Computer Engineering, was administered and discussed to the entire educational community at the HPSZ. The first WE (Women in Engineering) Challenge Competition was also launched for pre-university students. The challenges, devised by women, were designed to demonstrate the relevance of engineering in everyday life, to inspire pre-university students to pursue STEAM disciplines. The study examines responses to the questionnaire from both students and faculty at the HPSZ, analysing differences in perceptions between men and women regarding gender equality, the gender gap, and gender stereotypes. The findings indicate significant differences in perceptions of the gender gap, particularly among the student population. However, there are no significant differences in views on gender equality and gender stereotypes. Overall, the university community at the HPSZ recognises the importance of achieving gender equality and eliminating stereotypes. Nevertheless, there seems to be some uncertainty regarding their stance on the gender gap issue. Active engagement across all educational levels, focusing on early education, is crucial for reducing inequality in the STEAM field, eradicating gender stereotypes, and fostering interest in STEAM disciplines.
工程领域的性别差距是实现社会性别平等的问题之一。在这种背景下，萨拉曼卡大学萨莫拉高等理工学院（HPSZ）开展的“性别视角工程”项目是一项平等倡议，旨在了解大学社区对性别差距和性别平等的看法，并促进对STEAM（科学、技术、工程、艺术、数学）学科的兴趣，尤其是女生。为了实现这些目标，一份专门针对工程和建筑的问卷，之前已经在计算机工程中得到了验证，现在在HPSZ的整个教育界进行了管理和讨论。首届WE （Women in Engineering）挑战赛亦为大学预科生举办。这些挑战由女性设计，旨在展示工程与日常生活的相关性，以激励大学预科学生追求STEAM学科。该研究调查了HPSZ学生和教师对问卷的回答，分析了男女在性别平等、性别差距和性别刻板印象方面的看法差异。研究结果表明，人们对性别差距的看法存在显著差异，尤其是在学生群体中。然而，性别平等和性别刻板印象的观点没有显著差异。总体而言，HPSZ的大学社区认识到实现性别平等和消除刻板印象的重要性。然而，他们在性别差距问题上的立场似乎有些不确定。各级教育的积极参与，重点是早期教育，对于减少STEAM领域的不平等、消除性别刻板印象和培养对STEAM学科的兴趣至关重要。
{"title":"Perception Disparity Between Women and Men on the Gender Gap in STEM at a Spanish University","authors":"Ana Belén González-Rogado;Ana Belén Ramos-Gavilán;María Ascensión Rodríguez-Esteban;Alicia García-Holgado","doi":"10.1109/RITA.2024.3522255","DOIUrl":"https://doi.org/10.1109/RITA.2024.3522255","url":null,"abstract":"The gender gap in engineering is one of the problems in achieving gender equality in society. In this context, the “Engineering with a Gender Perspective” project, an equality initiative developed at the Higher Polytechnic School of Zamora (HPSZ), University of Salamanca, aims to understand the university community’s perception of the gender gap and gender equality and to promote interest in STEAM (Science, Technology, Engineering, Arts, Mathematics) subjects, particularly among female students. To achieve these objectives, a questionnaire tailored to Engineering and Architecture, which had previously been validated for Computer Engineering, was administered and discussed to the entire educational community at the HPSZ. The first WE (Women in Engineering) Challenge Competition was also launched for pre-university students. The challenges, devised by women, were designed to demonstrate the relevance of engineering in everyday life, to inspire pre-university students to pursue STEAM disciplines. The study examines responses to the questionnaire from both students and faculty at the HPSZ, analysing differences in perceptions between men and women regarding gender equality, the gender gap, and gender stereotypes. The findings indicate significant differences in perceptions of the gender gap, particularly among the student population. However, there are no significant differences in views on gender equality and gender stereotypes. Overall, the university community at the HPSZ recognises the importance of achieving gender equality and eliminating stereotypes. Nevertheless, there seems to be some uncertainty regarding their stance on the gender gap issue. Active engagement across all educational levels, focusing on early education, is crucial for reducing inequality in the STEAM field, eradicating gender stereotypes, and fostering interest in STEAM disciplines.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"1-11"},"PeriodicalIF":1.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361193","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 : 2024-12-09DOI: 10.1109/RITA.2024.3513702
Jesús Manuel Soriano-Alcantara;Francisco D. Guillén-Gámez;Julio Ruiz-Palmero
The purpose of this study was to have a more general and holistic view of the basic digital competencies self-perceived by the main agents of the educational community (teachers, students and parents) of all educational stages (Early Childhood Education, Primary Education, Secondary Education and Higher education). Specifically, the incidence of sociodemographic settings (urban-rural) and gender (female-male) in the Dominican Republic was analyzed and compared, for each educational stage. To achieve these purposes, an ex post facto design was used, and with a non-probabilistic sampling of 1149 participants. Among the main findings, the digital competencies of teachers are satisfactory and high in all educational stages and sociodemographic settings environments, while the group of students and parents shows lower scores, especially in the early educational stages. Regarding gender, no significant differences were found in the group of students for any educational stage, while in the group of teachers and parents, differences were found in some educational stages, in favor of the male gender. These findings suggest the need to design specific interventions to improve students and parents’ digital competencies, especially in Primary Education and rural areas, where minimum levels are observed. In addition, the importance of considering gender differences in the digital competencies of teachers and parents is highlighted to promote equity and equal access to digital education.
{"title":"Digital Transformation in the Educational Community of the Dominican Republic: Exploring the Role of Sociodemographic Environments and Gender in Digital Competence","authors":"Jesús Manuel Soriano-Alcantara;Francisco D. Guillén-Gámez;Julio Ruiz-Palmero","doi":"10.1109/RITA.2024.3513702","DOIUrl":"https://doi.org/10.1109/RITA.2024.3513702","url":null,"abstract":"The purpose of this study was to have a more general and holistic view of the basic digital competencies self-perceived by the main agents of the educational community (teachers, students and parents) of all educational stages (Early Childhood Education, Primary Education, Secondary Education and Higher education). Specifically, the incidence of sociodemographic settings (urban-rural) and gender (female-male) in the Dominican Republic was analyzed and compared, for each educational stage. To achieve these purposes, an ex post facto design was used, and with a non-probabilistic sampling of 1149 participants. Among the main findings, the digital competencies of teachers are satisfactory and high in all educational stages and sociodemographic settings environments, while the group of students and parents shows lower scores, especially in the early educational stages. Regarding gender, no significant differences were found in the group of students for any educational stage, while in the group of teachers and parents, differences were found in some educational stages, in favor of the male gender. These findings suggest the need to design specific interventions to improve students and parents’ digital competencies, especially in Primary Education and rural areas, where minimum levels are observed. In addition, the importance of considering gender differences in the digital competencies of teachers and parents is highlighted to promote equity and equal access to digital education.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"19 ","pages":"379-388"},"PeriodicalIF":1.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858895","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}
This study develops a bibliometric analysis of research on models of technology acceptance in education, identifying possible changes in the orientation of this research because of the Covid-19 pandemic. Using the Scopus database, 2,000 documents worldwide and 233 publications for Ibero-America were compared. The Bibliometrix package of the R-Studio software made it possible to analyse productivity through publication, visibility and impact metrics for both groups. The results support Lotka and Bradford’s Law, both globally and in Ibero-America. From the scientific mapping and the conceptual structure, structural equation models (SEM) are identified as the most frequently repeated estimation methodology, while TAM and TAM3 models reflect the highest number of repetitions, and in second place UTAUT and UTAUT2. The social structure shows the main collaborative networks in terms of authors and countries. In the case of the Ibero-American countries, Spain stands out with a high proportion of publications and citations.
{"title":"Models of Technology Acceptance in Education: A Bibliometric Analysis","authors":"Patricia Hernández-Medina;Diego Pinilla-Rodríguez;Gabriel Ramírez-Torres;María Paublini-Hernández","doi":"10.1109/RITA.2024.3486999","DOIUrl":"https://doi.org/10.1109/RITA.2024.3486999","url":null,"abstract":"This study develops a bibliometric analysis of research on models of technology acceptance in education, identifying possible changes in the orientation of this research because of the Covid-19 pandemic. Using the Scopus database, 2,000 documents worldwide and 233 publications for Ibero-America were compared. The Bibliometrix package of the R-Studio software made it possible to analyse productivity through publication, visibility and impact metrics for both groups. The results support Lotka and Bradford’s Law, both globally and in Ibero-America. From the scientific mapping and the conceptual structure, structural equation models (SEM) are identified as the most frequently repeated estimation methodology, while TAM and TAM3 models reflect the highest number of repetitions, and in second place UTAUT and UTAUT2. The social structure shows the main collaborative networks in terms of authors and countries. In the case of the Ibero-American countries, Spain stands out with a high proportion of publications and citations.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"19 ","pages":"342-352"},"PeriodicalIF":1.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821294","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 : 2024-11-26DOI: 10.1109/RITA.2024.3506868
Noelia Gerbaudo-González;Susana Feijoó-Quintas;Manuel Gandoy-Crego;María del C. Gutiérrez-Moar;María J. Diz-López;Samuel Furtado;Gil Gonçalves;David Facal
Entrepreneurship Education (EE) plays a pivotal role in stimulating entrepreneurial performance and nurturing innovative ideas. Recognizing the essential role of doctoral programs within the education system, it becomes crucial for Higher Education Institutions (HEIs) to integrate EE into these programs. This study presents the development of the Training for Innovation Driven Research program, specifically designed for Ph.D. students in the early stages of their research trajectories. The methodologies used to train participants include design thinking and lean start-up, and draw on the EntreComp network. To assess the training program’s effectiveness, a comprehensive set of tools has been designed, The training program consists of 32 hours of courses and 18 hours of autonomous work, designed to assist researchers in transferring the results of their projects or Ph.D. thesis to society and to promote innovation and entrepreneurship among Ph.D. students and researchers. Evaluation tools include observation checklists for ongoing evaluation, rubrics for evaluating tangible products against basic criteria, and surveys with structured questionnaires to assess the impact of the programme on the target population. The proposed training model offers a framework for fostering innovation, adaptability, and creativity among doctoral students, underscoring the importance of learning-by-doing approaches in realizing the objectives of EE within the academic setting. The program is intended to provide researchers with the competencies needed to navigate and lead in complex, innovation-driven environments, and to strengthen the role of HEIs in the global knowledge society.
{"title":"Promoting Entrepreneurial Education in Doctoral Programs: INVENTHEI’s Training for Innovation-Driven Research (2024)","authors":"Noelia Gerbaudo-González;Susana Feijoó-Quintas;Manuel Gandoy-Crego;María del C. Gutiérrez-Moar;María J. Diz-López;Samuel Furtado;Gil Gonçalves;David Facal","doi":"10.1109/RITA.2024.3506868","DOIUrl":"https://doi.org/10.1109/RITA.2024.3506868","url":null,"abstract":"Entrepreneurship Education (EE) plays a pivotal role in stimulating entrepreneurial performance and nurturing innovative ideas. Recognizing the essential role of doctoral programs within the education system, it becomes crucial for Higher Education Institutions (HEIs) to integrate EE into these programs. This study presents the development of the Training for Innovation Driven Research program, specifically designed for Ph.D. students in the early stages of their research trajectories. The methodologies used to train participants include design thinking and lean start-up, and draw on the EntreComp network. To assess the training program’s effectiveness, a comprehensive set of tools has been designed, The training program consists of 32 hours of courses and 18 hours of autonomous work, designed to assist researchers in transferring the results of their projects or Ph.D. thesis to society and to promote innovation and entrepreneurship among Ph.D. students and researchers. Evaluation tools include observation checklists for ongoing evaluation, rubrics for evaluating tangible products against basic criteria, and surveys with structured questionnaires to assess the impact of the programme on the target population. The proposed training model offers a framework for fostering innovation, adaptability, and creativity among doctoral students, underscoring the importance of learning-by-doing approaches in realizing the objectives of EE within the academic setting. The program is intended to provide researchers with the competencies needed to navigate and lead in complex, innovation-driven environments, and to strengthen the role of HEIs in the global knowledge society.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"19 ","pages":"353-360"},"PeriodicalIF":1.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825925","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 : 2024-11-18DOI: 10.1109/RITA.2024.3501933
Miguel Ángel Sánchez Vidales;Paula Lamo
Collaboration between universities and companies has become vital in training future professionals. However, on many occasions, this collaboration could be more agile and efficient to avoid a disconnect between the supply of trained professionals and the demand of the labor market. This situation is aggravated in the case of online training, where students are geographically delocalized and need the opportunity to interact directly with companies. To address this issue and ensure up-to-date and relevant student training, an innovative program that promotes collaboration between the university and various organizations in the Master in Industry 4.0 is presented. The proposal offers a new subject that uses learning based on challenges proposed and directed by leading companies in the sector. This allows students to apply their knowledge and gain insight into the industry in which they will work. Four calls have been carried out, and there are 14 challenges available, covering various industrial sectors and applications related to Industry 4.0/5.0. This paper presents the results of this program. Companies have obtained innovative and valuable solutions for their specific needs. In contrast, students have been able to apply their knowledge in real situations and gain valuable experience in the business world. Many students come from different geographical environments, especially from LATAM, and they value the subject and teaching staff positively. Due to the success of the program in Master in Industry 4.0, the same approach has been implemented in the university’s master’s program in the Internet of Things, with equally satisfactory results.
{"title":"Bridge the Gap: Using Challenge-Based Learning to Connect University and Industry","authors":"Miguel Ángel Sánchez Vidales;Paula Lamo","doi":"10.1109/RITA.2024.3501933","DOIUrl":"https://doi.org/10.1109/RITA.2024.3501933","url":null,"abstract":"Collaboration between universities and companies has become vital in training future professionals. However, on many occasions, this collaboration could be more agile and efficient to avoid a disconnect between the supply of trained professionals and the demand of the labor market. This situation is aggravated in the case of online training, where students are geographically delocalized and need the opportunity to interact directly with companies. To address this issue and ensure up-to-date and relevant student training, an innovative program that promotes collaboration between the university and various organizations in the Master in Industry 4.0 is presented. The proposal offers a new subject that uses learning based on challenges proposed and directed by leading companies in the sector. This allows students to apply their knowledge and gain insight into the industry in which they will work. Four calls have been carried out, and there are 14 challenges available, covering various industrial sectors and applications related to Industry 4.0/5.0. This paper presents the results of this program. Companies have obtained innovative and valuable solutions for their specific needs. In contrast, students have been able to apply their knowledge in real situations and gain valuable experience in the business world. Many students come from different geographical environments, especially from LATAM, and they value the subject and teaching staff positively. Due to the success of the program in Master in Industry 4.0, the same approach has been implemented in the university’s master’s program in the Internet of Things, with equally satisfactory results.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"19 ","pages":"371-378"},"PeriodicalIF":1.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858894","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 : 2024-11-18DOI: 10.1109/RITA.2024.3501218
Heylin Diaz;Peter Poór
Developing Communication protocols has played a crucial role in the industry’s success. The featured article deals with the main communication protocols used in industrial automation. Materials and methods section summarizes different technologies in primary communication protocols used between devices, represented by the automation pyramid that occurs at different levels or layers. The main contribution of the article is presented in results section as a practical study on using industry protocols with EdMES software to automate production processes. This is maintained by using different communication technologies in an automated process. The article’s conclusion specifies the communication technologies identified and how they allow the interaction between other process actors, which are a part of the earlier presented automation pyramid.
{"title":"Enhancing Industrial Automation: A Practical Study on Communication Protocols and EdMES Software Integration","authors":"Heylin Diaz;Peter Poór","doi":"10.1109/RITA.2024.3501218","DOIUrl":"https://doi.org/10.1109/RITA.2024.3501218","url":null,"abstract":"Developing Communication protocols has played a crucial role in the industry’s success. The featured article deals with the main communication protocols used in industrial automation. Materials and methods section summarizes different technologies in primary communication protocols used between devices, represented by the automation pyramid that occurs at different levels or layers. The main contribution of the article is presented in results section as a practical study on using industry protocols with EdMES software to automate production processes. This is maintained by using different communication technologies in an automated process. The article’s conclusion specifies the communication technologies identified and how they allow the interaction between other process actors, which are a part of the earlier presented automation pyramid.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"19 ","pages":"361-370"},"PeriodicalIF":1.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858893","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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