Reading fluency is the ability to decode connected text with accuracy and speed (Archer, Gleason, & Vachon, 2003; Daly, Neugebauer, Chafouleas, & Skinner, 2015), and is generally measured by how many words a student can read in a minute. Selfefficacy is the judgment people make about their own performance levels for specific abilities, which affects their motivation and behaviors concerning those abilities (Bandura, 1977). It is unknown if repeated reading or interval sprinting reading interventions have an effect on reading self-efficacy. Two third-grade students with low reading fluency participated in an alternate treatment design, using repeated reading and interval sprinting reading interventions. After each session, reading self-efficacy was assessed using the Children’s Intervention Rating Profile (CIRP; Witt & Elliot, 1985). Results indicated that neither student’s reading fluency increased as expected with single session dosage, but their reading self-efficacy did increase for both the repeated reading and interval sprinting interventions. Student 2 demonstrated an increase in reading fluency and reading self-efficacy following the repeated reading intervention when the intervention dosage was increased. Both students reported increases in reading self-efficacy, even when their reading fluency did not increase, suggesting these interventions may provide benefits beyond simply increasing the number of words a student can read in one minute

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Les protocoles avec alternance de traitements sont des protocoles expérimentaux à cas uniques utiles pour évaluer et pour comparer l’efficacité d’interventions. Pour l’analyse de ces protocoles, les meilleures pratiques suggèrent aux chercheurs et aux professionnels d’utiliser conjointement les analyses statistiques et visuelles, mais ces méthodes produisent des taux d’erreurs insatisfaisants sous certaines conditions. Dans le but de considérer cet enjeu, notre étude a examiné l’utilisation de réseaux de neurones artificiels pour analyser les protocoles avec alternance de traitements et a comparé leurs performances à trois autres approches récentes. Plus précisément, nous avons examiné leur précision, leur puissance statistique et leurs erreurs de type I sous différentes conditions. Bien qu’il ne soit pas parfait, le modèle de réseaux de neurones artificiels présentait en général de meilleurs résultats et une plus grande stabilité à travers les analyses. Nos résultats suggèrent que les réseaux de neurones artificiels puissent être des solutions prometteuses pour analyser des protocoles avec alternance de traitements. / Alternating-treatment designs are useful single-case experimental designs for the evaluation and comparison of intervention effectiveness. Most guidelines suggest that researchers and practitioners use a combination of statistical and visual analyses to analyze these designs, but current methods still produce inadequate levels of errors under certain conditions. In an attempt to address this issue, our study examined the use of artificial neural networks to analyze alternating-treatment designs and compared their performances to three other recent approaches. Specifically, we examined accuracy, statistical power, and type I error rates under various conditions. Albeit not perfect, the artificial neural networks model generally provided better and more stable results across analyses. Our results suggest that artificial neural networks are promising alternatives to analyze alternating-treatment designs.