ARLEM -Using Augmented Reality to Create Learning Environments
The Augmented Reality Learning Experience Model (ARLEM IEEE 1589) content model specifies a description of workplaces where people learn and/or work, and real-time delivery of (and interactions with) instructional content pertaining to activities and tools within intelligent spaces. The ARLEM activity model will support other LTSC specifications, such as xAPI learning record stores, permitting personalized instruction adaptation. In addition, the specification will provide a model for live data from connected objects in the spaces to ARLEM-compliant software running on devices designed for visualization using Augmented Reality.
Chair – Fridolin Wild email@example.com
Secretary – Brandt Dargue Brandt.W.Dargue@boeing.com
Child and Student Data Governance Working Group (P7004)
Goal: produce standards for vendors and educational institutions that will assure stakeholders about specific constraints on the collection, storage, distribution, and use of student data. The standard defines specific methodologies to help users certify how they approach accessing, collecting, storing, utilizing, sharing, and destroying child and student data. The standard provides specific metrics and conformance criteria regarding these types of uses from trusted global partners and how vendors and educational institutions can meet them.
Chair – Marsali Hancock – firstname.lastname@example.org
Technical Advisory Group (TAG) for xAPI.
Our initial purpose is to create an IEEE technical report as a reference and implementation guide for xAPI 1.0.3. More broadly, we’ll be providing an open place for discussion among xAPI stakeholders and we’ll potentially be making recommendations about needs to support widespread use of the specification based on our activity in writing the report. Our start point is the xAPI 1.0.3 specification. We’ll discuss all aspects of xAPI such as xAPI Profiles and the relation of xAPI to SCORM and cmi5. The end point is open-ended and in our discussion, we will work to define the scope of the TAG. Meetings are every Tuesday 2:30 PM EST
Chair Shelly Blake-Plock – email@example.com
Vice-Chair, Jono Poltrack
Secretary, Andy Johnson
Adaptive Instructional Systems (AIS) (P2274)
The purpose of the Adaptive Instructional Systems Study Group is to investigate the possible market need for standards across a group of technologies collectively known as Adaptive Instructional Systems (AIS). AIS include Intelligent Tutoring Systems and other related learning technologies. The output of the study group will be one or more PARs identifying needed standards activities.
- Chair—Robert Sottilare, Soar Technology, Inc., firstname.lastname@example.org
- Vice Chair—Eric Domeshek, Stottler Henke, Inc., email@example.com
- Treasurer—Jody Cockroft, University of Memphis, firstname.lastname@example.org
- Secretary—Drew Hampton, University of Memphis, email@example.com
IEEE Industry Connections Industry Consortium on Learning Engineering (ICICLE)
The IEEE Industry Connections Industry Consortium on Learning Engineering (ICICLE) is an open forum and community-driven platform for defining and supporting the profession of Learning Engineering. As part of the IEEE Standards Association’s (IEEE-SA) Industry Connections (IC) program, ICICLE’s membership spans the globe and is comprised of leading organizations in industry, academia, and government. The Industry Connections Program helps incubate new standards and related products and services by facilitating collaboration among organizations and individuals as they hone and refine their thinking on rapidly changing technologies.
While Learning Science research has generated many of these new technologies, neither the scientific community nor the instructional designers who create new learning activities offer much guidance concerning the capabilities and limitations of the underlying technologies; how to use them to accomplish instructional goals; and how to evaluate the effectiveness of both the technologies and the various pedagogical innovations they allow. Motivated by the need to provide this guidance, IEEE ICICLE was established by leading organizations across the world.
Chair Shelly Blake-Plock – firstname.lastname@example.org
Vice Chair Craig Wiggins – email@example.com
Mobile Learning Platforms (P7919.1)
This working group is developing standards for mobile learning platforms.
7919.1 – Requirements for eReaders to Support Learning Applications
Scope: The 7919.1 standard will describe and classify 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.
Purpose: the purpose of P1719.1 is enable eReader developers, eBook authors and publishers, and consumers to understand the capabilities and affordances offered by eReaders and required by “eBooks” that are used in learning, education, and training. In this standard, an “eBook” may range from an interactive traditionally organized eBook to a fully adaptive learning and teaching system. The standard will enable stakeholders to ensure that the capabilities and affordances offered match those that are required.
John Costa, Chair firstname.lastname@example.org
P9274 is a spinoff of the work of the xAPI Technical Working Group. P92741.1 (Base Spec), 92741.2 9 (Profile Spec), and 9274.2 (Individual Profiles) purpose is to develop a standard to provide an interoperable means to store and retrieve learning experience data as required by modern, data-intensive learning technologies. This project would standardize the data model format and communication protocol for learning experience data allowing vendors to build interoperable solutions and to take advantage of many products that support the xAPI.
Chair Jono Poltrack – jonopoltrack@GMAIL.COM
SCORM Renew Work Group
SCORM Renew is a workgroup that will examine the renewal of expiring key SCORM enabling standards – The first of these SCORM standards to be examined for renewal are – 1484.11.3 Learning Technology-Extensible Markup Language (XML) Schema Binding for Data Model for Content Object Communication and 1484.12.3 Extensible Markup Language (XML) Schema Definition Language Binding for Learning Object Metadata
CDSWG20: Competency Data Standards Working Group
Jim Goodell – email@example.com
Federated Machine Learning P3652.1
“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.