AI Insights

Using Advanced Data Mining Techniques for Educational Leadership

May 14, 2024

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Data mining techniques can be a powerful tool for advancing educational leadership by providing insights from large datasets. This guide outlines a methodology for collecting a corpus of scholarly articles relevant to K-12 education in the United States, using web scraping, APIs, and search tools. By following these steps, educational leaders can gather valuable data to inform decision-making and drive positive change.


To collect a suitable dataset for this project, the following requirements should be met:

  • Filetype: PDF
  • License: Open source and commercial CC BY license
  • Country of publication: United States
  • Text language: English
  • Recency: Articles published after 2018
  • Volume: 5000 PDFs to be collected

The Approach

Techniques Employed

This section outlines the key techniques and tools used to efficiently gather a large corpus of relevant scholarly articles. By leveraging advanced search capabilities, web scraping, and APIs, the data collection process can be streamlined and automated.

Google Advanced Search

Google Advanced Search is a powerful tool that enables users to find articles meeting specific criteria by filtering search results based on factors such as location, license, and filetype. By constructing targeted search queries and applying the appropriate filters, it is possible to narrow down the results to only those articles that are directly relevant to the research topic at hand. This technique helps to minimize the amount of manual filtering required later in the process.

Advanced Search

Apify’s SERPs Scraper

Apify’s SERPs (Search Engine Results Pages) scraper is a tool that automates the extraction of data from Google search results. Rather than manually navigating through pages of search results and copying the relevant information, the SERPs scraper can quickly and efficiently extract the data in a structured format. This greatly increases the speed and efficiency of the data gathering process compared to manual methods. is a comprehensive database of scholarly articles, patents, and other scientific documents. It provides access to a vast collection of content across multiple disciplines, making it a valuable resource for researchers. offers advanced search capabilities, allowing users to filter results based on various criteria such as publication date, author, institution, and more. Additionally, provides an API that allows for automated querying and retrieval of data, further streamlining the data collection process.

Connected Papers

Connected Papers is a tool that generates a visual network of related articles based on a set of input papers. By starting with a few exemplar papers on a given topic, Connected Papers can quickly identify a large number of related articles, providing an efficient way to expand the corpus of relevant literature. To extract data from the network generated by Connected Papers, a custom web scraper can be built using a tool like Playwright. This allows for the automated extraction of key information from the articles, such as titles, authors, abstracts, and links to full-text PDFs.

Connected Papers

Application of Techniques

Identifying Relevant Sources

To begin the data collection process, high-quality sources known to publish peer-reviewed articles relevant to K-12 education in the United States were identified. These sources included academic journals, research institutions, and government agencies, such as:

By focusing on these reputable sources, the likelihood of finding articles that meet the project’s criteria was increased.

Utilizing Google Advanced Search

With the target sources identified, Google Advanced Search was used to construct queries that would return relevant results from these sources. The search queries included keywords related to K-12 education in the United States, and filters were applied to limit the results to PDFs with open source or CC BY licenses published after 2018.

Apify’s SERPs scraper was then used to automate the extraction of data from the search results. The scraper was configured to extract the title, URL, and snippet for each result, and to navigate through multiple pages of results. This allowed for the efficient gathering of a large number of potentially relevant articles.

Apify's SERPs scraper


To further expand the corpus of articles,’s advanced search capabilities were leveraged. Queries were constructed using keywords related to K-12 education in the United States, and filters were applied to limit the results to articles published after 2018 with open source or CC BY licenses.

To automate the data extraction process, API access to was requested. This allowed for the use of scripts to automatically query the database and retrieve the relevant metadata and full-text PDFs for the articles matching the search criteria.

Leveraging Connected Papers

To identify additional relevant articles that may have been missed by the searches on Google and, Connected Papers was used. A set of exemplar papers on K-12 education in the United States was input into Connected Papers, generating a network of related articles.

To efficiently extract data from this network, a custom web scraper was built using Playwright. The scraper navigated through the network, extracting the title, authors, abstract, and link to the full-text PDF for each article. This allowed for the automated gathering of a large number of potentially relevant articles in a short amount of time.

Processing and Filtering the Data

After the initial data collection phase, the resulting dataset was processed and filtered to remove any articles that did not meet the project’s criteria. This involved:

  • Removing any articles published outside the United States
  • Removing any articles not written in English
  • Removing any articles not directly related to K-12 education

This filtering process helped to refine the dataset and ensure that only the most relevant articles were included.

To make the filtering process more efficient, the dataset was first deduplicated to remove any articles that were collected multiple times from different sources. Then, automated scripts were used to check the language and country of publication for each article, allowing for the quick removal of any non-English or non-US articles.

Finally, the remaining articles were manually reviewed to determine their relevance to K-12 education. This involved skimming the abstracts and full text of the articles to identify those that were most closely related to the topic of interest. The manual review process was time-consuming, but it was necessary to ensure the quality and relevance of the final dataset.

Manual Review and Quality Control

After the automated filtering steps were completed, the remaining articles underwent a manual review process to ensure their quality and relevance. This involved a team of subject matter experts closely reading each article and assessing its fit with the project’s goals.

During the manual review process, articles that were found to be off-topic, poorly written, or otherwise unsuitable were removed from the dataset. In some cases, articles were also removed if they were found to be duplicates or if they did not meet the project’s licensing or publication date criteria.

The manual review process was iterative, with multiple rounds of review and filtering until the team was satisfied with the quality and relevance of the remaining articles. The final dataset consisted of approximately 5,000 PDFs that were deemed to be the most useful for informing educational leadership and decision-making.

The manual review process was essential for ensuring the integrity and usefulness of the final dataset. By applying subject matter expertise and carefully curating the articles, the team was able to create a high-quality corpus of literature that could be used for further analysis and insight generation.

Potential Challenges

  • Non-US or Non-English Content: Some sources may contain non-US or non-English content despite applying filters. Manual review is necessary.
  • Relevance to K-12 Education: Not all collected articles may be directly relevant to K-12 education. Subject matter expertise is needed to curate the final dataset.
  • Achieving Target Volume: Achieving the target volume of 5000 PDFs may require expanding the search to additional sources.

Potential Uses of the Technology

The data mining techniques outlined in this guide can be applied to various domains beyond educational leadership, such as:

  • Business intelligence and market research
  • Scientific literature reviews
  • News media analysis
  • Social media sentiment analysis

By adapting the methodology to different contexts and sources, valuable insights can be uncovered from large text-based datasets.


Data mining is a valuable approach for educational leaders seeking to make data-driven decisions. By following the steps in this guide and leveraging tools like Google Advanced Search, Apify,, and Connected Papers, a comprehensive dataset of scholarly articles can be collected. While challenges may arise in filtering and curating the data, the potential insights gained make the effort worthwhile. As data becomes increasingly central to leadership, mastering these techniques will be a key skill for driving positive change.

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