Important Documents and Links

Important Documents

Other competency related initiatives for reference

  • Competency Model Cleaning House
  • IEEE SA Learning Technology Standards Committee (LTSC)
    • P1484.1
      C/LT/CM4LTS WG

      • Guide for Conceptual Model for Learning Technology Systems (CM4LTS)
        This guide provides a conceptual model and classification of data used by learning technology systems.
    • P2881
      C/LT/LMeta

      • Standard for Learning Metadata This standard intentionally builds on the IEEE 1484.12.1 standard but is explorative to new learning paradigms and modern technology practices. This standard will specify a conceptual data schema that defines the structure of a metadata instance. This conceptual data schema specifies the data elements which compose a metadata instance for multiple learning types. This standard does not define how a learning technology system implements the data schema.
    • P1484.20.2
      C/LT/1484.20.2 Recommended Practices for Defining Competencies

      • This document provides Recommended Practices for Defining Competencies. It supports IEEE 1484.20.1, a technical standard for competency definitions data. This document gives recommended practices for defining the kinds of information that the 1484.20.1 standard encodes as interoperable data, i.e. data that defines a skill, knowledge, ability, attitude, habit of practice, behavior or learning outcome.
    • P1484.2
      C/LT/ILR WG Recommended Practice for Integrated Learner Record (ILR)

      • The ILR Recommended Practices [1484.2] provides best practice guidance for learner records related to:
        1. Identity and Trust
        2. Open Ontology References
        3. Verifiable Assertions
        4. Integrated learner record payloads supporting the spectrum of formal or informal education and workplace learning, and military learning records.”
    • P2894
      C/LT/XAI Guide for an Architectural Framework for Explainable Artificial Intelligence

      • This guide specifies an architectural framework that facilitates the adoption of explainable artificial intelligence (XAI). This guide defines an architectural framework and application guidelines for XAI, including:
        1. description and definition of explainable AI
        2. the categorizes of explainable AI techniques;
        3. the application scenarios for which explainable AI techniques are needed
        4. performance evaluations of XAI in real application systems.
    • P7004.1
      C/LT/VCSPDG Recommended Practices for Virtual Classroom Security, Privacy and Data Governance

      • This recommended practice produces best practices for meeting the requirements of IEEE P7004: Standard for Child and Student Data Governance when designing, provisioning, configuring, operating, and maintaining an online virtual classroom experience for synchronous online learning, education, and training. The recommended practice includes language that can be referenced in requests for proposals (RFPs) for online (also known as virtual) classroom solutions, the operational runbook(s) for such solutions, and the assessment and certification guideline(s) for compliance process of such solutions.
    • P7004
      C/LT/WG-CSDG Standard for Child and Student Data Governance

      • This standard provides stakeholders with certifiable and responsible child and student data governance methodologies.
    • P1484.11.1
      C/LT/SCORMRENEW Standard for Learning Technology–Data Model for Content Object Communication

      • This Standard describes a data model to support the interchange of agreed upon data elements and their values between a learning-related content object and a runtime service (RTS) used to support learning management. This Standard does not specify the means of communication between a content object and an RTS nor how any component of a learning environment shall behave in response to receiving data in the form specified. This Standard is based on a related data model defined in the “Computer Managed Instruction (CMI) Guidelines For Interoperability,” version 3.4, defined by the Aviation Industry CBT Committee (AICC). To balance the need to support existing implementations with the need to make technical corrections and support emerging practice, this Standard selectively includes those data elements from the CMI specification that are commonly implemented; renames some data elements taken from the CMI specification to clarify their intended meaning; modifies the data types of data elements taken from the CMI specification to reflect ISO standard data types and internationalization requirements; removes some organizational structures used in the CMI specification to group data elements that are specific to the AICC community of practice and not generally applicable; and introduces some data elements not present in the CMI specification to correct known technical defects in data elements taken from that specification.
    • P2961
      C/LT/CEC Guide for an Architectural Framework and Application for Collaborative Edge Computing

      • This guide defines a machine learning framework that allows a computing task to be decomposed and distributed across edge and cloud nodes. This guide provides a blueprint for data usage, model learning, and computing collaboration in edge computing environments while meeting latency, privacy, security, and regulatory requirements. It defines the architectural framework and application guidelines for collaborative edge computing, including
        1. description and definition of collaborative edge computing,
        2. the types of collaborative edge computing,
        3. the application scenarios to which each type applies
        4. performance evaluation of collaborative edge computing in the real application system.
    • P2955
      C/LT/SLLL Standard for Creating Spoken Tutorials Enabling Self-learning in Local Language

      • This standard defines a protocol based robust framework that enables creation of spoken tutorials for self-learning. The tutorials use screencast technology that helps in computer-aided instruction bridging the literacy and language divides. The spoken tutorial is an audio-video instructional material created for self-learning through the screencast technology. The tutorials achieve the objective of creating documentation for free and open source software, overcome the challenge of the lack of teachers, enable IT literacy training accessible to students weak in English and serve as a mechanism to conduct tests for the participants and issue certificates. This standard highlights the pedagogical issues and lays stress on the methodology to create the tutorials. It also elaborates on the process of creation of spoken tutorials from content outline, writing the script, novice check, recording, maintaining the time and translation and dubbing. Furthermore this standard defines how an end user can effectively use spoken tutorials for self-learning by using the side by side method. This standard sets the platform for effective methods for creating audio-video tutorials for self-learning.
    • P2247.4
      C/LT/AIS Recommended Practice for Ethically Aligned Design of Artificial Intelligence (AI) in Adaptive Instructional Systems

      • This recommended practice describes ethical considerations and recommended best practices in the design of artificial intelligence as used by adaptive instructional systems. The ethical considerations derived from P2247.1, Standard for the Classification of Adaptive Instructional Systems is directly related to:
        • P2247.1 Standard for the Classification of Adaptive Instructional Systems
        • P2247.2 Interoperability Standards for Adaptive Instructional Systems (AISs)
        • P2247.3 Recommended Practices for Evaluation of Adaptive Instructional Systems
    • 1484.11.1
      C/LT/CMI_WG11 Standard for Learning Technology – Data Model for Content Object Communication

      • This Standard describes a data model to support the interchange of agreed upon data elements and their values between a learning-related content object and a runtime service (RTS) used to support learning management. This Standard does not specify the means of communication between a content object and an RTS nor how any component of a learning environment shall behave in response to receiving data in the form specified. This Standard is based on a related data model defined in the “Computer Managed Instruction (CMI) Guidelines For Interoperability,” version 3.4, defined by the Aviation Industry CBT Committee (AICC). To balance the need to support existing implementations with the need to make technical corrections and support emerging practice, this Standard selectively includes those data elements from the CMI specification that are commonly implemented; renames some data elements taken from the CMI specification to clarify their intended meaning; modifies the data types of data elements taken from the CMI specification to reflect ISO standard data types and internationalization requirements; removes some organizational structures used in the CMI specification to group data elements that are specific to the AICC community of practice and not generally applicable; and introduces some data elements not present in the CMI specification to correct known technical defects in data elements taken from that specification.
    • 1484.20.1
      C/LT/1484.20.1 Standard for Learning Technology – Data Model for Reusable Competency Definitions

      • This Standard defines a data model for describing, referencing, and exchanging competency definitions, primarily in the context of online and distributed learning. This Standard provides a way to represent formally the key characteristics of a competency, independent of its use in any particular context. It enables interoperability among learning systems that deal with competency information by providing a means for them to refer to common definitions with common meanings. This standard enables information about competencies to be encoded and exchanged. It does not define whether a competency is a skill, knowledge, ability, attitude or learning outcome but can be used to capture information about any of these. (The scope has not changed.)
    • 1484.13.1
      C/LT/RMLTWG13 Standard for Learning Technology – Conceptual Model for Resource Aggregation for Learning, Education, and Training

      • This Standard defines a conceptual model for interpreting externalized representations of digital aggregations of resources for learning, education, and training. The conceptual model defines a set of concepts and the relationships among them and is expressed as a formal ontology. Internal compositions and uses of digital resources are not specified nor are processing methods for resource aggregations.
    • 1484.13.5
      C/LT/RMLTWG13 Recommended Practice for Learning Technology – IETF RFC 4287 – Atom Syndication Format – Mapping to the Conceptual Model for Resource Aggregation

      • This Recommended Practice specifies how the elements and attributes defined in Atom Syndication Format (Atom) relate to the components of the conceptual model for resource aggregation defined in IEEE Std 1484.13.1.
    • 1484.13.3
      C/LT/RMLTWG13 Recommended Practice for Learning Technology – ISO 21000-2:2005 Information Technology — Multimedia Framework (MPEG-21) — Part 2: Digital Item Declaration Mapping to the Conceptual Model for Resource Aggregation This Recommended Practice specifies the how the elements and attributes defined in ISO 21000-2:2005 Multimedia Framework (MPEG-21) — Part 2: Digital Item Declaration (MPEG21 DID) relate to the components of the conceptual model for resource aggregation defined in IEEE Std 1484.13.1.
    • 1484.13.2
      C/LT/RMLTWG13 Recommended Practice for Learning Technology – Metadata Encoding and Transmission Standard (METS) Mapping to the Conceptual Model for Resource Aggregation

      • This Recommended Practice specifies how the elements and attributes defined in the Metadata Encoding and Transmission Standard (METS) relate to the components of the conceptual model for resource aggregation defined in IEEE Std 1484.13.1.
    • 1484.13.4
      C/LT/RMLTWG13 Recommended Practice for Learning Technology – IMS Content Packaging Information Model (CP) Version 1.2 – Mapping to the Conceptual Model for Resource Aggregation

      • This Recommended Practice specifies how the elements and attributes defined in the IMS Content Packaging Information Model (CP) Version 1.2 relate to the components of the conceptual model for resource aggregation defined in IEEE Std 1484.13.1.
    • 1484.13.6
      C/LT/RMLTWG13 Recommended Practice for Learning Technology – Open Archives Initiative Object Reuse and Exchange Abstract Model (OAI-ORE) – Mapping to the Conceptual Model for Resource Aggregation 5.2 Scope of Proposed Standard:

      • This Recommended Practice specifies how the elements and attributes defined in the Open Archives Initiative Object Reuse and Exchange (OAI-ORE) Abstract Model and expressed in the OAI-ORE Resource Map Implementation in RDF/XML relate to the components of the conceptual model for resource aggregation defined in IEEE Std 1484.13.1.
        1589 New C/LT/AR-LEM Standard for an Augmented Reality Learning Experience Model The proposed Augmented Reality (AR) learning experience model will specify how to represent learning activities and their according workplace reference models in a standardized interchange format in order to lower entry barriers for authoring of learning experience spanning real world interaction using sensors and computer vision, and web applications.
    • 1484.12.3 Revision
      C/LT/SCORMRENEW Standard for Extensible Markup Language (XML) Schema Definition Language Binding for Learning Object Metadata

      • This effort seeks to extend the 2005 Standard. This Standard specifies an eXtensible Markup Language (XML) binding of the learning object metadata (LOM) data model defined in IEEE 1484.12.1-2002 Standard for Learning Object Metadata. An implementation that conforms with this Standard shall conform to IEEE 1484.12.1-2002.
        • REVISED Scope: This Standard defines World Wide Web Consortium (W3C) Extensible Markup Language (XML) structure and constraints on the contents of XML 1.1 documents that can be used to represent learning object metadata (LOM) instances as defined in IEEE 1484.12.1-2002, Standard for Learning Object Metadata. This Standard defines the structure and constraints of the XML 1.1 documents in W3C XML Schema definition language. An implementation that conforms to this Standard shall conform to IEEE 1484.12.1-2002.
    • P7919.1
      C/LT/Mobile Requirements for eReaders to Support Learning Applications

      • This standard describes and classifies the capabilities of eReaders that enable them to be used as a platform for learning, education, and training and provides alternative methods for implementing these capabilities. Methods include applications of industry standards and may include open source reference code.
    • P9274.1.1
      C/LT/xAPI JavaScript Object Notation (JSON) Data Model Format and Representational State Transfer (RESTful) Web Service for Learner Experience Data Tracking and Access

      • This Standard describes a JavaScript Object Notation (JSON) data model format and a Representational State Transfer (RESTful) Web Service Application Programming Interface (API) for communication between Activities experienced by an individual, group, or other entity and a Learning Record Store (LRS). The LRS is a system that exposes the xAPI RESTful Web Service API for the purpose of tracking and accessing experiential data, especially in learning and human performance.
    • P1484.20.1 Revision
      C/LT/1484.20.1 Standard for Learning Technology-Data Model for Reusable Competency Definitions

      • This Standard defines a data model for describing, referencing, and sharing competency definitions, primarily in the context of online and distributed learning. In this Standard a competency may be defined for a skill, knowledge, ability, attitude, habit of practice, or learning outcome.
    • P2247.1
      C/LT/AIS Standard for the Classification of Adaptive Instructional Systems

      • This standard defines and classifies the components and functionality of adaptive instructional systems (AIS). This standard defines parameters used to describe AIS and establishes requirements and guidance for the use and measurement of these parameters.
    • P9274.4.1
      C/LT/xAPI Recommended Practice for the Implementation of the Experience Application Programming Interface (API) 1.0.3

      • This Recommended Practice for the Implementation of the Experience API (xAPI) 1.0.3 describes the technical implementation of xAPI and includes information an implementer would find useful including background on xAPI, a description of xAPI Profiles, matters of best practice in privacy and security, and related matters in conformance and testing.
    • 3652.1
      C/LT/FML Guide for Architectural Framework and Application of Federated Machine Learning Federated learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across data owners.

      • This guide provides a blueprint for data usage and model building across organizations while meeting applicable privacy, security and regulatory requirements. It defines the architectural framework and application guidelines for federated machine learning, including: 1) description and definition of federated learning, 2) the types of federated learning and the application scenarios to which each type applies, 3) performance evaluation of federated learning, and 4) associated regulatory requirements.
    • P2247.2
      C/LT/AIS Interoperability Standards for Adaptive Instructional Systems (AISs)

      • This standard defines interactions and exchanges among the components of adaptive instructional systems (AISs). This standard defines the data and data structures used in these interactions and exchanges and parameters used to describe and measure them and establishes requirements and guidance for the use and measurement of the data, data structures, and parameters.
    • P2247.3
      C/LT/AIS Recommended Practices for Evaluation of Adaptive Instructional Systems

      • This recommended practice defines and classifies methods of evaluating adaptive instructional systems (AIS) and establishes guidance for the use of these methods. This best practice incorporates and promotes the principles of ethically aligned design for the use of artificial intelligence (AI) in AIS.
    • 3527.1
      C/LT/DLSR Standard for Digital Intelligence (DQ) —

      • Framework for Digital Literacy, Skills and Readiness Digital intelligence is a comprehensive set of technical, cognitive, meta-cognitive, and socio-emotional competencies enable individuals to face the challenges of and harness the opportunities of digital life. The digital intelligence standard establishes a framework that encompasses digital literacy, skills, and readiness, comprising 8 areas of digital life – identity, use, safety, security, emotional intelligence, literacy, communication, and rights – across 3 levels of experience – citizenship, creativity, and competitiveness.
    • 1484.11.2 Revision
      C/LT/SCORMRENEW Standard for Learning Technology – ECMAScript Application Programming Interface for Content to Runtime Services Communication

      • This Standard describes an ECMAScript application-programming interface (API) for content-to-runtime-services communication. This Standard is based on an API defined in the “CMI Guidelines For Interoperability,” version 3.4, defined by the Aviation Industry CBT Committee (AICC). It defines common API services in the ECMAScript language that enable the communication of information between learning-related content and a runtime service (RTS) used to support learning management. This Standard does not address the data structures that may be transmitted, data security, or communication between an RTS and related management systems.
    • 1484.12.1 Revision
      C/LT/SCORMRENEW Standard for Learning Object Metadata

      • This Standard is a multi-part standard that specifies Learning Object Metadata. This Part specifies a conceptual data schema that defines the structure of a metadata instance for a learning object. For this Standard, a learning object is defined as any entity–digital or non-digital– that may be used for learning, education or training. For this Standard, a metadata instance for a learning object describes relevant characteristics of the learning object to which it applies. Such characteristics may be grouped in general, life cycle, meta-metadata, educational, technical, educational, rights, relation, annotation, and classification categories. The conceptual data schema specified in this part permits linguistic diversity of both learning objects and the metadata instances that describe them. This conceptual data schema specifies the data elements which compose a metadata instance for a learning object. This Part is intended to be referenced by other standards that define the implementation descriptions of the data schema so that a metadata instance for a learning object can be used by a learning technology system to manage, locate, evaluate or exchange learning objects. This Part of this Standard does not define how a learning technology system represents or uses a metadata instance for a learning object.
    • P9274.4.2
      C/LT/xAPI Recommended Practice for Cybersecurity in the Implementation of the Experience Application Programming Interface (xAPI)

      • This recommended practice describes the technical implementation of cybersecurity for xAPI and includes information an implementer would find useful related to matters of best practice in privacy and security, and related matters in conformance and testing. This recommended practice is directly related to P9274.1.1.
  • US Chamber of Commerce T3 Initiative
  • Open Skills Network