Academic and Related Skills Interventions for Autism

Academic and Related Skills Interventions for Autism: a 20-Year Systematic Review of Single-Case Research Fahad Alresheed, Wendy Machalicek, Amanda Sanford & Carmen Bano

Review Journal of Autism and Developmental Disorders ISSN 2195-7177 Rev J Autism Dev Disord DOI 10.1007/s40489-018-0141-9

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Author's personal copy Review Journal of Autism and Developmental Disorders https://doi.org/10.1007/s40489-018-0141-9

REVIEW PAPER

Academic and Related Skills Interventions for Autism: a 20-Year Systematic Review of Single-Case Research Fahad Alresheed 1 & Wendy Machalicek 2 & Amanda Sanford 3 & Carmen Bano 4 Received: 8 December 2017 / Accepted: 16 May 2018 # Springer Science+Business Media, LLC, part of Springer Nature 2018

Abstract This systematic literature review examines single-case intervention research targeting academic and related skills for children with autism spectrum disorder (ASD) in school settings. Fifty-four studies published between 1995 and 2014 met inclusion criteria. Tau-U was calculated for each study to examine the effectiveness of interventions. The mean score across all the studies was high (M Tau-U = 0.78), but ranged from weak to very high with scores between 0.15 and 1.00. The analysis demonstrated that school-based interventions were generally effective at improving the academic and related skills of students with ASD. The authors summarized some critical gaps in the research, and reviewed the quality of the research designs. Keywords Academic . Intervention . Autism spectrum disorder . ASD . Tau-U

Autism spectrum disorder (ASD) is an early emerging neurodevelopmental disorder characterized by deficits in social communication as well as restricted and repetitive behaviors and interests (American Psychiatric Association 2013). Children with ASD are also at increased risk for comorbid disorders including challenging behaviors like aggression and inattention (Hartley et al. 2008), attention deficit/ hyperactivity disorder, depression and mood disorders, and

* Wendy Machalicek [email protected]; https://www.researchgate.net/profile/ Wendy_Machalicek Amanda Sanford [email protected] Carmen Bano [email protected] 1

Department of Special Education, University of Oregon, 901 E.18th #139, Eugene, OR 97403-5253, USA

2

Department of Special Education and Clinical Sciences, University of Oregon, Eugene, OR 97403, USA

3

Portland State University, 1900 SW 4th Ave, Portland, OR 97201, USA

4

Home Based Program Manager ChildCareGroup, 1420 W Mockingbird Ln #300, Dallas, TX 75247, USA

anxiety disorders (Ghaziuddin et al. 2002; Matson and Williams 2013; Van Steensel et al. 2011). The confluence of ASD and comorbid disorders necessitates individualized and intensive instruction, thus presenting unique challenges to the local education agencies (LEAs) that are serving a growing number of children with an educational classification of ASD (Bagatell et al. 2010; Carnahan and Williamson 2013; Centers for Disease Control and Prevention 2010). From 2002 to 2010, the prevalence of ASD increased approximately by 123% (CDC 2014) and at the same time general education placement increased at a rate quicker than all other disabilities (Whitby 2013). Most intervention research with school-age children with ASD has rightly examined the effectiveness of strategies aimed at improving social communication (Chang and Locke 2016; Watkins et al. 2015) and play (Kossyvaki and Papoudi 2016). However, a growing emphasis on ensuring access to the general education curriculum and improving post-school outcomes related to employment, higher education, and independent living necessitates both the use of effective intervention in schools to improve functional academic skills for this population and further research in this area (Wehman et al. 2014). Below grade-level academic performance is not a defining characteristic of ASD, but academic performance is variable and many students require explicit instruction and support to access the general education curriculum and to address poor academic performance in one or more curricular areas (e.g.,

Author's personal copy Rev J Autism Dev Disord

McCurdy and Cole 2014). For example, students with ASD often present with delayed acquisition of reading and spelling skills (Gross 1994; O’Connor and Klein 2004). Few children with ASD develop literacy skills beyond functional sight word recognition (Vacca 2007), and these impairments are often more severe for the approximately 30 to 50% of school-age students with ASD who are minimally verbal (Anderson et al. 2007). In addition, students with ASD often lack skills that are supportive of learning academic content including selfmanagement and organizational skills (Moore 2002; Whalon et al. 2009). At the same time, students with ASD often present with barriers to participating in instruction including low levels of task engagement (National Research Council 2001), impaired attention to relevant instructional stimuli (Kinnealey et al. 2012), and escape-maintained challenging behavior such as self-injury, aggression, and destruction of property (Koegel et al. 2012). Notably, when students with ASD receive explicit instruction in self-management and organizational skills, which support performance in a curricular area such as literacy, the result can be improved academic productivity (AsaroSaddler and Saddler 2010). A significant and growing body of research has been evaluating the effects of educational interventions for students with ASD. In 2008, Machalicek and colleagues published a review of school-based instructional intervention studies for students with ASD published between 1995 and 2005. Spencer et al. published a second review in 2014 which reviewed studies published between 2000 and 2012. The authors concluded that most research included elementary-age students while few studies included secondary-age students. Most of the studies targeted reading, writing, and mathematics. Few studies focused on science and social studies instruction. Spencer and colleagues identified the following effective interventions: (a) visual support, (b) technology-based instruction, (c) concrete representation, (d) direct instruction, and (e) behavioral interventions. These findings align with the earlier findings of Machalicek and colleagues. Reviews have also examined the ASD intervention literature specific to curricular areas or type of instructional delivery including reading comprehension (Chiang and Lin 2007), writing (Pennington and Delano 2012), and math (Barnett and Cleary 2015; Gevarter et al. 2016). Chiang and Lin (2007) conducted a review of 11 studies targeting reading comprehension for students with ASD. The review identified several instructional methods recommended by the National Reading Panel (2000) to teach reading comprehension. These include multimedia methods and explicit instruction for vocabulary comprehension, and cooperative learning and question answering for text comprehension. Similarly, Pennington and Delano (2012) reviewed 15 studies targeting writing skills for students with ASD. The authors reported that most interventions used electronic technology. Also, they identified two types of interventions that were most effective in improving

writing skills: modeling and self-regulated strategy development (SRSD). Barnett and Cleary (2015) synthesized 11 studies of math interventions for students with ASD. The authors identified interventions using visual representation approaches as the most commonly researched intervention. These visual representation interventions included self-modeling, the use of manipulatives, Btouch point^ technique, and diagrams. More recently, Gevarter et al. (2016) conducted a review evaluating the effectiveness of math interventions for individuals with ASD. The authors reviewed 26 studies and concluded that most successful interventions to teach math skills combined educational strategies (e.g., touch points from TouchMath) and behavioral strategies, such as prompting, modeling, and reinforcement. Although the authors of the extant reviews have provided a comprehensive analysis of interventions including methodological quality, no quantitative measure of intervention effectiveness has been examined except Gevarter et al.’ (2016) who reported percentage of non-overlapping data (PND) to inform our understanding of the effectiveness of math interventions. The current review addresses this limitation by using another non-overlap index, Tau-U, to examine the effectiveness of academic interventions to improve targeted academic and related skills. The fact that single-case design (SCD) has historically relied on visual analysis of graphs to judge the effectiveness of an intervention has complicated the inclusion of SCD studies in evidence-based practices reviews (Valentine et al. 2016). Shadish et al. (2015) pointed to the importance of using good standardized effect size measures using scales that have the same meaning across reviewed studies. These standardized effect size measures can benefit SCD research by making research studies more accessible and more useful when informing policy decisions. While some researchers have noted that effect sizes are a necessity for understanding the findings of a study and comparison across studies (Shadish et al. 2015), other researchers consider reporting effect sizes best practice when presenting empirical research findings in many fields (Nakagawa and Cuthill 2007). What Works Clearinghouse (WWC) standards suggest that SCD research must include visual analysis as well as quantitative measurement to be scientifically validated (Kratochwill et al. 2010). Parker et al. (2011) introduced Tau-U, described in greater detail in the BMethod^ section, as a statistical analysis of single-case research data; therefore, this study utilized Tau-U as a quantifiable non-overlap index in order to compare findings across studies. Children with ASD need to learn a variety of skills that will allow them to learn, work, and live successful lives. Academic skills, including math and prerequisite skills, and general knowledge are important for children with ASD to function in society. Unfortunately, most research in this area targets literacy and less research has been done in other academic


areas (Pennington 2010; Spencer et al. 2014). Like the review conducted by Machalicek et al. (2008), the current review extends previous research because it reviews studies targeting all academic and related skills. Additionally, many of the prior reviews included studies carried out in a variety of environments such as family homes, clinics, and schools. The current review included only studies conducted in school settings. The school is one of the most important settings to carry out research that targets academic skills (Alibali and Nathan 2010). If we consider that the local context impacts the quality and the effectiveness of interventions (Horner et al. 2014), schools are the natural environment for conducting research on interventions to improve the academic skills of school-age children. The development and evaluation of academic interventions in classrooms may lead to a better contextual fit of such interventions. The purpose of the present review is to examine the effectiveness of school-based instructional intervention research addressing academic and related skills for students 3 to 21 years, from 1995 to 2014. Like other reviews, the authors assessed the methodological quality of each study. However, this review extends the timespan of prior reviews and addresses the limitations of previous reviews by utilizing a non-overlap index (Tau-U) to quantify the

effectiveness of interventions in each study and across the curricular domains of literacy, engagement, math, science, social studies, and prerequisite skills. Use of a nonoverlap index allows for quantification of the degree to which participating students have improved in targeted academic skills following interventions and easier comparison across studies and domains. The authors addressed three a priori research questions in the current review: 1. Which curricular domains, interventions, and academic and related skills have been targeted in studies with students with ASD in school settings? 2. How effective have school-based interventions been in improving targeted academic skills or skills related to academic performance? 3. What level of methodological rigor (i.e., design and procedures) exists in single-case research studies examining academic interventions for students with ASD?

Method Search and reliability procedures are illustrated in Fig. 1.

Fig. 1 Search and reliability procedures

Total number potential studies = 2,865

Total number studies utilizing single-case research teaching skills to students with ASD in school setting 125 studies

Initial search using electronic database (ERIC, PsycINFO, and MEDLINE) using the following descriptors: “autis*,” Asperger, pervasive developmental disorder or (pervasive developmental disorder-not otherwise specified; PDD-NOS) and intervention,” in combination with “academic,” “general education,” “literacy,” “math*,” “writing,” “science,” “organization,” or “task engagement” search studies targeting academic skills Exclusion of studies not utilizing single-case research and overlapping studies 2,740 studies were excluded

Ancestral search

Across the 131 studies abstract read to identify studies meeting the three inclusion criteria targeting academic skills, 72 studies were excluded

Method section read to make sure studies met inclusion criteria of the 59 studies

Total number of studies included 54

6 studies included

5 studies were excluded


Search Procedures

Rigor of the Studies

Electronic database searches were completed using in-text searches of ERIC, PsycINFO, and MEDLINE using the following keywords: Bautis*,^ Asperger, or pervasive developmental disorder (pervasive developmental disorder-not otherwise specified; PDD-NOS) and Bintervention,^ in combination with Bacademic,^ Bgeneral education,^ Bliteracy,^ Bmath*,^ Bwriting,^ Bscience,^ Borganization,^ or Btask engagement.^ The authors limited the search results to English language studies published in a peer-reviewed journal between 1995 and 2014. This search initially produced 2865 articles to review for potential inclusion. Fifty-four studies were identified for inclusion after eliminating duplicate articles, applying inclusion criteria and conducting ancestral searches.

To evaluate the rigor of the studies selected for the current review, the authors used Reichow (2011) guidelines. Reichow’s evaluator criteria provide a rubric with six primary indicators critical for demonstrating the internal validity of a study, and six secondary indicators. A study is considered strong if (a) each primary indicator is rated as high, and (b) the ratings are high or acceptable for at least three of the six secondary indicators. A study is considered adequate if (a) it receives a high score on at least four primary indicators (without any unacceptable scores in any other primary indicator), and (b) an acceptable or high rating in at least two of the six secondary indicators. A study is considered weak if (a) it receives a high score in fewer than four primary indicators, and (b) if fewer than two of the six secondary indicators are acceptable or high.

Inclusion and Exclusion Criteria

Certainty of Evidence

The BMethod^ section of each article was read to identify studies that (a) implemented intervention strategies to improve one or more academic skills or skills related to academic performance with at least one participant with a medical diagnosis or educational classification of ASD between the ages of 3 and 21 years; (b) employed a single-case research design; and (c) conducted intervention in a private or public school setting, including transition programs serving students aged 19 to 21 years. Interventions were considered school based if all sessions took place in a special education or general education classroom, playground, cafeteria, gymnasium, library, or any other area within the school campus. Exclusion criteria included (a) fewer than three data points in baseline phase, and (b) conducted in non-academic settings such as homes, community, or clinics.

To provide an objective measure of certainty of evidence, TauU scores were calculated for each participant, study, academic or related skill, curricular area, and type of intervention. Parker et al. (2011) introduced Tau-U as an alternative to regression-based and existing non-overlap models of statistical analysis of single-case research data. Tau-U combines assessment of non-overlap between A–B experimental phases while controlling for a positive baseline trend. To extract data for Tau-U calculation, the authors used pdfs of the published graphs for each article and the UN-SCAN-IT version 5.2 (Silk 1992) to manually digitize underlying x, y data points. Then, the authors saved the data in an Excel database sheet and later pasted it into the Tau-U calculator lab at the Single Case Research website (www.singlecaseresearch. org). Baselines were corrected, and both baselines and comparison phases or conditions were weighted to obtain final Tau-U scores. For multiple-baseline designs, Tau-U was calculated for each baseline–intervention (A1–B1) combination. For withdrawal designs (A1B1A2B2), Tau-U was calculated for each baseline and intervention pair (i.e., A1B1 and A2B2). In the case of alternating treatment designs, Tau-U was calculated by contrasting each intervention condition with the baseline phase data when a baseline phase was reported, or control condition data (e.g., A1B1, A1C1). The average of all scores for each of the studies was calculated. The authors first calculated Tau-U for each student (if only one student, Tau-U for each skill). Then, the mean Tau-U score across participants or skills in each study was calculated to obtain a total study score. Finally, the weighted mean Tau-Us for all 54 studies together were calculated to obtain the overall measure of effect. Tau-U scores range from 0 to 100% and can be interpreted using the following criteria: (a) 20% or lower suggests a weak effect; (b)

Data Extraction and Analysis Variable Coding The authors coded each study for the following variables: (a) type of single-case research design; (b) number of participating students with ASD; (c) participant demographics including age, gender, race and ethnicity, and other diagnoses; (d) type of school setting; (e) targeted academic or related skills; (f) intervention used and the role of the person implementing the intervention; (g) inter-rater reliability procedures and scores; (h) treatment integrity procedures and scores; (i) social validity assessment and outcomes; and (j) assessment of generalization and maintenance. The coding sheet is available from the authors by request.


between 20 and 60% suggests a medium effect; (c) between 60 and 80% suggests a high effect; and (d) above 80% suggests a high to very high effect (Vannest and Ninci 2015).

Inter-rater Reliability To guarantee that the data collected represents the variables measured, the author conducted the following three types of inter-rater reliability: (a) inclusion, (b) coding, and (c) Tau-U calculation. Inclusion A second doctoral student independently reviewed the method section of each study identified for inclusion against the inclusion criteria. Each article represented an occasion for an agreement or a disagreement. Inter-rater reliability was calculated by dividing the total number of agreements by the total number of agreements plus disagreements and multiplying by 100 (Cohen and Swerdlik 2005). Inter-rater reliability was 91%. The five studies with disagreement were discussed with the second author, and a consensus agreement made about inclusion or exclusion of each study. Five studies were excluded based on intervention setting or targeted skills. Coding The first author coded each of the included studies on the variables using a coding sheet developed for this review. A second doctoral student independently coded a random sample of 12 studies (22%) using the same coding scheme. Disagreements were discussed on the coding of four studies, and corrections were made accordingly. The author calculated inter-rater reliability using the same formula used in the inclusion inter-rater reliability. Inter-rater reliability for coding of articles was 98%. Tau-U analysis The first author calculated Tau-U scores as previously described. A second doctoral student independently calculated Tau-U scores for a random sample of 12 studies (22%). Each Tau-U score was considered an opportunity for agreement or disagreement. There were no disagreements. Inter-rater reliability for Tau-U scores was 100%.

Results Overview The 54 studies included in this review were published in 21 different journals between 1995 and 2014 and represent

intervention with a total of 150 students with ASD, some of them with comorbid diagnoses. Thirty-nine studies (72%) assessed treatment integrity or the degree to which interventions were implemented as intended. Fifty-three (98%) studies reported inter-rater agreement, ranging from 85 to 100%. Twenty-eight (52%) of the 54 studies included a maintenance phase in which the researchers removed the intervention. Fifteen (28%) studies reported data on the generalization of the skills acquired during the study. Only 26 (48%) of the 54 studies reported social validity data on stakeholder perceptions of the goals, intervention procedures, and outcomes of the intervention. The reader may refer to Table 1 for a description of studies by domain categories listed according to generalization and maintenance, intervention, target skills, type of design, interventionist, setting, Tau-U scores, and rigor of study.

Participants and Settings This review included a total of 150 student participants. Of the 147 students with gender reported, 129 (86%) were male, and 18 (12%) were female. The gender of three (2%) participants was not reported. Although most studies did not report the race or ethnicity of the students, 26% did, and the most common race or ethnicity of these students was white (47% of the participants with reported ethnicities or races). Regarding participants’ age, 74 (49%) students were elementary school-age and 43 (29%) students were middle school-age. Twenty-one (14%) students were high school-age and seven (5%) were preschool-age students. The ages of five (3%) students were not reported. Sixty-four students (43%) had comorbid diagnoses including intellectual disabilities, ADHD, speech and language impairments, emotional/behavioral disorders, and hearing impairments. In 25 studies (46%), the interventionists were teachers. In 20 (37%) of the studies, researchers implemented the intervention. Other interventionists included therapists, parents, and paraprofessionals. Most interventions took place either in special education classrooms (48%) or in general education classrooms (37%). In 15% of the studies, interventions took place in other settings (e.g., hallway, playground, and cafeteria).

Targeted Academic Skills and Intervention Strategies Literacy Skills Nineteen studies (35%) evaluated an intervention focused on teaching literacy skills. Ten (53%) of these studies implemented at least one type of evidence-based practice. The evidencebased practices (Odom et al. 2010) included (a) technologyaided instruction and intervention (40%), (b) video modeling (20%), (c) prompting (20%), (d) time delay (10%), and (e) visual support (20%). Four (21%) of the studies implemented

Table 1 Studies listed by domain categories listed according to description of participants, generalization and maintenance, intervention, target skills, type of design, interventionist, setting, Tau-U scores, and rigor of study Studies

N

Age

Diagnosis

G/M

Intervention

Target skill

Type of design

Interventionist

Setting

Tau-U

Study rigor

Armstrong and Hughes (2012)

5

7–8

ASD

N/N

Answering questions and retelling story

ATD

Researcher

GE

0.53

W

Asaro-Saddler (2014)

3

7–8

ASD

N/N

Teachers

SE

0.94

A

3

6–9

ASD and AS

N/Y

MPD across baselines

Researcher

GE

0.93

A

3

12–15

ASD and CD

N/Y

Planning for strategy to write a story Planning for strategy to write a story Matching sound-to-letter and decoding of novel words

MPD across baselines

Asaro-Saddler and Saddler (2010) Bailey et al. (2011)

MBD across participants

Researcher

GE

0.50

W

Carnahan and Williamson (2013)

3

13

ASD

N/Y

Teachers

PS

0.94

A

1

12

ASD

N/N

Increasing reading comprehension of science Teaching word identification

WD design (A–B–A–B)

Coleman-Martin et al. (2005)

WD design (A–B–A–B + C–A–C)

Teachers

SE

1.00

A

Flores and Ganz (2009)

2

12–14

ASD

N/Y

Repeated reading using computer and storybook Self-regulated strategy development Self-regulated strategy development Scaffold instruction lessons and picture book-based phonological awareness Compare-contrast text pattern using Venn diagram Nonverbal reading approach method with computer assistance Explicit instruction

MBD across behaviors

Researcher

PS

1.00

A

Johnston et al. (2009)

2

4–5

Autism and CD

N/Y

Increasing reading comprehension Identification of letter sound

ATD with baseline

Teacher

P

1.00

W

Kagohara et al. (2012)

2

10–12

AS and ADHD

N/Y


Researcher

GE

0.93

S

Ledford et al. (2008)

6

5–8

ASD and SLI

Y/N

MPD across behaviors

Teachers

SP

0.71

S

Ledford and Wehby (2014) Marcus and Wilder (2009) Mason et al. (2010)

5

5–6

ASD

Y/N


Researcher

SP

0.84

S

1

4

ASD

N/N

ATD with baseline

Therapist

P

0.76

W

1

14

ASD and EBD

N/Y

MPD across tasks

Researcher

SE

0.66

W

Mucchetti (2013)

4

6–8

ASD

N/N

MBD and ATD

Teacher

PS

0.99

S

Pennington et al. (2012)

1

7

ASD

N/Y


Teacher

SE

0.69

A

Pennington et al. (2011)

3

7–10

ASD

Y/Y

Use a spell-check function on a word processor Sight words in a small group arrangement Sight words, naming, and geometric shapes Identify or label Greek and Arabic letters Response parts and words used during/persuasive tasks Increasing comprehension and activity engagement Acquiring story construction and writing sentences of story Construct simple stories

MPD across participants

Teacher

SP

0.64

A

I. Literacy skills

Constant time delay procedure Small group instruction Peer video modeling and self-video modeling Self-regulated strategy development

Rev J Autism Dev Disord

Teacher-led adapted shared reading activities Simultaneous prompting using computer-assisted instruction Simultaneous prompting using computer-assisted instruction

Author's personal copy

Fixed or gradual array presenting target stimulus Video modeling instruction using iPad

Studies

Age

Diagnosis

G/M

Intervention

Target skill

Type of design

Interventionist

Setting

Tau-U

Study rigor

Reisener et al. (2014)

4

9–10

ASD, AS, and PDD/NOS

N/Y

Increasing oral reading fluency

WD (A–B–A–C) and (A–C–A–B)

Teachers

GE

0.77

W

Whalon and Hanline (2008)

3

7–8

ASD, AS, and PDD/NOS

N/N

Repeated readings and listening passage preview Reciprocal questioning strategy instruction


Peer and a researcher

GE

0.95

A

1

12

ASD

N/Y

Computer-based sight-word reading intervention

Generate and respond to questions from reading material Enhancing sight-word reading

MBD across tasks

Researcher

SP

1.00

W

ASD

N/N

Therapy ball chairs

(A–B–C) design

Teachers

GE

0.33

W

Visual interactive books and interactive books paired with music Social stories intervention

In-seat behavior and engagement during instruction Engagement during activities

WD design (A–B–C–A–C)

Teachers

SP

0.36

W

Social interaction, raising hand, and vocalization Task engagement and reducing teacher prompts Task engagement across 2 conditions: attention and escape Independent completion of assignments Attending to task and academic accuracy Attention during learning activates On-task behavior


Teachers

GE

0.45

W

MPD across settings and (A–B–A–B)

Teachers

GE

1.00

S

ATD

Teachers

GE

1.00

A

(A–B) design

SP

0.84

W


Researcher and parents Teachers

SP

0.89

A

(A–B–C) design

Researchers

PS

0.30

W


Researcher

SE

0.90

W

Task engagement

MBD across tasks

Teacher

SP

0.49

W

Reducing off-task behavior Academic engagement


Peer and a teacher

GE

0.97

A


Researcher

GE

0.15

W

Producing independent work


Researcher and teachers

SP

0.86

W

In-seat behavior and engagement Academic production

WD designs (A–B–A–B) and (B–A–B) WD design (A–B–A–B)

Teachers

SP

0.91

W

Researcher and teachers

SP

0.95

W

I. Literacy skills

Yaw et al. (2011)

II. Engagement and task completion Bagatell et al. (2010) 5 NA Carnahan et al. (2009)

5

6–11

ASD

N/N

Chan and O’Reilly (2008) Cihak et al. (2010)

2

5–6

ASD

N/Y

3

11–13

ASD

Y/N

Handheld self-modeling picture prompts

Cihak et al. (2012)

4

11–14

ASD and AS

N/N

Social stories and video self-modeling

Ferguson et al. (2005)

1

14

AS

N/N

Personal digital assistant

Holifield et al. (2010)

2

9–10

ASD

N/N

Self-monitoring

Kinnealey et al. (2012)

4

13–20

ASD

N/N

Legge et al. (2010)

2

11–13

ASD

N/Y

Massey and Wheeler (2000) McCurdy and Cole (2014) Nicholson et al. (2011)

1

4

ASD

N/Y

3

7–11

ASD

N/N

Sound-absorbing walls and halogen lighting Self-monitoring using MotivAider® Photographic activity schedule Peer support intervention

4

9

ASD

N/Y

Pelios et al. (2003)

3

5–9

ASD

Y/Y

Schilling and Schwartz (2004) Soares et al. (2009)

4

3–4

ASD

N/N

1

13

AS

N/N

III. Math skills

Physical activity intervention Time-delay schedule, prompt fading, and unpredictable supervision Therapy ball chairs Computer aided and self-monitoring


N


Table 1 (continued)

Table 1 (continued) Studies

N

Age

Diagnosis

G/M

Intervention

Target skill

Type of design

Interventionist

Setting

Tau-U

Study rigor

Akmanoglu and Batu (2004) Banda and Kubina (2009) Burton et al. (2013)

3

6–17

ASD and ID

Y/Y

Simultaneous prompting

Pointing to numerals


Researcher

SP

0.77

W

1

13

ASD and PDD

N/N

Researcher

SP

1.00

A

13–15

ASD

Y/Y

Initiating a low presence math problem Functional math skills


3


Teachers

SP

1.00

A

Cihak and Foust (2008)

3

7–8

ASD

N/N

Single-digit addition facts

ATD with baseline

Teachers

SP

0.73

A

Cihak and Grim (2008)

4

15–17

ASD and moderate ID

Y/Y

Independent purchasing skills

MPD across behaviors and settings

Teacher

SP

0.99

S

Fletcher et al. (2010)

2

13–14

ASD, moderate ID and HI

N/N

Single-digit math problems

ATD with baseline

Teacher

SP

0.96

A

Polychronis et al. (2004)

2

7–11

ASD

N/N

Time and geography

ATD with baseline

Teachers

SP

1.00

A

3

7–9

N/N

Researcher

GE

0.87

W

2

15–16


Teacher

SP

1.00

A

Whitby (2013)

3

13–14

ASD and AS

Y/Y

Solve It problem-solving routine

Complete math facts and math computation 3-digit money computational subtraction problems Solving multiple-step word mathematical problems


Waters and Boon (2011)

ASD, AS, ID and EBD ASD, AS, and mild ID

High and low preference strategy Video self-modeling on iPad Number line and touch point strategies Counting-on math technique with the next-dollar strategy TouchMath, a multisensory math program and number line Embedded instruction with a distribution schedule Response repetition technique TouchMath using touch points and regrouping


Teachers

GE

1.00

A

1

16

ASD and moderate ID

N/N

Video self-modeling using an iPad


Researcher

SP

0.72

W

Knight et al. (2012)

3

5–7

ASD

Y/Y

Explicit instruction

MPD across tasks

Researcher

GE

0.71

W

Riesen et al. (2003)

1

13

ASD

N/N

ATD with baseline

Paraprofessionals

GE

1.00

W

Smith et al. (2013)

3

11–12

ASD, ID, and ADHD

Y/Y

Constant time delay and simultaneous prompting Embedded computer-assisted explicit instruction

Correct unprompted responds in science class setting Acquire and generalize science descriptors Definitions of science terms Science terms and their applications


Researcher

GE

1.00

S

V. Social studies McKissick et al. (2013)

3

9–10

ASD

Y/Y


Researcher

SP

0.82

A

Schenning et al. (2013)

3

11–13

ASD and ID

Y/Y


Teachers

GE

0.98

S

Zakas et al. (2013)

3

11–13

ASD

Y/Y

Identifying of legend symbols on a map Adapting and comprehending social study lessons Improving expository text comprehension


Teacher

GE

1.00

A

I. Literacy skills

IV. Science skills Hart and Whalon (2012)

Computer-assisted instruction Structured inquiry using graphic organizer Graphic organizer


VI. Prerequisite skills

N/Y


Rapp et al. (2012)

Author's personal copy N number of students, G generalization, M maintenance, N no, Y yes, ASD autism, DS Down syndrome, ADHD attention deficit hyperactivity disorder, SLI speech language impairment, EBD emotional behavioral disorder, PDD/NOS pervasive developmental disorder not otherwise specified, PDD pervasive developmental disorder, ID intellectual disability, HI hearing impairment, CD cognitive delays, AS Asperger syndrome, NA not available, ATD alternating treatments design, MPD multiple-probe design, MBD multiple-baseline designs, WD withdrawal design, GE general education, SP special education, PS private school, P preschool, W weak, A adequate, S strong

A A PS GE Researcher Researcher MPD across stimulus MPD across participants Errorless learning Test-taking instruction N/N Y/Y ASD and ID ASD, AS, and ADHD 2 4 Graff and Green (2004) Songlee et al. (2008)

9–12 12–17

N/Y ASD and DS 5–7 4 Fentress and Lerman (2012)

I. Literacy skills

N Studies

Table 1 (continued)

0.16 0.83

PS Therapist MBD and ATD

G/M Age

Diagnosis

Intervention

Most-to-least prompting and no-no-prompt

Independent response, identification, imitation, and matching skills Visual discrimination Test-taking strategy

Setting Type of design Target skill

Interventionist

0.76

Tau-U

A

Study rigor


practices that had some empirical support but did not meet the criteria to be considered evidence-based practices. These include self-regulated strategy development (SRSD) (75%) and direct instruction (25%). Five studies (26%) implemented practices that have no empirical support. Four different areas of skills were targeted in literacy: (a) emergent literacy (i.e., letter identification), (b) phonics and fluency (i.e., word recognition, fluency), (c) reading comprehension (i.e., inferential skills), and (d) writing (i.e., composition, spelling). Engagement and Task Completion The engagement and task completion domain included 15 studies (28%). Twelve (80%) of these studies implemented, at least, one type of evidence-based practice. The evidencebased practices were (a) technology-aided instruction and intervention (25%), (b) self-management (25%), (c) visual support (17%), (d) social stories (17%), (e) video modeling (8%), (f) time delay (8%), (f) prompting (8%), (g) peer-mediated instruction and intervention (8%), and (h) exercise (8%). Three (20%) studies implemented practices that have no empirical support. The most common skills targeted in this domain included (a) task engagement, (b) in-seat behavior, (c) off-task behavior, and (d) completing assignments independently. Math Skills Math skills domain included ten studies (18%). Only two (20%) of these studies implemented, at least, one type of evidence-based practice. The evidence-based practices included (a) technology-aided instruction and video modeling (50%), and (b) prompting (50%). Three (30%) studies implemented a practice that had some empirical support but did not meet the criteria to be considered evidence-based practice. This practice was Touch-Point Instruction. Five (50%) studies implemented practices that have no empirical support. Targeted skills included (a) numbers and operations, (b) problem solving, and (c) functional math skills. Science Skills The science skills domain included four studies (7%). Three (75%) of these studies implemented, at least, one type of evidence-based practice. The evidence-based practices included (a) technology-aided instruction and intervention (33%), (b) technology-aided instruction and video modeling (33%), and (c) time delay and prompting (33%). One (25%) of the studies implemented a practice that had some empirical support but did not meet the criteria to be considered evidencebased practice. This practice was direct instruction. Studies on science addressed mostly two skills: comprehension and knowledge of scientific terminology.


Social Studies

Effectiveness Across Curricular Areas

The social studies domain included three studies (6%). These studies (100%) implemented at least one type of evidencebased practice. The evidence-based practices were visual support (67%) and technology-aided instruction and intervention (33%). The skills targeted in social studies included reading comprehension of texts and identifying map symbols.

This review also examined the effectiveness of interventions targeting literacy skills (19 studies), engagement and task completion (15 studies), math skills (10 studies), science skills (4 studies), social studies skills (3 studies), and prerequisite skills (3 studies). One study included a combination of literacy skills and engagement (i.e., Mucchetti 2013). Interventions that targeted math and social studies had the highest mean Tau-U scores (0.93 for social studies and 0.92 for math), indicating a high to very high effect of the interventions. Interventions that targeted science skills had a mean Tau-U score of 0.85 (high to very high effect). Interventions that targeted literacy skills had a mean Tau-U score of 0.81 (high to very high effect). Interventions that targeted prerequisite skills had a mean effect size of 0.67 (high effect). Interventions that targeted engagement and task completion had a mean Tau-U score of 0.64 (high effect).

Prerequisite Skills The prerequisite skills domain included three studies (6%). One of these studies implemented at least one type of evidence-based practice, which was prompting (33%). One of the studies implemented a practice that had some empirical support but did not meet the criteria to be considered evidence based. This practice was test-taking instruction (33%). One study implemented a practice that has no empirical support. These studies targeted a variety of prerequisite skills including (a) test taking, (b) visual discrimination, and (c) a combination of identification, imitation, matching, and following instructions.

Effectiveness across Interventions To analyze the effective across-intervention types, the authors did not include studies that combine two or more interventions. The weighted average Tau-U score across all 54 research studies was high (M Tau-U = 0.78; range = 0.15–1.00) with a mode of 1.00 and a median of 0.93. Interventions involving the use of technology (e.g., computer, video, iPad, personal digital assistant, MotivAider) were the most common interventions, appearing in 12 studies (20%). These interventions were also effective. The weighted M Tau-U score for interventions using technology was 0.78 (range = 0.53– 1.00). After calculating the weighted average across participants by intervention, the authors discovered that three evidence-based practices (EBPs) had high to very high effectiveness. These interventions were: peer support (Tau-U = 0.99), video modeling (Tau-U = 0.93), and self-management (Tau-U = 0.89). Four EBPs proved to be highly effective: technology-aided interventions (Tau-U = 0.78), prompting (Tau-U = 0.76), visual support (Tau-U = 0.74), and time delay (Tau-U = 0.71). Social stories had medium effectiveness (TauU = 0.45), and the effect of exercise was weak (Tau-U = 0.15) when used to teach academic skills. Regarding practices with some evidence, all interventions proved to have high to very high effectiveness. These interventions were self-regulated strategy development (Tau-U = 0.90), TouchMath (Tau-U = 0.87), direct instruction (Tau-U = 0.83), and test-taking instruction (Tau-U = 0.83).

Methodological Quality of the Studies The authors evaluated the rigor of design for all the 54 studies included in this review, following Reichow (2011) guidelines. Only eight (14%) studies were considered strong. Twentythree (43%) studies met the criteria for adequate strength, where 23 (43%) of the studies were rated as being weak regarding the strength of the research report. Regarding the primary quality indicators, dependent variables received the highest score. Forty-three (79%) studies operationally defined the dependent variable in a way that was replicable and with appropriate measurement data collected. On the other hand, participant characteristic indicators received the lowest score. Thirty-two (59%) studies failed to report the operationalized diagnostic instrument as well as standardized test scores and instruments used to obtain those scores. Regarding secondaryquality indicators, inter-observer agreement was the most common indicator used, with 53 studies (98%). Kappa and use of blind raters were the least used indicators. One study (2%) reported kappa, and two studies (4%) used raters who were blinded to treatment conditions for all participants. See Table 1 for a summary of quality ratings.

Discussion In general, academic interventions in this study were effective in increasing the frequency of targeted academic and related skills. However, the authors identified gaps in the research concerning specific curricular areas, targeted skills, and ages of children involved in the research. The current review identified only two studies (Johnston et al. 2009; Marcus and Wilder 2009) evaluating the effects of interventions on early literacy skills. Both studies targeted letter identification in


preschool-age children. A third study (Bailey et al. 2011) targeted letter identification in adolescents with ASD and severe communication impairments. These results support the findings from Westerveld et al. (2015) regarding the current lack of research on emergent literacy skills in preschool. More research is needed on early literacy development of children with ASD. Whalon and Hart (2010) conducted a qualitative study focusing on reading instruction of children with ASD in the general education classroom. Consistent with previous research, the children with ASD in the study had good decoding skills but poor reading comprehension. Whalon and Hart identified a lack of focus on comprehension during reading instruction. The present review confirms this trend. Finally, Whalon and Hart found that reading instruction was successful when teachers used strategies to make tasks more concrete and contextualized. These strategies included visual supports, schedules, scripts, and modeling. Using these supports appears to increase the effectiveness of those interventions reviewed here. In the present review, most studies targeting math skills addressed number and operations. These findings align with the findings of Browder et al.’s (2008) meta-analysis on teaching mathematics to students with significant cognitive disabilities. The authors found that most studies addressed numbers and computation, or measurement (i.e., money skills). Considering this limited scope of targeted skills, it is important for researchers to evaluate the effects of interventions targeting other content areas like algebra, geometry, measurement, and data analysis and probability. Findings of the present review also overlap with the findings of Spooner et al. (2011) concerning limited studies targeting science for students with severe disabilities including students with ASD. In addressing engagement and task completion, these studies showed that self-management strategies like self-assessment, self-recording, self-evaluation, decision-making, goal setting, and self-reinforcement besides self-monitoring effectively increased engagement and task completion, as well as decreased off-task behavior. Although engagement and task completion are often associated with challenging behavior (e.g., off-task behavior, noncompliance, task avoidance), none of the studies targeting engagement and task completion in the current review used functional behavior assessment (FA) except Cihak et al. (2012). These authors used a brief functional behavior analysis to identify the putative operant function of behavior prior to developing an intervention. Cihak and colleagues explained that FA is the most accurate way to know the function of a behavior. However, FA can be time intensive so the authors suggested the use of brief-FA as an alternative. Like analog FA, brief-FA can demonstrate a functional relation between a behavior and the variables maintaining such behavior (Iwata et al. 1982/1994). Future research evaluating the impact of interventions on task engagement and

completion during academic instruction should examine the utility and effectiveness of implementing brief FA prior to developing an intervention. Regarding effectiveness of evidence-based practices and practices with some evidence, the authors found that these practices were more effective to teach specific skills. In this analysis, an intervention was considered very effective to teach a specific skill when the Tau-U score was 0.90 or higher. For example, technology-aided intervention was most effective when used to teach word recognition than used to teach other skills (Coleman-Martin et al. 2005; Yaw et al. 2011; Smith et al. 2013). Visual supports were most effective to teach reading comprehension (Carnahan and Williamson 2013; Schenning et al. 2013; Zakas et al. 2013). Also effective when teaching reading comprehension was direct instruction (Flores and Ganz 2009). Video modeling was consistently more effective when teaching functional skills (Kagohara et al. 2012; Burton et al. 2013). Peer support was most effective to teach on task behavior as shown by McCurdy and Cole (2014). Assaro-Saddler and Saddler (2010) and AssaroSaddler (2014) demonstrated that SRSD can be very effective to help students plan writing strategies. Equally, TouchMath has been shown to be very effective to teach math computation (Fletcher et al. 2010; Waters and Boon 2011). The authors compared the outcomes of the current review with the review of studies targeting content area instruction conducted by Spencer et al. (2014). Spencer and colleagues identified the following effective interventions: (a) visual supports, (b) technology-based instruction, (c) concrete representation, (d) direct instruction, and (e) behavioral interventions. The current review supports these findings except in the visual supports category. After calculating Tau-U, only two of the five studies on visual supports proved effective (Tau-U greater than 0.98). The other three studies targeted students with a low level of functioning, and the outcomes of the interventions were less positive (Tau-U lower than 0.50). Spencer’s interventions that were also proven to be effective or very effective in the current review included (a) technology-based instruction (e.g., computer-assisted instruction, video-modeling, and MotivAider); (b) concrete representation (e.g., graphic organizers); (c) direct instruction (e.g., Solve It curriculum and peer support); and (d) behavioral interventions (e.g., time delay). Considering the great number of studies that used technology-based interventions, the authors were interested to know the effectiveness of such interventions. The weighted mean Tau-U for all studies using technology was 0.78 (high effect). The authors identified 46 (85%) studies that did not attain a strong quality rating. There were several reasons studies failed to meet the strong quality rating, but the main one was not reporting critical characteristics of the participants such as their performance on diagnostic instruments used to assess the participant, standardized test scores, or the instruments


used to obtain those scores. By explaining more about participants using standardized assessment, it allows researchers and practitioners to understand which participants can benefit from each type of intervention. The interventions included in the current research were generally effective at improving targeted academic skills of students with ASD. However, only 28% of the studies documented generalization of skills. Effective interventions can be of little benefit if they do not lead to the generalization of learned skills (Koegel et al. 2008). The goal of education is to help students learn skills that can be generalized to different contexts (i.e., settings, stimuli and persons) and can be maintained over time. Generalization naturally occurs for many typically developing children, but children with ASD often struggle to generalize or maintain their acquired skills; therefore, generalization and maintenance of skills must be a critical component of any intervention for children with ASD (Dawson and Osterling 1997). Generalization and maintenance of skills are also an important measure of intervention effectiveness. Therefore, future research should include measures of generalization and maintenance whenever possible. Among the 31 studies using evidence-based practices, 26 (83%) utilized a single type of intervention. In five (17%) of the studies (Cihak et al. 2012; Pelios et al. 2003; Soares et al. 2009; Pennington et al. 2011; Pennington et al. 2012), the evidence-based practices were combined into idiosyncratic packages. For example, Cihak et al. (2012) combined the use of video modeling and social stories procedures to increase task engagement of four middle school boys. This combination of practices proved to have a very high effect (TauU = 1.00). However, these idiosyncratic intervention packages were unique and not replicated in other studies. The presence of these idiosyncratic packages shows that practitioners and researchers can use a combination of evidence-based practices to individualize intervention. Although there is some evidence that these programs are effective, more research is needed to prove the effectiveness and validity of using a combination of interventions in academic settings.

Further research is needed to inform intervention in different curricular areas. Relatively few studies targeted the curricular areas of science and social studies. Studies targeting prerequisite skills for academic performance, such as test taking, imitation, attention, matching, classroom routines, following directions, and visual discrimination, were limited in number. Given the current emphasis on test taking and academic achievement (Linn 2011), as well as the characteristics of children with ASD, it seems appropriate to devote more effort and resources to research interventions targeting these critical skills. Similarly, there is a lack of research targeting handwriting, grammar, and especially involving non-verbal students who will likely require individualized instruction. The authors were unable to identify any single-case research targeting early math skills for preschool-age children such as sorting by size, shape, and patterns, counting verbally, numeral recognition, one-to-one correspondence, and magnitude comparison. In addition, a limited number of studies have targeted high school students, which confirms the findings and recommendations of Spencer et al. (2014). Older students with ASD also need to learn functional academic skills because of their unique impairments (Szidon et al. 2015). It is recommended to devote more resources to study interventions to teach literacy, engagement and task completion, and prerequisite skills. These skills are critical if students with ASD are to transition effectively to independent adult life. Finally, only eight (14%) studies were identified as having a strong experimental design. Most designs were either adequate or weak. The authors recommend researchers to implement one of the guidelines (e.g., Task force on evidence-based interventions for school psychology, 2003; Kratochwill et al. 2010) to assess the rigor of single-case designs before conducting new research projects. This would strengthen the methodology. Future research should also include the following quality elements that were most commonly absent from the studies reviewed: (a) description of participant characteristics, especially race and ethnicity and reporting of performance on standardized norm-reference assessments; (b) measures of generalization and maintenance (c) use of blind raters; and (d) use of Kappa indicator for inter-observer agreement.

Future Research The findings of this review bring attention to several recommendations that can advise future research. Most studies failed to report the race or ethnicity of participants; this is a frequently discussed gap in intervention research for children with ASD (Wong et al. 2015) and clearly remains an issue. Contextual factors like race and ethnicity are important when trying to tailor interventions to individual students (West et al. 2016). Future research should include more information about contextual factors, especially regarding participants’ characteristics. This will help make a more accurate judgment about the effectiveness of the interventions.

Limitations This review has several limitations. First, the content overlaps 20% with the recent review of Machalicek et al. (2008) since 11 studies appear in both reviews. Seven studies (13%) of the current review appear in Spencer et al. (2014) review. Additionally, the author did not assess publication bias in this literature review. Studies with positive results are more likely to be published (Shadish et al. 2016). Nevertheless, this review offers the additional measurement of intervention


effectiveness (Tau-U) and the evaluation of the rigor of the studies using Reichow (2011) guidelines.

Conclusion This review examined 20 years of school-based instructional intervention research for students with ASD. The authors addressed the limitations of previous reviews by restricting the scope of the studies to interventions conducted in school settings and by utilizing a non-overlap index (Tau-U) to measure the effectiveness of interventions. The selected studies identified the most effective interventions used to teach academic skills to students with ASD. Also, the review identified the academic skills targeted and several gaps in the research. For example, we suggest future researchers examine emergent literacy, science, and other math content areas (e.g., algebra, geometry, measurement, data analysis, probability). Also, we recommend the use of visual supports and technology with students with a high level of functioning. Finally, we noted the importance of reporting participant characteristics (e.g., race or ethnicity, and standardized test scores), maintenance, and generalization data. However, these findings must be interpreted with caution due to the low level of rigor of the studies.

Compliance with Ethical Standards Conflict of Interest interest.

The authors declare that there is no conflict of

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