Introducing High School Statistics Teachers to Predictive Modelling and APIs Using Code-Driven Tools

Tasks for teaching predictive modelling and APIs often require learners to use code-driven tools. Minimal research, however, exists about the design of tasks that support the introduction of high school students and teachers to these new statistical and computational methods. Using a design-based re...

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Bibliographic Details
Published inStatistics education research journal Vol. 21; no. 2; pp. 1 - 25
Main Authors Fergusson, Anna, Pfannkuch, Maxine
Format Journal Article
LanguageEnglish
Published International Association for Statistical Education and the International Statistical Institute 01.07.2022
International Association for Statistical Education
Subjects
Coding
Computation
Computer Interfaces
Decision Making
Educational statistics
Faculty Development
Foreign Countries
High school students
High School Teachers
High schools
Instructional Design
Mathematical Models
Mathematics Teachers
Prediction
Programming
Sciences education
Statistics Education
Teacher Workshops
Teachers
Teaching Methods
Thinking Skills
New Zealand
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Summary:Tasks for teaching predictive modelling and APIs often require learners to use code-driven tools. Minimal research, however, exists about the design of tasks that support the introduction of high school students and teachers to these new statistical and computational methods. Using a design-based research approach, a web-based task was developed. The task was constructed using our design framework and implemented within a face-to-face professional development workshop involving six high school statistics teachers. The teachers were guided through the process of developing a prediction model using: an informal approach; visual prediction intervals; data about movie ratings from an API; and R code that ran in the browser. Our findings from this exploratory study indicate that the web-based task supported the development of new statistical and computational ideas related to predictive modelling and APIs.
ISSN:1570-1824
1570-1824
DOI:10.52041/serj.v21i2.49