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A novel interaction for competence assessment using micro-behaviors: Extending CACHET to graphs and charts

dataset
posted on 2023-04-05, 14:19 authored by Fiorenzo ColarussoFiorenzo Colarusso, Peter ChengPeter Cheng, Grecia Garcia GarciaGrecia Garcia Garcia, Aaron Stockdill, Daniel Raggi, Mateja JamnikMateja Jamnik

Data for paper published in:  Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23)


These files contain:

  • The data from the Graph Familiarity questionnaire used in our study (competence assessment using micro-behaviours_Demographic data and questionnaire)
  • The interactions produced by our participants on each stimulus (competence assessment using micro-behaviours _interactions on all stimuli)
  • All the pauses produced by our participants on each stimulus (competence assessment using micro-behaviours _Pauses)

Paper abstract

Competence Assessment by Chunk Hierarchy Evaluation with Transcription-tasks (CACHET) was proposed by Cheng [14]. It analyses micro-behaviors captured during cycles of stimulus view- ing and copying in order to probe chunk structures in memory. This study extends CACHET by applying it to the domain of graphs and charts. Since drawing strategies are diverse, a new interactive stimulus presentation method is introduced: Transcription with In- cremental Presentation of the Stimulus (TIPS). TIPS aims to reduce strategy variations that mask the chunking signal by giving users manual element-by-element control over the display of the stimulus. The potential of TIPS, is shown by the analysis of six participants transcriptions of stimuli of different levels of familiarity and com- plexity that reveal clear signals of chunking. To understand how the chunk size and individual differences drive TIPS measurements, a CPM-GOMS model was constructed to formalize the cognitive process involved in stimulus comprehension and chunk creation.


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Engineering and Physical Sciences Research Council

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Automating Representation Choice for AI Tools

Engineering and Physical Sciences Research Council

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Automating Representation Choice for AI Tools

Engineering and Physical Sciences Research Council

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How to (re)represent it?

Engineering and Physical Sciences Research Council

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