Anonymized Attendance Chart¶
Run the Anonymized Attendance Chart MicroSim fullscreen
About This MicroSim¶
This chart is an argument, not just a report. The argument is that aggregate data is sufficient for the questions a club leader actually asks about attendance.
Everything is here: the trend across sixteen weeks, the sharp dip at week 9, the recovery immediately after, and -- once you tick the prior-semester series -- whether this year is running ahead of last. A leader can act on all of it.
And there are no names in the underlying data. Not hidden, not access-controlled: absent. There is no drill-down from a bar to a student because the numbers behind the bar are counts, not lists.
The week 9 dip is worth pausing on with a group. It looks alarming until you know it is the school break week, which is the other lesson: a number needs its context before it needs an explanation.
How to Use¶
- Hover a bar for the exact count and week number.
- Show prior semester adds a second series for year-over-year comparison; the legend toggles it too.
Lesson Plan¶
Bloom level: Understand (L2) -- interpret
Learning objective: Given an anonymized attendance chart, the learner interprets the weekly trend without any access to individually identifiable data.
Before the sim (5 min). Ask what a club needs attendance data for. The answers -- is it growing, did something go wrong, do we need more mentors -- are all answerable from totals.
With the sim (10 min). Read the trend. Ask what happened at week 9 before revealing it. Then turn on the prior semester and ask whether this year is healthier.
After the sim (10 min). List every attendance question the club has asked in the last year and mark which ones need a name to answer. Most will not.
Check for understanding. Ask: "A mentor wants to know which student missed the most sessions. Can this chart answer that?" No -- and whether the club should answer it, and from where, is the discussion worth having.
Embedding This MicroSim¶
Paste this into any page of the book, adjusting the relative path to
docs/sims/ for the page's depth:
<iframe src="../../sims/anonymized-attendance-chart/main.html" width="100%" height="522" scrolling="no"></iframe>
Specification¶
The full specification below is extracted from Chapter 24: Tracking Student Data and Managing Club Communication.
Type: chart
**sim-id:** anonymized-attendance-chart<br/>
**Library:** Chart.js<br/>
**Status:** Specified
Chart type: Bar chart
Purpose: Show how weekly attendance can be reported as a fully aggregate number, with hover detail but no names anywhere in the underlying data.
Bloom Taxonomy: Understand (L2)
Bloom Taxonomy Verb: interpret
Learning objective: Given an anonymized attendance chart, the learner interprets the weekly trend without any access to individually identifiable data.
X-axis: Week number (1 through 16)
Y-axis: Number of students present (count only)
Data series:
1. Current Semester (blue bars): sample values ranging 10-16 students per week across 16 weeks, with a visible dip around week 9 (a school break week) and a recovery afterward
2. Prior Semester (light gray bars, toggle to show/hide): a comparable sample series for year-over-year comparison
Title: "Weekly Attendance -- Aggregate Count Only"
Legend: Position top-right, includes a toggle for the "Prior Semester" series
Interactive features:
- Hover any bar to see the exact count and week number in a tooltip
- Click the legend entry to toggle the "Prior Semester" series on or off
- No drill-down to individual students is possible from this chart, by design
Annotation: A caption below the chart reading "This chart contains no student names -- only weekly totals."
Implementation: Chart.js bar chart with two datasets and legend-click toggling enabled.
References¶
- Chapter 24: Tracking Student Data and Managing Club Communication -- the chapter this MicroSim supports.
- Student Data Privacy Principles -- the four practices protecting whatever data is kept.
- Guardian Consent Collection Workflow -- what may be collected in the first place.
- Data anonymization -- aggregation as a privacy technique.
- Aggregate data -- what is preserved and what is lost.