Skip to main content
SimilarExport ZIP
100%

Many plots. Part 1

✓ Published1🌍 Public
AAlena Egorova
Last edited Apr 7, 2023
Created on Apr 6, 2023
Forked from Experiment AE

The visualization is a small-multiple grid of scatter plots, one per participant, showing eye-tracking metrics across 24 math problems. Each plot displays the problem order on the x-axis and a selectable y-axis metric (average fixation duration, no-fixation percentage, or response time), with points colored by correctness. A red trend line shows the linear relationship, and a color legend filters points by hovering. The interface uses a dropdown menu to change the y-axis metric. Data comes from a WPI MAPLE Lab eye-tracking study with 86 students, parsed from CSV and rendered using the d3 library (v7), with helper modules for axes, color legend, and scatter plot rendering.

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

Similar vizzes

Loading thumbnail…

Many plots. Part 2

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.

AAlena Egorova
93% match
Loading thumbnail…

Many plots. Part 3

This visualization presents a small-multiple grid of scatter plots, with each plot showing data from one participant who solved math problems while their eye movements were tracked. The x-axis consistently represents problem order, while the y-axis can be toggled via a menu to display either average fixation duration, no-fixation percentage, or response time. A second menu sorts the individual participant plots by their average response time or percentage of correct answers. Overlaid red trend lines reveal whether the chosen y-axis measure changes over the course of the experiment. The plots are colored by correctness, and a legend allows filtering points to view only correct or incorrect responses. The visualization is built with D3 v7, using its selection API and scale functions, and renders participant-specific trend lines calculated from the data.

AAlena Egorova
92% match
Loading thumbnail…

Experiment AE

This interactive scatterplot explores eye-tracking data from a WPI MAPLE Lab study, showing how problem order relates to metrics like average fixation duration, response time, or no-fixation percentage. Users select the y-axis variable via a menu, and each point is colored by correctness. The visualization uses d3.scaleLinear for axes, d3.scaleOrdinal for color mapping, and displays a red trend line computed via linear regression. Hovering over the d3 color legend highlights correct or incorrect data points. Data is loaded from a CSV and parsed with d3.csvParse.

AAlena Egorova
86% match
Loading thumbnail…

Interactive Menu in Scatter Plot

This scatter plot visualizes eye-tracking data from a study of 86 undergraduate students solving math problems. The x-axis shows the order of problems in the experiment, while the y-axis displays the average duration of fixations in milliseconds. Each point is colored according to whether the participant's response was correct or not, with a color legend that users can hover over to highlight points of a specific outcome. The visualization is rendered as an SVG using D3 v7, with axes and tick marks generated via D3's `scaleLinear` and `axisLeft`/`axisBottom` functions. Data is loaded asynchronously from a GitHub gist containing the CSV dataset.

AAlena Egorova
85% match
Loading thumbnail…

Scatter Plot Simple - AE

A scatter plot displays the relationship between problem order and average duration of fixations for participants in a WPI MAPLE Lab eye-tracking study. Each circle represents a single math problem solved by one participant, with the x-axis showing the problem's order in the experiment and the y-axis the average fixation duration in milliseconds. The visualization uses d3.scaleLinear and d3.extent to map data values to screen coordinates, with circles rendered via selection joins. Data is loaded from a CSV file using d3.csvParse.

AAlena Egorova
84% match
Loading thumbnail…

Scatter Plot with mean trend

This scatter plot displays the relationship between problem order and average fixation duration for 86 undergraduate students, with each point colored by whether the response was correct. A red trend line, calculated via linear regression using cumulative sums, shows the overall direction of the data. The visualization uses d3.v7 with SVG rendering, employing `selectAll` joins for marks and axes, `scaleOrdinal` with `schemeCategory10` for coloring, and `extent` for domain scaling. Data originates from a WPI MAPLE Lab eye-tracking study from 2021-2022.

AAlena Egorova
82% match
Loading thumbnail…

MathPerception - map

This map visualizes eye-tracking data from a WPI MAPLE Lab study of 86 undergraduates solving math problems. It displays participant ID, problem order, correctness, and average fixation duration per digit, revealing how attention patterns vary across individuals and tasks. The visualization uses d3.csvParse to load the dataset and renders the raw state data as JSON within a pre element. D3 v7 select and join methods manage the display, updating whenever new data is fetched and processed.

AAlena Egorova
79% match
Loading thumbnail…

Dropdown menu AE

This interactive scatterplot lets users explore eye-tracking data from a study of 86 undergraduates solving math problems. A dropdown menu, built with D3.js, dynamically switches the y-axis between average fixation duration, no fixation percentage, and response time. Each point represents a problem, colored by correctness, with a regression line revealing trends over problem order. The visualization highlights associations between response time, fixation patterns, and performance. Data is fetched from a remote CSV and rendered using D3's scaleLinear, scaleOrdinal, and axes utilities.

AAlena Egorova
78% match