Learning analytics basics

Welcome — this topic is a compact, practical guide for teachers who want to use classroom data wisely. No dashboards that suck your time. Just simple, meaningful measures you can collect, interpret and act on — in the spirit of the Top Teacher approach: student-centered, formative, respectful of learners’ prior knowledge, motivation and self‑esteem.

Think of this as “low-cost analytics”: small, regular evidence that helps you tune teaching, strengthen feedback, and support individual learners.

Why bother with learning analytics?

Briefly:

• It helps you find what students already know (and what they don’t) — Ausubel and Piaget insist: learning must anchor to prior knowledge.
• It makes formative assessment practical and focused — feedback that actually improves learning.
• It reveals patterns (who’s confident, who’s shaky, who’s slipping) so you can intervene before rapport or self‑esteem suffers.
• You can use simple numbers (average, spread, gain) as conversation-starters with students and parents — not as labels.

Goal: collect a few reliable signals that guide your next lesson — not measure everything.

Keep it simple: choose up to 3 indicators

Limit yourself. Pick three indicators that match your lesson goals. Example sets:

Option A — Foundation check

• Pre‑test average (quick 5 Q)
• Exit‑ticket correctness (3 Q)
• Student confidence (self-rated 1–4)

Option B — Skills & transfer

• Mastery grid (skill checklist: yes/partly/no)
• Ratio of students scoring ≥ target
• SD (spread) of test scores

Option C — Engagement & process

• Attendance/participation
• Number of formative attempts (e.g., quiz retakes)
• Quality of self‑reflection journal entries (rubric 0–3)

Why 3? Because anything more becomes hard to interpret and act upon without extra time or support.

What to collect (easy, low friction)

• Micro‑pretests (3–7 items) to check prior knowledge
• 3‑question exit tickets:
  1. One factual check (correct/incorrect)
  2. One connection to previous learning (short answer)
  3. One confidence self-rating (I’m confident / somewhat / unsure / lost)
• Short polls (single multiple‑choice) during class
• Observation checklist (2–5 items) when circulating (e.g., “on task”, “collaborating”, “asks questions”)
• Simple rubrics for one main skill (0–3)
• Quick peer/self‑assessments (e.g., “I can explain this to a classmate: yes/no/maybe”)
• LMS quiz logs or Google Form responses (automated capture)
• Attendance + activity completion (homework or digital task)

Tools: paper slips, sticky notes, Google Forms, Kahoot/Quizzes, your LMS gradebook, a simple spreadsheet.

How to collect without overload

• Automate where possible: Google Forms → spreadsheet; LMS quizzes → reports.
• Timebox: collect 3 exit tickets in 5 minutes at lesson end.
• Rotate deep checks: not every lesson needs a pretest. Use sampling (e.g., one group per week).
• Reuse: same 3 exit‑ticket questions format saves marking time.
• Delegate: students can collect peer‑feedback, or use self‑assessment to generate class summary.
• Visual cues only: use traffic lights or sticky notes for quick confidence checks.

Quick analyses you can do (no statistics degree needed)

1. Class average — snapshot of how the group did.
2. Standard deviation (SD) — how spread out scores are. Interpretation:
   • Small SD + high average = most learned it.
   • Small SD + low average = everyone struggled → reteach/adjust.
   • Large SD = mixed learning: differentiate; some students need extra support.
     (Petri Lounaskorpi: large dispersion often means teaching didn’t reach all learners.)
3. Item difficulty — percent correct per question. Helps spot misunderstood content.
4. Pre/post gain — % correct on pretest vs. posttest; shows learning progress.
5. Mastery grid counts — how many have “yes/partly/no” per skill.
6. Confidence vs. correctness cross‑tab — reveal over/underconfidence.

Please take the quizs to proceed: