Education 4.0: AI Empowers Smart Blended Learning Process with Biofeedback

Ciolacu, Monica; Svasta, Paul

doi:10.1109/educon46332.2021.9453959

Cited by 28 publications

(10 citation statements)

References 11 publications

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“…Problematizando el tema de estudio, a pesar de haber diversos estudios que resaltan la eficacia de la Educación 4.0 aplicada a un entorno semipresencial (Ciolacu et al, 2020;Ciolacu & Svasta, 2021;Muawiyah et al, 2018;Raman & Rathakrishnan, 2019), existe la necesidad de una descripción paso a paso de una metodología que haya sido probada experimentalmente, así como la comparación de distintos enfoques para el logro de competencias propias de la industria 4.0 (Stek, 2022).…”

Section: José Laboriunclassified

Efectividad de Educación 4.0, e-learning y metodología tradicional en el logro de competencias de investigación universitaria

Labori

2024

Rev. Innova Educación

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In this study, the effectiveness of three educational methodologies: Education 4.0, traditional education, and e-learning, was sought to be compared in the development of investigative and statistical competencies among university students. A quantitative-qualitative research was conducted with a sample of 96 students from the University of Aconcagua (Chile) during the period between August and December 2023. Data collection was carried out through "ad hoc" surveys designed to meet validity and reliability standards. The results indicate that the use of Education 4.0 showed a significant increase in statistical competencies, particularly in the handling of Power BI software, reaching 73.61%, contrasted with the 5.56% observed in students who only used e-learning (p<0.001). Furthermore, the evaluation of statistical competencies for research, measured on a scale from 1 to 5, where 5 represents the maximum level, obtained an average of 4.355 in the group that applied the methodology with Education 4.0, significantly surpassing the 3.429 obtained with the traditional method (p<0.05). In conclusion, the findings suggest that Education 4.0 proves to be a more effective strategy than e-learning and traditional teaching for the development of statistical and investigative competencies. These results provide solid grounds for promoting an innovative and practical approach in the comprehensive training of future professionals.

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Section: José Laboriunclassified

Efectividad de Educación 4.0, e-learning y metodología tradicional en el logro de competencias de investigación universitaria

Labori

2024

Rev. Innova Educación

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“…São grupos de pessoas que dividem uma paixão por algo, interagindo na busca de aprender mais sobre o assunto (Lave e Wenger, 1991). Na Educação 4.0, associa-se as comunidades de prática como um sinônimo de colaboração, em um ciclo em que se propõe a utilização de tecnologias computacionais para apoiar o processo educacional no ensino superior (Ciolacu e Svasta, 2021). A colaboração é uma das habilidades que precisam ser potencializadas entre os estudantes na Educação 4.0, necessária para a vida dos jovens no século XXI (Puncreobutr, 2016).…”

Section: Definições Iniciaisunclassified

Comunidades de Prática em Ambientes Virtuais de Aprendizagem para o Desenvolvimento de Habilidades da Educação 4.0

Oliveira

Pimentel²,

Silva³

2022

Anais Estendidos Do XI Congresso Brasileiro De Informática Na Educação (CBIE 2022)

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Neste estudo, busca-se debater o fomento ao aspecto social da aprendizagem a partir das comunidades de prática, que entendem o processo educacional e o ganho de aprendizagem com base na interação social entre pessoas. São propostos recursos e requisitos com base em comunidades de prática para um Ambiente Virtual de Aprendizagem (AVA), além de duas ferramentas computacionais para a aplicação e avaliação destes requisitos e recursos. Busca-se compreender o impacto do desenvolvimento de comunidades de prática em AVAs para potencializar a colaboração e a interação entre estudantes no processo formal de ensino.

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“…In addition, positive emotions embedded in learning processes are a predictor of effort, perseverance, and belief in one's own ability and in hard work being the main cause of achieving academic achievement [47][48][49][50][51][52][53][54][55]. In fact, it has been concluded that it is important to implement emotional education and mental health plans across all university studies as a good practice linked to the wellbeing of students, lower dropout rates, overcoming academic adversity, and perseverance in the achievement of high-performance goals and personal, academic and professional success, in general [56][57][58]. In this line, several studies have suggested specific programs to successfully train emotional intelligence in university students and demonstrate the multiple benefits that it entails [59][60][61][62][63][64].…”

Section: Key Variablesmentioning

confidence: 99%

Predictors of University Attrition: Looking for an Equitable and Sustainable Higher Education

et al. 2022

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The failure and dropout of university studies are issues that worry all nations due to the personal, social, and economic costs that this they entail. Because the dropout phenomenon is complex and involves numerous factors, to reverse it would involve a comprehensive approach through interventions aimed at the factors identified as key in the decision to drop out. Therefore, the main objective of this work is to determine the profile of students who enter the EPN (STEM higher-education institution) to analyze the characteristics that differentiate students who drop out early in their career and those who stay in school. A sample of 624 students who accessed the EPN leveling course (a compulsory course at the beginning of their studies) participated in the study. A total of 26.6% of the participants were women. A total of 50.7% of the participants passed the course. Data referring to social, economic, and academic variables were analyzed. Comparison techniques, as well as artificial neural networks, were used to compare characteristic profiles of students who passed the leveling course and those who dropped out. The results showed significant differences between the profiles of the students who passed and those who dropped out with regard to the variables related to previous academic performance and motivational and attributional aspects. The artificial neural networks corroborated the importance of these variables in predicting dropout. In this research, the key variables predicting whether a student continues or leaves higher education are revealed, allowing the identification of students at possible risk of dropping out and thus promoting initiatives to provide adequate academic support and improve student retention.

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Education 4.0: AI Empowers Smart Blended Learning Process with Biofeedback

Cited by 28 publications

References 11 publications

Efectividad de Educación 4.0, e-learning y metodología tradicional en el logro de competencias de investigación universitaria

Efectividad de Educación 4.0, e-learning y metodología tradicional en el logro de competencias de investigación universitaria

Comunidades de Prática em Ambientes Virtuais de Aprendizagem para o Desenvolvimento de Habilidades da Educação 4.0

Predictors of University Attrition: Looking for an Equitable and Sustainable Higher Education

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