Child Performance Concerns? Do This First

When a child is not demonstrating a skill, it is easy to conclude that the child simply cannot do it, but that conclusion is often premature. Much of the time, what looks like a child's performance problem is really a question of how the target is being run. Before changing the child's program, an effective supervisor looks at the conditions around the skill: the materials, the setting, the way the instruction is delivered, and whether the child is being asked to perform in only one narrow context. Skills frequently appear once the context shifts, which is why flexibility is the first tool rather than the last. Treating a struggling target as a puzzle to solve, instead of a verdict on the child, keeps supervision constructive and accurate.

The people running daily trials usually hold the most useful information, so a good early step is to ask the technician what barriers they notice with the target. Their answer often reveals the real obstacle — materials that are not motivating, instructions that vary between sessions, a distracting environment, or a skill that lands better when embedded in play. When more than one technician works with the same child, consistency becomes essential, because mixed delivery produces mixed data that is hard to interpret. It also helps to have each technician show how they run the target, so the supervisor can confirm the procedure is being implemented as written rather than only described. Gathering this information before acting prevents changing a program for the wrong reason.

From there, it helps to widen the lens and consider several explanations at once. The supervisor can ask whether motivation is strong enough or the reinforcers need refreshing, whether the child has the prerequisite skills, whether the issue is really generalization rather than initial performance, and whether ordinary factors like fatigue or a hard transition affected the session. A single rough session is not the whole story, so looking at the data trend across sessions — and across technicians and times of day — prevents overreacting to a one-time dip. Once the likely obstacle is identified, the productive response is to collaborate: model a new way to run the target, let the technician practice, and set a follow-up plan. Approached this way, performance concerns become shared problem-solving rather than blame, which is the stance effective supervision is built on (Sellers, Valentino, & LeBlanc, 2016), and it keeps decisions grounded in data rather than impressions (Cooper, Heron, & Heward, 2020).

References

Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied behavior analysis (3rd ed.). Pearson.

Sellers, T. P., Valentino, A. L., & LeBlanc, L. A. (2016). Recommended practices for individual supervision of aspiring behavior analysts. Behavior Analysis in Practice, 9(4), 274–286. https://doi.org/10.1007/s40617-016-0110-7