Digital Technologies for Heritage Teaching: Trend Analysis in New Realities

Arteaga, Jacob Vargas; Gravini-Donado, Marbel Lucía; Riva, Lorenzo Domenico Zanello

doi:10.3991/ijet.v16i21.25149

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…Therefore, it is necessary to locate and comprehend the status and trends of the study issue to build and appropriately support it [27], [28]. Bibliometric analyses may offer a strong base, field advancement objective, and pertinent scientific publications data [29], [30]. The author will distinguish findings between conference papers and articles through journal websites in this research.…”

Section: Artificial Neuralmentioning

confidence: 99%

Trend and Visualization of Artificial Intelligence Research in the Last 10 Years

et al. 2023

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Technology is permeating every aspect of our everyday lives, and businesses are increasingly turning to learning algorithms. These technologies are commonly associated with Artificial Intelligence (AI), Deep Learning (DL), Machine Learning (ML), and Artificial Neural Network (ANN). Due to previous research exciting findings, implications, and limitations, the research publication on AI, ML, DL, and ANN will increase each year. Finally, the growth analysis of AI, ML, DL, and ANN publications in last 10 years brings up the results that the publications significantly increase each year. This research utilizes descriptive analyses and bibliometrics. Implications of the investigation of specific keyword mapping visualization results on AI, ML, DL, and ANN, can be created and enhanced for more study. A different area of future research might focus on using fewer terms, particularly to study learning methods like AI, ML, DL, and ANN in a convolutional neural network.

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Section: Artificial Neuralmentioning

confidence: 99%

Trend and Visualization of Artificial Intelligence Research in the Last 10 Years

et al. 2023

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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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“…They suggest a hierarchical multi-label metadata classification with a machine-learning algorithm to improve the search and classification of educational content. Moreover, this research devotes itself to existing studies by presenting a hierarchical multi-label learning object of the dataset in a suitable format [24].…”

Section: Multi-label Classification Algorithms For Learning Object Cl...mentioning

confidence: 99%

Multi-Label Classification of Learning Objects Using Clustering Algorithms Based on Feature Selection

Amane¹,

Aissaoui²,

Berrada

2022

Int. J. Emerg. Technol. Learn.

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In the field of online learning, the development of learning objects (LOs) has increased. LOs promote reusing and referencing educational content in various learning environments. However, despite this progress, the lack of a conceptual model for sharing suitable LOs between learners makes multiple challenges. In this regard, multi-label classification plays a significant role to make high-quality LOs, which can be accessible and reusable. This article highlights a new way of using learning objects based on Multi-Label Classification (MLC) and clustering algorithms with feature selection techniques. It suggests a new system that makes the most suitable choice among many alternative sources based on the Sharable Content Object Reference Model (SCORM). The proposed algorithm has been tested on a real-world application dataset related to the data analysis service for the learning science community. The experimental results show that our algorithm outperforms the traditional approach and produces good results.

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Digital Technologies for Heritage Teaching: Trend Analysis in New Realities

Cited by 13 publications

References 39 publications

Trend and Visualization of Artificial Intelligence Research in the Last 10 Years

Trend and Visualization of Artificial Intelligence Research in the Last 10 Years

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

Multi-Label Classification of Learning Objects Using Clustering Algorithms Based on Feature Selection

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