Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wentorai/research-plugins --skill mooc-analytics-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wentorai/research-plugins/mooc-analytics-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/mooc-analytics-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/mooc-analytics-guide/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wentorai/research-plugins/mooc-analytics-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/mooc-analytics-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.01832 |
| Opus 5 | $0.00010 | $0.00916 |
| Sonnet 5 | $0.00004 | $0.00366 |
| Haiku 4.5 | $0.00002 | $0.00183 |
Grade A, and why
mooc-analytics-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MOOC Analytics Guide
A skill for analyzing Massive Open Online Course data, implementing learning analytics pipelines, and extracting actionable insights from online education platforms. Covers clickstream processing, engagement modeling, dropout prediction, and A/B testing for course design.
Data Sources and Formats
Common MOOC Data Schemas
MOOC platforms export several standard data types:
| Data Type | Description | Typical Format |
|---|---|---|
| Clickstream logs | Page views, video plays, pauses, seeks | JSON event logs |
| Forum posts | Discussion text, timestamps, thread structure | CSV/JSON |
| Grade records | Assignment scores, quiz attempts, certificates | CSV |
| Course structure | Module hierarchy, release dates, prerequisites | XML/JSON |
| Survey responses | Pre/post course surveys, demographics | CSV |
Accessing Open MOOC Datasets
Several open datasets are available for research:
- MOOCdb: Standardized schema from MIT, includes clickstream, forum, and grade data
- Stanford MOOCPosts: 30,000+ labeled forum posts for sentiment and urgency classification
- Open University Learning Analytics (OULAD): Anonymized data for 30,000+ students across 7 courses
- edX Research Data Exchange: Available to institutional partners via application
import pandas as pd
# Load OULAD dataset (publicly available)
students = pd.read_csv("studentInfo.csv")
assessments = pd.read_csv("assessments.csv")
interactions = pd.read_csv("studentVle.csv")
# Basic engagement metric: total clicks per student per course
engagement = (
interactions
.groupby(["id_student", "code_module", "code_presentation"])
.agg(total_clicks=("sum_click", "sum"),
active_days=("date", "nunique"))
.reset_index()
)
print(engagement.describe())
Engagement and Retention Analysis
Defining Engagement Metrics
Key metrics used in learning analytics research:
- Session count: Number of distinct learning sessions (gap-based, e.g., 30-min inactivity threshold)
- Time on task: Total seconds spent on content pages and videos
- Video completion ratio: Fraction of video duration actually watched
- Forum participation rate: Posts + replies per student per week
- Assignment submission rate: Fraction of graded assignments submitted on time
- Regularity index: Entropy of daily activity distribution (lower entropy = more regular)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 207 lines · 19 tokens per session scan A 62766b6ff813
mooc-analytics-guide is a skill published in the GitHub repository wentorai/research-plugins (291 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 1,832 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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