Facilitator guide: Module 5, Library data literacy workshop
A teacher-facing companion to Module 5. The module is the full lesson; this guide adds the facilitation layer: a preparation checklist, timing cues, guidance for the discussion questions, and notes on what to emphasize and where participants get stuck. Slides and a printable version are below.
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At a glance
- Length: 60 minutes. A 45 minute and a 75 minute variant are noted under Teaching notes.
- Level: general audience. Public library patrons, community members, or lifelong learners with no background in finance, statistics, or computers beyond comfort using a web browser.
- Access: the no-code sandbox at sandbox.altfndata.com only. No API key or coding tool is needed. A personal email is acceptable for registration for this general-audience session, though a work or school email speeds automatic approval.
- Goal: participants leave able to open the sandbox on their own, use the data dictionary and coverage browser to understand a field before trusting it, filter to a question they generated themselves, and name at least one reason to be cautious about a data-driven conclusion.
Before class
- Register your own sandbox account and run the guided demo end to end, so you have known-good results on screen before anyone arrives.
- Confirm with venue staff that the guest wifi and the sandbox's registration flow work smoothly together; test this on the venue's own network if you can, not just your own.
- Look up the brand or category you plan to use in the demo through the coverage browser beforehand, and note the record and vendor counts so you can speak to real numbers live.
- Load the slides and confirm the projector or screen shows the coverage browser and data dictionary tabs clearly, since this session leans on those tools rather than the SQL editor.
- Print or have ready a short list of familiar brand names across a couple of categories, in case participants freeze when asked to pick one themselves.
Timed agenda with cues
| Time | Segment | What to do | Watch for |
|---|---|---|---|
| 0 to 10 | Welcome and framing | Ask the group what a dataset is in their own words, then introduce the idea of looking at real auction records for watches, handbags, and similar goods. | Participants assuming this requires technical skill. Reassure them early that no coding or spreadsheet experience is needed. |
| 10 to 20 | Sandbox orientation | Everyone registers together and opens the homepage, coverage browser, and data dictionary tabs as a group. | Registration email typos or a slow venue network. Have a backup shared account ready so no one is stuck watching. |
| 20 to 40 | Guided demo | Filter one familiar brand to sold items only and build a chart of realized price over time, narrating each step. | Moving too fast through the data dictionary. Read each field's plain-language description aloud before using it. |
| 40 to 50 | Participant exploration | Each person picks a brand or category they are curious about and filters to it themselves, with you circulating to help. | Someone picking a brand with very few records. Nudge them to check the coverage browser's count first. |
| 50 to 60 | Group share-out and discussion | A few participants describe what they found, then work through the discussion questions as a group. | Running short on time. Protect at least five minutes for the discussion questions rather than letting share-out run long. |
Guidance for the discussion questions
Use these as the "what to listen for" behind each question in the module. They are talking points, not a graded key.
- What surprised participants about the dataset's actual contents. Listen for: expectations of a simple price list versus the reality of fields like status and vendor that require reading before trusting a number.
- Why it matters whether a price was actually paid versus merely asked. Listen for: an asking price only reflects a seller's hope, while a realized price reflects what a buyer was actually willing to pay, which is why status must be checked before drawing a conclusion.
- What questions to ask about how the data was collected before trusting it. Listen for: which auction houses are included, how far back coverage goes, and whether a category or brand is thinly covered.
- What would make a recent price trend trustworthy versus doubtful. Listen for: connect directly to the recency gap, since the newest months can look thin or volatile simply because collection has not caught up, not because the market moved.
- How reading a real dataset differs from reading a news article that summarizes a trend. Listen for: a news summary hands you someone else's framing and selection, while filtering the data yourself lets you see the actual records and their limits firsthand.
- An everyday, non-financial decision where reading a real dataset would help. Listen for: any example where the participant would otherwise rely on a secondhand summary, such as checking a product's real resale history before buying or selling something themselves.
Teaching notes
- Most common misconception: participants treat any listed price as what an item sold for. Point back to the status field and show a side-by-side of an unsold listing and a sold one so the distinction is concrete.
- Second misconception: finding few results for a brand and concluding the dataset has no data on it, rather than trying a shorter or differently spelled version of the name. Model this correction live in the coverage browser.
- 45 minute variant: drop the participant exploration segment and move straight from the guided demo to a shortened group discussion using two or three of the questions above.
- 75 minute variant: after participant exploration, have a few pairs briefly show their filtered view to the group before the discussion questions, so the room sees more than one brand or category in action.
- If the sandbox is slow or blocked on the venue network: fall back to the pre-run screenshots you captured in preparation, and invite participants to try the sandbox on their own device or at home afterward.
For the full lesson content, queries, and homework, see Module 5. Questions or a class API key: info@altfndata.com.