Integrating Data Science into a General Education Information Technology Course

Haynes, Malcolm; Groen, Joshua; Sturzinger, Eric; Zhu, Danny; Shafer, Justin; McGee, Timothy J.

doi:10.1145/3349266.3351417

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…We utilize techniques followed in [13] to balance theory and practice in the realm of data science and machine learning. Furthermore, recent works focus on how to maximize student retention and integrate data science into early undergraduate courses for introductory and non-technical students, [12] and [7], respectively. We apply contributions presented in this work to create material relatable for the general student population by focusing on environmental applications such as weather analysis.…”

Section: Computer Science K-12 Educationmentioning

confidence: 99%

Interactive Visualizations to Introduce Data Science for High School Students

Chittora

Baynes

2020

Proceedings of the 21st Annual Conference on Information Technology Education

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Artificial intelligence integrates with our daily life in more ways each day with the invention of new tools and applications. For example, businesses utilize machine learning, which is a subfield of artificial intelligence, to identify the ideal customer for targeted advertisement. Machine learning and data science scale previous manual problems to a wider customer base. With all the various applications for artificial intelligence, there is still a noticeable gap in the number of qualified job applicants who can solve some of the machine learning problems of the day. There is a movement to introduce computer science education earlier to K-12 students. One motivation behind an earlier introduction is to better prepare students to study computer science at universities and fulfill these coveted technology positions in the future. Given this motivation, there needs to be more computer science instructional activities and lesson plans to support K-12 teachers. Currently there are several new tools and curricula available for computer science. However, there is a need for beginner and K-12 accessible data science and machine learning instructional methods. These topics are advanced computer science electives but an earlier introduction can still benefit students. Interactive visual activities support high school students to learn challenging topics. In this paper, we consider how a visual analytic process can present difficult data science concepts. We present an interactive visual educational tool to teach data science to high school students. The first activity introduces the visual analytic pipeline. The next interactive activities present machine learning classification and regression topics. We select datasets and applications which are relatable to our main audience. This paper describes the design and implementation of the data science educational tool for high school students. CCS CONCEPTS • Applied computing → Interactive learning environments.

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Section: Computer Science K-12 Educationmentioning

confidence: 99%

Interactive Visualizations to Introduce Data Science for High School Students

Chittora

Baynes

2020

Proceedings of the 21st Annual Conference on Information Technology Education

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“…There are also inconsistencies among the existing learning programs. Organizing learning programs around data science process models has been suggested (Haynes et al, 2019;Jaggia et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Data Science Education – A Scoping Review

2023

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Aim/Purpose: This study aimed to evaluate the extant research on data science education (DSE) to identify the existing gaps, opportunities, and challenges, and make recommendations for current and future DSE. Background: There has been an increase in the number of data science programs especially because of the increased appreciation of data as a multidisciplinary strategic resource. This has resulted in a greater need for skills in data science to extract meaningful insights from data. However, the data science programs are not enough to meet the demand for data science skills. While there is growth in data science programs, they appear more as a rebranding of existing engineering, computer science, mathematics, and statistics programs. Methodology: A scoping review was adopted for the period 2010–2021 using six scholarly multidisciplinary databases: Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, and the AIS Basket of eight journals. The study was narrowed down to 91 research articles and adopted a classification coding framework and correlation analysis for analysis. Contribution: We theoretically contribute to the growing body of knowledge about the need to scale up data science through multidisciplinary pedagogies and disciplines as the demand grows. This paves the way for future research to understand which programs can provide current and future data scientists the skills and competencies relevant to societal needs. Findings: The key results revealed the limited emphasis on DSE, especially in non-STEM (Science, Technology, Engineering, and Mathematics) disciplines. In addition, the results identified the need to find a suitable pedagogy or a set of pedagogies because of the multidisciplinary nature of DSE. Further, there is currently no existing framework to guide the design and development of DSE at various education levels, leading to sometimes inadequate programs. The study also noted the importance of various stakeholders who can contribute towards DSE and thus create opportunities in the DSE ecosystem. Most of the research studies reviewed were case studies that presented more STEM programs as compared to non-STEM. Recommendations for Practitioners: We recommend CRoss Industry Standard Process for Data Mining (CRISP-DM) as a framework to adopt collaborative pedagogies to teach data science. This research implies that it is important for academia, policymakers, and data science content developers to work closely with organizations to understand their needs. Recommendation for Researchers: We recommend future research into programs that can provide current and future data scientists the skills and competencies relevant to societal needs and how interdisciplinarity within these programs can be integrated. Impact on Society: Data science expertise is essential for tackling societal issues and generating beneficial effects. The main problem is that data is diverse and always changing, necessitating ongoing (up)skilling. Academic institutions must therefore stay current with new advances, changing data, and organizational requirements. Industry experts might share views based on their practical knowledge. The DSE ecosystem can be shaped by collaborating with numerous stakeholders and being aware of each stakeholder’s function in order to advance data science internationally. Future Research: The study found that there are a number of research opportunities that can be explored to improve the implementation of DSE, for instance, how can CRISP-DM be integrated into collaborative pedagogies to provide a fully comprehensive data science curriculum?

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Exploring Interdisciplinary Data Science Education for Undergraduates: Preliminary Results

Xiao

et al. 2021

Diversity, Divergence, Dialogue

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Integrating Data Science into a General Education Information Technology Course

Cited by 5 publications

References 23 publications

Interactive Visualizations to Introduce Data Science for High School Students

Interactive Visualizations to Introduce Data Science for High School Students

Data Science Education – A Scoping Review

Exploring Interdisciplinary Data Science Education for Undergraduates: Preliminary Results

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