Pub Date : 2025-03-25DOI: 10.1109/RITA.2025.3554310
Alba Ayuso-Lanchares;Noemí Merayo
As the world rapidly digitizes, there is a growing emphasis on engaging young people, particularly girls, in STEM (Science, Technology, Engineering, and Mathematics) fields where they remain underrepresented. This study investigates the influence of primary and secondary school teachers on students’ STEM engagement within the Spanish education system, with a focus on gender equality. Using a descriptive correlational design, the research gathered responses from 664 teachers, revealing differences in beliefs and practices between primary and secondary educators regarding STEM promotion. The findings indicate that while primary teachers often emphasize career opportunities, secondary teachers focus on explaining STEM study content. Interestingly, only a minority of teachers (30-40%) report receiving gender equality training, underscoring a need for better teacher education in this area. The study also identifies key gender-related barriers, including stereotypes, lack of female role models, and limited STEM career visibility. These barriers are compounded by societal pressures, leading to fewer women in technology-related studies. The results call for education systems to enhance curricula with gender-sensitive practices and align primary and secondary teaching approaches to cultivate technological interest from an early age. Improved STEM training for teachers, as well as activities that promote female role models, can foster a supportive environment for girls. This study contributes to understanding the role of educators in shaping STEM aspirations and highlights the urgent need for systemic changes to address the gender gap in STEM fields.
{"title":"Teachers’ Perspectives on Gender Equity in STEM: Beliefs, Challenges, and Opportunities","authors":"Alba Ayuso-Lanchares;Noemí Merayo","doi":"10.1109/RITA.2025.3554310","DOIUrl":"https://doi.org/10.1109/RITA.2025.3554310","url":null,"abstract":"As the world rapidly digitizes, there is a growing emphasis on engaging young people, particularly girls, in STEM (Science, Technology, Engineering, and Mathematics) fields where they remain underrepresented. This study investigates the influence of primary and secondary school teachers on students’ STEM engagement within the Spanish education system, with a focus on gender equality. Using a descriptive correlational design, the research gathered responses from 664 teachers, revealing differences in beliefs and practices between primary and secondary educators regarding STEM promotion. The findings indicate that while primary teachers often emphasize career opportunities, secondary teachers focus on explaining STEM study content. Interestingly, only a minority of teachers (30-40%) report receiving gender equality training, underscoring a need for better teacher education in this area. The study also identifies key gender-related barriers, including stereotypes, lack of female role models, and limited STEM career visibility. These barriers are compounded by societal pressures, leading to fewer women in technology-related studies. The results call for education systems to enhance curricula with gender-sensitive practices and align primary and secondary teaching approaches to cultivate technological interest from an early age. Improved STEM training for teachers, as well as activities that promote female role models, can foster a supportive environment for girls. This study contributes to understanding the role of educators in shaping STEM aspirations and highlights the urgent need for systemic changes to address the gender gap in STEM fields.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"67-76"},"PeriodicalIF":1.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865240","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 : 2025-03-25DOI: 10.1109/RITA.2025.3554596
Giuseppe S. Silva;Pedro N. Vasconcelos;Antonio C. Zambroni de Souza
Engineering projects address society’s demands and are multidisciplinary by nature. The occurrence of catastrophic failures and the imperative for responsible innovation evidence the importance of linking education with holistic solutions to complex global issues. However, integrating scientific and technological information with social demands has been increasingly neglected. In universities, the division of knowledge into independent subjects reinforces the principle of fragmentation, challenging the perception of problems in professional training. This manuscript advocates a transverse and transdisciplinary approach to teaching ethics in engineering to explore the role of future professionals as agents of socioeconomic and environmental transformation. The concept of transverse education emerges where a given discipline traverses disciplinary boundaries and is integrated into academic training without increasing the course curriculum. A rapid literature review is conducted to assess the current state of engineering ethics education and identify whether Transverse Education in Engineering Ethics (TEEE) is being applied in engineering academic institutions. Documents indexed by Web of Science and Scopus databases were considered in the review, complemented by a supplementary search to reduce the risk of geographical bias. Records were selected based on publication date, focus on engineering ethics education, and application maturity of TEEE according to international guidelines and the Brazilian National Common Curricular Base. Low worldwide adherence to TEEE is revealed, with 5% of the selected engineering teaching/learning records addressing ethics in a transversal manner. This article highlights the importance of TEEE and provides insights for integrating ethics into engineering education, serving as a tool for immediate application and future research.
{"title":"Ethics as Transverse Subject in Engineering Education: A Review of Current Efforts","authors":"Giuseppe S. Silva;Pedro N. Vasconcelos;Antonio C. Zambroni de Souza","doi":"10.1109/RITA.2025.3554596","DOIUrl":"https://doi.org/10.1109/RITA.2025.3554596","url":null,"abstract":"Engineering projects address society’s demands and are multidisciplinary by nature. The occurrence of catastrophic failures and the imperative for responsible innovation evidence the importance of linking education with holistic solutions to complex global issues. However, integrating scientific and technological information with social demands has been increasingly neglected. In universities, the division of knowledge into independent subjects reinforces the principle of fragmentation, challenging the perception of problems in professional training. This manuscript advocates a transverse and transdisciplinary approach to teaching ethics in engineering to explore the role of future professionals as agents of socioeconomic and environmental transformation. The concept of transverse education emerges where a given discipline traverses disciplinary boundaries and is integrated into academic training without increasing the course curriculum. A rapid literature review is conducted to assess the current state of engineering ethics education and identify whether Transverse Education in Engineering Ethics (TEEE) is being applied in engineering academic institutions. Documents indexed by Web of Science and Scopus databases were considered in the review, complemented by a supplementary search to reduce the risk of geographical bias. Records were selected based on publication date, focus on engineering ethics education, and application maturity of TEEE according to international guidelines and the Brazilian National Common Curricular Base. Low worldwide adherence to TEEE is revealed, with 5% of the selected engineering teaching/learning records addressing ethics in a transversal manner. This article highlights the importance of TEEE and provides insights for integrating ethics into engineering education, serving as a tool for immediate application and future research.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"57-66"},"PeriodicalIF":1.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821896","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 : 2025-03-17DOI: 10.1109/RITA.2025.3552226
Jalberth F. Araújo;Izadora S. Cardoso;Henrique D. Silva
In this paper is analyzed the teaching and learning process of the Undergraduate Course in Electrical Engineering at the Federal University of Campina Grande. The analysis is based on the application of forms to the professors and graduating students and pre-graduating students. The assertions of the forms are based on the questionnaire of National Student Performance Examination (ENADE), in the report of the National Curriculum Guidelines for Engineering Courses and in the Pedagogical Project of the Electrical Engineering Program at the Federal University of Campina Grande. With the answers of teachers and students, analyzes were carried out to obtain information of agreement or disagreement of the participants about the assertions addressed. In addition, the results obtained in some assertions were compared with the results obtained in the survey conducted during the National Student Performance Examination of 2017. This comparison aims to identify if the opinions of the students are undergoing modifications or remaining similar. From the results of this work, it is believed that it is possible to identify the factors that affect the teaching and learning process of the Undergraduate Course in Electrical Engineering of the Federal University of Campina Grande, so that future solutions can be proposed with the aim of improving the learning experience offered in the course.
{"title":"Analysis of the Teaching and Learning Process of the Undergraduate Course in Electrical Engineering at the Federal University of Campina Grande","authors":"Jalberth F. Araújo;Izadora S. Cardoso;Henrique D. Silva","doi":"10.1109/RITA.2025.3552226","DOIUrl":"https://doi.org/10.1109/RITA.2025.3552226","url":null,"abstract":"In this paper is analyzed the teaching and learning process of the Undergraduate Course in Electrical Engineering at the Federal University of Campina Grande. The analysis is based on the application of forms to the professors and graduating students and pre-graduating students. The assertions of the forms are based on the questionnaire of National Student Performance Examination (ENADE), in the report of the National Curriculum Guidelines for Engineering Courses and in the Pedagogical Project of the Electrical Engineering Program at the Federal University of Campina Grande. With the answers of teachers and students, analyzes were carried out to obtain information of agreement or disagreement of the participants about the assertions addressed. In addition, the results obtained in some assertions were compared with the results obtained in the survey conducted during the National Student Performance Examination of 2017. This comparison aims to identify if the opinions of the students are undergoing modifications or remaining similar. From the results of this work, it is believed that it is possible to identify the factors that affect the teaching and learning process of the Undergraduate Course in Electrical Engineering of the Federal University of Campina Grande, so that future solutions can be proposed with the aim of improving the learning experience offered in the course.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"47-56"},"PeriodicalIF":1.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716454","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 : 2025-03-13DOI: 10.1109/RITA.2025.3551086
Eric Fujiwara
Equivalent circuits (EC) and bond graphs (BG) are inventive approaches to represent energy interactions in complex multidomain systems. Undergraduate and graduate students in engineering and physics courses benefit from such intuitive representations as alternatives to abstract space-state models and block diagram forms. However, differences between EC and physical counterparts frustrate their implementation and simulation in network analysis software like the widespread SPICE-based ones. Therefore, this paper proposes a method for converting EC and BG into realizable models through modulated effort/flux sources supported by systematic node labeling. Comparative transient simulations in LTspice and MATLAB encompass examples of mechanical, electrical, and magnetic systems, yielding compatible results apart from the multidomain interfaces. The presented technique supports elaborated models’ development and validation through an open-source and lightweight software, motivating further applications in didactic projects and virtual labs.
{"title":"Multidomain Systems Modeling With SPICE: Equivalent Circuit and Bond Graph Approaches","authors":"Eric Fujiwara","doi":"10.1109/RITA.2025.3551086","DOIUrl":"https://doi.org/10.1109/RITA.2025.3551086","url":null,"abstract":"Equivalent circuits (EC) and bond graphs (BG) are inventive approaches to represent energy interactions in complex multidomain systems. Undergraduate and graduate students in engineering and physics courses benefit from such intuitive representations as alternatives to abstract space-state models and block diagram forms. However, differences between EC and physical counterparts frustrate their implementation and simulation in network analysis software like the widespread SPICE-based ones. Therefore, this paper proposes a method for converting EC and BG into realizable models through modulated effort/flux sources supported by systematic node labeling. Comparative transient simulations in LTspice and MATLAB encompass examples of mechanical, electrical, and magnetic systems, yielding compatible results apart from the multidomain interfaces. The presented technique supports elaborated models’ development and validation through an open-source and lightweight software, motivating further applications in didactic projects and virtual labs.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"39-46"},"PeriodicalIF":1.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688154","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 : 2025-02-13DOI: 10.1109/RITA.2025.3541541
Oscar Revelo Sánchez;Alexander Barón Salazar;Manuel Ernesto Bolaños
Concepts and skills related to the basic constructs of programming (variables, types, expressions, assignment, simple input and output, and conditional control and iteration structures) are a key component in the education of future software developers. Initial programming courses (CS1) tend to have a high academic mortality rate. This is reflected in low student grades, indicating that students are not achieving the required academic competencies. Seeking alternative ways to improve student performance in CS1 courses, this paper proposes as a didactic learning strategy the use of collaborative programming/coding, mediated by the integrated development environment Visual Studio Code and its add-on Live Share, in solving computer programming problems or challenges. The results of a controlled experiment in which this collaborative tool was used in different CS1 courses of the Faculty of Engineering of the University of Nariño in San Juan de Pasto - Colombia are presented.
{"title":"Collaborative Programming as a Didactic Learning Strategy in CS1 Courses","authors":"Oscar Revelo Sánchez;Alexander Barón Salazar;Manuel Ernesto Bolaños","doi":"10.1109/RITA.2025.3541541","DOIUrl":"https://doi.org/10.1109/RITA.2025.3541541","url":null,"abstract":"Concepts and skills related to the basic constructs of programming (variables, types, expressions, assignment, simple input and output, and conditional control and iteration structures) are a key component in the education of future software developers. Initial programming courses (CS1) tend to have a high academic mortality rate. This is reflected in low student grades, indicating that students are not achieving the required academic competencies. Seeking alternative ways to improve student performance in CS1 courses, this paper proposes as a didactic learning strategy the use of collaborative programming/coding, mediated by the integrated development environment Visual Studio Code and its add-on Live Share, in solving computer programming problems or challenges. The results of a controlled experiment in which this collaborative tool was used in different CS1 courses of the Faculty of Engineering of the University of Nariño in San Juan de Pasto - Colombia are presented.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"32-38"},"PeriodicalIF":1.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496457","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}
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.
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