Teaching students to manage data


Teaching Data Literacy: Home page

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Welcome to Teaching Data Literacy

Human beings have always used stories to make sense of their world. Only in the past few centuries have we begun to validate our stories with statistical evidence. Data is the critical ingredient in this process. This mini-course aims to teach instructors in K-12 environments how to introduce their students to fundamental concepts of data literacy. This course recommends that teachers use a Knowledge Building framework to teach their students to develop ideas that they can explore with data, and to identify, gather, and analyze data that supports or refutes those ideas.

Knowledge building refers to a specific constructivist framework for inquiry-based learning that lends itself well to the study of data literacy. In a knowledge building community of learners:

  • students work collaboratively to develop knowledge as a community
  • students view knowledge creation as a process that improves ideas continuously, not as finite problem solving
  • students use discourse to solve problems collectively

Knowledge building theory represents the collective work of many scholars over the past three decades. Before proceeding with this course, please review the website linked above, and some of the articles on knowledge building listed at the end of this introduction to the course.

Outcomes of the mini-course

Data literacy is a vast subject that matters to all of us as both lifelong learners and citizens of a world guided increasingly by information created, saved, analyzed, and applied on a scale never before experienced in all of human history. This mini-course is simply a brief introduction to this subject that aims specifically to provide teachers with a framework for introducing data literacy to their students in an age appropriate context. The course presents the key elements of data literacy as an iterative three-part process:

  1. idea formation
  2. data organization
  3. statistical analysis of data

By the end of this course, you will:

  1. learn and understand the Data Management Cycle
  2. learn to apply the steps of each part of the process
  3. learn how to prepare a lesson plan for a grade of your choice using the methodology of this course

Instructors taking this course should view it as a cookbook. Each step is itself a recipe for some component of what I call the Data Management Cycle. Not every step is appropriate for every student. I have tried to identify areas that apply only to students at a certain level, but more importantly, I have stressed the importance of conceptual understanding over the ability to perform any of the underlying mathematical or computer science skills involved in any step of the process. Becoming data literate does not require either. A course on any aspect of this vast topic will be successful if it teaches students the origins, meaning, and uses of data, and the many reasons to always question it.

Course prerequisites

Watch this video that provides an introduction to data literacy in education.

Check out these websites that contain information that will help you to design lesson plans to teach data literacy

Links to course modules

Module 1: Idea formation and abstraction

Module 2: Data organization

Module 3: Analysis Modeling & Simulation

Module 4: Dynamic Data Analysis

Module 5: Course library

Data literacy-related standards for K-12 education

No single set of curricular standards for data literacy exists. The following links lead to websites containing some of the curricular standards for computer science, mathematics, and science proposed by various policy and regulatory organizations that pertain directly to the teaching of data literacy.

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