Monday, November 16, 2020

2020 Updates

 2020 Publications:

 


Science of Remote Learning

Jim Goodell & Aaron Kessler



May 22, 2020 
 publication descriptionMIT Open Learning

This document was created as a resource for those working on developing, implementing, and engaging in remote teaching and learning. One goal of this work is to provide a starting point for transitioning away from emergency online instruction in response to COVID-19 toward increasingly effective remote learning. This resource is written in plain language to be broadly applicable to professional educators and non-professionals; teams designing remote learning experiences, people supporting at-home learning (mostly parents) and learners. While we do not believe this document provides a single best process for creating and supporting online learning - learning is a deeply contextual process that requires attention to the needs of learners in your context- our hope is that in considering the strategies contained within, grounded in research based principles, you can iteratively improve and support remote learners and learning.

Design Recommendations for Intelligent Tutoring Systems, Volume 8 - Data Visualization (Book) - Chapter 2 - A Learning Engineering Approach to Data Visualization

Jim Goodell & Bridget E. Thomas



December 29, 2020 
 publication description

US Army Combat Capabilities Development Command - Soldier Center


This chapter  presents  the development  of  data visualizations as  a learning  engineering  exercise.  The  approach  is based on  the  theory  that  a viewer’s  interaction with  a data visualization is a  learning  experience intended  to  address  specific  learning  objectives.  The process  starts with questions like:  What  do you want the viewer/learner  to know, be able  to do, or  do  after  experiencing  the  data visualization? Is it  a call  to  action? Do you want  to change the viewer’s behavior? Do you want  the viewer  to be  able  to  apply  the new knowledge in some way?  The approach applies  human-centered design, incorporates  key  elements of  cognitive science, uses  data  to  inform  design decisions, and iteratively  develops and tests  aspects of  the visualization to  optimize it  for  the desired outcomes.

A Learning Engineering Model for Learner-Centered Adaptive Systems

Jim Goodell & K.P. Thai



International Conference on Human-Computer Interaction
HCII 2020: HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games pp 557-573

Goodell J., Thai KP.. (2020) A Learning Engineering Model for Learner-Centered Adaptive Systems. In: Stephanidis C. et al. (eds) HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games. HCII 2020. Lecture Notes in Computer Science, vol 12425. Springer, Cham. https://doi.org/10.1007/978-3-030-60128-7_41


Applying Self-Sovereign Identity Principles to Interoperable Learner Records

(Technical Consultant)


Jun 16, 2020 - U.S. Chamber of Commerce Foundation


Learning and Employment Record (LER) Wrapper and Wallet -- A Universal Cross-Standard Digital Container for Self-Sovereign Management of Learning and Employment Records with Cross-Standard LER Wrappers

publication date(Editors) Jim Goodell, Alex Jackl, Joe Andrieu, Jim Kelly



Jul 10, 2020  publication descriptionU.S. Chamber of Commerce Foundation


An LER is a digital record of learning and work that can be linked to an individual and combined with other digital records for use in pursuing educational and employment opportunities.  An LER can document learning wherever it occurs, including at the workplace or through an education experience, credentialing, or military training. It can also include information about employment history and earnings. LERs are similar to electronic health records (EHRs) and have the potential to improve education and hiring outcomes in the same way that EHRs have improved healthcare delivery. What makes LERs unique is their ability to be fully transferable and recognized across student information, learning management, employer HR, and military systems. 


LERs go by many names and are also referred to as an interoperable learning record (ILR).This draft specification is being developed within the T3 Innovation Network (T3 Network) with assistance from project teams to leverage existing LER standards, not replace them. This draft specification was developed in the public domain and will be offered for recognition as a standard by relevant standards organizations concerned with Learning and Employment Records (LERs). The editors facilitated cooperation with a large group of standards and stakeholders including industry, academia, and standards organizations. Contributors included representatives from Access 4 Learning (A4L), Common Education Data Standards (CEDS), IMS Global Learning Consortium, Postsecondary Electronics Standards Council (PESC), HR Open Standards Consortium, and World Wide Web Consortium (W3C), among others. In addition, IEEE approved a workgroup for a new guide to interoperable learner records that will be informed by this document. This draft specification will be reviewed and pilot-tested by LER pilot teams to improve and update the specification over time.



publication descriptionThis draft specification is being developed in the public domain and will be offered for recognition as a standard by relevant standards organizations concerned with Learning and Employment Records (LERs). An LER is a digital record of learning and work that can be linked to an individual and combined with other digital records for use in pursuing educational and employment opportunities. An LER can document learning wherever it occurs, including at the workplace or through an education experience, credentialing, or military training. It can also include information about employment history and earnings. LERs are similar to electronic health records (EHRs) and have the potential to improve education and hiring outcomes in the same way that EHRs have improved healthcare delivery. What makes LERs unique is their ability to be fully transferable and recognized across student information, learning management, employer HR, and military systems. LERs go by many names and are also referred to as an interoperable learning record (ILR).This draft specification is being developed within the T3 Innovation Network (T3 Network) with assistance from project teams to leverage existing LER standards, not replace them. It will be offered for adoption by various standards organizations. This work has been done in cooperation with a large group of standards and stakeholders including Access 4 Learning (A4L), Common Education Data Standards (CEDS), IMS Global Learning Consortium, Postsecondary Electronics Standards Council (PESC), HR Open Standards Consortium, and World Wide Web Consortium (W3C), among others. In addition, IEEE approved a workgroup for a new guide to interoperable learner records that will be informed by this document. This draft specification will be reviewed and pilot-tested by LER pilot teams to improve and update the specification over time.

IEEE IC INDUSTRY CONSORTIUM ON LEARNING ENGINEERING, Proceedings of the 2019 Conference on Learning Engineering, IEEE SA INDUSTRY CONNECTIONS

Blake‐Plock, S., Goodell, J., Kurzweil, D., Kessler, A., Olsen, J. (Editors)

publication dateJul 24, 2020  publication descriptionIEEE SA INDUSTRY CONNECTIONS


 

Proceedings of the 2019 Conference on Learning Engineering


What we Discovered at the Roots of Learning Engineering

Jim Goodell, Mark Lee, & Jodi Lis
image of document


This article examines the practice and process of learning engineering. It is based on site visits and interviews we conducted in Pittsburgh, PA in November 2018 with representatives from several departments at Carnegie Mellon University, Duolingo, Carnegie Learning, and Acrobatiq.

CEDS Data Model Guide - Version 8 

Jim Goodell


May 6, 2020  
ceds.ed.gov 

The CEDS Data Model Guide describes how to use the Common Education Data Standards Data Models published on the CEDS website (https://ceds.ed.gov) and Open Source Community (https://github.com/CEDStandards). CEDS includes a broad scope of data elements definitions spanning much of the P-20W spectrum (pre-kindergarten through workforce education). The CEDS Domain Entity Schema (DES) includes a hierarchical schema of domains and entities—as a nontechnical reference showing CEDS elements in context. The Integrated Data Store (IDS) is a fully normalized logical, or physical, model. The Guide also includes examples showing CEDS elements in the context of other types of data models, such as the star schema typically used in dimensional data warehouse design.



CEDS Conceptual Model Guide - Version 8

Jim Goodell/QIP/AEM


May 6, 2020  
ceds.ed.gov 

This overview of the CEDS Conceptual Model contains general information that can be applied broadly across the four domains of P-20W education and training:
1. Early Childhood
2. K-12
3. Higher Education
4. Workforce (including military)
The CEDS Conceptual Data Model is built on four key concepts: Person, Organization, Resource, and Relationship. These key concepts are modeled as CEDS Entity super classes. They also serve as broad categories for understanding and organizing P-20W longitudinal data. The model supports longitudinal data, recognizing that data and relationships change over time:
● People have roles in Organizations for specific periods of time.
● The status of a Person, Organization, Resource, or Relationship may be different at different points in time.
● Events involving one or more Persons, Organizations, Resources, or Relationships occur at a point in time and over periods of time.


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