Pick a lens and a metric. The Oracle plots every entity over time and forecasts the next 1–3 months using a linear trend fit.
Select a teacher to view their rubric stats, observation history, and coaching notes.
A premium tour of every feature - load, filter, analyze, save, share, and compare with AI.
Load your dataset, apply filters, and reveal insights in seconds.
Drop in an EM2 (student performance) or GMW (classroom observation) CSV. The visualizer auto-detects the format and reshapes the data accordingly.
Scope the view to exactly what matters: School, Grade, Assessment Type, Performance Band, and a date range. For GMW, Type becomes Curriculum and Band becomes Module, plus a horizontal strip of observer/district/state/teacher filters.
Click the copper Apply button to recompute KPIs, insight cards and charts. Use Ctrl/⌘ + click to select multiple values in a single list. Reset filters clears everything instantly.
Snapshot the current charts into the Dashboard panel. Give it a name (e.g. "Spring Benchmark - Grade 4 Focus") and it's stored locally so you can revisit or compare later.
Download a tidy CSV of the current school × grade summary - averages, weighted points, and band percentages - ready for Excel, Sheets, or further analysis.
avg_overall_percentage, points_pct, and per-band percentages.Copy a short link that encodes your exact view - filters, active tab, date window, and GMW extra filters. Recipients open the link and land on the same scene, with charts recomputed from their CSV copy.
?qs=A7F2K9Q).Turn the automatically-detected insight cards (largest drop, most consistent school, top band shift, largest gain, etc.) into a concise narrative suitable for a memo or stakeholder brief.
A second surface for reviewing saved snapshots side by side.
Every chart bundle you save from the Overview tab appears as a card here, sorted by most recent. Names are truncated elegantly; timestamps stay readable.
Hold Ctrl/⌘ and click multiple saved entries to compare them simultaneously. The right panel arranges them in a responsive grid.
Selected snapshots render as a live preview of their charts - images when captured, canvases when re-rendered - each labeled with its save name and timestamp.
Generate a single summary for one snapshot or an academic-report-style comparison across multiple snapshots - trends, differences, and notable patterns, in plain language.
A grounded, coach-flavored conversation surface. Ask in plain English, get back specific, actionable answers pulled directly from the rows currently in scope.
Every question is answered against a compact data context block built from your current School, Grade, and date selections. Change a filter and the next turn sees the new slice automatically.
The system prompt casts the model as a veteran instructional coach. Answers favor specifics, invite action, and suggest next coaching moves where the question calls for it.
Start with a one-click prompt (weakest dimension, who to observe next, what changed), or free-type. The last several turns stay in context so follow-ups feel continuous.
A generalized over-time view. Pick a lens, a metric, and a horizon. The Oracle plots every entity over time and projects the next one to three months using a simple linear-trend fit.
Group by School, Grade, Teacher, Observer, Role, State, District, School Type, Curriculum, Module, or individual Rubric Dimension. Metric options adapt to the dataset (EM2 percentages vs GMW rubric scores).
Each entity's history is fit with a least-squares linear regression. The fit is extended forward as a dashed line in the same color as the history. No hidden ML, no black box: the math is auditable and explained on the page.
After the chart renders, three cards call out the steepest rise, sharpest drop, and most stable entity by fitted slope, so reviewers see the story without having to read the lines themselves.
A character-select screen for teachers. Browse rubric stats as RPG-style attributes, compare multiple teachers, and review coaching notes - all powered by GMW observation data.
Every teacher in the GMW dataset appears as a premium card with their initials avatar, school, grade, observation count, and a Level badge (Lv. 10–30) derived from their overall rubric average.
Click a teacher to reveal their full stat sheet: a radar chart of all 8 dimensions, HP/MP-style gradient bars with color-coded scores (orange=Emerging, amber=Developing, blue=Achieving), and a pulsing Power Level orb showing their composite score.
Search and add teachers as chips in a Facebook Messenger-style input. Select two or more to see an overlaid radar chart comparing their profiles, with a gold dashed line showing the averaged score across selected teachers.
Every observation comment is organized into 8 collapsible sections, one per rubric dimension. Each note shows the observer name, date, and the score given for that observation - so coaches can trace feedback over time.
The Walkthrough Tool captures classroom data across eight dimensions grouped into four categories. Hover any dimension label in the dashboard - radar axes, bar charts, Teacher Stats stat bars, accordion headers, or character-card mini-bars - to see the full definition in context.
A teacher's skills in facilitating Great Minds lesson components and pedagogical elements.
Emphasis on students' cognitive lift and discourse.
How teachers use curriculum-embedded formative and summative assessments and lesson activities to provide focused, actionable feedback to students.
Participation in collaborative and independent lesson activities.
Full rubric definitions for each dimension. These same descriptions appear as hover tooltips across the dashboard.
How effectively the teacher enacts each part of the lesson as designed, with attention to the purpose of each component. Focuses on whether transitions, directions, and facilitation moves ensure that students engage in the intended work of each phase (e.g., sense-making, practice, synthesis), rather than just moving smoothly through the lesson.
The degree to which the teacher uses content-appropriate instructional moves to support how students learn the material. Focuses on whether the teacher applies key pedagogical approaches (e.g., modeling, questioning, scaffolding, use of representations or texts) in ways that deepen understanding and align to the demands of the discipline, rather than relying on generic or procedural teaching strategies.
The level of thinking students are doing. Emphasizes whether students are doing the "heavy lifting" (reasoning, problem-solving, explaining) rather than the teacher doing most of the work.
The quality and structure of student talk. Focuses on how students engage in meaningful discussion, explain ideas, respond to others, and use academic language.
How the teacher gathers information about student understanding during the lesson. Includes curriculum-embedded assessment data, observation, and use of student work to monitor progress in real time.
How the teacher uses evidence of student thinking to adjust instruction and provide feedback. Looks at responsiveness, specificity of feedback, and whether it moves student learning forward.
The extent to which students engage meaningfully with peers to build understanding. Focuses on whether collaboration involves shared thinking - such as explaining, questioning, and responding to ideas - rather than simply dividing work or participating superficially.
The extent to which students are actively and productively engaged in individual work that advances their understanding. Focuses on whether students are thinking, applying, and making sense of content on their own, rather than passively completing tasks or waiting for direction.
A chronological record of what shipped, organized by branch. Merges are highlighted; in-progress branches are labeled in color.
Need more help? Reach out to the AIMS team.