In this blog post, I delve into the development and pilot testing phase of my "Invitation to Learning Engineering" project, where I apply core concepts from the Learning Experience Design (LXD) field. This project centers on creating a foundational learning engineering module, with a specific focus on aligning design decisions with evidence-based practices to create an impactful learning experience. Guided by key readings, including North’s (2023) chapters on “The Message” and “The Results,” as well as essential LXD templates and evaluation tools, this phase allowed me to integrate academic theories into a practical instructional design project.
The goal of this reflection is not only to outline the steps taken in developing and pilot testing the module but also to share insights on the challenges and growth I encountered along the way. By incorporating specific examples and adhering to module standards, this post demonstrates how theory can be translated into practice in a way that contributes to the emerging field of learning engineering. Through this reflection, I aim to document my development process, critical analyses, and the personal and professional growth that emerged from this experience.
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The "Invitation to Learning Engineering" project is centered on designing a dynamic e-learning course incorporating established instructional design methodologies and emerging technologies. Developed in Articulate Rise, this self-paced course uses scenario-based learning (SBL) to provide real-world applications of core learning engineering principles, immersing learners in practical, context-rich environments that enhance problem-solving and decision-making skills. Scenario-based assessments and immediate feedback loops, embedded throughout the course, help ensure that learners can evaluate and adjust their understanding incrementally.
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Agile Project Management and Tool Integration
Project management for this development phase is conducted within an agile framework using Jira, which supports iterative design, testing, and continuous improvement based on SME and user feedback. The project also employs the LEED Tracker and LE Case Guide 1.0, two tools central to tracking alignment with evidence-based practices in learning engineering, ensuring rigorous adherence to industry standards (Totino & Kessler, 2024; Kessler et al., 2024). These tools enable the project team to monitor learner engagement metrics and adapt content delivery to meet evolving needs (Research-Based Rationale | Push Pull Curation Plan).
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Leveraging AI for Efficient, Adaptable Content Creation
To streamline development and provide tailored learning experiences, this project integrates several emerging technologies. For instance, the custom GPT model "Learning Engineering Coach" is used to generate scenario responses, personalizing learner engagement and fostering a supportive environment. Articulate AI Assistant speeds up content generation, creating interaction-rich materials that align with instructional objectives, while Google’s NotebookLM synthesizes research findings to supplement course content. Together, these tools ensure a balance between rigorous instructional quality and flexible adaptability for learners (Emergent Technology Plan | Push Pull Curation Plan).
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The "Invitation to Learning Engineering" course is grounded in scenario-based learning (SBL) principles, which provide a bridge between theoretical knowledge and practical application. Scenario-based learning immerses learners in realistic environments where they can apply complex concepts to simulated, high-stakes situations. This approach is especially relevant to learning engineering, as it enables learners to actively engage with course content while developing critical skills for real-world problem-solving (Clark, 2021). According to Chernikova et al. (2020), SBL is particularly effective in professional education, where it enhances learning outcomes by using authentic scenarios that mirror actual work contexts, fostering critical thinking and adaptability.
Impactful scenario example in the Rise course (▲ click to preview course)
To support this integration, the course utilizes Donald Clark’s (2021) principles of effective scenario design. These principles emphasize focusing on the most impactful scenarios and breaking down content into manageable segments for easier comprehension, as illustrated in the example provided. Clark’s "80/20 rule" in scenario design encourages concentrating on the 20% of scenarios that address the most critical competencies. This approach not only enhances the efficiency of learning but also directs attention to the high-priority skills that professionals are likely to encounter frequently.
The course also benefits from the meta-analysis by Chernikova et al. (2020), which emphasizes the importance of scaffolding in SBL. In this project, scaffolding is provided through immediate feedback mechanisms and reflection prompts within each scenario, helping learners regulate their learning and reinforce key concepts. Reflection phases, a form of self-regulated scaffolding, have proven valuable in supporting experienced learners, while more guided feedback assists those new to the field (Chernikova et al., 2020).
Erol et al. (2016) emphasize the significance of Scenario-Based Learning (SBL) in preparing individuals for rapidly evolving fields, particularly in Industry 4.0 education, where technology and automation are essential. SBL helps learners in engineering adapt to fast-paced demands by enabling them to analyze complex data and make informed decisions. This course creates a safe yet challenging environment for practicing these skills through scenarios that mirror real-world industry challenges, such as data-driven decision-making and iterative development.
Incorporating insights from Kolodner et al. (2003), the course utilizes problem-based learning (PBL) alongside scenario-based learning (SBL). PBL encourages students to view scenarios as opportunities to tackle underlying challenges. This method aligns with the Learning Engineering Evidence and Decision (LEED) Tracker and LE Case Guide 1.0 tools, which help learners contextualize scenarios within industry standards and learning objectives (Totino & Kessler, 2024).
By combining scenario-based and problem-based methodologies, the course achieves a practical integration of theory and practice. This approach not only meets the theoretical framework outlined in North (2023) but also provides a hands-on learning experience that enhances knowledge transfer, bridging the gap between academic learning and professional practice.
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Our summative evaluation plan leverages the Learning-Transfer Evaluation Model (LTEM) (Thalheimer, 2024) framework to assess the effectiveness and real-world impact of the “Invitation to Learning Engineering” e-learning course. Each LTEM tier is represented to provide a comprehensive approach, from basic completion metrics to work performance and broader organizational impact.
Metric: Track course completion rates to ensure all participants reach the end of the e-learning course.
Purpose: Serve as a baseline to identify who completed the course.
Metric: Monitor learner engagement through interactive elements, like quizzes and discussion prompts.
Purpose: Evaluate engagement levels to ensure active participation, though not a direct indicator of learning success.
Metric: Use performance-focused learner surveys assessing learners' perceptions of their own understanding, motivation to apply content, and perceived relevance to their work.
Purpose: Gather insights into learners’ motivation and perceived value of the course content for practical application.
Metric: Conduct assessments on foundational concepts (4A) necessary for applying learning engineering principles in real scenarios.
Purpose: Validate learners’ understanding of key principles needed for decision-making. Assess trivial knowledge (4B) minimally to avoid overemphasis on non-essential details.
Metric: Scenario-based questions requiring learners to make decisions in realistic learning engineering contexts.
Purpose: Measure the ability to apply foundational knowledge in decision-making situations relevant to learning engineering.
Metric: Provide a practical task or simulation where learners must demonstrate a core learning engineering skill (e.g., designing an instructional intervention).
Purpose: Assess real-world task performance without aid to evaluate readiness for on-the-job application.
Metric: Conduct follow-up surveys with participants’ managers or collect observational feedback to gauge the application of learning engineering skills on the job.
Purpose: Validate that skills learned are being effectively transferred and utilized in workplace contexts.
Metric: Evaluate broader organizational impact metrics, such as improvements in project outcomes linked to learning engineering interventions.
Purpose: Assess the downstream effects of skill transfer on organizational performance indicators and stakeholder impact.
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An adaptation of the Learning Engineering Case Guide 1.0 in FIgma
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