AI-based multidisciplinary framework to assess the impact of gamified video-based learning through schema and emotion analysis

Vidanaralage, A.J.; Dharmaratne, Anuja; Haque, Shamsul

doi:10.1016/j.caeai.2022.100109

Cited by 5 publications

(3 citation statements)

References 233 publications

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“…E-learning ( 6), Engineering education ( 6), Education [3], [5], [9], [22], [28], [31], [37], [38], [40], [44] Cluster 3 (amarillo)…”

Section: Referencias De La Revisión Sistemáticamentioning

confidence: 99%

Rueda de la Pedagogía para la Inteligencia Artificial: adaptación de la Rueda de Carrington

Jiménez-García,

Orenes-Martínez,

López-Fraile

2023

RIED

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La integración efectiva de la Inteligencia Artificial (IA) en la educación es necesaria para aprovechar sus beneficios en el proceso de enseñanza-aprendizaje. Este artículo propone la adaptación de la Rueda de la Pedagogía de Carrington a una Rueda de la Pedagogía para la IA, con el fin de ofrecer un marco pedagógico para integrar la IA en la educación. La metodología de investigación utilizada se basa en una revisión y mapeo sistemático junto a un estudio bibliométrico del análisis de co-ocurrencia de términos para identificar los clusters temáticos relevantes que respalden científicamente la necesidad de la adaptación de la Rueda. La nueva rueda atiende a los cuatro clusters obtenidos (Integración de la IA para mejorar la educación, Uso de tecnologías educativa en el proceso de enseñanza y aprendizaje, Diseño e innovación pedagógica y Educación Sostenible y Ética) y presenta anillos concéntricos que explican cómo incorporar gradualmente la IA en diferentes niveles cognitivos (Taxonomía de Bloom) e integración tecnológica (Modelo SAMR) ambos adaptados a la IA, con ejemplos de herramientas y aplicaciones. Además, se incluye un nivel Reflexivo-Metacognitivo que aborda la ética y responsabilidad en el uso de la IA. En conclusión, la rueda adaptada a la IA es una opción viable para mejorar la eficacia y eficiencia de la educación, con la condición de que los docentes participen en la planificación y ejecución del proceso de enseñanza y aprendizaje para garantizar su éxito. Cabe mencionar la importancia de mantener la rueda actualizada debido a la aparición constante de nuevas aplicaciones.

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“…E-learning ( 6), Engineering education ( 6), Education [3], [5], [9], [22], [28], [31], [37], [38], [40], [44] Cluster 3 (amarillo)…”

Section: Referencias De La Revisión Sistemáticamentioning

confidence: 99%

Rueda de la Pedagogía para la Inteligencia Artificial: adaptación de la Rueda de Carrington

Jiménez-García,

Orenes-Martínez,

López-Fraile

2023

RIED

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“…Classification and identification research focus on AI’s ability to recognize emotions in individuals for various purposes, such as evaluative ( Vidanaralage et al, 2022 ), classificatory ( Lian, 2023 ), or as a preliminary step for emotional regulation in individuals ( Wetcho and Na-Songkhla, 2022 ). These classification and identification-oriented uses have also been employed to measure mental health ( Xu et al, 2022 ), sentiment analysis ( Peng et al, 2022 ), or even to identify warning signs regarding mental health, such as identifying depression in college students ( Ding et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence

Roda-Segarra,

Mengual-Andrés,

Payà Rico

2024

Front. Artif. Intell.

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Research in the field of Artificial Intelligence applied to emotions in the educational context has experienced significant growth in recent years. However, despite the field’s profound implications for the educational community, the social impact of this scientific production on digital social media remains unclear. To address this question, the present research has been proposed, aiming to analyze the social impact of scientific production on the use of Artificial Intelligence for emotions in the educational context. For this purpose, a sample of 243 scientific publications indexed in Scopus and Web of Science has been selected, from which a second sample of 6,094 social impact records has been extracted from Altmetric, Crossref, and PlumX databases. A dual analysis has been conducted using specially designed software: on one hand, the scientific sample has been analyzed from a bibliometric perspective, and on the other hand, the social impact records have been studied. Comparative analysis based on the two dimensions, scientific and social, has focused on the evolution of scientific production with its corresponding social impact, sources, impact, and content analysis. The results indicate that scientific publications have had a high social impact (with an average of 25.08 social impact records per publication), with a significant increase in research interest starting from 2019, likely driven by the emotional implications of measures taken to curb the COVID-19 pandemic. Furthermore, a lack of alignment has been identified between articles with the highest scientific impact and those with the highest social impact, as well as a lack of alignment in the most commonly used terms from both scientific and social perspectives, a significant variability in the lag in months for scientific research to make an impact on social media, and the fact that the social impact of the research did not emerge from the interest of Twitter users unaffiliated with the research, but rather from the authors, publishers, or scientific institutions. The proposed comparative methodology can be applied to any field of study, making it a useful tool given that current trends in accreditation agencies propose the analysis of the repercussion of scientific research in social media.

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“…In the era of society 5.0, technological progress is growing tremendously (Bartoloni et al, 2022;Fukuda, 2020;Huang et al, 2022). Advanced technology is accompanied by the development of science (Junius et al, 2022). The development of science also has an impact on the education sector (Orji et al, 2022;Phillips, 2021).…”

Section: Introductionmentioning

confidence: 99%

Development of accounting learning media based on wetland environment for high school students in South Kalimantan

Chandra

Maulana

Bariroh

et al. 2023

EBASR

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This study aims to develop a wetland-based accounting learning media for high school students in South Kalimantan. This study measures the feasibility of learning media developed with the validation of media experts and practitioners. The form of research and development uses model ADDIE which consists of five stages, namely analysis (analysis), design (design), development (development), implementation (implementation), and evaluation (evaluation). The instruments used are validation sheets and student response questionnaires. The method used in data collection is interviews for initial research data and validation. The research step of the learning media development process starts from stage (1) Analysis, The analysis stage is the stage of analyzing the need for the development of new learning models/methods and analyzing the feasibility and requirements of developing new learning models/methods. The analysis phase includes curriculum analysis and student characteristics analysis. (2) Design, At this stage, the design of learning media development is carried out in the form of an initial design that is in accordance with the analysis that has been done previously. (3) Development, the development stage is the stage of product realization that is ready to be implemented. (4) Implementation, at this stage, the implementation is carried out in a limited manner at the designated school as the research site. (5) Evaluation, at this stage, researchers make revisions to the developed media based on criticism and suggestions obtained from the response questionnaire or field notes.

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AI-based multidisciplinary framework to assess the impact of gamified video-based learning through schema and emotion analysis

Cited by 5 publications

References 233 publications

Rueda de la Pedagogía para la Inteligencia Artificial: adaptación de la Rueda de Carrington

Rueda de la Pedagogía para la Inteligencia Artificial: adaptación de la Rueda de Carrington

Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence

Development of accounting learning media based on wetland environment for high school students in South Kalimantan

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