AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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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}

}