# Introduction
The human capital supply chain is a complex network of systems with inherent challenges to accommodating TLA operability. Even within a single organization, the specific composition and arrangement of learning technologies will differ and change over time. This becomes a greater issue when looking across several organizations. The capabilities desired for a DoD learning ecosystem come not from individual components or databases, but from the enterprise-level collection, dissemination, and analysis of data that support the planning and controlling of human capital accession, including education and training. The Total Learning Architecture (TLA) defines a set of policies, specifications, business rules, and standards for enabling this enterprise level learning ecosystem. It is designed to benefit from modern computing technologies, such as cloud-based deployments, microservices, and high Quality of Service (QoS) messaging services. TLA standards help organize the learning-related data required to support lifelong learning and enable defense-wide interoperability across DoD learning tools, products, and data.
The TLA was developed in collaboration with stakeholders from across the defense community, professional standards organizations, industry, and academia. It is maintained by the ADL Initiative (opens new window). The ADL Initiative is also the steward for DoD Instruction (DoDI) 1322.26 (opens new window) for Distributed Learning and its Fungible References (opens new window). DoDI 1322.26 establishes the policy, responsibilities, and requirements for developing, managing, providing, and evaluating distributed learning for DoD military and civilian personnel. The Instruction also emphasizes distributed learning interoperability, in terms of both the systems and the data being produced. It also directs the use of common TLA standards to ensure interoperability of learning technology products, services, and data. The DoDI is updated twice annually by the Defense ADL Advisory Committee (DADLAC) (opens new window).
The TLA Data Strategy (opens new window) provides a common set data standards and technical specifications designed to be implemented across DoD’s education and training community. This overarching strategy ensures that all data resources are designed in a way that they can be used, shared, and moved efficiently across the organization. The ADL Initiative is working with the Institute of Electrical and Electronics Engineers (IEEE), an internationally recognized standards-development organization, to formally establish the data standards required for successful TLA implementation. While these standards will continue to evolve, DoD education and training communities are urged to adopt and employ them now. These commercial standards describe the data within the four pillars of the TLA Data Strategy.
As a policy driven architecture, the TLA data pillars do not require any mandatory components. There are only required behaviors, organized into functional groups around each data pillar. The TLA data pillars must be exposed through common interfaces, asynchronous services, and standard data formats for communicating and storing data. Interfaces between components and data stores use the Secure Hypertext Transfer Protocol (HTTPS – part of an architectural pattern called Representational State Transfer or REST) and message payloads are created using the Experience Application Programming Interface (xAPI), and a JavaScript Object Notation (JSON) for encoding grammatical triples (noun/verb/object). Physically, the interfaces may be exposed at any point or points, depending on the physical components. This allows a gradual migration of legacy systems by having them expose interfaces rather than having to replace components.
The TLA Sandbox is a testing environment built around the ADL's TLA reference implementation. The reference implementation includes the core systems for managing and transforming learner data into meaningful information. The sandbox is available for DoD stakeholders to testing and evaluating software systems, learning activities, instructional strategies, and other related information systems for compatibility with the TLA's data interoperability standards. Additional information on the technical underpinnings of the TLA Reference Implementation including various DoDAF views, the System/SubSystem Design Documentation, and benchmarks used in its development are included in the 2019 TLA Report (opens new window). Updates to the 2019 report are available in the 2022 TLA Functional Requirements Document (opens new window).
# Document Scope and Intended Audience
The intended audience for this document is developers or engineers who are familiar with TLA standards. Readers are not required to know all the details about each standard. However, some knowledge of xAPI basic concepts is advantageous for reading this document. Readers should also have a fundamental understanding of how data is exchanged across the internet including how to represent and exchange data using JavaScript Object Notation (JSON), and how to access RESTful API's on the web. Server-side Javascript experience using web frameworks such as Node.js and NoSQL databases (e.g., Mongo DB) are also recommended.
The purpose of the TLA Reference Implementation is to enable DoD stakeholders to test, evaluate, or stand up their own TLA sandbox implementations in a way that exposes TLA data so that it may be shared across components and organizations. This document covers the specific implementation details of the ADL Initiative's TLA Reference Implementation and its various components, connections, data consumers, and data producers. It demonstrates how ADL engineers have implemented the various TLA tools, components, and technologies to demonstrate the data transformations required by the TLA. However, this is only one possible way of implementing these capabilities.
# Understanding the Total Learning Architecture (TLA)
The TLA project started in 2016 with the strategic vision of establishing a common data strategy across the education and training industry that enables lifelong learning. This goal required a multi-faceted understanding of the tools, technologies, modalities, and learning science used to support education and training within different communities of practice.
Learning takes place both inside and outside the classroom. The digital world around us creates a web of learning resources that are accessible in the workplace, from home, through social circles, and on a growing variety of media and devices. The exponential growth of data generated by these systems has the potential to enable better insights while reducing costs through continuous process improvement across all education and training activities. The next generation of learning activities cannot be defined within the context of a single LMS. In today’s world, an individual’s lifelong learning continuum is distributed across numerous technology platforms that use different instructional methodologies and learning activities. To fully realize the vision of lifelong learning, the future learning ecosystem will be defined by personalized and competency-based learning environments that rely on the availability and accessibility of learner data across organizational and institutional boundaries.
Learner data is a critical asset that enables effective decision making for both trainees to identify gaps in competencies, and for organizations to track employee capabilities across emerging needs. The key to managing lifelong learning data within the TLA is the interoperability afforded through the technical standards, specifications, and practices that underpin an integrated data strategy. The TLA Data Strategy is necessary to provide the semantic interoperability required for enterprise-level analysis and decision support. Data-driven decisions are enabled through enterprise-level analyses of learning data, supporting the continual refinement of occupational skills and the creation, selection, and maintenance of learning activities necessary to achieve proficiency.
The TLA Data Strategy provides a common set of goals and objectives across DoD’s education and training community to ensure data are used effectively. This overarching strategy will ensure that all data resources are positioned in a way that they can be used, shared, and moved efficiently across the organization. The ADL Initiative is working with the Institute of Electrical and Electronics Engineers (IEEE), an internationally recognized standards-development organization, to formally establish the data standards required for successful TLA implementation. While these standards will continue to evolve, DoD education and training communities are urged to adopt and employ them now. These commercial standards describe the data within the four pillars of the TLA Data Strategy:
IEEE P9274.1 Experience API (xAPI) 2.0 (opens new window): Learner performance tracking within different learning activities use the Experience API (xAPI) (opens new window) to capture learning activity streams. This standard defines how learner performance is captured, communicated, and shared via a Learner Record Store (LRS), the server-side implementation of xAPI. The xAPI standard also includes xAPI Profiles (opens new window) such as cmi5 and the TLA’s Master Object Model. The IEEE xAPI Profile Working Group (opens new window) is working to standardize the current specification.
IEEE P2881 Standard for Learning Metadata (opens new window): Descriptions of learning resources (e.g., learning activities, events, instructional content) are stored in the TLA’s Experience Index (XI). While not currently part of the IEEE standard, the Experience Index is intended to be the server-side implementation of P2881. This draft standard builds upon the IEEE 1484.12.1 Learning Object Metadata (LOM) (opens new window) to increase the granularity of how learning resources are defined. The standard includes a P2881 Core that is required for every learning resource and P2881 Profiles that are created for specific types of learning resources (e.g., course, eBook, webinar). The P2881 standard leverages other educational data standards such as the Department of Education's Common Education Data Standards (CEDS) (opens new window), the Postsecondary Electronic Standards Council (PESC) (opens new window), the Credential Engine (opens new window) Learning Opportunity Type, the Learning Resource Metadata Initiative (LRMI) (opens new window), and Schema.org (opens new window). This draft standard also includes numerous data types and properties that were derived from MILHDBK 29612, TRADOC FM 350-70, and the USAF 36-2235 Instructional Systems Design guidebook.
IEEE 1484.20.3 Sharable Competency Definitions (SCD) (opens new window): The SCD standard enables a common approach for describing competencies, aligning competencies together in the context of a competency framework, and defining the assessment and evaluation criteria for the evidence a learner must demonstrate to help measure proficiency. This standard is being designed to facilitate a common language for describing the knowledge, skills, abilities, and other behaviors (KSAOs) required for performing different jobs, duties, and tasks associated with an occupational specialty. The SCD standard follows best practices by defining a core set of metadata for each competency definition with the ability to create different profiles for the different types of competencies used within an organization (e.g., institutional competencies, occupational competencies, process competencies, skill definitions). SCD's provide a common approach for aligning education and training activities to the desired operational performance expected from learners to perform with proficiency. Additional information about SCDs can be found in the Competency Data Standards Work Group (opens new window).
IEEE P2997 Standard for Enterprise Learner Records (opens new window): The purpose of this Standard is to define a data model and API for communicating learner data between connected systems and across organizational boundaries in the enterprise. Using this standard, organizations will be able to aggregate and manage learner data generated from connected systems available within an organization so that it can be shared with other systems or organizations that require it. The data model defines a ledger of learner records generated by a variety of different systems used across the enterprise. It provides linkages to learner performance data (i.e., evidence of learner performance), descriptions of learning activities that generate this evidence, and the definition of competencies or credentials that each learning experience is aligned to. The ELR standard also defines how this data is shared with other systems required throughout the human capital supply chain.
Ongoing TLA research is centered around achieving the vision of learning with “any device, anytime, anywhere.” This requires understanding how devices are to be discovered, connected, secured, identified, and instrumented to generate understandable learning data, while maintaining the loosely coupled (opens new window) nature required of a true ecosystem. This work requires adherence to the DoD's Identity, Credential, and Access Management Services (opens new window) and the work required to support the DoD's Zero Trust Reference Architecture (opens new window) that was finalized in April 2021. The ADL is currently working through the development and testing paths to ensure proper DoD authentication, authorization, and accountability is in place to establish and provide attribution of unique identifiers for all DoD personnel.
# How this Document is organized
This guide follows the different maturity levels described in the TLA Capability Maturity Model (opens new window): and walks the reader through the different systems, settings, and tools used within each level of maturity. The TLA CMM allows organizations to self-assess their level of maturity using their own tools, technologies, and workflows. While this guide is focused on the technology, it's important to note that only a portion of the CMM is focused on the technology. The CMM looks at the maturity of the organizational processes, leadership perspectives, and feedback mechanisms for improving workplace performance. .
The current TLA Reference Implementation utilizes a range of different applications to achieve CMM Level 3 conformance. This document provides detailed instructions for installing, configuring, and managing the tools, technologies, and systems running inside the TLA Sandbox.