The project "An Invitation to Learning Engineering" is an open-access e-learning course designed to introduce learners to the core principles of learning engineering. The course is developed using Articulate Rise, with Jira as the project management tool, and an OpenAI custom GPT for assistance in generating scenario-based learning content and responses to use in the course. The prospective learners are professionals in the field of learning engineering who will engage in scenario-based learning activities to build practical skills.
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Learning Experience Design Essentials Designing for Users and Impact-Notebook.html
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Technology-Enhanced Learning Experiences
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The workbook linked below contains three distinct checklists that align with key learning experience design (LXD), Universal Design for Learning (UDL), and e-learning quality standards. These checklists are designed to guide the evaluation and development of online courses, training programs, and learning materials by ensuring accessibility, instructional alignment, and overall course quality.
Professionals involved in designing and implementing learning solutions—such as instructors, instructional designers, and educational technologists—often rely on intuition or established practices without systematically testing and validating their approaches. This results in learning solutions that may not effectively address learners' needs or leverage the full potential of the learning sciences (Thai et al., 2023).
Our project aims to develop a comprehensive e-learning micro-credential program that equips professionals with the knowledge, skills, and tools to apply learning engineering principles effectively. Phase one comprises the Level 1 credential—an open-access primer on learning engineering. Our e-learning course is “An Invitation to Learning Engineering.”
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To capture a deeper understanding of the program’s purpose, structure, and status, click on the project’s website below:
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“The set of personas is used during design to decide which of several options will work better and to guide adaption so that those options work more broadly” (Thai et al., 2023).
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