Planning School Resource Scraper

A powerful data extraction tool designed to collect structured educational resources from Planning School. It helps researchers, educators, and analysts transform resource listings into clean, usable datasets for analysis, reporting, and content reuse.

     

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Introduction

Planning School Resource Scraper systematically gathers published resources and detailed content from an educational platform, converting scattered information into structured data. It solves the challenge of manually collecting and organizing large volumes of learning material. This project is ideal for educators, researchers, content analysts, and data teams working with educational resources.

Educational Resource Collection

• Collects complete resource listings with metadata
• Supports filtering by keywords, authors, and categories
• Extracts both summaries and full resource content
• Outputs data in structured, analysis-ready formats
• Designed for scalable and repeatable data collection

Features

Feature	Description
Resource Listing Extraction	Collects all available resources with titles and summaries.
Detailed Content Scraping	Retrieves full content including text, images, and metadata.
Advanced Filtering	Filters resources by search terms, authors, or categories.
Multiple Export Formats	Supports JSON, HTML, and plain text outputs.
Configurable Limits	Controls volume with maximum resource limits.

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What Data This Scraper Extracts

Field Name	Field Description
id	Unique identifier for each resource.
title	Title of the resource.
summary	Short description or excerpt.
content	Full textual content of the resource.
categories	Associated topics or classifications.
author	Resource author details.
publishedAt	Original publication date.
updatedAt	Last update timestamp.
url	Canonical resource URL.
featuredImage	Main image associated with the resource.

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Example Output

[
    {
        "id": 14,
        "title": "What are carbon fiber composites and should you use them?",
        "summary": "An overview of carbon fiber composites, their benefits, and applications.",
        "slug": "carbon-fiber-composite-materials",
        "publishedAt": "March 17th, 2025",
        "updatedAt": "March 18th, 2025",
        "author": "Arun Chapman",
        "categories": ["Guides", "Features"],
        "url": "https://www.gotoplanningschool.com/resource?p=carbon-fiber-composite-materials"
    }
]

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Directory Structure Tree

planning-school-resource-scraper/
├── src/
│   ├── main.py
│   ├── collectors/
│   │   ├── resource_list.py
│   │   └── resource_detail.py
│   ├── parsers/
│   │   ├── content_parser.py
│   │   └── metadata_parser.py
│   ├── exporters/
│   │   └── json_exporter.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• Researchers use it to collect educational articles, so they can perform content analysis and trend studies.
• Educators use it to archive learning materials, enabling structured curriculum planning.
• Content teams use it to monitor new resources, helping them stay updated with industry insights.
• Data analysts use it to build datasets for text mining and knowledge discovery.

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FAQs

Can I limit the number of resources collected? Yes, the scraper supports configurable limits to control how many resources are processed in a single run.

Does it support filtering by author or topic? Yes, filtering options allow precise selection based on keywords, authors, or categories.

Are full articles included or just summaries? Both options are supported. You can extract only summaries or complete resource content.

What formats are supported for output? Data can be exported in structured formats such as JSON, HTML, or plain text.

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Performance Benchmarks and Results

Primary Metric: Processes up to 40–60 resources per minute depending on content size.

Reliability Metric: Maintains a success rate above 98% across repeated collection runs.

Efficiency Metric: Optimized parsing minimizes memory usage while handling large text bodies.

Quality Metric: Achieves high data completeness with consistent extraction of metadata and content fields.

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