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(seeing data as numbers in context, reading charts, graphs and tables, understanding numerical and graphical summaries of data, etc.) Statistics as a way of understanding the world: Can I use existing data to help make decisions? (using census data, birth and death rates, disease rates, CPI, ratings, rankings, etc., to describe, decide and defend)
Google Charts is notable for its use in education. The NCBI article titled "Teaching Data Science to Medical Students: A Multi-Disciplinary Approach" (NCBI article [3]) covered how Google Charts has helped medical students learn about data science in a hands-on way. Google Charts allows for the visualization of complex data sets, which may ...
Data literacy is distinguished from statistical literacy since it involves understanding what data means, including the ability to read graphs and charts as well as draw conclusions from data. [7] Statistical literacy, on the other hand, refers to the "ability to read and interpret summary statistics in everyday media" such as graphs, tables ...
Instead, students were guided to develop their own invented algorithms through working with concrete representations of number such as manipulatives and drawings as well as more traditional number sentences. Additional activities include journaling, cutting and pasting, interviewing (for data collection) and playing conceptual games.
Statistical graphics have been central to the development of science and date to the earliest attempts to analyse data. Many familiar forms, including bivariate plots, statistical maps, bar charts, and coordinate paper were used in the 18th century.
Visual learning is a learning style among the learning styles of Neil Fleming's VARK model in which information is presented to a learner in a visual format. Visual learners can utilize graphs, charts, maps, diagrams, and other forms of visual stimulation to effectively interpret information.
Dr. Wolfgang Greller and Dr. Hendrik Drachsler defined learning analytics holistically as a framework. They proposed that it is a generic design framework that can act as a useful guide for setting up analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency.
Students learn to use graphical and numerical techniques to analyze distributions of data (including univariate, bivariate, and categorical data), the various methods of data collection and the sorts of conclusions one can draw therefrom, probability, and statistical inference (point estimation, confidence intervals, and significance tests).
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