The ADDIE model is a systematic instructional design framework used to guide the design and development of high-quality learning experiences. The five steps of the ADDIE model are:

 

  1. Analysis: In this first phase, the instructional designer gathers information about the learning needs, objectives, audience characteristics, existing resources, and constraints. The intent is to understand the context and to identify the gap between the current state and the desired state of learning.
  2. Design: After analysis, the next step is designing the learning solution. The designer will define learning objectives, select appropriate instructional strategies and media, create a detailed lesson plan or curriculum, and outline assessments to measure learning outcomes. Drafts of activities, exercises, and content are created and reviewed.
  3. Development: During development, the actual learning materials are created based on the design specifications. This may involve revising drafts, developing multimedia elements, designing final forms of activities and exercises, and building assessments. The focus is on creating effective, engaging learning materials that align with the instructional goals.
  4. Implementation: After development, the designed learning solution is put into action. This may involve delivering the training to learners through face-to-face instruction, online courses, or blended learning approaches. The implementation phase also includes instructor training and logistical planning.
  5. Evaluation: The final phase of the ADDIE model involves evaluating the effectiveness of the learning solution. This includes assessing learner performance, collecting feedback from learners and stakeholders, measuring the achievement of learning objectives, and identifying areas for improvement. Evaluation data is used to inform revisions to the learning materials and to make recommendations for future iterations of the instructional design process.

The ADDIE model is iterative, meaning that the results of the evaluation phase can inform revisions to any of the previous phases, leading to continuous improvement of the learning solution.