The use of technology-based self monitoring (SM) interventions is becoming more popular, increasing the likelihood that SM interventions will have greater contextual fit within classrooms. SM has resulted in increases in desired school behaviors, such as increases in on task behavior, academic performance, and task completion for students diagnosed with disabilities. The purpose of this study was to extend the findings from Clemons et al. and examine student and teacher use of a web based SM application, I-Connect, and reinforcement to increase on task behavior for three U.S. elementary school students with or at risk for an emotional behavioral disorder using extended SM intervals. All three participants demonstrated significant increases in on task behavior with the use of the I-Connect application plus reinforcement intervention in the classroom.
Amato-ZechN. A.HoffK. E.DoepkeK. J. (2006). Increasing on-task behavior in the classroom: Extension of self-monitoring strategies. Psychology in the Schools, 43(2), 211–221. https://doi.org/10.1002/pits.20137
2.
BedesemP. L. (2012). Using cell phone technology for self-monitoring procedures in inclusive settings. Journal of Special Education Technology, 27(4), 33–46. https://doi.org/10.1177/06264341202700403
3.
BenazziL.HornerR. H.GoodR. H. (2006). Effects of behavior support team composition on the technical adequacy and contextual fit of behavior support plans. The Journal of Special Education, 40(3), 160–170. https://doi.org/10.1177/00224669060400030401
4.
BouckE. C.SavageM.MeyerN. K.Taber-DoughtyT.HunleyM. (2014). High-tech or low-tech? Comparing self-monitoring systems to increase task independence for students with autism. Focus on Autism and Other Developmental Disabilities, 29(3), 156–167. https://doi.org/10.1177/1088357614528797
5.
BradleyR.DoolittleJ.BartolottaR. (2008). Building on the data and adding to the discussion: The experiences and outcomes of students with emotional disturbance. Journal of Behavioral Education, 17(1), 4–23.
6.
BradleyR.HendersonK.MonforeD. A. (2004). A national perspective on children with emotional disorders. Behavioral Disorders, 29(3), 211–223. https://doi.org/10.1177/019874290402900301
7.
BruhnA. L.GilmourA.RilaA.Van CampA.SheafferA.HancockE.FernandoJ.WehbyJ. (2022). Treatment components and participant characteristics associated with outcomes in self-monitoring interventions. Journal of Positive Behavior Interventions, 24(2), 156–168. https://doi.org/10.1177/1098300720946651
8.
BruhnA. L.McdanielS.KreighC. (2015). Self-monitoring for students with behavior problems: A systematic review of current research. Behavioral Disorders, 40(2), 102–121. https://doi.org/10.17988/BD-13-45.1
9.
BruhnA. L.WallerL.HasselbringT. S. (2016). Tweets, texts and tablets: The emergence of technology-based self-monitoring. Intervention in School and Clinic, 51(3), 157–162. https://doi.org/10.1177/1053451215585803
10.
ClemonsL. L.MasonB. A.Garrison-KaneL.WillsH. P. (2016). Self-monitoring for high school students with disabilities: A cross-categorical investigation of I-Connect. Journal of Positive Behavior Interventions, 18(3), 145–155. https://doi.org/10.1177/1098300715596134
11.
CookB. G.CookS. C. (2013). Unraveling evidence-based practices in special education. The Journal of Special Education, 47(2), 71–82. https://doi.org/10.1177/0022466911420877
DowdyA.PeltierC.TincaniM.SchneiderW. J.HantulaD. A.TraversJ. C. (2021). Meta-analyses and effect sizes in applied behavior analysis: A review and discussion. Journal of Applied Behavior Analysis, 54(4), 1317–1340. https://doi.org/10.1002/jaba.862
14.
EnnisR. P.LaneK. L.OakesW. P. (2018). Empowering teachers with low-intensity strategies to support instruction: Self-monitoring in an elementary resource classroom. Preventing School Failure, 62(3), 176–189. https://doi.org/10.1080/1045988X.2017.1408055
15.
EstrapalaS.RilaA.BruhnA. L. (2018). Don’t quit cold turkey: Systematic fading to promote sustained behavioral change. TEACHING Exceptional Children, 51(1), 54–61. https://doi.org/10.1177/0040059918790567
16.
FreemanK. A.Dexter-MazzaE. T. (2004). Using self-monitoring with an adolescent with disruptive classroom behavior. Behavior Modification, 28(3), 402–419. https://doi.org/10.1177/0145445503258982
17.
Graham-DayK. J.GardnerR.HsinY. (2010). Increasing on-task behaviors of high school students with attention deficit hyperactivity disorder: Is it enough?Education and Treatment of Children, 33(2), 205–221. https://doi.org/10.1353/etc.0.0096
18.
GulchakD. J. (2008). Using a mobile handheld computer to teach a student with an emotional and behavioral disorder to self-monitor attention. Education and Treatment of Children, 31(4), 567–581. https://www.jstor.org/stable/42899996
19.
HunterW.WilliamsonR. L.JasperA. D.CaseyL. B.SmithC. (2017). Examining self-monitoring interventions for academic support of students with emotional and behavioral disorders. Journal of International Special Needs Education, 20(2), 67–78. https://doi.org/10.9782/2159-4341-20.2.67
20.
KratochwillT. R.HornerR. H.LevinJ. R.MachalicekW.FerronJ.JohnsonA. (2021). Single-case design standards: An update and proposed upgrades. Journal of School Psychology, 89, 91–105. https://doi.org/10.1016/j.jsp.2021.10.006
21.
LedfordJ. R.ZimmermanK. N. (2023). Rethinking rigor in multiple baseline and multiple probe designs. Remedial and Special Education, 44(2), 91–167. https://doi.org/10.1177/07419325221102539
22.
LevinJ. R.FerronJ. M. (2021). Different randomized multiple-baseline models for different situations: A practical guide for single-case intervention researchers. Journal of School Psychology, 86, 169–177. https://doi.org/10.1016/j.jsp.2021.03.003
23.
McLaughlinT. F.KrappmanV. F.WelshJ. M. (1985). The effects of self-recording for on-task behavior of behaviorally disordered special education students. Remedial and Special Education, 6(4), 5–61. https://doi.org/10.1177/074193258500600407
24.
MiltenbergerR. G.FlessnerC.GatheridgeB.JohnsonB.SatterlundM.EgemoK. (2004). Evaluation of behavioral skills training to prevent gun play in children. Journal of Applied Behavior Analysis, 37(4), 513–516. https://doi.org/10.1901/jaba.2004.37-513
25.
MooneyP.RyanJ. B.UhingB. M.ReidR.EpsteinM. H. (2005). A review of self-management interventions targeting academic outcomes for students with emotional and behavioral disorders. Journal of Behavioral Education, 14(3), 203–221. https://doi.org/10.1007/s10864-005-6298-1
26.
MorinK. L.LindstromE. R.KratochwillT. R.LevinJ. R.BlaskoA.WeirA.Nielson-PheifferC. M.KellyS.JanuntsD.HongE. R. (2023). Nonconcurrent multiple-baseline and multiple-probe designs in special education: A systematic review of current practice and future directions. Exceptional Children, 90, 126–147. https://doi.org/10.1177/00144029231165506
27.
OteroT. L.HautJ. M. (2016). Differential effects of reinforcement on the self-monitoring of on-task behavior. School Psychology Quarterly, 31(1), 91–103. https://doi.org/10.1037/spq0000113
28.
ParkerR. I.VannestK. J.DavisJ. L.SauberS. B. (2011). Combining nonoverlap and trend for single-case-research: Tau-U. Behavior Therapy, 42(2), 284–299. https://doi.org/10.1016/j.beth.2010.08.006
29.
PophamM.CountsJ.RyanJ. B.KatsiuannisA. (2018). A systematic review of self-regulation strategies to improve academic outcomes of students with EBD. Journal of Research in Special Educational Needs, 18(4), 239–253. https://doi.org/10.1111/1471-3802.12408
RockM. L. (2005). Use of strategic self-monitoring to enhance academic engagement, productivity, and accuracy of students with and without exceptionalities. Journal of Positive Behavior Interventions, 7(1), 3–17. https://doi.org/10.1177/10983007050070010201
32.
RomansS. K.WillsH. P.HuffmanJ. M.Garrison-KaneL. (2020). The effect of web- based self-monitoring to increase on-task behavior and academic accuracy of high school students with autism. Preventing School Failure: Alternative Education for Children and Youth, 64(3), 249–260. https://doi.org/10.1080/1045988x.2020.1732282
33.
RosenbloomR.MasonR. A.WillisH. P.MasonB. A. (2016). Technology delivered self-monitoring application to promote successful inclusion of an elementary student with autism. Assistive Technology, 28(1), 9–16. https://doi.org/10.1080/10400435.2015.1059384
34.
RosenbloomR.WillsH. P.MasonR.HuffmanJ. M.MasonB. A. (2019). The effects of a technology-based self-monitoring intervention on on-task, disruptive, and task-completion behaviors for adolescents with autism. Journal of Autism and Developmental Disorders, 49(12), 5047–5062. https://doi.org/10.1007/s10803-019-04209-4
35.
SanettiL.Collier-MeekM. A.FallonL. (2016). Fidelity and flexibility: Treatment acceptability and individualized adaptations of evidence-supported treatments. In MaltzmanS. (Ed.), The Oxford handbook of treatment processes and outcomes in psychology: A multidisciplinary biopsychosocial approach (pp. 289–308). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199739134.013.25
36.
ScheibelG.ZimmermanK. N.WillsH. P. (2023). Increasing on-task behavior using technology-based self-monitoring: A meta-analysis of I-Connect. Journal of Special Education Technology, 38(2), 146–160. https://doi.org/10.1177/01626434221085554
37.
SlocumT. A.PinkelmanS. E.JoslynP. R.NicholsB. (2022). Threats to internal validity in multiple-baseline design variations. Perspectives on Behavior Science, 45, 619–638. https://doi.org/10.1007/s40614-022-00326-1
38.
StrainP. S.WilsonK.DunlapG. (2011). Prevent-teach-reinforce: Addressing problem behaviors of students with autism in general education classrooms. Behavioral Disorders, 36(3), 160–171. https://doi.org/10.1177/019874291003600302
39.
TarlowK. R. (2017). An improved rank correlation effect size statistic for single-case designs: Baseline corrected Tau. Behavior Modification, 41(4), 427–467. https://doi.org/10.1177/0145445516676750
WillsH. P.MasonB. A. (2014). Implementation of a self-monitoring application to improve on-task behavior: A high-school pilot study. Journal of Behavioral Education, 23, 421–434. https://doi.org/10.1007/s10864-014-9204-x
43.
WittJ. C.ElliotS. N.MartensB. K. (1984). Acceptability of behavioral interventions used in classrooms: The influence of amount of teacher time, severity of behavior problem, and type of intervention. Behavioral Disorders, 9(2), 89–155. https://doi.org/10.1177/019874298400900211
44.
WolfeK.BartonE. E.MeadanH. (2019). Systematic protocols for the visual analysis of single-case research data. Behavior Analysis in Practice, 12(2), 491–502. https://doi.org/10.1007/s40617-019-00336-7
45.
ZhangY.CookC. R.LyonA. R. (2022). A simple matter of time? School-level analysis of the relationship between time allocation, treatment integrity and student outcome. School Mental Health, 14, 73–87. https://doi.org/10.1007/s12310-021-09412-2
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