# Learning Analytics Interpretation Guide

Interpret learning analytics data and translate dashboard findings into actionable...

- Repository: https://github.com/GarethManning/education-agent-skills (939 stars)
- Category: Writing
- License: see repository
- Last updated: 2026-10-22
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add GarethManning/education-agent-skills --skill learning-analytics-interpretation-guide -g -a claude-code -y`
Codex: `npx skills add GarethManning/education-agent-skills --skill learning-analytics-interpretation-guide -g -a codex -y`
Cursor: `npx skills add GarethManning/education-agent-skills --skill learning-analytics-interpretation-guide -g -a cursor -y`
Gemini CLI: `npx skills add GarethManning/education-agent-skills --skill learning-analytics-interpretation-guide -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add GarethManning/education-agent-skills`

## Description

Interpret learning analytics data and translate dashboard findings into actionable teaching decisions. Use when reviewing LMS data, quiz patterns, or engagement metrics.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Learning Analytics Interpretation Guide

## What This Skill Does

Guides a teacher through interpreting a specific learning dataset, assessment results, engagement metrics, study behaviour patterns, or any other quantitative or qualitative data about student learning, to identify actionable patterns and inform specific teaching decisions. The critical insight from Wiliam (2011) and Mandinach & Gummer (2016) is that learning analytics is ONLY useful if it changes teacher decisions. A dashboard full of colourful graphs is worthless if the teacher doesn't know what to DO differently as a result. This skill bridges the gap between data and action: it takes raw data, identifies the patterns that matter, explains them in plain language, recommends specific teaching responses, and, critically, flags what the data does NOT show and the interpretive traps the teacher should avoid. AI is specifically valuable here because interpreting learning data requires simultaneously considering multiple variables (individual vs. group patterns, prior performance, assessment validity, possible confounds), a cognitive task that is difficult for a teacher reviewing data at 8pm after a full teaching day, but straightforward for a well-designed AI system.

## Evidence Foundation

The rest: https://github.com/GarethManning/education-agent-skills/blob/HEAD/skills/ai-learning-science/learning-analytics-interpretation-guide/SKILL.md