Tailored Data Science Education Using Gamification

Zicari, Roberto V.; Tolle, Karsten; Manieri, Andrea

doi:10.1109/cloudcom.2016.0108

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Project-based pedagogies have been mentioned as one of the appropriate pedagogy for teaching data scientists (Donoghue et al, 2021;Saltz & Heckman, 2016;Takemura, 2018). Other teaching practices have been applied to promote data skills, such as gamification (Hee et al, 2016), and social student events like hackathons and datathons (Anslow et al, 2016;Huppenkothen et al, 2018). The common features among the mentioned teaching practices are that they are student-centered, and enforced hands-on learning that integrates real business scenarios and data (A. Y.…”

Section: Teaching Pedagogiesmentioning

confidence: 99%

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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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Section: Teaching Pedagogiesmentioning

confidence: 99%

“…There have been some developments in teaching strategies and tools that improve the teaching of STEM subjects such as gamification and metaverse which have been shown to improve science education (Hee et al, 2016). These are some strategies that may be considered for DSE.…”

Section: Conclusion Implications Limitations and Areas For Further Re...mentioning

confidence: 99%

Data Science Education – A Scoping Review

2023

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“…It can, therefore, be used to perform a gap analysis following the EDISON classification to identify mismatches between education offering and business sectors demand. Students, data analysts, educators, and other stakeholders can use this tool to identify the gaps in their skills and competencies and identify the most suitable educational path to fill these gaps . Moreover, by constantly collecting data from sources like job Ads and postgraduate programs, we will be able to identify trends from both the job market and education.…”

Section: Competencies Classification Service Architecturementioning

confidence: 99%

Bridging the demand and the offer in data science

Belloum

Koulouzis

Wiktorski

et al. 2019

Concurrency and Computation

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Summary During the last several years, we have observed an exponential increase in the demand for Data Scientists in the job market. As a result, a number of trainings, courses, books, and university educational programs (both at undergraduate, graduate and postgraduate levels) have been labeled as “Big data” or “Data Science”; the fil‐rouge of each of them is the aim at forming people with the right competencies and skills to satisfy the business sector needs. In this paper, we report on some of the exercises done in analyzing current Data Science education offer and matching with the needs of the job markets to propose a scalable matching service, ie, COmpetencies ClassificatiOn (E‐CO‐2), based on Data Science techniques. The E‐CO‐2 service can help to extract relevant information from Data Science–related documents (course descriptions, job Ads, blogs, or papers), which enable the comparison of the demand and offer in the field of Data Science Education and HR management, ultimately helping to establish the profession of Data Scientist.

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