Behavior analysts commonly use visual inspection to analyze single-case graphs, but studies on its reliability have produced mixed results. To examine this issue, we compared the Type I error rate and power of visual inspection with a novel approach-machine learning. Five expert visual raters analyzed 1,024 simulated AB graphs, which differed on number of points per phase, autocorrelation, trend, variability, and effect size. The ratings were compared to those obtained by the conservative dual-criteria method and two models derived from machine learning. On average, visual raters agreed with each other on only 75% of graphs. In contrast, both models derived from machine learning showed the best balance between Type I error rate and power while producing more consistent results across different graph characteristics. The results suggest that machine learning may support researchers and practitioners in making fewer errors when analyzing single-case graphs, but replications remain necessary.

Prior research found that without the naming cusp, children did not learn from instructional demonstrations presented before learn units (IDLUs) (i.e., modeling an expected response twice for a learner prior to delivering an instructional antecedent), however, following the establishment of naming, they could. The present study was designed to compare the rate of learning reading and mathematics objectives in children who showed naming using IDLUs compared to standard learn units (SLUs) alone (comparable to three-term contingency trials). In Phase 1, a pre-screening phase, we demonstrated that four typically developing males, 3 to 4 years of age, had naming within their repertoire, meaning they were able to master the names of novel 2-D stimuli as both a listener and a speaker without explicit instruction. Using the same participants in Phase 2, we compared rates of learning under two instructional methods using a series of repeated AB designs where conditions (IDLUs versus SLUs) were counterbalanced across dyads and replicated across participants. The participants learned more than twice as fast under IDLU conditions and showed between 30% and 50% accuracy on the first presentation of a stimulus following a model. The IDLU condition was more efficient (fewer trials to criterion) than the SLU condition. These findings, together with prior findings, suggest that the onset of naming allows children to learn faster when instructional demonstrations are incorporated into lessons.

We implemented a delayed multiple probe across participants design to analyze the effects of behavioral skills training (BST) on teaching assistants' effective delivery of instruction as measured through their performance on the Teacher Performance Rate and Accuracy (TPRA) scale. Effective instruction is defined as instruction that is both accurate and fluent. Three adult teaching assistants, newly hired at a kindergarten readiness program that employed the principles of applied behavior analysis, were selected to participate. The participants had no previous experience implementing three‐term contingency trials. Dependent variables included two components of the TPRA scale measured pre‐ and post‐intervention: (1) percent of correctly delivered trials and (2) rate of trial delivery. The results indicated that BST increased the accurate delivery of correct three‐term contingency trials by teaching assistants as measured through TPRA scale observations. The intervention also successfully increased the teaching assistants' rate of trials delivered per minute.