Using LMS & Assessment Data to Identify Students Needing Support

01 Purpose

Most schools already collect more than enough data to spot students who are slipping — it's just scattered across the LMS gradebook, assessment reports, and attendance records. This session gives teachers a simple, repeatable routine for turning that existing data into a short, prioritized list of students to act on each week, plus a shared language for the support that follows. The goal is confident, independent use — not a one-time demo.

02 Learning objectives

By the end of this module, participants will be able to:

• Locate and interpret the three core data views in the LMS: missing assignments, current grade, and recent assessment performance.
• Apply a consistent set of "flag" criteria to identify students who need timely support.
• Distinguish between an engagement concern, a mastery concern, and an attendance concern — because each calls for a different response.
• Record a next step for each flagged student and hand it off using the school's intervention workflow.

03 Target audience & prerequisites

Classroom teachers and instructional support staff who already have an LMS account and grade-level rosters. No data or technical background is assumed; participants only need to be able to sign in and view their own classes. Facilitators should pre-load each participant's real classes (or the synthetic demo course) so the practice is hands-on from minute one.

04 Module outline

1. Hook (5 min) — A one-slide story: a student who quietly fell behind, and the three data points that would have caught it two weeks earlier.
2. Model (10 min) — Facilitator walks the three LMS data views live, thinking aloud while applying the flag criteria to the synthetic demo course.
3. Guided practice (15 min) — Participants open their own classes and build a flag list alongside the facilitator, pausing to compare decisions.
4. Independent practice (15 min) — Participants finish their list and assign one next step per student using the job aid.
5. Share & troubleshoot (10 min) — Quick round of edge cases ("what if the grade looks fine but assignments are missing?") and how to handle them.
6. Close (5 min) — Set the weekly routine, confirm where lists are recorded, and point to the LMS-hosted job aid and follow-up.

05 Facilitator guide (key moves)

• Normalize the work. Open by framing this as "15 minutes a week," not a new initiative. Adoption depends on it feeling lighter than what teachers do now.
• Show, then transfer. Model on the synthetic course first so no one is exposed; only then move participants into their own data.
• Anticipate the sticking point. The most common confusion is a passing grade hiding missing work — call it out explicitly during the model.
• End on the handoff. A flag list with no next step is noise. Close every example by naming the action and where it goes.

06 Quick-reference job aid

A one-glance reference participants keep after the session (printable and LMS-hosted):

07 Check for understanding

• A student has a B but four missing assignments this week. Which concern is this, and what's your first move?
• Name the three LMS data views you'd open to build your weekly flag list.
• Why does every flagged student need a recorded next step before the list is "done"?

08 Implementation notes

• Cadence: tie the routine to an existing weekly touchpoint (e.g., the Monday planning block) so it doesn't compete for new time.
• Platform fit: the three data views map to standard LMS/SIS reports; where a view is missing, this is exactly the gap a custom report or dashboard can fill.
• Privacy: all examples in this sample use synthetic data. In a live rollout, follow the school's data-privacy and FERPA practices and limit access to each teacher's own roster.
• Follow-through: host the job aid in the LMS and revisit flag lists in the next session to reinforce adoption and surface obstacles.