From “Authentic Tools” to Authenticity: Using CT to Enable Discovery in Statistics Classrooms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

CS Educators pushing to integrate computational thinking (CT) into mathematics classrooms has quickly influenced statistics classrooms where students now analyze data using modern computational tools. Yet while teachers perceive using “authentic tools” in the classroom as providing an “authentic learning experience,” the power of computational tools to help reimagine existing content is often overlooked. Our team worked with teachers to co-design two CT-integrated statistics units across two years. We use a model of professional growth to discuss the teachers’ changing beliefs over the course of the two co-design projects. We see how the enactment of a programming-focused CT-integrated unit led to changes in teachers’ beliefs about the pedagogical impact of CT. These informed the teachers’ approach in a second co-design, resulting in a unit that emphasized empowering students to discover statistical concepts via an authentic learning experience using authentic computational tools.

Original languageEnglish (US)
Title of host publicationISLS Annual Meeting 2021 Reflecting the Past and Embracing the Future - 15th International Conference of the Learning Sciences, ICLS 2021
EditorsErica de Vries, Yotam Hod, June Ahn
PublisherInternational Society of the Learning Sciences (ISLS)
Pages1115-1116
Number of pages2
ISBN (Electronic)9781737330615
StatePublished - 2021
Event15th International Conference of the Learning Sciences, ICLS 2021 - Virtual, Online
Duration: Jun 8 2021Jun 11 2021

Publication series

NameProceedings of International Conference of the Learning Sciences, ICLS
ISSN (Print)1814-9316

Conference

Conference15th International Conference of the Learning Sciences, ICLS 2021
CityVirtual, Online
Period6/8/216/11/21

Funding

This work was made possible through generous support from the National Science Foundation (grants CNS-1138461, CNS-1441041, DRL-1020101, DRL-1640201 and DRL-1842374) and the Spencer Foundation (Award #201600069).

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Education

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