Education Division Scholarship
Conceptualizing Learning Engineering
Document Type
Research Poster
Streaming Media
Publication Date
4-24-2025
Abstract
The Learning Engineering Toolkit (Author et al., 2022) defines Learning Engineering (LE) as a process and practice that applies the learning sciences using human-centered engineering design methodologies and data-informed decision-making to support learners and their development (Kessler et al., 2022). The LE process involves developing an understanding of the learner, the context, and the team addressing the central learning challenge. Solutions are targeted to specific learners and contexts with data collected and analyzed to inform continuous improvement through iterative design (Author et al., 2022). The LE process makes data central to decision-making and emphasizes iterative development and refinement.
Figure 1: The Learning Engineering Process (Kessler et al., 2022)
We aim to show how LE is moving forward as a practice. LE uses an iterative human-centered design approach and the results of a rich body of research from the learning sciences and related fields to (a) understand affordances and constraints of a learning venue, (b) identify salient characteristics of the targeted learners, including variations in what learners bring to their learning, (c) propose design solutions that are informed by what is known about learning and how to support it, (d) iteratively work towards learning solutions that are engaging, effective, equitable, and impactful, and (e) utilize data to understand the effectiveness of learning interventions (Author et al., 2022; Kolodner, 2023). LE incorporates the best learning and design disciplines into its processes and practices.
While science aims to discover truths about the world, engineering focuses on developing practical and scalable solutions using scientific knowledge as a tool (Author et al., 2022; Craig et al., 2023; Dede et al., 2019). LE embodies this distinction (Kolodner, 2023) by using what is known about how people learn and how to support learning in the design of curriculum, learning technologies, learning resources, environments for informal learning, training materials, and more (Dede et al., 2019). The LE approach is designed to close the loop on efforts to improve learning outcomes and bring solutions to scale (Saxberg, 2017).
LE emphasizes practical application, scalability, and the optimization of learning experiences through interdisciplinary methods and data analytics. LE uses various theoretical lenses on learning, identifies ways to instrument and analyze data, and iterates designs. It encourages attention to theoretical underpinnings and systematically incorporates data instrumentation for continuous improvement of solutions.
LE integrates principles from multiple fields to create effective educational interventions, emphasizing collaboration among stakeholders to apply research findings practically. It is a team activity, as a wide variety of expertise is needed to design learning solutions that will work: expertise on different literatures related to learning processes, targeted learners, pedagogical approaches, technology, data collection and analysis, subject matter, and project management (Author et al., 2020; Roberts & Miller, 2019). A central tenet of learning engineering is the need for a variety of expertise and shared understanding needed to solve a learning challenge (see Figure 2).
Figure 2: Learning Engineering Teams (Author et al., 2022)
Publication Title
https://convention2.allacademic.com/one/aera/aera25/index.php?cmd=Online+Program+View+Paper&selected_paper_id=2195726&PHPSESSID=kjvtuh5126b9mrglgp3fhui7am