Turning Big Data into “Educational Intelligence”

by | Jun 14, 2017

Blog post 1

For the past few years, “big data” and analytics have become hot topics in the educational space, along with strategies and best practices with which to leverage these new capabilities. In this new series of blog posts, we at Intellify Learning hope to educate readers on what is possible in this exciting time as “Educational Intelligence” continues to evolve.

Through our project work with both customer and data standards communities, we see both excitement and trepidation surrounding the possibilities that data acquisition and analytics provide to educational institutions. But before we go forward, we should understand what led us to this point.

Since the late 1980’s businesses around the world have been using data-driven analytics to make decisions in a field that was soon termed “Business Intelligence.” BI, and more recently labeled Business Analytics, fuels the mission of today’s companies in a variety of ways, some of which are now visible by the end-user or customer. Whenever Netflix suggests a show for you to watch or Amazon suggests a product in which you may have interest, their more advanced BI analytics is drawing those conclusions through three forms of analytics:

  • Descriptive analytics: Based on one or more raw data streams and some basic computations, insights are primarily historical business metrics and operational status. This intelligence is the baseline of more “descriptive” insights to inform decision-making.
  • Predictive analytics: Insights are based on computation/machine learning or AI to forecast the likely outcome for a scenario. For example, at Amazon if you bought a single-serve coffee maker and have a history of purchasing grocery items, you will probably be interested in purchasing some coffee as well.
  • Prescriptive analytics: These insights are based on machine learning or AI to suggest potential actions across multiple scenarios and determine the implications of each decision scenario. For example, if my business has the opportunity to invest in W or X technology, and the market behaves like Y or Z, what are the implications for the different decision path scenarios on my business?

Business Intelligence and its underlying analytics continues to positively advance informed decision-making action. With that, it has been a prominent motivating influence on other fields such as education to steadily embrace the value of data, analytics and intelligence.

Given our experience in educational focused analytics, we have a view of Educational Intelligence and what it means within the educational landscape:

Educational Intelligence (EI) is the byproduct of applying data management, data analytics, and data science to aggregate high volumes of data from multiple academic platforms and applications. EI provides academic stakeholders tailored insights, allowing for more informed and timely decisions and actions to continuously improve operational performance and academic outcomes.

Although still in its infancy, forward-thinking educational institutions are using analytics capabilities to increasingly impact recruitment, retention, personalized and adaptive learning, curriculum efficacy, instructor-student interactions and intervention, and more. These insights foster informed decision-making and action to enhance performance and outcomes for both the operations and academics of the education “business.”

But the adoption of EI is not as simple as migrating a business intelligence solution into the education space. To enable EI across the academic enterprise, there are some unique characteristics and requirements associated with the business of education that need to be addressed, such as:

  • Understanding and defining the data governance policy and implementation required.
  • Determining the threshold for, and balancing of, the use of analytics to to inform and drive action vs. automating insights and intervention decisions.
  • Defining what sort of data needs to be captured, where it comes from, and how it is stored in real-time and asynchronously.
  • Identifying and adopting EI solutions that span the entire student lifecycle as well as offer the depth needed for functional insights (e.g., recruitment,enrollment, academics, retention).
  • Ensuring that the EI capabilities are sufficiently tailored to the specific requirements of key stakeholders in the operational and academic segments.

This early but steadily advancing stage of EI is very exciting. Participants have an opportunity to shape the field moving forward and create analytics solutions that can be truly transformative for institutions. Advancements in EI can improve the quality of the overall educational experience – including enhanced teaching and learning outcomes.

In our next post, we will be discussing the impact EI can have on stakeholders across the board. Check it out and join us for the journey!

Intellify Learning transforms data into actionable insights that empower education. Intellify Learning enables educators to use data analytics with greater ease and drive decisions on critical issues in education.

Chris Vento is a founder and Chief Visionary Officer for Intellify Learning. An edtech innovator and leader for nearly 20 years, Chris co-founded the company in 2013 to make analytics part of mainstream educational technology and best practice.

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