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Many plots. Part 2

✓ Published1🌍 Public
AAlena Egorova
Last edited Apr 14, 2023
Created on Apr 9, 2023
Forked from Many plots. Part 1

This gallery example presents a small-multiple scatter plot matrix exploring eye-tracking data from a WPI MAPLE Lab study of 86 students solving math problems. Each scatter plot shows a different participant’s data, plotting problem order on the x-axis against a selected metric (average fixation duration, no-fixation percentage, or response time) on the y-axis, with points colored by correctness. A dropdown menu switches the y-axis variable, while a legend filters points by hover. The visualization displays the raw data with linear trend lines, built using d3.v7’s scaleLinear and scaleOrdinal APIs, and fetches the dataset from a gist.

AI-generated description

This is a dataset from WPI MAPLE Lab study conducted in 2021-2022. 86 undergraduate students solved math problems in the lab and their eye-movements during the solving process were collected with eye-tracking software. Each participant solved 24 problems: 12 with and 12 without brackets.

Data dictionary:

  • Participant_ID' - Anonymized participant ID. Categorical.
  • 'Problem_ID' - Problem title. Categorical.
  • 'Order' - The order of the problem in the experiment. Ordinal.
  • 'Brackets' - If equation contains brackets. Categorical.
  • 'Response_time' - How long did it take fot participant to response. Quantitative.
  • 'Correct_or_not' - If participant’s response was correct ot not. Categorical.
  • 'Average_duration_of_fixations' - How long participant fixated on each digit (on average). Quantitative.
  • 'No_fixation_percentage' - On how many digits participant did not fixate? Quantitative.

The following information could be visualized:

  1. Did average fixation duration change over the time of the experiment? Did it change differently in different participants?
  2. Was response time associated with correctness? Did this association change over the time of the experiment?
  3. Did people tent to answer longer in problems with brackets and did this association change over the time of the experiment?
  4. Was tendency to use peripheral vision associated with response corectness? Did this association change over the time of the experiment?
MIT Licensed

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