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An analysis framework for collaborative problem solving in practice-based learning activities: a mixed-method approach

Published: 25 April 2016 Publication History

Abstract

Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.

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        cover image ACM Other conferences
        LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge
        April 2016
        567 pages
        ISBN:9781450341905
        DOI:10.1145/2883851
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        New York, NY, United States

        Publication History

        Published: 25 April 2016

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        Author Tags

        1. analysis framework
        2. collaborative learning
        3. practice-based learning
        4. problem solving

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        LAK '16 Paper Acceptance Rate 36 of 116 submissions, 31%;
        Overall Acceptance Rate 236 of 782 submissions, 30%

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        • (2024)Enforcing STEM-Driven CS Education Through Collaborative LearningEvolution of STEM-Driven Computer Science Education10.1007/978-3-031-48235-9_5(135-163)Online publication date: 1-Jan-2024
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