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Full or Lite Decision Explorer

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About This MicroSim

The book describes three ways to run a learning record store: LRS-Lite (no always-on servers, one course of up to about 150 students, an estimated one to five dollars a month), a single-server tier of the full LRS (about 10,000 concurrent active students on one host, $300 to $2,500 a month) and the Full distributed LRS (districts, about $10,300 a month on demand). The choice turns on what each tier gives up, not only on price:

Question Lite Full or single server
Always-on servers acceptable? No servers Yes, and staff to run them
Scale One course, about 150 students Many classes, districts
Freshness As of each student's last sync Server-side, near real time
Evidence integrity Self-reported by the student's browser Server-authenticated ingestion
Cross-class analytics On demand only, or not supported Native
Push alerts Scheduled function only Native

Describe a school with the controls. Each tier card shows its cost and scale estimates and a red label for every requirement it fails; a card with no unmet requirement is highlighted, and the banner recommends the cheapest one. Show reasoning lists each requirement and the tiers it rules out. Hover a card for the source of each figure. All figures are design or planning-level estimates, and the rule set is a teaching heuristic derived from the trade-off table, not an official sizing tool.

Learning objective: The learner will recommend Full, single-server or Lite for a described school by weighing scale, freshness, evidence integrity and budget, and will justify the recommendation against the trade-off table.

Bloom's taxonomy level: Evaluate (verb: recommend)

How to Use

  1. At the defaults (30 students, $50 a month, nothing checked) the banner recommends Lite. Press Show reasoning to see why the other two tiers are ruled out.
  2. Tick Grades depend on evidence. Which tier drops out, and what would it take for another tier to fit?
  3. Tick Staff to run servers and raise the budget until a tier is recommended.
  4. Drag Students past 150 and then past 10,000. Watch each scale limit rule a tier out.
  5. For each scenario below, set the controls, read the recommendation, and write one sentence of justification that cites a row of the trade-off table.

Both sliders use a logarithmic scale, so small values are easy to set.

Iframe Embed Code

You can add this MicroSim to any web page by adding this to your HTML:

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<iframe src="https://dmccreary.github.io/microsims/sims/full-or-lite-decision-explorer/main.html"
        height="642px"
        width="100%"
        scrolling="no"></iframe>

Lesson Plan

Audience

Teachers, instructional designers, learning-technology developers and learning-analytics practitioners (college undergraduate and professional development).

Duration

20 minutes

Prerequisites

  • The full LRS capacity and cost model (Chapter 21)
  • LRS-Lite sync and browser-side dashboards (Chapters 22 and 23)

Activities

  1. Scenarios (10 min): Recommend a tier for each school and justify it against the table.

    • A single teacher piloting formative sims with 28 students, no IT staff, $20 a month.
    • A high school of 1,200 students whose course grades use MicroSim evidence, with one systems administrator and $1,500 a month.
    • A district of 18,000 students that needs real-time at-risk alerts, with an operations team and $12,000 a month.
    • Break the recommendation (5 min): For the first school, find the single change that forces a move away from Lite. Name the row of the trade-off table it corresponds to.
    • Critique the heuristic (5 min): Name one factor the rule set ignores (for example high availability, migration effort or the Neo4j license) and explain how it could change a recommendation.

Assessment

  • The learner recommends a tier for a described school and justifies it with at least two rows of the trade-off table.
  • The learner identifies which requirement rules out Lite (evidence integrity, cross-class analytics, real-time alerts or scale) in a given case.
  • The learner explains why the figures are planning estimates and the rule set is a heuristic.

References

  1. Experience API - Wikipedia. The statement format both tiers store, and the role of a learning record store.
  2. xAPI specification - Advanced Distributed Learning (ADL) on GitHub. What an LRS must accept and store.
  3. High availability - Wikipedia. What the single-server tier gives up compared with the distributed tier.
  4. Serverless computing - Wikipedia. The pay-per-use model behind LRS-Lite's cost estimate.
  5. p5.js reference: createCheckbox - p5.js. The control used for the four requirements.