How does the ADDIE model encourage reflection and iteration?

The ADDIE model encourages reflection and iteration through its five stages: Analysis, Design, Development, Implementation, and Evaluation. After each stage, there is an opportunity for reflection and review, allowing for adjustments and improvements to be made before moving on to the next stage. This iterative process ensures that the training or learning program is continually improved and refined based on feedback and evaluation.

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Learning & Training with ADDIE

An effective training program can boost the performance and morale of current team members and newcomers. This Learning & Training deck utilizes the A...

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ADDIE is a training development and learning model and was originally created for the U.S. Army. Since then, it has been implemented across not only all branches of the Armed Forces, but also a variety of companies and industries. ADDIE is an acronym that stands for the five stages of a training and learning process: Analysis, Design, Development, Implementation, and Evaluation. It relies on each stage being done in the given order but with a focus on reflection and iteration. Most modern training models are based on this five-phase framework or its variation. (Slide 2)

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How has the ADDIE model evolved since its creation for the U.S. Army?

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The ADDIE model, originally created for the U.S. Army, has evolved to be implemented across all branches of the Armed Forces and various companies and industries. While it still maintains its five-stage process - Analysis, Design, Development, Implementation, and Evaluation - the focus has shifted to reflection and iteration. This means that while the stages are done in order, there is an emphasis on revisiting and refining each stage as needed. Most modern training models are based on this five-phase framework or its variations.

What are the challenges of implementing the ADDIE model?

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The challenges of implementing the ADDIE model can include the time and resources required for each stage, the need for expertise in each area, and the potential for a lack of flexibility due to the linear nature of the model. Additionally, the model requires constant evaluation and iteration, which can be resource-intensive. Finally, the model may not be suitable for all types of training or learning scenarios.

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