Di, Collaborative Learning, and Computer-assisted Instruction Reading Autism

Abstract

A meta-analysis of single-subject inquiry was conducted examining the effectiveness of figurer-assisted interventions (CAI) for teaching a wide range of skills to students with autism spectrum disorders (ASD) inside the school-based context. Intervention furnishings were measured by computing improvement charge per unit deviation (IRD), which is a simple approach to visual assay that correlates well with both parametric and non-parametric effect size measures. Overall, results propose that CAI may be a promising approach for teaching skills to students with ASD. Still, several concerns brand this determination tenuous. Recommendations for future research are discussed.

Equally a group, individuals with autism spectrum disorders (ASD) demonstrate relatively strong skills in responding to visual media (Quill 1997; Wetherby and Prizant 2000). This affinity to visual materials underscores the success of interventions that use picture-based cues to help students with ASD organize daily events and activities, communicates more than effectively, imitates appropriate behaviors, and/or acquires both academic and functional skills. In fact, strategies that comprise visual presentation and that let for repeated imitation of skills and/or behaviors currently are considered best practice for educating individuals with ASD (National Research Council; NRC 2001). Due to the increasing number of students with ASD, there is a demand to develop and systematically validate new and innovative visual-based interventions. One such method that builds upon the visual learning strengths of students with ASD and tin can be adapted to fit inside a diversity of educational contexts is the use of computers.

Traditionally, the term reckoner has been used to refer to personal desktop and laptop devices that most people utilize. While the personal estimator is all the same the most pop type of device, advancing applied science has broadened our view of what a computer looks like. Today, computers stand for any electronic device that accepts, processes, stores, and/or outputs data at high speeds. As such, computers have come to represent an array of gadgets beyond desktop and laptop computers and now include mobile devices like smartphones (due east.g., Android and iPhones) and tablets (e.one thousand., iPad and Kindle). Beyond how computers may be defined, they accept become a ubiquitous part of the global world and their employ will continue to grow in the future.

The use of computers for enhancing the academic, behavioral, and social outcomes of students with ASD is a relatively new surface area of enquiry, but one that has great potential. Many parents report their children'southward fascination with and propensity for learning from visually based media such as computers (Nally et al. 2000). In add-on, researchers have identified that individuals with ASD non only demonstrate meaning skill acquisition when taught via computers, but likewise have a preference for instruction delivered through such devices (e.m., Bernard-Opitz et al. 2001; Moore et al. 2000; Shane and Albert 2008). Given such preferences, the apply of computer-assisted interventions (CAI) emerges as an ideal method for teaching students with ASD for several reasons. Starting time, students with ASD oftentimes find the world confusing and unpredictable, and take difficulty dealing with modify. As such, educational practices should brand every effort to describe expectations, provide routines, and present firsthand and consistent consequences for responding (Iovannone et al. 2003). Computers non merely provide a predictable learning environs for the educatee with ASD, only also produce consistent responses in a manner that likely will maintain interest and, possibly, increase motivation. 2d, viewing of educational activity through electronic media may allow individuals with ASD to focus their attention on relevant stimuli (Charlop-Christy and Daneshvar 2003; Shipley-Benamou et al. 2002). Considering students with ASD take difficulty screening out unnecessary sensory information (Quill 1997), focusing on a figurer, where only necessary information is presented, may maximize their attention. Third, the use of computers likely creates an surroundings for learning that appears to individuals with ASD equally less threatening (Sansosti et al. 2010). That is, computers are free from social demands and probable tin can exist viewed repeatedly by the student without fatigue.

Recently, there has been a proliferation of unmarried-subject enquiry investigating the utility of schoolhouse-based, CAI for students with ASD. For example, CAI for students with ASD have been demonstrated to increase: (a) object labeling and vocabulary acquisition (e.1000., Coleman-Martin et al. 2005; Massaro and Bosseler 2006), (b) correct letter sequences and spelling (east.g., Kinney et al. 2003; Scholosser and Blischak 2004), (c) reading skills (e.1000., Mechling et al. 2007), (d) appropriate classroom behaviors (e.g., Mechling et al. 2006; Whalen et al. 2006), and (eastward) social skills (e.g., Simpson et al. 2004; Sansosti and Powell-Smith 2008). From the data available, it appears that computers can exist harnessed to support a wide variety of skills to children with ASD.

With the proliferation of research in this area, several recent reviews accept made attempts to summarize the extant single-subject area research examining the effectiveness of CAI. For case, Pennington (2010) provided a descriptive review of CAI research conducted between 1997 and 2008. Specifically, this review examined the effectiveness of CAI for teaching skills related to literacy. Overall, Pennington concluded that computer-based interventions take promise for pedagogy academic skills to students with ASD. Wainer and Ingersoll (2011) also conducted a descriptive review of enquiry focused on the utilise of interactive multimedia for didactics individuals with ASD language content and pragmatics, emotional recognition, and social skills. In their review of the extant literature, interactive multimedia programs were found to be both engaging and beneficial for ASD learners due to their known strengths in the area of visual processing and determined to have hope as an educational approach. More recently, Ramdoss et al. (2011a, b) conducted 2 systematic reviews on the effects of CAI for improving literacy skills (e.g., reading and sentence construction) and improving song and not-song communications, respectively. Taken together, the results of these separate reviews provide initial insight regarding the efficacy of CAI for students with ASD. In fact, each of the reviews suggests that CAI is a promising approach for supporting the needs of students with ASD. Yet, each of these reviews did not provide whatsoever metric that measured the overall magnitude of effect of CAI for students with ASD.

Need for Quantitative Synthesis of Single-Subject Enquiry

Today, schoolhouse-based practitioners are faced with ever-increasing demands to identify and utilize show-based practices (Reichow et al. 2008; Simpson 2008). Such demands began with the mandates prepare along by the No Child Left Behind Human activity (NCLB 2001) and were extended further by the Individuals with Disabilities Education Improvement Act, which required educators to select appropriate education strategies that are "…based on peer-reviewed research to the extent applied…" (IDEIA 2004, 20 UsaC. 1414 §614, p. 118). More recent demands accept chosen for the calculations of result size and conviction intervals to establish those strategies that promote the greatest amount of expected change (east.g., What Works Immigration House, see http://ies.ed.gov/ncee/wwc/; Whitehurst 2004). Therefore, failure to provide a statistical summary indicating the amount of behavior change runs contrary to gimmicky do and leaves practitioners to rely on the conclusions drawn by the studies' authors. In an era when empirically validated approaches are routinely demanded within schoolhouse contexts, it is crucial to differentiate betwixt promising and show-based practices (Yell and Drasgow 2000).

Given the increment in single-subject research investigating the employ of computers to teach students with ASD in contempo years combined with the demand for more than stringent design and analysis of enquiry conducted within schoolhouse-based contexts, determination of whether or not CAI represents an evidence-based arroyo is warranted. First, such decision should exist based on specific criteria for evaluating unmarried-bailiwick enquiry studies that are considered to be of high quality. Equally part of their piece of work with the Council for Exceptional Children, Division of Research, Quality Indicator Job Force, Horner et al. (2005) provided a set of guidelines for determining when unmarried-subject research documents a practise as evidence-based. These guidelines affirm that (a) both the strategy and context in which the strategy is used have been clearly defined; (b) the efficacy of the approach has been documented in at least 5 published studies in peer-reviewed journals; (c) the enquiry has been conducted across iii dissimilar geographical locations and includes at least 20 total participants; (d) the strategy was implemented with fidelity, (e) social validity (i.e., acceptability of the intervention) has been measured, and (f) results demonstrate experimental control through the use of multiple baseline, reversal, and/or alternating treatment designs. 2nd, efforts should be taken to systematically summarize the extant unmarried-subject literature by employing some course of effect size metric. Traditionally, single-subject research has been interpreted past visual inspection of graphed information. Through visual inspection, a large treatment effect is indicated past a stark contrast in the levels of data betwixt the baseline and intervention phase(s). However, such analysis can be subjective and often fails to demonstrate the impact of an intervention when only pocket-sized alter is indicated. Over the years, adding of the percentage of non-overlapping data (PND) has been suggested as an culling for systematically synthesizing single-subject research studies (Scruggs and Mastropieri 1998, 2001) as it provides a method for quantifying outcomes objectively and tin can be calculated on whatsoever type of unmarried-subject research pattern (Parker et al. 2007). PND, a not-parametric approach to summarizing research, determines the magnitude of behavior change from baseline to treatment phase past calculating the proportion of non-overlapping data between those phases. Specifically, the PND is calculated by counting the number of data points in the treatment condition(southward) that do not overlap with the highest (or lowest if appropriate) baseline data indicate, divided by the total number of handling data points, and multiplied by 100 (to summate a percentage). The higher the percentage obtained, the stronger the intervention effectiveness. Although PND is the oldest and about widely known non-parametric method for analyzing single-discipline data, information technology has been scrutinized for its confounding condition for floor or ceiling datum (Wolery et al. 2008) and variability in data trends (Ma 2006). Due to such limitations, other indices of non-overlapping data such as the percentage of information exceeding the median (PEM; Ma 2006), the percent of all overlapping data (PAND; Parker et al. 2007), and the pairwise data overlap squared (PDO2; Wolery et al., 2008) have been promoted. Despite efforts to provide accurate synthesis of single-subject area research, each of these iterations likewise has been criticized for not being sensitive plenty to detect the important characteristics of trends and variability within fourth dimension-serial data (Wolery et al. 2008), as well every bit beingness unable to calculate confidence intervals (Parker et al. 2009).

Parker et al. (2009) and Parker et al. (2014) suggest using the improvement charge per unit difference (IRD) to supplement visual inspection of graphs and for calculating event size of single-case research. IRD has been used for decades in the medical field (referred to "take a chance reduction" or "risk divergence") to draw the absolute alter in hazard that is owing to an experimental intervention. This metric is valued within the medical community due to its ease of interpretation, every bit well as the fact that it does not crave specific data assumptions for confidence intervals to be calculated (Altman 1999). IRD represents the difference between two proportions (baseline and intervention). More specifically, information technology is the difference in comeback rates between baseline and intervention phases (Higgins and Dark-green 2009; Parker et al. 2009. By knowing the absolute difference in improvement, practitioners can determine the result of an intervention and if the alter in behavior is worth repeating. To summate IRD, a minimum number of data points are removed from either baseline or intervention phases to eliminate all overlap. Information points removed from the baseline phase are considered "improved," pregnant they overlap with the intervention. Data points removed from the intervention phased are considered "not improved," meaning they overlap with the baseline. The proportion of information points "improved" in baseline is then subtracted from the proportion of data points "improved" in the intervention phase (IR _I –IR _B  =IRD). The maximum IRD score is 1.00 or 100 % (all intervention data exceed baseline). An IRD of 0.70 to 1.0 indicates a large effect size, 0.50 to 0.70 a moderate effect size, and less than 0.fifty a small or questionable outcome size (Parker et al. 2009). An IRD of 0.50 indicates that half of the scores between baseline and handling phase were overlapping so there is only hazard-level improvement. One distinct advantage of IRD is that it affords the power to calculate confidence intervals. Practitioners can interpret the width of a conviction interval equally the precision of the arroyo (big intervals betoken that the IRD is not trustworthy, whereas narrow intervals bespeak more precision). In addition to this practical reward, Parker et al. (2009) found that IRD correlated well with the R ² and Kruskal–Wallis W outcome sizes (0.86) and with PND (0.83). To date, research utilizing IRD calculations has been embraced more than inside the biosciences. Nevertheless, there is growing support for its use within educational research due to the demands for research to include stronger designs and issue size calculations with confidence intervals (Higgins and Greenish 2009; Whitehurst 2004). IRD has been utilized in a variety of single-case research meta-analysis (e.g., Ganz et al. 2012a, b; Miller and Lee 2013; Vannest, et al. 2010.

Purpose

The purpose of this written report was to provide a quantitative meta-analysis of existing single-subject research studies that have investigated the use of school-based CAI for children with ASD. As stated previously, there is an increased demand for educators to implement evidence-based practices within schools. While in that location have been a myriad of single-subject studies demonstrating the effectiveness of CAI, it is necessary to evaluate the extant literature using a common metric such as issue size via a meta-assay (Kavale 2001). A synthesis of the available unmarried-bailiwick research would add substantial data to our existing knowledge regarding the efficacy of CAI for children and adolescents with ASD within schoolhouse-based contexts and would provide practitioners with much needed information necessary for educational controlling. To this terminate, a meta-analysis was conducted of unmarried-bailiwick studies that included the use of CAI for students with ASD. Thus, this investigation was interested in primarily answering the following question: Are school-based CAI effective for students with ASD? Specifically, this meta-analysis determines the overall bear upon of calculator-based technologies for teaching students with ASD using the improvement rate departure (IRD). In addition, this meta-assay provides data as to whether estimator-assisted interventions can exist considered an evidence-based do as outlined in Horner et al. (2005).

Method

Identification of Studies

Studies included in this meta-assay were located by conducting a search of journal manufactures published from 1995 to 2013 using PsycINFO and EBSCO databases. Multiple searches were conducted using a combination of the following descriptors: autism, autistic disorder, autism spectrum disorder, loftier functioning autism (HFA), Asperger's syndrome (Equally), pervasive developmental disorder (PDD), computers, reckoner-assisted pedagogy, and computer-assisted learning. An ancestral search of studies using the reference lists of each study located through PsycINFO or EBSCO as well was conducted in an effort to locate additional studies that did not appear in the online searches. In add-on, manual searches of the journals Periodical of Autism and Developmental Disorders, Focus on Autism and Other Developmental Disabilities, Journal of Positive Behavior Interventions, and Inquiry in Autism Spectrum Disorders were conducted to identify references that were not located through electronic search. In total, 76 studies were located (69 empirical studies and 7 case studies) that examined the utilise of computers or computer-assisted instruction for education children and adolescents with ASD.

Post-obit the initial location of articles, the authors reviewed each study to determine inclusion eligibility based on the following criteria. First, the written report was conducted between 1995 and 2013 and was published in a peer-reviewed journal. 2nd, participants in the study were diagnosed/identified with an ASD (i.e., autism, As, and PDD). In a handful of studies, participants without ASD were included in the study. In these instances, only data for the private with ASD were analyzed every bit office of the meta-analysis. Tertiary, the report employed a single-subject research design that demonstrated experimental command (east.g., multiple baseline and reversal pattern). Non-experimental, AB designs and non-empirical case studies were excluded from the assay considering such approaches practice not provide sufficient information to rule out the influence of a host of misreckoning variables (Kazdin 1982), making it hard to determine the natural class that the beliefs(s) would have taken had no intervention occurred (Risely and Wolf 1972). Fourth, the study presented information in graphical displays that depicted individual data points to allow for calculation of the IRD. Studies that incorporated dichotomous-dependent variables (e.g., right/wrong), or that had fewer than three probes in a multiple-probe design, were excluded from the analysis because they could not permit advisable calculation of the IRD. In addition, studies that employed group designs were excluded in order to provide a uniform metric of treatment effectiveness and because of the difficulty combining the effect size measures with IRD assay. 5th, the study utilized outcome measures targeting academic, beliefs, and/or social skills.

Out of the 76 original studies found, 48 were excluded for the post-obit reasons: the use of group designs (north = 13); the inclusion of participants whose main diagnoses were non ASD (n = three); the inclusion of participants who were adults or who were not attending school (north = 2); the employ of AB designs, case studies, or other designs that provided only descriptive interpretations of findings (n = eleven); the commodity was an unpublished doctoral dissertation (n = ii); and insufficient information and/or graphical displays that permit the calculation of IRD (n = 17). Equally a effect, a full of 28 studies met the multiple inclusion criteria and were included in the quantitative analysis.

Coding of Studies

Each of the 28 studies included in the meta-analysis was summarized and data coded for further analysis. Specifically, a summary table was prepared that provided data regarding: (a) participant characteristics, including number, diagnosis, and historic period; (b) setting characteristics describing the location where the intervention was implemented; (c) blazon of enquiry design; (d) description of the target skill(southward) or dependent variable(s); (e) intervention (contained variable) description, including type/format of computer-assisted strategy utilized and the length of the intervention; and (f) confirmation of whether the written report measured inter-observer reliability, handling integrity, and/or social validity (run into Table 1 for a descriptive assay of the included studies). The nature of the coding system utilized allowed for assessment of whether or not CAI met the criteria equally an show-based practice using the guidelines set along by Horner et al. (2005).

Tabular array 1 Studies included in the meta-analysis

Total size table

Reliability of Coding

To establish inter-rater reliability for the coding procedure, the first two authors independently rated all studies using the categories mentioned higher up and compared their results. An understanding was recorded when both raters indicated whatsoever of the study features as being the same, and a disagreement was recorded when only one rater coded a specific study characteristic. Inter-rater reliability was established every bit a per centum understanding betwixt both raters and was calculated by dividing the full number of agreements past the total number of agreements plus disagreements and multiplying by 100. Inter-rater reliability for coding of study features was 97 % (range 94–100 %). Cohen'southward kappa (κ) also was calculated. This mensurate of reliability (used for qualitative items) is more than bourgeois and adjusts for chance agreement (Suen and Ary 1989). The kappa calculation was 0.92.

Data Extraction

In social club to ensure that data utilized within the meta-analysis were authentic, graphs from each of the 28 published studies were digitized using GraphClick software (Arizona Software 2008). By creating digitized graphs, we were able to recreate the original information points. Specifically, nosotros were able to pinpoint and recreate X-axis and Y-centrality data values digitally and transfer these values into an Excel spreadsheet (see Parker et al. 2009, for a more detailed description of the process). This process permitted the ability to generate graphs that was not crowded (i.e., hard to view).

Effect Size Adding

Using the digitized graphs, two raters calculated IRD scores for every participant for baseline and handling contrasts, excluding generalization and maintenance information points. Specifically, IRD was calculated by determining the pct of improved information points in the treatment phase divided by the total number of data points while eliminating any overlapping information points between baseline and treatment conditions. To ensure accuracy of calculations, the two raters independently computed IRD scores for each of the studies and and so compared their calculations. Initially, these two raters calculated their overall reliability for 8 randomly selected studies included in the meta-assay (30 % of the studies). For these get-go eight studies, inter-rater reliability was 83 %. While this initial reliability statistic was slightly lower than expected, the two raters met to resolve discrepancies through further inspection of data for each of the viii studies. Information technology was discovered that the two raters differed on how to calculate scores that tied across phases (eastward.thousand., one data point of 20 % during baseline and one point of 20 % during intervention). For this study, such data points were considered as overlapping. Following this discovery, both raters independently recalculated scores for the offset 8 studies, resulting in an inter-rater reliability of 95 %. IRD scores so were calculated independently for the remaining studies. In improver to the two raters' calculations, one independent reviewer (an advanced graduate student trained in computing IRD) coded eight randomly selected studies. Overall, the independent reviewer demonstrated 100 % inter-rater agreement with the two raters.

Additionally, procedures were utilized in order to create conviction intervals (CI) for each of the IRD calculations. The current data were analyzed using NCSS Statistical Software (REF). Specifically, we conducted a test of two proportions with the option to include exact 84 % CIs based on bootstrap for IRD calculation. The 84 % CI was selected for judging the precision of IRD scores for the following reasons. Commencement, an 84 % confidence limit is liberal enough to permit clinical conclusion-making (due east.thou., altering interventions) when such decisions are not high stakes (Ganz et al. 2012a, b). 2nd, using the 84 % CI is equivalent to making an inference examination of differences at the p = 0.05 level (Schenker and Admirer 2001; Payton et al. 2003).

Results

Written report Characteristics

The 28 studies selected for inclusion in this meta-analysis were published betwixt 1995 and 2013 and measured the furnishings of computer-assisted interventions on a full of 93 participants with ASD. These studies appeared in a total of xiii journals, with approximately half of them (46 %) published in either the Journal of Autism and Developmental Disorders or Focus on Autism and Other Developmental Disabilities. All of the studies included in the meta-analysis employed single-subject research designs. Among these, 46 % used a multiple baseline design (northward = thirteen), 25 % used a multiple probe design (n = vii), xiv % used an ABAB reversal blueprint (n = 4), 7 % used a changing conditions blueprint (n = 2), and 7 % used an alternate treatment design (n = 2). Inter-observer reliability was reported in 26 of the 28 studies (93 %), and intervention fidelity was measured in 21 studies (75 %). However, social validity was measured in only x studies (36 %). A summary of the participants, research design, target skills, strategies utilized, and reliability/validity is presented in Tabular array 1.

Participant and Intervention Characteristics

The 93 participants ranged in age from 3 years, 2 months to xviii years (mean age = 9 years, 5 months; SD = iii.53). Most of the study participants were boys (n = 77, 83 %) and elementary school historic period (vi–11 years; n = 56, 60 %). There was a relatively equal representation of preschool (three–5 years; due north = 16, 17 %), eye school (12–14 years; n = 15, 16 %), and loftier school (sixteen–20 years; n = half-dozen, half dozen %) students in the overall sample. All of the studies analyzed included participants who had a diagnosis of autism (n = 86, 92 %), Asperger syndrome (n = 3, three %), or were categorized as ASD with no specification (northward = 4, iv %).

Each of the studies included in the meta-analysis targeted various characteristics of social, behavioral, and academic difficulties in children with ASD and were conducted within schoolhouse-based settings. Specifically, seven of the studies targeted social behaviors (Bernard-Opitz et al. 2001; Cheng and Ye 2010; Hetzroni and Tannous 2004; Murdock et al. 2013; Sansosti and Powell-Smith 2008; Simpson et al. 2004; Whalen et al. 2006), nine studies targeted behavioral skills (Ayres et al. 2009; Bereznak et al. 2012; Cihak et al. 2010; Flores et al. 2012; Hagiwara and Smith-Myles 1999; Mancil et al. 2009; Mechling et al. 2006, 2009; Soares et al. 2009), and 12 studies targeted academic skills (Bosseler and Massaro 2003; Coleman-Martin et al. 2005; Ganz et al. 2014; Hetzroni and Shalem 2005; Pennington 2010; Schlosser and Blischak 2004; Simpson and Keen 2010; Smith 2013; Smith-Myles et al. 2007; Soares et al. 2009; Yaw et al. 2011). The estimator-assisted interventions implemented within the studies ranged in length from 3 to 30 sessions. Almost of the studies employed interventions that were of medium length or xi–xx sessions long (northward = xvi, 57 %), while brief (ane–ten sessions; northward = 4, 14 %) and long interventions (over 20 sessions; n = 8, 29 %) were less common.

Overall (Omnibus) Furnishings of CAI

Information from this study yielded, 151 separate furnishings sizes from a total of 28 studies. Total mean IRD for all studies included within the meta-analysis was 0.61 CI₈₄ [0.48, 0.74], indicating a moderate effect. That is, CAI intervention data showed a 61 % improvement charge per unit from baseline to intervention phases on a range of outcomes, and we are reasonably certain the range of comeback is within 48 to 74 %. There was pregnant variation beyond studies, contributing to the lower boilerplate IRD adding. Effigy 1 illustrates the IRD and 84 % CIs for each CAI intervention and past individual study.

Fig. 1

Wood plot depicting IRD and 84 % confidence intervals by study and overall

Full size paradigm

Variation in Effects past Targeted Outcomes

Differences in IRD scores also were examined relative to blazon of intervention. That is, separate IRD analyses were calculated beyond the three categories of dependent variables: academic skills, behavioral skills, and social skills. Analysis of bookish skill outcomes yielded 73 separate effect sizes from a full of thirteen studies. The full mean IRD value for interventions targeting bookish skills was 0.66 CI₈₄ [0.63, 0.69; moderate effect]. For studies targeting behavioral skills, a total of 35 split up effects sizes from seven studies yielded a mean IRD of 0.44 CI₈₄ [0.38, 0.49; small effect]. Similarly, analysis of social skills outcomes yielded 43 dissever effect sizes from a total of eight studies with a full mean IRD calculation of 0.29 CI₈₄ [0.24, 0.33; small effect]. Taken together, results of analysis by type of intervention suggest that CAI may exist more than effective when targeting academic skills. Results from behavioral and social skills variables should be considered preliminary due to low numbers of studies evaluating these outcomes combined with the high level of variability of findings from private studies (see Fig. one). Figure 2 illustrates the overall IRD and 84 % CIs for targeted outcomes.

Fig. 2

Forest plot depicting IRD and 84 % conviction interval by targeted behavior

Total size prototype

Variation in Effects by Historic period

Additional IRD calculations were conducted to examine differences across historic period levels of students (preschool, uncomplicated, center, and loftier). For preschool age students, eighteen split up consequence sizes from iv studies yielded an IRD score of 0.43 CI₈₄ [0.36, 0.49; small effect]. Elementary-anile students had lxx separate effect sizes from 11 studies. The mean IRD for elementary-aged students was 0.41 CI₈₄ [0.37, 0.45; small result]. For heart-schoolhouse-aged students, 38 split up result sizes from ix studies yielded an IRD of 0.39 CI₈₄ [0.34, 0.44; small effect]. High school-aged students had 24 carve up effects sizes from four studies and yielded an overall IRD of 0.64 CI₈₄ [0.57, 0.70; moderate upshot]. Virtually of the studies included in this meta-analysis examined the effects of CAI with elementary-aged students (n = 11), and but a handful of studies were examined preschool (north = 4) and loftier schoolhouse (n = 4) populations. Equally such, the calculations should be interpreted with caution. Figure 3 illustrates overall IRD and 84 % CIs by age.

Fig. three

Forest plot depicting IRD and 84 % confidence interval by age

Full size image

Word

Descriptively, the extant literature appears to comply with the guidelines offered by Horner et al. (2005) for determining if a exercise is prove-based. Specifically, the results of CAI were synthesized across 28 peer-reviewed studies conducted past 25 master researchers across 15 different geographical locations (eleven states and iv countries), and cumulatively included 93 participants. In addition, all of the studies included in this analysis demonstrated experimental control and the bulk of studies (n = 26; 93 %) provided a measure of inter-observer agreement and treatment integrity (north = 21; 75 %). Unfortunately, only ten studies (36 %) assessed social validity. Despite the failure of the majority of studies to collect data on fidelity of implementation and intervention acceptability, the extant literature appears to comply with the guidelines offered by Horner et al., permitting CAI to be considered an evidence-based arroyo.

Despite the alignment with the aforementioned features, results of IRD calculations suggest that CAI demonstrates only a moderate impact to students with ASD. As such, we provide a less than enthusiastic endorsement of CAI and posit that such interventions possess the potential to impact students with ASD positively, only likely are impacted by a host of boosted factors that account for effects. Such a tempered consideration largely is due to the possibility of additional factors/variables (i.e., participant and intervention characteristics) that may business relationship for effects. Commencement, mean IRD calculations were highly variable (ranging from −0.05 to 1.00). Although many of the studies included within this meta-assay demonstrated effective results, a handful of studies (e.g., Bernard-Opitz et al. 2001; Hetzroni and Tannous 2004; Mancil, et al. 2009; Murdock et al. 2013) demonstrated questionable outcomes despite having a good experimental command. Such variability suggests that CAI may be effective for some students and not for others. Given this information, it is possible that CAI is suited to participants with certain characteristics (i.e., increased language and cognitive functioning; power to understand basic social behaviors). It is worthy to annotation that while all students in the studies reviewed were identified as existence on the autism spectrum, quantification of the caste or severity of autism-related symptoms and bookish, behavioral, and social skills difficulties rarely was provided. Also, the manner in which computer technologies were used in the studies varied greatly and may have influenced the results. In some studies, computers were used as an broaden to other forms of teaching. In other studies, participants were taught skills through independent interactions with computers. Information technology is likely that certain individuals were better able to utilise computers independently and others needed a greater level of back up. Again, specific characteristics of participants may be an of import variable impacting the effectiveness of CAI. As such, the claim that CAI is an effective strategy should exist tempered until future enquiry provides more conclusive findings based on thorough analysis of multiple participant and intervention variables.

Second, results of this analysis point that outcomes of CAI were mixed based on the blazon of intervention designed. That is, CAI appears to be more effective for education academic skills to students with ASD than for improving behavioral and/or social skills. The fact that CAI demonstrated limited effectiveness for teaching behavioral and social skills may not be surprising given the highly variable nature of behavioral and social interactions. It may be that students have learned the skills through CAI, but neglect to apply the skills within real-world contexts. Still, this finding suggests that CAI does not demonstrate the same outcomes across unlike domains of skill acquisition and/or improvement. As a result, more conclusive evidence is needed to make a full conclusion of whether or not CAI is an evidence-based modality for all levels of instruction.

Third, much of the research combines CAI with the use of additional intervention strategies (i.e., self-monitoring and consequent strategies). As such, it becomes difficult to ascertain which element (due east.yard., prompting, reinforcement, and CAI) is the disquisitional component of the intervention, or whether a combination of approaches has the greatest consequence. While there is no clear show of a difference in the present analysis amidst those studies that used CAI alone versus those that employed other strategies, several confounding variables were axiomatic. The misreckoning of CAI with other strategies is a problem that should be overcome. Specifically, research is needed examining the extent to which CAI individually contributes to outcomes.

From the preceding give-and-take, it is apparent that CAI has positive claims that suggest information technology is a promising strategy for supporting skill acquisition of students with ASD in school-based contexts. However, it is unclear from the present analysis that CAI is an testify-based strategy. In a prior descriptive review, Pennington (2010) suggested that CAI may have promise as an constructive literacy intervention for students with autism. The results of our analysis support Pennington'southward claim and contribute added knowledge regarding the effectiveness of CAI for promoting behavioral and socials skills in children with ASD. Overall, the results of this study provide information suggesting that CAI has noteworthy potential for improving the bookish, behavioral, and social outcomes of students with ASD, merely are not yet an evidence-based strategy.

Limitations of Current Assay

The results of this meta-analysis should exist viewed as preliminary due to several limitations. First, application of rigorous inclusion criteria resulted in a express sample size. Although 28 studies are sufficient, such a limited number of studies prevent a thorough assay of some of the variables of involvement (e.g., specific diagnoses, cognitive and language levels, setting characteristics, and intervention features). Second, interpretation of the results was limited by the method of analysis. That is, the assay was based on a subset of studies that yielded IRD information. Third, interpretation of the results was limited further by the degree of variation in pattern and implementation of CAI. While some of the enquiry reviewed demonstrated clear examples of well-controlled studies, much of the extant literature combines CAI with the utilise of boosted intervention strategies (i.due east., self-monitoring and consistent strategies). This raises the possibility that boosted blueprint characteristics may be important to the success of the intervention. Fourth, all studies located and included inside this analysis were conducted within school-based contexts. Therefore, claims that CAI is an constructive approach outside of school-based contexts cannot be made.

Futurity Enquiry Recommendations

In that location are several recommendations for future research examining the effectiveness of CAI for students with ASD that were exposed equally part of this meta-analysis. First, more methodologically robust investigations should exist incorporated into hereafter studies that include methods for information collection across the intervention phase. Specifically, future enquiry should include extended data collection on maintenance and generalization of skills, social validity of the intervention, and handling fidelity. The inclusion of such elements of data drove and subsequent assay would permit for more definitive claims of the efficacy of CAI. Second, hereafter studies must provide more detailed information pertaining to descriptions of participants. Of detail importance is information pertaining to cognitive and language power, severity of deficits, specific (and confirmed) diagnosis of participants on the autism spectrum (e.g., severity) and setting characteristics. More acceptable participant descriptions would go far easier to determine whether participant related variables moderate the effect of CAI in addition to allowing the creation of profiles of "responders" and "non-responders" to interventions. 3rd, the present meta-analysis included CAI within school-based contexts only. Time to come research should utilise similar methodology to examine the outcomes of reckoner-assisted interventions in other settings, such as clinic-based settings and home/community environments. Fourth, future research should further examine the overall effectiveness of CAI with ASD populations using more than rigorous methods of data analysis (i.e., hierarchical linear modeling) that permit the power to examine moderating variables.

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Author information

Affiliations

1. Kent Country University, Kent, OH, USA

   Frank J. Sansosti, Mary Lynn Doolan & Brittany Remaklus

2. West Branch Local Schools, Beloit, OH, United states

   Alison Krupko

3. State Support Team Region 8, Cuyahoga Falls, OH, United states of america

   Jenine Thou. Sansosti

4. Lifespan Evolution and Educational Sciences, College of Education, Wellness, and Human Services, Kent Country University, 405 White Hall, Kent, OH, 44242, USA

   Frank J. Sansosti, Mary Lynn Doolan & Brittany Remaklus

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Correspondence to Frank J. Sansosti.

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Sansosti, F.J., Doolan, M.L., Remaklus, B. et al. Reckoner-Assisted Interventions for Students with Autism Spectrum Disorders within Schoolhouse-Based Contexts: A Quantitative Meta-Analysis of Unmarried-Subject field Enquiry. Rev J Autism Dev Disord 2, 128–140 (2015). https://doi.org/x.1007/s40489-014-0042-five

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• Received: xiv Nov 2014

• Accepted: 17 November 2014

• Published: 02 Dec 2014

• Issue Date: June 2015

• DOI : https://doi.org/ten.1007/s40489-014-0042-5

Keywords

• Autism spectrum disorder
• Computers
• Estimator-assisted instruction
• Engineering science
• Schoolhouse-based intervention
• Meta-assay
• Single subject

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