Session details: Paper Session: Undergrad Education: Data Science and Gaming

2018 ◽  
Author(s):  
Brett Becker
Keyword(s):  
Data Science ◽  
Paper Session ◽  
Education Data

scholarly journals Record linkage of routine data with cohorts’ data of infants under European and Portuguese law

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Doetsch ◽  
I Lopes ◽  
R Redinha ◽  
H Barros
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Protection ◽  
Record Linkage ◽  
Data Science ◽  
Personal Data ◽  
Routine Data ◽  
Cohort Data ◽  
Education Data ◽  
Explicit Consent

Abstract The usage and exchange of “big data” is at the forefront of the data science agenda where Record Linkage plays a prominent role in biomedical research. In an era of ubiquitous data exchange and big data, Record Linkage is almost inevitable, but raises ethical and legal problems, namely personal data and privacy protection. Record Linkage refers to the general merging of data information to consolidate facts about an individual or an event that are not available in a separate record. This article provides an overview of ethical challenges and research opportunities in linking routine data on health and education with cohort data from very preterm (VPT) infants in Portugal. Portuguese, European and International law has been reviewed on data processing, protection and privacy. A three-stage analysis was carried out: i) interplay of threefold law-levelling for Record Linkage at different levels; ii) impact of data protection and privacy rights for data processing, iii) data linkage process' challenges and opportunities for research. A framework to discuss the process and its implications for data protection and privacy was created. The GDPR functions as utmost substantial legal basis for the protection of personal data in Record Linkage, and explicit written consent is considered the appropriate basis for the processing sensitive data. In Portugal, retrospective access to routine data is permitted if anonymised; for health data if it meets data processing requirements declared with an explicit consent; for education data if the data processing rules are complied. Routine health and education data can be linked to cohort data if rights of the data subject and requirements and duties of processors and controllers are respected. A strong ethical context through the application of the GDPR in all phases of research need to be established to achieve Record Linkage between cohort and routine collected records for health and education data of VPT infants in Portugal. Key messages GDPR is the most important legal framework for the protection of personal data, however, its uniform approach granting freedom to its Member states hampers Record Linkage processes among EU countries. The question remains whether the gap between data protection and privacy is adequately balanced at three legal levels to guarantee freedom for research and the improvement of health of data subjects.

scholarly journals Data for Students: The Potential of Data and Analytics for Student Success

2020 ◽  
Author(s):  
Katherine Leu
Keyword(s):  
Student Success ◽  
Financial Aid ◽  
Postsecondary Education ◽  
Data Analytics ◽  
Data Science ◽  
Postsecondary Institutions ◽  
College Completion ◽  
Ethical Challenges ◽  
Education Data ◽  
Data Linkages

Postsecondary education is awash in data. Postsecondary institutions track data on students’ demographics, academic performance, course-taking, and financial aid, and have put these data to use, applying data analytics and data science to issues in college completion. Meanwhile, an extensive amount of higher education data are being collected outside of institutions, opening possibilities for data linkages. Newer sources of postsecondary education data could provide an even richer view of student success and improve equity. To explore this potential, this brief describes existing applications of analytics to student success, presents a framework to structure understanding of postsecondary data topics, suggests potential extensions of these data to student success, and describes practical and ethical challenges.

scholarly journals New digital laboratories of experimental knowledge production: Artificial intelligence and education research

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
Ben James Williamson
Keyword(s):  
Artificial Intelligence ◽  
Education Research ◽  
Knowledge Production ◽  
Data Science ◽  
Learning Science ◽  
Data Intensive ◽  
Education Data ◽  
Brain Sciences ◽  
Disciplinary Expertise ◽  
Scientific Expertise

Education data scientists, learning engineers and precision education specialists are new experts in knowledge production in educational research. By bringing together data science methodologies and advanced artificial intelligence (AI) systems with disciplinary expertise from the psychological, biological and brain sciences, they are building a new field of AI-based learning science. This article presents an examination of how education research is being remade as an experimental data-intensive science. AI is combining with learning science in new ‘digital laboratories’ where ownership over data, and power and authority over educational knowledge production, are being redistributed to research assemblages of computational machines and scientific expertise.

scholarly journals Balancing Plurality and Educational Essence: Higher Education Between Data-Competent Professionals and Data Self-Empowered Citizens

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 10
Author(s):  
Nils Hachmeister ◽  
Katharina Weiß ◽  
Juliane Theiß ◽  
Reinhold Decker
Keyword(s):  
Higher Education ◽  
Data Science ◽  
Literacy Education ◽  
Institutions Of Higher Education ◽  
Data Literacy ◽  
Fields Of Study ◽  
Education Data ◽  
Study Programs ◽  
Potential Part ◽  
High Degree

Data are increasingly important in central facets of modern life: academics, professions, and society at large. Educating aspiring minds to meet highest standards in these facets is the mandate of institutions of higher education. This, naturally, includes the preparation for excelling in today’s data-driven world. In recent years, an intensive academic discussion has resulted in the distinction between two different modes of data related education: data science and data literacy education. As a large number of study programs and offers is emerging around the world, data literacy in higher education is a particular focus of this paper. These programs, despite sharing the same name, differ substantially in their educational content, i.e., a high plurality can be observed. This paper explores this plurality, comments on the role it might play and suggests ways it can be dealt with by maintaining a high degree of adaptiveness and plurality while simultaneously establishing a consistent educational “essence”. It identifies a skill set, data self-empowerment, as a potential part of this essence. Data science and literacy education are still experiencing changeability in their emergence as fields of study, while additionally being stirred up by rapid developments, bringing about a need for flexibility and dialectic.

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