How does Tuva facilitate data literacy and real-world learning?

Tuva’s philosophy is to put technology and real datasets at students’ fingertips so they can explore data visually from different perspectives with ease. We believe that if students engage with data often and in different contexts, they will develop lifelong skills in reasoning and solving problems by seeking evidence.

Real-World Datasets for Authentic Learning

Tuva’s searchable dataset library provides a wide selection of real-world datasets that relate to science, math, and social sciences so students can engage with data relevant to different aspects of their lives. Tuva’s datasets come from publicly available local, national, and international agencies and published researchers. They are curated for classroom accessibility to give students targeted opportunities to learn about phenomena and places from the patterns they discover in the data.

• Data are about real phenomena and relevant to students' lives and experiences.
• Datasets are curated to be understandable and accessible.
• Tuva’s datasets are kept current to reflect the dynamic nature of data.
• Datasets can be searched and filtered by disciplinary topics, learning standards, grade level, or by dataset properties.
• Students and teachers can upload and analyze their own data.

Tuva’s Resources for Science, Math, and Social Sciences Instruction

Data literacy has core value in all disciplines. Math skills come alive when they are rooted in real-world situations and numbers; data is the lifeblood of science; understanding of social sciences is based on large sets of geographic, demographic, and economic data. Telling stories that arise from data provides a rich opportunity to develop language arts.

Tuva’s learning activities prompt students to work analytically and across disciplines as they explore and interpret data about a wide variety of real phenomena and problems. Activities provide teachers with sample solutions and teaching strategies.

• A variety of content for different grade levels and abilities, some guided, some student-directed.
• Sample solutions and teaching tips are provided for teachers.
• Teachers can collect and view students’ responses and graphs and provide individual feedback directly in the platform.
• Dataset guides for teachers provide sample questions that can be explored with a dataset, as well as sample ways to graph the data to investigate those questions.
• Tutorial videos and reference handouts support using the tools to reason statistically when analyzing and interpreting data.