My instructional approach is grounded in data-informed decision-making and supported by proven design models. I use assessment data, learner needs, and performance trends to drive the planning and development of effective learning experiences. Each strategy I apply supports purposeful, aligned, and responsive instruction.
Below are key models that guide my work, along with visuals showcasing how I collect and analyze data to inform design decisions.
I use the ADDIE model primarily after larger assessments or during long-term planning cycles.
It guides me through analyzing data to identify learner needs, designing aligned instruction, developing materials, implementing support, and evaluating effectiveness through performance and feedback.
This model ensures structure, alignment, and reflection across complex or multi-phase learning solutions.
I apply the SAM model in fast-paced projects or small-group settings where iteration is key.
It allows me to quickly prototype content, gather feedback, and make real-time adjustments based on learner performance.
SAM supports agility and collaboration, making it ideal for evolving needs and rapid design timelines.
I begin with the end in mind, identifying the desired learning outcomes based on benchmarks or performance goals.
I then design assessments and scaffold instruction to ensure all components align with those outcomes.
This approach is especially effective for multi-step planning and maintaining instructional coherence.
I use Bloom’s to structure learning objectives that align with various levels of cognitive demand.
It helps me differentiate tasks and build progression from foundational understanding to higher-order thinking.
This strategy ensures that instruction supports meaningful learning, application, and performance growth.