Learning Analytics Interpretation Guide Agent Skill

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

Context cost
5.7k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 22.7 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

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

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

analyticsassessmentdatadata-literacyformativeinterpretationsiemenswiliam

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md22.7 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: learning-analytics-interpretation-guide disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-learning-science/learning-analytics-interpretation-guide" skill_name: "Learning Analytics Interpretation Guide" domain: "ai-learning-science" version: "1.0" evidence_strength: "moderate" evidence_sources: - "Siemens & Long (2011) — Penetrating the fog: analytics in learning and education" - "Bienkowski et al. (2012) — Enhancing teaching and learning through educational data mining and learning analytics (US DoE report)" - "Wiliam (2011) — Embedded formative assessment (data use for formative purposes)" - "Mandinach & Gummer (2016) — What does it mean for teachers to be data literate?" - "Wise (2014) — Designing pedagogical interventions to support student use of learning analytics" input_schema: required: - field: "dataset_description" type: "string" - field: "decision_context" type: "string" optional: - field: "student_level" type: "string" - field: "subject_area" type: "string" - field: "comparison_data" type: "string" - field: "time_constraints" type: "string" output_schema: type: "object" fields: - field: "data_interpretation" type: "object" - field: "actionable_patterns" type: "array" - field: "recommended_actions" type: "array" - field: "cautions" type: "array" chains_well_with: - "formative-assessment-loop-designer" - "ai-feedback-design-principles" - "retrieval-practice-designer" - "diagnostic-question-designer" teacher_time: "5 minutes" tags: ["analytics", "data", "formative", "Siemens", "Wiliam", "data-literacy", "assessment", "interpretation"] ---
67# Learning Analytics Interpretation Guide
68
69## What This Skill Does
70
71Guides 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.
72
73## Evidence Foundation
74
75Siemens & Long (2011) articulated the foundational vision for learning analytics: using data generated by learners to understand and optimise learning. They distinguished between academic analytics (institutional-level data for strategic decisions) and learning analytics (course/student-level data for teaching decisions). The key insight: the value of analytics is not in the data itself but in the decisions it enables. Bienkowski et al. (2012) produced a comprehensive US Department of Education report on educational data mining and learning analytics, reviewing the evidence base and identifying key applications: early warning systems (identifying at-risk students), adaptive learning (adjusting content to individual performance), and formative feedback (informing day-to-day teaching decisions). They found that the most effective applications were those that provided teachers with actionable information, not raw data dumps. Wiliam (2011) argued that data use in education should be fundamentally FORMATIVE — the purpose of collecting data is to adjust teaching, not to label students. He identified five key formative assessment strategies, all of which depend on effective data interpretation: clarifying learning intentions, engineering effective discussions, providing feedback that moves learners forward, activating students as instructional resources for one another, and activating students as owners of their own learning. Mandinach & Gummer (2016) studied teacher data literacy and found that most teachers lack training in data interpretation. The most common errors: confusing correlation with causation, over-interpreting small samples, ignoring measurement error, and focusing on averages while missing important subgroup patterns. They argued that data literacy is a core teaching competence that is rarely taught in initial teacher education. Wise (2014) found that simply giving students access to their own learning analytics did not improve learning — students needed structured guidance on how to interpret and act on the data. The same principle applies to teachers.
76
77## Input Schema
78
79The teacher must provide:
80- **Dataset description:** What data is available. *e.g. "Year 10 mock exam results: class of 28, scores range from 23% to 91%, mean 58%, median 54%. Question-level data available: Q1 (recall) 78% correct, Q2 (application) 45% correct, Q3 (analysis) 31% correct, Q4 (evaluation) 28% correct" / "Kaku engagement data for Year 7 maths: 24 students completed homework last week, 6 didn't. Of those who completed it, average time spent was 18 minutes. Three students spent over 40 minutes. Two students completed in under 5 minutes with 100% accuracy" / "Reading log data: 30 students, 3 weeks of daily reading minutes. Class average dropped from 22 min/day in week 1 to 14 min/day in week 3"*
81- **Decision context:** What the teacher needs to decide. *e.g. "I need to plan the next two weeks of revision lessons — what should I focus on?" / "I'm deciding whether to set more challenging homework or to go back and reteach the basics" / "I'm concerned about the reading decline and need to decide whether to intervene or whether this is normal variation"*
82
83Optional (injected by context engine if available):
84- **Student level:** Year group and proficiency
85- **Subject area:** The curriculum subject
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,361 words
Files1
Licencenot declared
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈770
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
4,905
on trigger
The instruction body, read only when the skill fires.
2.8%
of a 200k window
Ten skills this size would take about 28% of the window before you open a file.
050k100k150k200k context window

5.7k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

1 file, 22.7 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md22.7 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 1 file you can review in full before installing.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Learning Analytics Interpretation Guide · 5.7k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/learning-analytics-interpretation-guide/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/learning-analytics-interpretation-guide" ] && { [ ! -e .claude/skills/learning-analytics-interpretation-guide ] || { mv .claude/skills/learning-analytics-interpretation-guide "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/learning-analytics-interpretation-guide" .claude/skills/ && echo "Installed .claude/skills/learning-analytics-interpretation-guide"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/learning-analytics-interpretation-guide

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/learning-analytics-interpretation-guide/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/learning-analytics-interpretation-guide/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/learning-analytics-interpretation-guide" ] && { [ ! -e ~/.claude/skills/learning-analytics-interpretation-guide ] || { mv ~/.claude/skills/learning-analytics-interpretation-guide "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/learning-analytics-interpretation-guide" ~/.claude/skills/ && echo "Installed ~/.claude/skills/learning-analytics-interpretation-guide" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/learning-analytics-interpretation-guide/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Version1.0
Publishedno release date on file
PriceFree
Referencegarethmanning/learning-analytics-interpretation-guide

Versions

v1.0 is what is on the shelf; no release here carries a date. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

v1.0
  • No earlier releases have been published to the marketplace.
Pinning

The install command always fetches the release that is current the day you run it — v1.0 today — and cannot ask for an earlier one. The copy on your disk does not change until you run it again.

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