2020 Publications:
Science of Remote Learning
Jim Goodell & Aaron Kessler
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
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
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
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
(Editors) Jim Goodell, Alex Jackl, Joe Andrieu, Jim Kelly
Jul 10, 2020 U.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.
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)
Jul 24, 2020 IEEE SA INDUSTRY CONNECTIONS
Proceedings of the 2019 Conference on Learning Engineering
What we Discovered at the Roots of Learning Engineering
CEDS Data Model Guide - Version 8
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
