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Teacher Workload Calculator

The arithmetic of teaching β€” made visible

πŸ“ Classroom Setup
25 students
180 days
8 hrs
20 topics

Adding 5 minutes of individualized feedback per student sounds reasonable. Run the math and see what it costs β€” and who pays.

The Extra Burden

Using: 25 students Β· 180 days Β· 8 hrs/day
Adjust class size, calendar, and hours in Classroom Setup above.

5 min

This could be individualized feedback, one-on-one check-ins, or active learning facilitation. Whatever the new method requires.

The Cost

When you add burden to students, it distributes. When you add it to the teacher β€” it doesn't.

Per student
5 min
easily dismissed
Per teacher, per day
125 min
falls on one person
Per teacher, per week
10.4 hrs
β€”
Per teacher, per year
375 hrs
β€”
That's equivalent to…
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Why this matters

This is why good teaching methods fail. Active learning, Socratic questioning, individualized feedback β€” the evidence that these work is overwhelming. But they all share one fatal property: they require teacher time that doesn't scale.

The lecture persists not because it's effective. It persists because it's the only format that doesn't blow up this calculation.

From constraint-analysis.md, EduOpsLab framework

IEP documentation is mandatory, high-stakes, and unreasonably time-consuming. This calculator shows what it costs today β€” and what micro-process tools can recover.

Your Documentation Load

8 students
3 goals
3 observations
Time per observation
10 min

Find your binder, locate the student, fill in the form, write a note, file it.

1 min

4-tap flow on a phone during a natural pause. Student β†’ Goal β†’ Rating β†’ Note. Done.

The Numbers

Total observations per week 72
Batch Method
Micro-Process
Hours/week
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Hours/month
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Hours/year
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Time recovered per year
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The design principle

The transaction cost of using the tool must be lower than the cognitive cost of not using it. If the tool demands a context switch β€” open laptop, navigate to app, fill out form β€” it's a batch interrupt. It steals time from the production process.

The tool must fit inside the teacher's natural workflow like a reflex. The teacher contributes the high-value micro-judgment. The machine handles everything downstream.

From micro-process design principles, EduOpsLab framework

Microservices cost more to create individually than lectures β€” but a shared library changes the economics. Here's where the break-even is.

Your Course Structure

Topics per course: 20 topics β€” adjust in Classroom Setup above.

Batch Teaching Prep batch
3 hrs

Slides, notes, examples. Publisher materials reduce this further.

20%

Year 1 is full prep. Year 2+ you refresh a portion of slides.

Microservice Prep flow
6 units

Each micro-unit is a bounded, precise teaching interaction β€” a mini-activity, prompt, or check-in.

4 hrs

Higher than a lecture slide β€” a microservice must work standalone, with no surrounding lecture to carry it.

40%

Like publisher PowerPoints for lectures. A mature library raises this to 70–80%+.

10%

Well-designed microservices are stable β€” content changes less than presentation.

Prep Hours Comparison

Batch
Micro-Service
Total prep hours
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Hours built from scratch
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Hours sourced / reused
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Break-even library share rate
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The library economics argument

Publisher-supplied PowerPoints and test banks already subsidize batch teaching at scale β€” individual teachers don't build from scratch. A shared microservice library applies the same economic model to individualized teaching: the creation cost is amortized across thousands of classrooms, not borne by one teacher.

The transition cost is real: Year 1 with a thin library costs more than batch. Year 2+ with a deep library costs less β€” and the quality of each micro-interaction is higher because specialists built it, not an overloaded classroom teacher under time pressure.

From MP-5, ideas.md, EduOpsLab framework

πŸ“Š

Batch Variability Simulator

An animated visualization showing what happens when students with different learning rates are forced through the same batch schedule.

  • Configure N students with randomly distributed "processing times"
  • Watch the batch move β€” some students bored, some drowning
  • See WIP accumulation and defect compounding in real time
  • Compare side-by-side: batch schedule vs. individual-paced flow

The asymmetry and observation calculators lay the quantitative foundation. The batch simulator adds the visual argument for why batching fails β€” not just how much it costs.