Learning Engineering Uses a Variety of Tools (Poster 2)

• In Event: From Research to Application: Learning Engineering as a Process for Learning-Sciences-Informed Educational Design

Abstract

Educational and learning sciences researchers have made great discoveries in the past half-century about how people learn and in developing theoretical frameworks for the practical application of these discoveries, including motivational and contextual conditions that foster learning (e.g. Clark & Saxberg, 2018; Bloom, 1984; Bransford, 1999; Koedinger, et al. 2012; National Academies of Sciences, Engineering, and Medicine, 2018). Likewise, the disciplines of learning analytics and education data mining have emerged in the past quarter century (International Educational Data Mining Society, n.d.; SoLAR, n.d.). However, key methods and discoveries from these disciplines have yet to be applied at scale.
The learning engineering community of practice is addressing the theory-to-practice gap with process tools for practitioners and organizations to apply the learning sciences using human-centered and engineering design methodologies and data-informed decision-making (e.g. Author, 2022; Sandoval, 2014; Totino & Kessler, 2024).
Tools can be organized by applicable stages in the learning engineering process or those used throughout the process. They can also be categorized by discipline, such as tools helping learning engineering teams apply the learning sciences, employ human-centered design, leverage systems engineering, instrument learning experience data, employ learning analytics, or use action research methodologies. Finally, tools may be categorized by the type of challenge being addressed in a given iteration of the learning engineering process.
Tools for Understanding the Challenge
The learning engineering process starts with understanding the challenge for an iteration of a learning solution or improvement. Some examples of process tools that have been adapted from other domains for learning engineering are performance task analysis, cause and effect analysis, and failure mode and effects analysis (Author et al., 2022).
Tools from the Learning Sciences
Learning sciences category checklists (Author et al., 2020, 2022) and design patterns–templates for repeatable solutions to commonly occurring learning engineering problems can help learning engineering teams root decisions in science. These can be used with decision-tracking tools to ensure the fidelity of process-linking decisions to supporting learning sciences.
Data Instrumentation Tools
Software and technology standards, frameworks, and libraries support the rapid development of a scalable and secure collection of data from learning experiences, for example, the IEEE Experience API (xAPI) standards.
Data Analysis Tools
More can be done to make recent discoveries in learning analytics and educational data mining accessible for application by practitioners. Some current tools and resources help build learning analytics capacity in learning engineering teams, common awareness of questions that can be asked of data, and how to translate general questions into data questions (e.g., Author et al., 2022; Baker, 2024).
Tools Used Throughout the Learning Engineering Process
Examples of tools used in all stages of the process are conjecture maps (Sandoval, 2014) and the Learning Engineering Evidence and Decision (LEED) tracker (Totino & Kessler, 2024). Tools used by learning engineering teams support lean and agile methodologies, human-centered design, ethical decision-making, and implementation.
More work is needed to grow awareness of currently available tools and to continue to grow the toolset for scaled impact in human learning.

Authors

• Erin Czerwinski, Carnegie Mellon University

• Jim Goodell, Quality Information Partners

• Rob Nyland, U.S. Air University