# Education Learning Analytics Design

> Use for learning analytics, educational data mining, LMS log analysis, AI platform behavior data, learner modeling, prediction, clustering, sequence mining, early warning, dashboard design, and explainable analytics in education research.

- Skill: `ilog3/education-learning-analytics-design` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ilog3/education-learning-analytics-design`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ilog3/education-learning-analytics-design/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: ilog3 (https://skillmd.com/u/ilog3)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/ilog3/education-learning-analytics-design

---


# Education Learning Analytics Design

## Goal

Turn platform/log/behavior data into a rigorous education research design.

## Inputs

- Data source: LMS, AI platform, app logs, assignments, assessments, clickstream
- Outcome of interest
- Unit of analysis: student, session, class, school, item
- Time window
- Privacy constraints

## Workflow

1. Define research questions and unit of analysis.
2. Build data dictionary.
3. Plan cleaning, anonymization, and feature engineering.
4. Select analysis type: descriptive, clustering, prediction, sequence, causal inference.
5. Plan validation and interpretability.
6. Design visualizations/dashboards if needed.
7. Draft methods and limitations.

## Tool Calls

Python:

```bash
pip install pandas numpy scikit-learn statsmodels seaborn matplotlib shap
```

R:

```r
install.packages(c("tidyverse", "caret", "tidymodels", "lme4", "TraMineR"))
```

Tools:

```text
Jupyter, RStudio, Orange, Weka, Power BI, Tableau
```

## Output Format

| Data Field | Meaning | Feature Engineering | Privacy Risk |
|---|---|---|---|

Include:

- Data dictionary
- Feature plan
- Modeling plan
- Evaluation metrics
- Ethics/privacy checklist

## Quality Rules

- Avoid black-box prediction without educational interpretation.
- Do not use identifiable student data without anonymization and consent.
- Define leakage risks when predicting outcomes.