Deeper dive on next generation digital learning environment (NGDLE) interoperability: part a: interoperable app inputs

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#4 of a series in preparation for May 2016 Learning Impact Leadership Institute

In the previous post of the series I laid out a perspective on interoperability for next generation digital learning environments from the perspective of an application being able to fit into a configurable constellation of educational apps.  I would now like to elucidate the key features that make said app and environment “next generation.” I will once again refer to the “Anatomy of an Interoperable Learning App” figure introduced in the previous post.

What might next gen learning apps know about the learner? What should they be allowed to know? What can be personalized in the user experience from that knowledge?

For next gen learning apps the objective is to enable better information inputs to allow the app to personalize the learning experience.  What is the information and what does the app do with it? Shown in the figure are some categories of potential inputs (on the left hand side) that are discussed further here:

Institutional context: This is the type of information that is typically found in a student information system (students, student grade level, courses, sections or other groupings). There are potential common languages for this from interoperability standards like OneRoster and Learning Information Services, as well as the Rostering (aka Membership) Services of Learning Tools Interoperability. While exchange of this sort of information is not especially “next gen,” enabling this exchange without the need for custom integrations and code writing is. The information is typically used to authorize use of the app as well as group users together (such as in a collaborative app). Information about who the user is can also be used to bring the student back to the place where they left off, store results in progress specific to the user, etc.

User context: This is information about the learning activities that the student is currently engaged in (reading, assessment, discussion, assignment, media, etc) when the app is launched.  User context is also information that the app can generate and output in the form of an activity description.  User context is an area that requires more work to develop a common language.  Standards work applicable to this is Caliper Analytics and Learning Design.  User context information could be used by the app to personalize how the app behaves. For instance, an assessment app might act differently if it is launched in conjunction with a reading activity, versus if it is launched as a separate assessment.  Again, this is a more advanced topic.  It is very “next gen” in that it enables an app to respond to the circumstances (activity sequence) in which it is launched.

User Preferences:  We all know that apps typically have some ability to allow the user to specify preferences.  For “next gen” digital learning environments to be personalized in a scalable way, there needs to be a way for the user to specify preferences one time and have that information propagated to all relevant apps.  These are not “app specific” preferences, but rather preferences that could be pertinent to most apps.  For instance, when a user interacts with apps on their smart phone they may have certain preferences for size of fonts, use of media, etc.  There is a rich body of interoperability work called Access for All that has developed a rich framework for describing personal needs and preferences (PNP).  Access for All is applicable to all users, but also has a rich foundation in accessibility (and has even been published as an ISO/IEC standard). PNP has been applied to high stakes assessment accessibility via the Accessible Portable Item Protocol (APIP) standard.  One of the most important preference areas in education is privacy.  While the “setting privacy preferences in every app” model of consumer app stores may make sense for that world, it does not make sense for education. Students or parents need to be able to set privacy preferences once in the context of their institutional experience and then have those privacy options set as defaults for every app.  Therefore, for “next gen” learning environments, our expectation is that the privacy preferences will be selected from an interoperable privacy framework that is provided as an input to each app for each user.

Learner Profiles:  One of the potentially most valuable interoperable inputs for next gen learning apps is a learner profile. This is information that lets the app know where the student is in their learning experience and progression. It can be used to provide a personalized experience for the student. In some ways, the learner profile is the “holy grail” of next gen learning in that the better a learner’s “state” can be described the more personalized the learning experience could be. The problem of course is that no one knows exactly how to describe what a person knows or doesn’t know.  A learner “state” must be generated and kept for every adaptive learning or assessment app.  Is that state record standardized or interoperable? No, not at this point. In fact, such a description is probably considered to be the “secret sauce” of such products.  If the products are tracking progress toward agreed upon competencies or learning standards (like the U.S. K-12 Common Core) then it makes the possibilities of exchanging learning profiles greater.  But there are still a lot of nuances.  More light will potentially emerge at the end of that tunnel as adaptive summative testing becomes more mature.  Such testing will require well-defined “levels” of mastery to agreed upon learning standards.  However, even in the short-term there are some interesting possibilities.  For instance, in IMS right now we see tow more tractable paths to building and exchanging learner profiles.  In higher education the IMS community is developing ways to exchange competency frameworks among cooperating products in the educational enterprise.  In K-12 the IMS community is looking at taking small, but potentially powerful steps looking at profile items like reading level.  IMS does have some past work on learner profiles and competencies as well that may become applicable: Learner Information Package, ePortfolio and Reusable Definition of Competency or Educational Objective.

Ramifications on Next Gen Architectures

Getting the architecture “right” for next generation digital learning presents numerous opportunities and challenges.  Here are some questions that the education community needs to think through:

  1. Is there a software application that is best suited to be the keeper and provider (in terms of interoperability) of learner profiles? Should the learning management system or student information system do this?  Or, perhaps a competency management product? What about the new category that the Gates Foundation has been encouraging, Integrated Planning and Advising Systems (IPAS). Yet another possibility is a competency-oriented assessment product.
  2. Same question for entering, storing and sharing personal needs and preferences, including the all important privacy preferences?
  3. What pragmatic steps do we take in moving our institutional or product architecture forward as we build toward this future?

Next up in the series:  Deeper dive on next generation digital learning environment (NGDLE) interoperability: part b: interoperable app outputs