AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
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You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2024
Fostering Personalized Learning in Data Science: Integrating Innovative Tools and Strategies for Diverse Pathways Proceedings Article
In: IEEE Int. Conf. Eng. Educ.: Dissem. Adv. Eng. Educ. using Artif. Intell., ICEED, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835036741-6 (ISBN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, ChatGPT-4, Content recommendation, Content recommendations, Contrastive Learning, Data Science, Data science education, Federated learning, Individualized learning, Individualized learning experience framework, Learning experiences, Prerequisite skill identification, Science education, Self-directed learning, Teaching approaches, Virtual environments, Virtual Reality
@inproceedings{noauthor_fostering_2024,
title = {Fostering Personalized Learning in Data Science: Integrating Innovative Tools and Strategies for Diverse Pathways},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001849041&doi=10.1109%2fICEED62316.2024.10923798&partnerID=40&md5=cfec507f601df5ffc3b07db0df6d80a7},
doi = {10.1109/ICEED62316.2024.10923798},
isbn = {979-835036741-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Int. Conf. Eng. Educ.: Dissem. Adv. Eng. Educ. using Artif. Intell., ICEED},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper introduces an innovative teaching approach in data science tailored for students in non-computer science pathways, specifically Business Information Technology (BIT) and Computing and Information Technology (CIT). Over a five-year period, a unique teaching approach has been developed incorporating a virtual reality (VR) game event and ChatGPT-4 as a generative artificial intelligence (AI) tool. To address the inherent complexities of learning data science, particularly the diverse prerequisite skills, this study introduces a framework including a diagnostic assessment centered around a specific education research question: 'How can the learning experiences of individual students be customized to address the multifaceted challenges of data science education?' Through a diagnostic assessment process, conducted via a survey completed by students, this framework identifies students' unique requirements and skill areas facilitating the delivery of personalized content recommendations within the initial week of teaching. By fostering a culture of self-directed learning, the approach aims to enable students to concentrate on essential customized learning materials. This paper also highlights the overall student satisfaction with the module averaged 4.5 out of 5 with a standard deviation of 0.9 indicating a high level of contentment with the teaching approach. The discussion encompasses the framework's implications for teaching and its alignment with educational theories. This paper contributes to the computing education field by addressing the research question and offering insights for future research and teaching practices. © 2024 IEEE.},
keywords = {Adversarial machine learning, ChatGPT-4, Content recommendation, Content recommendations, Contrastive Learning, Data Science, Data science education, Federated learning, Individualized learning, Individualized learning experience framework, Learning experiences, Prerequisite skill identification, Science education, Self-directed learning, Teaching approaches, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper introduces an innovative teaching approach in data science tailored for students in non-computer science pathways, specifically Business Information Technology (BIT) and Computing and Information Technology (CIT). Over a five-year period, a unique teaching approach has been developed incorporating a virtual reality (VR) game event and ChatGPT-4 as a generative artificial intelligence (AI) tool. To address the inherent complexities of learning data science, particularly the diverse prerequisite skills, this study introduces a framework including a diagnostic assessment centered around a specific education research question: 'How can the learning experiences of individual students be customized to address the multifaceted challenges of data science education?' Through a diagnostic assessment process, conducted via a survey completed by students, this framework identifies students' unique requirements and skill areas facilitating the delivery of personalized content recommendations within the initial week of teaching. By fostering a culture of self-directed learning, the approach aims to enable students to concentrate on essential customized learning materials. This paper also highlights the overall student satisfaction with the module averaged 4.5 out of 5 with a standard deviation of 0.9 indicating a high level of contentment with the teaching approach. The discussion encompasses the framework's implications for teaching and its alignment with educational theories. This paper contributes to the computing education field by addressing the research question and offering insights for future research and teaching practices. © 2024 IEEE.
2023
Marquez, R.; Barrios, N.; Vera, R. E.; Mendez, M. E.; Tolosa, L.; Zambrano, F.; Li, Y.
A perspective on the synergistic potential of artificial intelligence and product-based learning strategies in biobased materials education Journal Article
In: Education for Chemical Engineers, vol. 44, pp. 164–180, 2023, ISSN: 17497728 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Bio-based, Bio-based materials, Biobased, ChatGPT, Chemical engineering, Chemical engineering education, Education computing, Engineering education, Formulation, Generative AI, Learning strategy, Learning systems, Material engineering, Materials, Students, Sustainable development, Teaching approaches, Traditional materials, Virtual Reality
@article{marquez_perspective_2023,
title = {A perspective on the synergistic potential of artificial intelligence and product-based learning strategies in biobased materials education},
author = {R. Marquez and N. Barrios and R. E. Vera and M. E. Mendez and L. Tolosa and F. Zambrano and Y. Li},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162078243&doi=10.1016%2fj.ece.2023.05.005&partnerID=40&md5=76cd274af795123f1e31e345dd36eded},
doi = {10.1016/j.ece.2023.05.005},
issn = {17497728 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Education for Chemical Engineers},
volume = {44},
pages = {164–180},
abstract = {The integration of product-based learning strategies in Materials in Chemical Engineering education is crucial for students to gain the skills and competencies required to thrive in the emerging circular bioeconomy. Traditional materials engineering education has often relied on a transmission teaching approach, in which students are expected to passively receive information from instructors. However, this approach has shown to be inadequate under the current circumstances, in which information is readily available and innovative tools such as artificial intelligence and virtual reality environments are becoming widespread (e.g., metaverse). Instead, we consider that a critical goal of education should be to develop aptitudes and abilities that enable students to generate solutions and products that address societal demands. In this work, we propose innovative strategies, such as product-based learning methods and GPT (Generative Pre-trained Transformer) artificial intelligence text generation models, to modify the focus of a Materials in Chemical Engineering course from non-sustainable materials to sustainable ones, aiming to address the critical challenges of our society. This approach aims to achieve two objectives: first to enable students to actively engage with raw materials and solve real-world challenges, and second, to foster creativity and entrepreneurship skills by providing them with the necessary tools to conduct brainstorming sessions and develop procedures following scientific methods. The incorporation of circular bioeconomy concepts, such as renewable resources, waste reduction, and resource efficiency into the curriculum provides a framework for students to understand the environmental, social, and economic implications in Chemical Engineering. It also allows them to make informed decisions within the circular bioeconomy framework, benefiting society by promoting the development and adoption of sustainable technologies and practices. © 2023 Institution of Chemical Engineers},
keywords = {Artificial intelligence, Bio-based, Bio-based materials, Biobased, ChatGPT, Chemical engineering, Chemical engineering education, Education computing, Engineering education, Formulation, Generative AI, Learning strategy, Learning systems, Material engineering, Materials, Students, Sustainable development, Teaching approaches, Traditional materials, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The integration of product-based learning strategies in Materials in Chemical Engineering education is crucial for students to gain the skills and competencies required to thrive in the emerging circular bioeconomy. Traditional materials engineering education has often relied on a transmission teaching approach, in which students are expected to passively receive information from instructors. However, this approach has shown to be inadequate under the current circumstances, in which information is readily available and innovative tools such as artificial intelligence and virtual reality environments are becoming widespread (e.g., metaverse). Instead, we consider that a critical goal of education should be to develop aptitudes and abilities that enable students to generate solutions and products that address societal demands. In this work, we propose innovative strategies, such as product-based learning methods and GPT (Generative Pre-trained Transformer) artificial intelligence text generation models, to modify the focus of a Materials in Chemical Engineering course from non-sustainable materials to sustainable ones, aiming to address the critical challenges of our society. This approach aims to achieve two objectives: first to enable students to actively engage with raw materials and solve real-world challenges, and second, to foster creativity and entrepreneurship skills by providing them with the necessary tools to conduct brainstorming sessions and develop procedures following scientific methods. The incorporation of circular bioeconomy concepts, such as renewable resources, waste reduction, and resource efficiency into the curriculum provides a framework for students to understand the environmental, social, and economic implications in Chemical Engineering. It also allows them to make informed decisions within the circular bioeconomy framework, benefiting society by promoting the development and adoption of sustainable technologies and practices. © 2023 Institution of Chemical Engineers