Data analytics and interoperability are two ubiquitous—and interdependent–topics on the minds of institutional leaders as they strive to implement innovative teaching and learning models to improve outcomes. What interoperability issues and technologies do institution need to address in order to gain greater insight into their students’ learning outcomes?
As digital learning continues to evolve, the user experience and underlying interoperability of courseware, tools, apps can enable the aggregation of data from multiple sources to drive personalized learning. Today, digital resource integration within institutional systems can be lengthy, expensive process. In some cases, proprietary tools can’t be integrated at all which leads to student learning data locked inside, where it is not readily usable by educators or combinable with other data sources.
Dr. Rob Abel, Chief Executive Officer of IMS Global Learning Consortium and Dr. Jon Mott, Chief Learning Officer of Learning Objects recently addressed these vital, ongoing questions in a live forum with hundreds of administrators from across the country.
As institutions implement next generation digital learning environments that will enable them to have usable and meaningful data, they should consider specific guiding principles that will enable them to achieve their innovation and learning improvement goals: Interoperability, Personalization, and Actionable Analytics. Well-established learning technology standards and specifications enable the rapid realization of these principles:
- Learning Tool Interoperability (LTI) which enables a seamless user experience for both the user and administrator integrate
- Learning Information Services (LIS) and OneRoster that incorporates systems such as the LMS, VLE, ePortfolios, Personalized/Adaptive/CBE platforms, SIS, and all management systems
- Caliper Analytics which then unifies all tools & events allowing a unified learning dashboard to be created for the end user and the administrator
Once the institution has standards and specifications in place they can redevelop their Infrastructure rapidly and at a much lower cost (relative to proprietary, application-by-application integrations). In most institution’s infrastructure the LMS, SIS, and other course section data is inconsistently formatted, disaggregated, and often incomplete.
Building on the foundation of these standards, institutions can implementing a new. learning-centric architecture powered by Learning Objects (www.learningobjects.com). In the Learning Objects framework, all courseware and assessment data feeds back to a common program-level “System of Record” and an associated Comprehensive Program Performance Data store.
From this, students, instructors, and administrators can view Capability Dashboards that are dynamically updated as learners achieve and demonstrate capabilities.
As an example, Learning Objects—in partnership with IMS, C-BEN, and Lumina–have been developing a next generation “extended” Capability Transcript which validates student attainment of capabilities, including authentic evidence of those capabilities.
Once the boxes have been checked for standards, specifications, infrastructure, and architecture, institutions are able to support a variety of new, innovative, learning and learner-centric educational models, including CBE. Per Mark Leuba’s EDUCAUSE REVIEW article about the future of the CBE infrastructure, “Competency programs can provide a rich and descriptive understanding of what the learner knows and has demonstrated — capabilities of keen interest to students and prospective employers. The traditional systems of record (the SIS) are mostly incapable of producing such reports.”
Competency-based learning and credentialing demand a fundamentally different architecture—one that supports program-level design, alignment, delivery, data aggregation, and analytics. When program learning outcomes (or capabilities), learning activities, and assessments all speak to each other seamlessly, technology fades to the background and transformational experience are possible for learners. Students operating in such an integrated environment can take charge of their futures, completing particular programs and other learning experiences that help them demonstrate and acquire the capabilities they need to pursue their career and life goals. These integrated, data-driven learning journeys are fundamentally personalized each student as they seamlessly navigate multiple learning environments, contexts, and even institutions.
Note: Special thanks to Jon Mott for his contribution to this article.