NET in the Wild Natural Environment Teaching & In-Vivo Data Collection

Natural Environment Teaching (NET) is more than playing with a learner and following their lead. Effective NET requires behavior technicians to recognize meaningful learning opportunities, respond to changing motivation, arrange the environment intentionally, use appropriate prompting and reinforcement procedures, and collect accurate data—all while maintaining natural and meaningful interactions. In this interactive virtual RBT Professional Development Unit (PDU), Sean Yocum of the Rad N Bad Podcast and Hickory Learning Group, takes participants beyond the buzzwords surrounding “naturalistic” and “learner-led” intervention and examine what high-quality NET actually looks like in practice. Through interactive scenarios, knowledge checks, simulated clinical situations, and in-vivo data exercises, participants will practice distinguishing purposeful NET from unstructured interaction, identifying naturally occurring learning opportunities, recognizing motivating operations, selecting appropriate measurement procedures, and collecting defensible data without disrupting the natural interaction. The goal is simple: stop simply playing therapy and start engineering meaningful learning opportunities.

1. 1Upon completion of this training, participants will be able to: 1. Differentiate Natural Environment Teaching, structured teaching, and unstructured interaction by identifying the observable behavioral components of each approach. 2. Identify naturally occurring and appropriately arranged learning opportunities within common play, leisure, social, adaptive, and daily living activities. 3. Identify changes in motivating operations and relevant environmental variables that may influence learner responding and the effectiveness of reinforcement during NET. 4. Describe how antecedent arrangement, prompting, prompt fading, and naturally related reinforcement can be incorporated into NET while maintaining meaningful learner engagement. 5. Differentiate naturally related reinforcement contingencies from arbitrary reinforcement and identify when the outcome of a learner's behavior can function as the reinforcer. 6. Select appropriate in-vivo measurement procedures—including frequency, opportunity-based recording, duration, latency, first-trial probes, task analysis, and permanent-product measurement—based on the behavior being measured. 7. Demonstrate through scenario-based exercises accurate in-vivo data collection while distinguishing directly observed behavior from retrospective estimation or reconstructed data. 8. Evaluate simulated NET sessions to identify missed learning opportunities, measurement barriers, changes in learner motivation, and potential improvements to treatment implementation.