Computational Skills by Stealth in Secondary School Data Science

• URL: http://arxiv.org/abs/2010.07017v1
• Date: Thu, 8 Oct 2020 09:11:51 GMT
• Title: Computational Skills by Stealth in Secondary School Data Science
• Authors: Wesley Burr, Fanny Chevalier, Christopher Collins, Alison L Gibbs, Raymond Ng, Chris Wild
• Abstract summary: We discuss a proposal for the stealth development of computational skills in students' first exposure to data science. The intent of this approach is to support students, regardless of interest and self-efficacy in coding, in becoming data-driven learners.
• Score: 16.960800464621993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The unprecedented growth in the availability of data of all types and qualities and the emergence of the field of data science has provided an impetus to finally realizing the implementation of the full breadth of the Nolan and Temple Lang proposed integration of computing concepts into statistics curricula at all levels in statistics and new data science programs and courses. Moreover, data science, implemented carefully, opens accessible pathways to stem for students for whom neither mathematics nor computer science are natural affinities, and who would traditionally be excluded. We discuss a proposal for the stealth development of computational skills in students' first exposure to data science through careful, scaffolded exposure to computation and its power. The intent of this approach is to support students, regardless of interest and self-efficacy in coding, in becoming data-driven learners, who are capable of asking complex questions about the world around them, and then answering those questions through the use of data-driven inquiry. This discussion is presented in the context of the International Data Science in Schools Project which recently published computer science and statistics consensus curriculum frameworks for a two-year secondary school data science program, designed to make data science accessible to all.

Related papers

• DSBench: How Far Are Data Science Agents to Becoming Data Science Experts? [58.330879414174476]
  We introduce DSBench, a benchmark designed to evaluate data science agents with realistic tasks. This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions. Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG)
  arXiv  Detail & Related papers  (2024-09-12T02:08:00Z)
• The Future of Data Science Education [0.11566458078238004]
  The School of Data Science at the University of Virginia has developed a novel model for the definition of Data Science. This paper will present the core features of the model and explain how it unifies various concepts going far beyond the analytics component of AI.
  arXiv  Detail & Related papers  (2024-07-16T15:11:54Z)
• Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice [0.6597195879147557]
  We present insights and experiences from the Data Science Education Community of Practice (DSECOP) DSECOP brings together graduate students and physics educators from different institutions to share best practices and lessons learned from integrating data science into undergraduate physics education. Our goal is to provide guidance and inspiration to educators who seek to integrate data science into their teaching, helping to prepare the next generation of physicists for a data-driven world.
  arXiv  Detail & Related papers  (2024-03-01T20:21:42Z)
• On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
  There have been severe concerns over the trustworthiness of AI technologies. Machine and deep learning algorithms depend heavily on the data used during their development. We propose a framework to evaluate the datasets through a responsible rubric.
  arXiv  Detail & Related papers  (2023-10-24T14:01:53Z)
• A compendium of data sources for data science, machine learning, and artificial intelligence [17.857341127079305]
  Recent advances in data science, machine learning, and artificial intelligence are leading to an increasing demand for data. Data sources are application-specific, and it is impossible to produce an exhaustive list of such data sources. The goal of this publication is to provide just such an (inevitably incomplete) list -- or compendium -- of data sources across multiple areas of applications.
  arXiv  Detail & Related papers  (2023-09-10T19:15:22Z)
• Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey [67.7834898542701]
  We focus on reviewing privacy-preserving techniques of graph machine learning. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information.
  arXiv  Detail & Related papers  (2023-07-10T04:30:23Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• CateCom: a practical data-centric approach to categorization of computational models [77.34726150561087]
  We present an effort aimed at organizing the landscape of physics-based and data-driven computational models. We apply object-oriented design concepts and outline the foundations of an open-source collaborative framework.
  arXiv  Detail & Related papers  (2021-09-28T02:59:40Z)
• Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley [81.01051375191828]
  The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses. These courses emphasize computational thinking, inferential thinking, and working on real-world problems. These courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.
  arXiv  Detail & Related papers  (2021-02-13T22:51:24Z)
• A fresh look at introductory data science [0.0]
  We present a case study of an introductory undergraduate course in data science that is designed to address these needs. This course has no pre-requisites and serves a wide audience of aspiring statistics and data science majors as well as humanities, social sciences, and natural sciences students. We discuss the unique set of challenges posed by offering such a course and in light of these challenges, we present a detailed discussion into the pedagogical design elements, content, structure, computational infrastructure, and the assessment methodology of the course.
  arXiv  Detail & Related papers  (2020-08-01T18:39:34Z)
• Data Science: A Comprehensive Overview [42.98602883069444]
  The twenty-first century has ushered in the age of big data and data economy, in which data DNA has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons and thinking about data science and analytics.
  arXiv  Detail & Related papers  (2020-07-01T02:33:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.