Collaborative learning with block-based programming: investigating human-centered artificial intelligence in education.

In this article, we investigate human-centered artificial intelligence (HCAI) in an educational context where pupils used block-based programming in small groups to solve tasks given by the teacher. We used a design-based research approach in which we, together with the teachers, created a maker spa...

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Publicado en:Behaviour & Information Technology Vol. 41; no. 9; pp. 1830 - 1848
Autores principales: Andersen, Renate, Mørch, Anders I., Litherland, Kristina Torine
Formato: pictorial research tables/charts Journal Article
Publicado: Taylor & Francis Ltd Jul2022
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jul2022
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        10.1080/0144929X.2022.2083981
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        atl: Collaborative learning with block-based programming: investigating human-centered artificial intelligence in education.
      aug:
        au:
          Andersen, Renate
          Mørch, Anders I.
          Litherland, Kristina Torine
        affil: Dept. of Primary and Secondary Teacher Education, Oslo Metropolitan University, Oslo, Norway
      sug:
        subj:
          Artificial Intelligence
          Learning Methods
          Software
          Human
      ab: In this article, we investigate human-centered artificial intelligence (HCAI) in an educational context where pupils used block-based programming in small groups to solve tasks given by the teacher. We used a design-based research approach in which we, together with the teachers, created a maker space for explorative science learning and organised teaching interventions wherein the pupils met online three hours a week for 16 weeks for an entire school year. Due to COVID-19, data were collected through Zoom, with collaborative learning situations captured through screen sharing and online communication using webcams. We employed three data analysis techniques: interaction analysis, visual artifact analysis, and thematic analysis. We developed an analytical framework for integration using thematic coding that combined concepts from computer-supported collaborative learning (CSCL) and domain-oriented design environments. We report the following findings: 1) Three types of rules between design units were identified with visual artifact analysis: latent, generic, and domain-specific rules; 2) two types of CSCL artifacts (technology and discussions) were intertwined and developed in parallel, along with a computer-based scaffolding scenario that offloads domain-specific scaffolding from humans to computers.
      pubtype: Academic Journal
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        pictorial
        research
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      ougenre: Article
    language: English
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