20 March 2013

Progress Report: The Personalized Learning Model

A bit more than two years ago my colleagues and I at the Gates Foundation came up with the Personalized Learning Model. Eighteen months ago I introduced it on this blog. Two weeks ago, at SXSWedu, we celebrated the launch of inBloom which is a set of services that support the Personalized Learning Model.

The concept of personalized learning was not new or unique to us. Indeed, we chose it because the benefits have been well-proven. Our model was a way to describe how technological supports could be designed to facilitate personalized learning. As we've been working on this for a couple of years now, it's time for a progress report.

Learning Objectives
In 2010 a consortium of states, coordinated by the Council of Chief State School Officers (CCSSO) and the National Governor's Association (NGA), introduced the Common Core State Standards for English/Literacy and Mathematics. They were rapidly adopted by 45 U.S. states. Having common standards across states is, of course, convenient but these standards seek to be an improvement on the previous generation.
The Common Core State Standards were written by building on the best and highest state standards in existence in the U.S., examining the expectations of other high performing countries around the world, and careful study of the research and literature available on what students need to know and be able to do to be successful in college and careers. No state in the country was asked to lower their expectations for their students in adopting the Common Core. The standards are evidence-based, aligned with college and work expectations, include rigorous content and skills, and are informed by other top performing countries. They were developed in consultation with teachers and parents from across the country so they are also realistic and practical for the classroom. (From the CCSS FAQ.)
In August of 2012, the CCSSO and NGA released official identifiers and an XML representation of the Common Core thereby facilitating alignment of digital learning resource to the core standards. Driven by the need to measure and prove coverage of the standards, finer-grained identifiers are being assigned to individual learning objectives within the common core standards.

The Next Generation Science Standards are also under development with an expected release before the end of March. Following their release, state education boards will consider adoption.

Postsecondary education is taking a different approach. There's little formal agreement between colleges and universities on the learning objectives that compose common courses. However, college and university departments are defining the objectives for core curriculum and there is growth in the sharing of these objectives within university systems. Colleges are also considering use of the Common Core for developmental education courses.

Student Data
Common Education Data Standards (CEDS) is a project to create a common data dictionary and logical data model for education data. Applications that align to CEDS use the same definitions for data fields making data exchange easier and increasing fidelity.

The inBloom Data Store uses CEDS for its data model and ingests data in SIF and Ed-Fi data formats. It offers an API through which personalized learning applications can store and retrieve common student data. Security features preserve the privacy of data and ensure that only authorized people can access it.

Newer data stores align student activity and assessment data to standard learning objectives. The goal is derive a model of what the student knows, what the student is learning and what the student has yet to learn. This enables rich reporting on student competency levels on an objective-by-objective basis and the stimulation of targeted interventions.

Content
I prefer to talk about educational content as learning activities. There are the traditional passive media such as reading, lectures, video and so forth. More engaging are interactive activities like virtual labs, simulations virtual worlds and games. For both active and passive content, education doesn't need special formats. The web content formats managed by the W3C are adequate and well-supported. What is needed is a way to represent the alignment between the content or activities and the standard learning objectives.

The Learning Resource Metadata Initiative (LRMI) is a standard way to describe educational materials including their alignment to standards. It's based on the Schema.org metadata standard adopted by Google, Yahoo!, Bing and Yandex.

LRMI metadata can be shared between systems using the Learning Registry. The inBloom index consumes LRMI data from the learning registry and offers a search service that can find educational content suited to specific student needs.

IMS Global defines standards for packaging learning content for import into learning management systems. However, I prefer the approach IMS uses for Learning Tools Interoperability. Instead of packaging content, this protocol allows content from other sites on the web to be seemlessly integrated into the learning experience. Integration in this way avoids limitations imposed by the packaging format and lets the developers of learning activities collect data about the use and effectiveness of their products.

Assessments
In my opinion, assessments are presently the weakest part of the Personalized Learning Model but that's changing rapidly. Two multistate assessment consortia, Smarter Balanced and PARCC are developing new assessments aligned to the Common Core State Standards. Both are committed to supplying formative and interim assessments in addition to year-end summative exams. CoreSpring is pooling assessments from a more than six different sources to supply a bank of good quality assessments that can be used in class, for quizzes and in interactive learning environments. MOOC developers such as Coursera, edX and Udacity are having to invent new ways to offer interactive assessments at extremely large scale.

In the long run, I expect the line between learning activities and assessment activities to blur. After all, much of learning occurs when the student demonstrates understanding. With adequately instrumented activities, the accumulated data about student competencies should reduce the need for big summative exams at the end of the year.

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We've come a long way in the last couple of years. Pioneers in this space like DreamBox, Knewton, Read180 and GrockIt had to build a whole infrastructure. But now there's a solid set of building blocks on which developers can build personalized learning applications. I anticipate a lot more innovation at the place where student data and content come together.

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