Retraction: A Project-Based Case Study of Data Science Education

Turek, Daniel; Suen, Anthony; Clark, Dav

doi:10.5334/dsj-2016-010

Cited by 3 publications

(2 citation statements)

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“…The discipline of DS has emerged over the past decade as a convergence of high-power computing, data visualization and analysis, and data-driven application domains. Prominent research institutions and private sector industries have embraced DS, but foundations for effective tertiary-level DS education remain absent (Turek et al, 2016).…”

Section: How Harvard's Case Methods Can Help Bridge the Gap Between Ds And Decision Makingmentioning

confidence: 99%

Success Factors for Using Case Method in Teaching Applied Data Science Education

Chkoniya

2021

European Journal of Education

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In a world where everything involves data, an application of it became essential to the decision-making process. The Case Method approach is necessary for Data Science education to expose students to real scenarios that challenge them to develop the appropriate skills to deal with practical problems by providing solutions for different activities. Data science combines multiple fields like statistics, scientific methods, and data analysis to extract value from data, being an umbrella term used for multiple industries, such as data analytics, data mining, machine learning, big data, business intelligence, and predictive analytics. This paper gives an overview of success factors for using the Case Method in teaching Applied Data Science education. Showing that close analysis provides a deeper understanding of implications, connects theory to practice, and classes unfold without a detailed script when successful instructors simultaneously manage content and process. This synthesis of current research can be used by Applied Data Science educators to more effectively plan the use of the Case Method as one possible teaching method.

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Section: How Harvard's Case Methods Can Help Bridge the Gap Between Ds And Decision Makingmentioning

confidence: 99%

Success Factors for Using Case Method in Teaching Applied Data Science Education

Chkoniya

2021

European Journal of Education

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“…As for the middle circle, it corresponds to the specialized paradigms inspired by statistics, operations research, linguistics, and artificial intelligence that altogether frame the theoretical bed to settle: knowledge discovery in databases (Rath, Jones, Hale, & Shenoi, 2016), machine learning (Rogers & Mark, 2016), data mining (Witten, Frank, Hall, & Pal, 2016), data sciences (Turek, Suen, & Clark, 2016), big data (Erl, Khattak, & Buhler, 2016), natural language processing (NLP; Sergei & McShane, 2016), text mining (Ramya, Venugopal, Iyengar, & Patnaik, 2017), and analytics (Kudyba, 2014) whose details of this last will be mentioned later.…”

Section: Underlying Domainsmentioning

confidence: 99%

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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Educational Data Science: An “Umbrella Term” or an Emergent Domain?

Peña-Ayala

2023

Big Data Management

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Retraction: A Project-Based Case Study of Data Science Education

Cited by 3 publications

References 0 publications

Success Factors for Using Case Method in Teaching Applied Data Science Education

Success Factors for Using Case Method in Teaching Applied Data Science Education

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Educational Data Science: An “Umbrella Term” or an Emergent Domain?

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