Powering the Journey Toward Educational Intelligence
The LMS continues to expand as the “hub” to facilitate and orchestrate the online learning experience. Given the breadth of intrinsic learning activities (e.g., enrollment, discussion, assessment, assignments) available in the LMS, the system generates a wealth of data that is a solid foundation for analyzing and addressing curriculum and student performance throughout the learning experience. Furthermore, with the emergence and adoption of IMS Caliper and xAPI, “live” event data is being captured with greater interoperability and timeliness. This enhances analysis to a more granular level as required for more specific actions to improve the learning experience.read more
In earlier blog posts, we discussed how Educational Intelligence is realized and the benefits that result. This video explains how Intellify Essentials eases the data analytics and management process so educators can realize and apply EI quickly to drive student retention, learning efficacy, and other critical institutional issues.read more
If you’ve just started exploring, planning or implementing an initial analytics project, you are not alone! Leveraging analytics to provide Educational Intelligence (EI) insights across the student life cycle is very much in it’s early yet exciting and rapidly evolving stages. There has certainly been an abundance of positive hype and discussion amidst the education community at conferences, in social media, articles and blogs, and a growing number of institutions about analytics in education. In addition, the education focused analytics solution providers and products, standards, and data analytics technology in general have emerged within the edtech community to supply some concrete initial analytics capabilities to build upon.
With all of this excitement and early stage activity, it is easy to feel overwhelmed, and possibly already in the back of the analytics pack in terms of getting started. The reality is, actual implementations of EI analytics have been slow to get started and still very much in its infancy. However, there is great potential and value to be realized.read more
In our initial post introducing our perspective on Educational Intelligence or “EI,” we set out to address the nature and state of analytics in the education space. We defined EI and discussed its evolution as it relates to BI / Business Analytics, including some differentiating considerations when applying analytics to education.
In education, the student experience and supporting functions drive analytics needs. The student experience lifecycle is comprised of three overarching areas: teaching and learning, student success, and recruitment and enrollment. In this post, we’ll take a closer look at how EI impacts key stakeholders across the student lifecycle, including the student, instructor, curriculum designer, administrator, success coach, and researcher.read more
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:read more