Total Learning Architecture (TLA)
The Total Learning Architecture (TLA) is an evolving set of standardized Web service specifications to responsibly share essential learning data between applications using common API specifications and data models. Just as it is vital that our health records be responsibly shared between providers of healthcare to improve health outcomes, it is important to enable the responsible sharing of learning data between providers of education and training to improve learning outcomes.
The Experience API (xAPI) is the first of these standardized Web services focused on sharing data about learning experiences. Future services will focus on other data about competencies, learner profiles, and learning content.
Track and gain access to learner data based on their interaction with learning experiences.
Enable tools and systems to reference common competencies, to report learner information in comparison to competency structures, and to align resources with competencies for recommendation.
Data about learners including preferences, course completion history and scores, and mastered competencies are provided.
Manage content to support just-in-time learning and enable sequencing. Appropriate content will be delivered to a learner based on the device they are using.
Why the TLA
- Enables use of new types of content including VR, AR, MR, Games, Mod/Sim, sensors, wearables, etc.
- Unlocks a learner’s data in order to collaborate for better learning outcomes by responsibly sharing it with other applications, teammates, instructors, and leaders
- Provides better visibility to instructors/administrators into the learner’s progress on a granular level
- Provides better visibility to a unit leader (or training officers/NCOs) on their Soldiers’ capabilities, experience, most recent training, etc.
- Provides visualizations and analytics to improve content/curricula based on real data
- Allows systems to securely access learner data so they can then adapt based on individual needs
- Reduces redundant training for learners and increases receiving credit when a learner transitions between applications or institutions
- Addresses the problem of what to learn next by acquiring data points regarding previous performance and learning tasks
- Provides the opportunity for better discovery of applications that help similar learners and instructional designers
- And much more!