Abstract
Players can build implicit understanding of challenging scientific concepts when playing digital science learning games [1]. In this study, we examine implicit computational thinking (CT) skills among upper elementary and middle school students during Zoombinis gameplay. We report on the development of a human labeling system for gameplay evidence of four CT skills: problem decomposition, pattern recognition, algorithmic thinking, and abstraction. We define labels that identify use of these skills in three Zoombinis puzzles, based on analysis of video data from both CT novices (upper elementary and middle school students) and CT experts (computer scientists and expert Zoombinis players). Future work will involve the construction of detectors for implicit CT skills based on these human labels, in order to analyze gamelog data at scale and give feedback to teachers.
Original language | English (US) |
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Pages | 195-200 |
Number of pages | 6 |
DOIs | |
State | Published - Oct 15 2017 |
Externally published | Yes |
Event | 4th ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, CHI PLAY 2017 - Amsterdam, Netherlands Duration: Oct 15 2017 → Oct 18 2017 |
Conference
Conference | 4th ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, CHI PLAY 2017 |
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Country/Territory | Netherlands |
City | Amsterdam |
Period | 10/15/17 → 10/18/17 |
Keywords
- Computational thinking
- Implicit learning
- Learning games
- Video analysis
ASJC Scopus subject areas
- Human-Computer Interaction