It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

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Computational Thinking 計算論的思考

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This sixth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Learning In a NetworKed Society (LINKS) Israeli Center of Research Excellence (I-CORE).
Themes:
• Big-data inquiry: thinking with data
• Learners making science
• Navigating post-truth societies
• Immersive learning
• Learning with internal values
• Student-led analytics
• Intergroup empathy
• Humanistic knowledge-building communities
• Open Textbooks
• Spaced Learning

Innovating Pedagogy 2017: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. Open University Innovation Report 6

The emerging field of Data Science has had a large impact on science and society. This has led to over a decade of calls to establish a corresponding field of Data Science Education. There is still...

Situating Data Science: Exploring How Relationships to Data Shape Learning.

Healthcare in general, and surgery/interventional care in particular, is evolving through rapid advances in technology and increasing complexity of care with the goal of maximizing quality and value of care. While innovations in diagnostic and therapeutic technologies have driven past improvements in quality of surgical care, future transformation in care will be enabled by data. Conventional methodologies, such as registry studies, are limited in their scope for discovery and research, extent and complexity of data, breadth of analytic techniques, and translation or integration of research findings into patient care. We foresee the emergence of Surgical/Interventional Data Science (SDS) as a key element to addressing these limitations and creating a sustainable path toward evidence-based improvement of interventional healthcare pathways. SDS will create tools to measure, model and quantify the pathways or processes within the context of patient health states or outcomes, and use information gained to inform healthcare decisions, guidelines, best practices, policy, and training, thereby improving the safety and quality of healthcare and its value. Data is pervasive throughout the surgical care pathway; thus, SDS can impact various aspects of care including prevention, diagnosis, intervention, or post-operative recovery. Existing literature already provides preliminary results suggesting how a data science approach to surgical decision-making could more accurately predict severe complications using complex data from pre-, intra-, and post-operative contexts, how it could support intra-operative decision-making using both existing knowledge and continuous data streams throughout the surgical care pathway, and how it could enable effective collaboration between human care providers and intelligent technologies. In addition, SDS is poised to play a central role in surgical education, for example, through objective assessments, automated virtual coaching, and robot-assisted active learning of surgical skill. However, the potential for transforming surgical care and training through SDS may only be realized through a cultural shift that not only institutionalizes technology to seamlessly capture data but also assimilates individuals with expertise in data science into clinical research teams. Furthermore, collaboration with industry partners from the inception of the discovery process promotes optimal design of data products as well as their efficient translation and commercialization. As surgery continues to evolve through advances in technology that enhance delivery of care, SDS represents a new knowledge domain to engineer surgical care of the future.

Surgical data science: The new knowledge domain

committee was convened by the Board on Science Education in Washington, DC with support from the Amgen Foundation and the Carnegie Corporation of New York. Opinions and statements included in the paper are solely those of the individual author, and are not necessarily adopted, endorsed, or verified as accurate by the Board on Science Education or the National Academy of Sciences, Engineering, and Medicine. Data Use by Middle and Secondary Students in the Digital Age: A Status Report and Future Prospects Victor R. Lee, Utah State University Michelle H. Wilkerson, University of California, Berkeley

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Data Use by Middle and Secondary Students in the Digital Age: A Status Report and Future Prospects

The Park City Math Institute 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in data science. The group consisted of 25 undergraduate faculty from a variety of institutions in the United States, primarily from the disciplines of mathematics, statistics, and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in data science.

/pdf/curriculum-guidelines-for-undergraduate-programs-in-data-35vg25lb66.pdf

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science.

/pdf/curriculum-guidelines-for-undergraduate-programs-in-data-2i5st6vco7.pdf

Ann Moskol

Papers

Curriculum Guidelines for Undergraduate Programs in Data Science

Curriculum Guidelines for Undergraduate Programs in Data Science