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As technology rapidly accelerates the capacity for all things data, schools have an obligation to leverage the power of learning analytics to catalyze learning success for all learners and gain insights that guide planning and decision making. Our learning analytics platform and custom data visualizations allow schools to use learning analytics solutions that reveal new patterns, provoke new questions, inform action, and share data in clear and compelling ways.


The Learning Analytics Collaborative (LAC) is a partnership between data scientists and schools around the world, focused on using and developing customized learning analytics to measure, collect, and analyze data for teaching and learning. Through this Collaborative, each participating school has full access to:

  • A Customizable, and secure Learning Analytics platform

  • Dynamic data visualizations for analyzing and sharing student data

  • An evolving suite of school-tested Learning Analytics tools

Learn more about the Learning Analytics Collaborative at


Why Audit Learning Data Assets?

In order to effectively manage learning data holdings and fully realize their potential, any school must first be aware of the location, condition, and value of its learning data assets. Conducting an audit will provide this information, raising awareness of collection strengths and areas of improvement, and data issues to improve overall strategy. A learning data audit highlights duplication of efforts and areas that require additional investment, allowing any school to optimise its resources. It will also highlight inadequacies in data creation and control practices, suggesting changes to minimise the risks. Broadly speaking, auditing data yields benefits such as:

  • prioritisation of resources that leads to efficiency savings

  • ability to manage risks associated with data loss, misuse and irretrievability

  • increasing the value of data assets through improved access and reuse

  • enabling effective and efficient learning analytics and data visualization for data-informed decision making

The Learning Data Audit addresses five core questions:

  1. What data assets currently exist and where are these assets located?

  2. How have these been managed to date and how should they be managed in future?

  3. What is their quality and condition?

  4. Which data assets should be prioritized or streamlined to enable data-informed decision making and which of these assets need to be maintained in the long term?

  5. Do current data management practices place these assets at risk?

Please email if you'd like to conduct a Learning Data Audit.

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