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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Bibliographic Details
Published in:Behaviour & information technology Vol. 41; no. 9; pp. 1830 - 1847
Main Authors: Andersen, Renate, Mørch, Anders I., Litherland, Kristina Torine
Format: Journal Article
Language:English
Published: London Taylor & Francis 04.07.2022
Taylor & Francis Ltd
Subjects:
Analysis
Artificial intelligence
Block-based programming
CAI
Collaboration
Collaborative learning
Collaborative virtual environments
Computer assisted instruction
Computer mediated communication
computer-supported collaborative learning
Computers
Cooperative learning
COVID-19
Data analysis
Design
domain-oriented design environment
Education
human-centered artificial intelligence
Humans
Interaction analysis
Interpersonal communication
knowledge-based rules
Learning
Learning environment
physical computing in education
Research design
Scaffolding
Small groups
Students
Teachers
Teaching
Webcams
ISSN:0144-929X, 1362-3001
Online Access:Get full text
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Summary: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.
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ISSN:0144-929X
1362-3001
DOI:10.1080/0144929X.2022.2083981