Integrating Data Science and Machine Learning to Chemistry Education: Predicting Classification and Boiling Point of Compounds

Kim, Shin-Yu; Jeon, Inseong; Kang, Seong-Joo

doi:10.1021/acs.jchemed.3c01040

Cited by 2 publications

(1 citation statement)

References 11 publications

(16 reference statements)

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“…Another challenge is the limited resources and infrastructure available for implementing AI and data science education [15]. Schools and educational institutions, especially those in underserved areas, may lack the necessary technology, software, and hardware to support these programs [13].…”

Section: Introductionmentioning

confidence: 99%

Developing Standards for Educational Datasets by School Level: A Framework for Sustainable K-12 Education

Jeon,

Kim,

Kang

2024

Sustainability

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As artificial intelligence (AI) and data science education gain importance in K-12 curricula, there is a growing need for well-designed sustainable educational datasets tailored to different school levels. Sustainable datasets should be reusable, adaptable, and accessible to support long-term AI and data science education goals. However, research on the systematic categorization of difficulty levels in educational datasets is limited. This study aims to address this gap by developing a framework for sustainable educational dataset standards based on learners’ developmental stages and data preprocessing requirements. The proposed framework consists of five levels: Level 1 (grades 1–4), where data preprocessing is unnecessary; Level 2 (grades 5–6), involving basic data cleaning; Level 3 (grades 7–9), requiring attribute manipulation; Level 4 (grades 10–12), involving feature merging and advanced preprocessing; and Level 5 (teachers/adults), requiring the entire data science process. An expert validity survey was conducted with 22 elementary and secondary school teachers holding advanced degrees in AI education. The results showed high validity for Levels 1–4 but relatively lower validity for Level 5, suggesting the need for separate training and resources for teachers. Based on the CVR results and expert feedback, the standards for Educational Datasets were revised, particularly for Stage 5, which targets teachers and adult learners. The findings highlight the importance of expert validation, step-by-step experiences, and an interdisciplinary approach in developing educational datasets. This study contributes to the theoretical understanding of educational datasets and provides practical implications for teachers, students, educational institutions, and policymakers in implementing effective and sustainable AI and data science education in K-12 settings, ultimately fostering a more sustainable future.

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Section: Introductionmentioning

confidence: 99%

Developing Standards for Educational Datasets by School Level: A Framework for Sustainable K-12 Education

Jeon,

Kim,

Kang

2024

Sustainability

Self Cite

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Utilization of artificial intelligence and machine learning in chemistry education: a critical review

Iyamuremye,

Niyonzima,

Mukiza

et al. 2024

Discov Educ

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The current study aimed to criticize the existing literature on the utilization of artificial intelligence (AI) and machine learning (ML) in teaching and learning chemistry. A comprehensive critical literature review was conducted using electronic databases such as Scopus, PubMed, ISI, Google Scholar, ERIC, Web of Science, and JSTOR. In this regard, 62 articles were extracted from these electronic databases. During the selection of the literature inclusion and exclusion criteria were applied. The inclusion criteria include empirical and theoretical studies examining the effectiveness, challenges, and opportunities of AI/ML, and articles from 2018 to 2024 and written in English. On the other side, the exclusion criteria include literature that unrelated to education, lacking empirical evidence, or not peer-reviewed, as well as non-English publications, and published before 2018. This was done to gain insights into the current implementation status of AI and ML as well as critical issues of using these approaches in chemistry education. The study employed a critical review of the literature, which involves a critical analysis of the themes and concepts that emerge from the selected literature and identifies the opportunities and challenges surrounding the utilization of these technologies. The results revealed that there are opportunities for the integration of AI and ML in chemistry education, including personalized learning experiences, teacher assistance, and accessibility to learning materials. In this regard, intelligent tutoring systems and adaptive learning platforms were identified as potential aides for teachers in various aspects of teaching. The study also revealed the limitations and challenges surrounding AI and ML, such as the dependence on preexisting data, potential biases in models, and concerns around data privacy and security. Moreover, the findings also indicated that the implementation of AI and ML in chemistry education is still in its juvenile stage. Thus, teacher training programs are needed to equip teachers with the necessary skills for the use of these technologies effectively in the classroom. In addition, more efforts should be made to facilitate research, collaboration, and the development of policies and regulations that ensure responsible use of these technologies in the teaching and learning process.

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Integrating Data Science and Machine Learning to Chemistry Education: Predicting Classification and Boiling Point of Compounds

Cited by 2 publications

References 11 publications

Developing Standards for Educational Datasets by School Level: A Framework for Sustainable K-12 Education

Developing Standards for Educational Datasets by School Level: A Framework for Sustainable K-12 Education

Utilization of artificial intelligence and machine learning in chemistry education: a critical review

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