Analytics at Realizeit
This blog post is the first in a series which will focus on analytics at Realizeit. Today we’ll begin with a short overview of our approach to analytics and over the coming weeks, we will go into more detail on some of the topics mentioned here and on results from collaborating with our partner institutions.
With the increasing prevalence of educational technology, we now have the opportunity to gather data on learners in a manner which was previously unachievable. At the forefront of this advance are true adaptive learning platforms such as Realizeit. This platform gathers highly granular data on each learner, at each step in the learning process. This data yields insights not only into learner achievement and progress, but also into learner behavior, course effectiveness and instructor engagement. Within Realizeit, this data and the associated insights are used both to power the adaptive learning experience and to provide evidence-based feedback and information to all stakeholders in the learning process.
Analytics in education is broadly broken into two categories: Learning Analytics and Academic Analytics. Take a look at the table below taken from “Penetrating the Fog” by Phil Long and George Siemens (Educause Review, September/October 201). They describe how Learning Analytics is centered on providing feedback to the learners and instructors. At Realizeit, our focus is to provide this feedback in real time, make it easily understandable and for it to be actionable so that is can have a positive impact while the learning is still taking place. This is achieved by integrating the learning analytics into both the student and instructor dashboards and making it part of how they use the system as opposed to a separate tool or component.
Academic Analytics focuses on delivering insights at the Department, Institution or Government level. We take two complimentary approaches to Academic Analytics. The first is by making tools available through the system by which stakeholders can directly access the data, evidence and analysis. These include both the manual exploration of the data and metrics through reporting tools and by automated analytics generation tools. This can yield evidence supported insights into the performance of students, instructors, courses and institutions.
The second approach is to form a research collaboration directly with the institution to aid them in the exploration of their learner data, to help them understand the impact of adaptive learning, to improve their courses and to drive outcomes. This form of collaboration between a learning institution and a provider is a new approach in the field of educational technology and has the potential to deliver benefits and improved results for both parties. Currently, we are engaged in projects with several large institutions in the United States and hope to share some of their findings soon.