Coursera Course Review Scraper

What does Coursera Course Review Scraper do?

Coursera Course Review Scraper collects public learner reviews from one or more Coursera course or program URLs. Provide a course, specialization, Professional Certificate, or review-tab URL and receive structured records containing the review text, star rating, author, date, completion status, helpful votes, course URL, and collection context.

Use the dataset for course research, learner sentiment analysis, education-market monitoring, recommendation systems, review archives, and AI or RAG workflows.

Why use Coursera Course Review Scraper?

• Review-focused data - Collect individual learner reviews instead of only course-card summaries.
• Useful review context - Keep rating, review date, completion status, helpful votes, course identity, and result rank together.
• Flexible collection - Process one course or multiple course URLs in a single run.
• Controlled results - Set a per-course result limit and page cap for predictable datasets.
• Filtering and ordering - Collect all ratings or one star rating, optionally require course completion, and sort by recent or most helpful reviews.
• Automation-ready exports - Download JSON, CSV, Excel, or XML, or connect the dataset to downstream services through Apify.

What data can you extract from Coursera?

Field	Description
reviewId	Unique Coursera review identifier
courseId	Coursera course identifier
courseSlug	Course slug from the supplied URL
courseUrl	Course page URL
authorName	Public review author name when available
rating	Star rating from 1 to 5
reviewText	Clean learner review text
reviewedAt	Review date in ISO 8601 format
productCompleted	Whether the learner marked the course completed
mostHelpfulVoteCount	Helpful votes recorded for the review
isMarkedHelpful	Helpful status returned for the current request
sortBy	Ordering used for the request: recent or helpful
ratingFilter	Star filter used, when one was selected
productCompletedFilter	Completion filter used, when selected
reviewRank	Position in the collected reviews for that course
page	Page number used to collect the review
totalReviews	Total matching reviews reported for the selected filters

Empty and null values are left out of individual records.

How to scrape Coursera reviews

1. Open the Actor in Apify Console.
2. Add one or more public Coursera course URLs.
3. Choose the review ordering and optional filters.
4. Set the number of reviews and maximum pages per course.
5. Run the Actor and review the dataset preview.
6. Export the results or connect the dataset to your workflow.

Course pages such as https://www.coursera.org/learn/course-slug, specializations such as https://www.coursera.org/specializations/program-slug, Professional Certificates such as https://www.coursera.org/professional-certificates/program-slug, and their review-tab variants with /reviews or #reviews are accepted. Query strings, fragments, trailing slashes, and Coursera subdomains are also supported.

Input Parameters

Parameter	Type	Required	Default	Description
startUrls	Array of strings	Yes	One working course URL	Coursera course or program URLs to process. Course, specialization, Professional Certificate, /reviews, and #reviews URLs are accepted.
sort_by	String	No	recent	Use recent or helpful.
rating_filter	String	No	all	Use all, 1, 2, 3, 4, or 5.
product_completed	Boolean	No	-	If true, keep only reviews marked as completed.
results_wanted	Integer	No	20	Maximum reviews per supplied URL; program URLs distribute this limit across constituent courses.
max_pages	Integer	No	10	Maximum review pages to request per course. Each page contains up to 50 reviews.

Output Data

Each dataset item is one learner review. Reviews from duplicate URLs are deduplicated by review identifier.

Field	Type	Description
reviewId	String	Unique review identifier
courseSlug	String	Course slug
courseUrl	String	URL of the course page
programSlug	String	Program slug when input is a program
programUrl	String	Original program URL when applicable
authorName	String	Public author name
rating	Integer	Rating from 1 to 5
reviewText	String	Review content without presentation markup
reviewedAt	String	ISO 8601 review timestamp
productCompleted	Boolean	Learner completion state
mostHelpfulVoteCount	Integer	Helpful vote count when available
reviewRank	Integer	Collected order for the course
page	Integer	Collection page
totalReviews	Integer	Matching review count

Usage Examples

Basic Extraction

Collect 20 recent reviews from one course page:

{
    "startUrls": ["https://www.coursera.org/learn/python-for-applied-data-science-ai"],
    "results_wanted": 20
}

Multiple Course URLs

Collect the same number of reviews from several courses:

{
    "startUrls": [
        "https://www.coursera.org/learn/python-for-applied-data-science-ai",
        "https://www.coursera.org/learn/machine-learning"
    ],
    "sort_by": "recent",
    "results_wanted": 50,
    "max_pages": 4
}

Program Reviews

Collect 20 reviews across all courses in a specialization or Professional Certificate. The limit is per supplied URL and is distributed across its constituent courses:

{
    "startUrls": [
        "https://www.coursera.org/specializations/ai-healthcare#reviews",
        "https://www.coursera.org/professional-certificates/google-it-support"
    ],
    "results_wanted": 20,
    "max_pages": 10
}

Filtered Helpful Reviews

Collect the most helpful five-star reviews that indicate course completion:

{
    "startUrls": ["https://www.coursera.org/learn/python-for-applied-data-science-ai/reviews"],
    "sort_by": "helpful",
    "rating_filter": "5",
    "product_completed": true,
    "results_wanted": 100,
    "max_pages": 5
}

Sample Output

{
    "reviewId": "216543659~COURSE!~ejOz7RDUEei99hK0xs-tsg",
    "courseId": "COURSE~ejOz7RDUEei99hK0xs-tsg",
    "courseSlug": "python-for-applied-data-science-ai",
    "courseUrl": "https://www.coursera.org/learn/python-for-applied-data-science-ai",
    "authorName": "Example Learner",
    "rating": 5,
    "reviewText": "The practical exercises made the concepts easier to understand.",
    "reviewedAt": "2026-08-25T10:30:00.000Z",
    "productCompleted": true,
    "mostHelpfulVoteCount": 12,
    "isMarkedHelpful": false,
    "sortBy": "recent",
    "reviewRank": 1,
    "page": 1,
    "totalReviews": 43772
}

Tips for Best Results

• Use the canonical Coursera course URL, with or without the /reviews suffix.
• Start with results_wanted: 20 to confirm the output before larger collections.
• Use helpful sorting for frequently endorsed feedback and recent sorting for current learner sentiment.
• Combine rating_filter and product_completed for focused analysis.
• Use max_pages with larger result limits to control run duration.
• Review the dataset preview because course review totals and ordering can change over time.

Integrations and Export Formats

Option	Useful for
JSON	Data pipelines, AI agents, and RAG preparation
CSV	Spreadsheet analysis and sentiment review
Excel	Reports and course comparisons
Google Sheets	Collaborative review analysis
Airtable	Searchable course-feedback catalogs
Webhooks	Triggering downstream jobs after a run
Make or Zapier	No-code workflows
Apify API	Starting runs and reading datasets programmatically

Frequently Asked Questions

Can I use a Coursera /reviews URL?

Yes. Course URLs and their /reviews variants are accepted. Tracking parameters and trailing slashes do not affect course identification.

Can I collect only five-star reviews?

Yes. Set rating_filter to 5. Values from 1 through 5 are supported, as well as all.

Can I collect completed-course reviews?

Yes. Set product_completed to true to request reviews marked as completed by the learner.

Can I export Coursera reviews to CSV or Excel?

Yes. Apify datasets can be downloaded in CSV, Excel, JSON, XML, and other supported formats.

Can I schedule recurring review monitoring?

Yes. Create an Apify schedule to repeat a run daily, weekly, or at another interval and compare the resulting datasets.

Is it legal to collect Coursera reviews?

Public-data collection rules vary by jurisdiction and use case. Review Coursera's terms, privacy requirements, and applicable laws before collecting or redistributing review data.

Related Actors

• edX Course Scraper - Collect course data from edX.
• Udacity Course Scraper - Build comparable datasets from Udacity courses.
• Skillshare Courses Scraper - Collect course information from Skillshare.

Support

For issues or feature requests, use the Issues tab on the Actor page. Include the input JSON, course URL, and a small output example.

Legal Notice

This Actor is intended for lawful collection and analysis of publicly available Coursera review data. Users are responsible for complying with Coursera's terms, applicable privacy requirements, intellectual property rules, and all other laws that apply to their collection and use of the data.