Technology-Aided Interventions And Instruction For
J Autism Dev Disord (2015) 45:3805–3819DOI 10.1007/s10803-014-2320-6ORIGINAL PAPERTechnology-Aided Interventions and Instruction for Adolescentswith Autism Spectrum DisorderSamuel L. Odom Julie L. Thompson Susan Hedges Brian A. Boyd Jessica R. Dykstra Michelle A. Duda Kathrine L. Szidon Leann E. Smith Aimee BordPublished online: 3 December 2014 Springer Science Business Media New York 2014Abstract The use of technology in intervention andinstruction for adolescents with autism spectrum disorder(ASD) is increasing at a striking rate. The purpose of this paperis to examine the research literature underlying the use oftechnology in interventions and instruction for high schoolstudents with ASD. In this paper, authors propose a theoreticaland conceptual framework for examining the use of technologyby and for adolescents with ASD in school, home, and community settings. This framework is then used to describe theresearch literature on efficacy of intervention and instructionthat utilizes technology. A review of the literature from 1990 tothe end of 2013 identified 30 studies that documented efficacyof different forms of technology and their impact on academics,adaptive behavior, challenging behavior, communication,independence, social competence, and vocational skills.S. L. OdomCenter on Secondary Education for Students with ASD,Chapel Hill, NC, USAB. A. BoydDivision of Occupational Sciences, Department of Allied Health,University of North Carolina at Chapel Hill,CB 8040, Chapel Hill, NC 27599-8040, USAe-mail: brian boyd@med.unc.eduS. L. Odom (&)Frank Porter Graham Child Development Institute, University ofNorth Carolina at Chapel Hill, 105 Smith Level Road,CB 8180, Chapel Hill, NC 27599-8180, USAe-mail: slodom@unc.eduJ. L. ThompsonCounseling, Education, Psychology, & Special Education,Michigan State University, Erikson Hall, Rm 344, East Lansing,MI 48824, USAe-mail: thom1281@msu.eduS. Hedges J. R. DykstraFrank Porter Graham Child Development Institute, University ofNorth Carolina at Chapel Hill, CB 8040, Chapel Hill,NC 27599-8040, USAS. Hedgese-mail: hedges@live.unc.eduJ. R. Dykstrae-mail: jessica.dykstra@unc.eduKeywords Technology Autism spectrum disorder Adolescents Natural settingsFew individuals in the world are untouched by some form oftechnology; they wear it on their wrists, carry it in theirpockets or purses, go to sleep and wake up to it, and may evendepend on it to keep their heart beating at the right pace. Therapid ‘‘uptake’’ of technology in interventions and teachingstrategies that affect the daily lives of individuals with autismspectrum disorder (ASD) is a prime example of thisM. A. DudaFrank Porter Graham Child Development Institute, University ofNorth Carolina at Chapel Hill, Campus Box 8185, Chapel Hill,NC 27599-8185, USAe-mail: duda@unc.eduK. L. Szidon L. E. SmithWaismann Center, University of Wisconsin, Madison, WI, USAK. L. Szidone-mail: szidon@waisman.wisc.eduL. E. Smithe-mail: lsmith@waisman.wisc.eduA. BordUC Davis MIND Institute, 2825 50th Street, Sacramento,CA 95817, USAe-mail: aimee.bord@ucdmc.ucdavis.edu123
3806phenomenon and is reflected in the large number of studiesthat have emerged in recent years (Boser et al. 2014; Grynszpan et al. 2014; Keintz et al. 2013; Knight et al. 2013;Mechling 2011; Pennington 2010; Ploog et al. 2013; Ramdoss et al. 2011a, b, 2012; Wainer and Ingersoll 2011). Theunique appeal of electronic technology for children andyouth with ASD (Kuo et al. 2014; Mazurek et al. 2012;Mineo et al. 2009; Shane and Albert 2008) has engenderedmuch excitement about its use in educational, clinical, andcommunity settings. This enthusiasm has led to a somewhatunbridled adoption of applications and equipment with littleregard for, or knowledge about, the efficacy of suchapproaches, or their potential collateral effects. Discussingcommunication needs of individuals with ASD, Shane et al.(2012) noted, ‘‘caution must continue to be exercised toensure that the dazzle of this impressive technology does notreplace a methodical, clinical process that matches a person with optimal communication technology available (p.1229).’’ This statement holds true for other forms of technology that have yet to be demonstrated effective via highquality research studies.The purpose of this paper is to examine the current researchon the use of technology in educational settings thecommunity, and in homes for adolescents with ASD and theirfamilies. The rationale for focusing on adolescents is that inthe autism intervention literature, researchers and scholarshave paid less attention to this age group, with research studiesprimarily including preschool- and elementary-age childrenas participants (Wong et al. 2014). However, given the dismalpost-school outcomes for young adults with ASD (Shattucket al. 2012) and the fact that they are approaching the end ofthe traditional years of public education where they mightaccess support for technology use, adolescents with ASD are apopulation for which technology-assisted interventions isvery important. In this paper, the authors begin with a workingdefinition of technology, propose a conceptual framework formatching the user, the technology, and the activity(i.e., goals), and use this conceptual framework for organizingresearch findings. A review of research published between1990 and 2013 follows with a discussion of findings about theusers involved in the research, activity addressed, types oftechnology, and contexts. The paper concludes with a discussion of the implications for current practice and futuredirections.Definition, Theoretical Foundation, and ConceptualFrameworkThe development and use of technology to assist individuals with ASD is a decidedly interdisciplinary work. Professionals contributing to this development are from the123J Autism Dev Disord (2015) 45:3805–3819fields of human computer interaction within the broaderfield of computer science, design, assistive technology,occupational sciences and therapy, rehabilitation engineering, speech-language pathology, learning sciences/psychology, and special education (Porayska-Pomsta et al.2012). Because disciplines often use different terminologies, a discussion about technology should begin with acommon working definition. To establish such a definitionfor this paper, members of the Technology Work Groupfrom the Center on Secondary Education for Students withASD (CSESA) drew from the United States (US) federaldefinition of assistive technology (PL 108-364, PLAW108publ364.htm) and the definition established by theCanadian Association for Occupational Therapy (2012).For the purposes of this paper, technology refers to anelectronic item/equipment, application, or virtual networkthat is used to intentionally increase, maintain, and/orimprove daily living, work/productivity, and recreation/leisure capabilities of adolescents with autism spectrumdisorders (CSESA Technology Group 2013). ‘‘Low tech’’or soft technologies, while often effective, are different ininstructional features (e.g., two dimensional, non-electronic), and the authors did not include them in this review.Theoretical FoundationPersuasion Theory underlies work in the field of human–computer interaction (Fogg et al. 2002). Although its rootsstretch back to Aristotle, contemporary interests in Persuasion Theory lie in social psychology, rhetoric, and business.Persuasion Theory focuses on factors within the individual(i.e., capabilities, interests, attitudes), the characteristics ofmessages or information conveyed, and features of specificcontexts, all with the intent of understanding their influenceson behavior and attitude change (Reardon 1981). Forexample, persuasion (e.g., a teacher trying to promote thesocial communication of a nonverbal adolescent with ASD)may occur through both the content of the message (e.g., aspecific greeting to a peer), the cues in the informationcontext that are attractive (e.g., an iPad with voice activation), and motivation provided by the context (e.g., aninterest in the peer responding, interest in assistance with atask, engaging in a fun game; Petty and Brinol 2008).Employing this theoretical model to the development oftechnology, Fogg et al. (2002) proposed that PersuasiveTechnology is ‘‘any type of computing system, device, orapplication that was designed to change a person’s attitudesor behavior in a predetermined way (n.p.).’’Persuasive Technology consists of two key concepts. Thefirst is credibility (Fogg and Tseng 1999), which refers to the
J Autism Dev Disord (2015) 45:3805–3819trustworthiness (i.e., inherent goodness or morality) andexpertise (i.e., functionality) of technology. An example ofcredibility is a visual schedule application (more commonlytermed ‘‘app’’) on a smartphone with tactile cuing thatreliably signals an upcoming transition to an adolescent withASD and visually displays the next activity. The second keyconcept, Kairos, is the delivery of a message at the right timeand/or place (Fogg and Eckles 2007). An example of Kairoswould be an adolescent with ASD using the smartphonevisual schedule app, just described, during the times of theday and locations in which transitions are the most troublesome. Although not without critics, Persuasive Technology has the value of focusing technology design onfunctionality, perceived value, and its use in context (Mintzand Aagaard 2012). For example, the developers of a projectentitled Helping Autism-diagnosed Teenagers Navigatingand Developing Socially (HANDS, http://hands-project.eu/index.php?page proj) directly employed principles of Persuasive Technology in their design of a mobile smartphoneapplication for adolescents with ASD (Mintz 2013; Mintzet al. 2012).Conceptual FrameworkThe CSESA Technology Group (2013) has proposed aframework for conceptualizing variables affecting the useof technology for adolescents with ASD that is consistentwith the principles of Persuasive Technology. It also drawsfrom the Human Activity Assistive Technology Model(HAAT) established by Cook and Hussey (2008). As canbe seen in Fig. 1, this model consists of characteristics ofthe user (e.g., adolescents with ASD), the activity that is toFig. 1 Conceptual framework for technology-use for adolescentswith ASD3807be supported by the technology, and the technology itself.The overlap of these factors represents the ideal useractivity-technology match. These factors are situatedwithin the broader ecological context of school, home, and/or community (Bronfenbrenner 1979).UsersThe current generation of adolescents is the first to havecomputer and online technology as a part of their livessince early childhood, and its use is pervasive. In a recentnational survey, investigators with the Pew Foundationreported that 78 % of respondents between 12 and 17 yearshad a smartphone and 95 % were online through someform of technology (Madden et al. 2013). Although thesurvey did not report information about respondents withASD, it is reasonable to assume that this demographicdescription applies to many adolescents with ASD (i.e.,they are teenagers as well as being individuals with ASD)and it certainly reflects a peer group context that is technologically active.For many youth with ASD, technology appears to beparticularly engaging (Keintz et al. 2013). Visual presentation of information is a preferred form of learning andsupport for many adolescents with ASD (Shane and Albert2008). There is evidence that animated or video presentations are more effective in conveying information thanstatic visual presentations (Van Laarhoven et al. 2010),large screen displays may be more effective than smallerscreen displays (Mechling and Ayres 2012), certain typesof visual screen media (e.g., seeing self on screen, virtualreality) may be preferred over others (Mineo et al. 2009)and learning tasks presented via a computer and visualmedium may result in more efficient performances than thesame tasks presented in a tangible format (Mechling et al.2006). In addition, when given a choice, adolescents withASD prefer to access and use technology relative to othersocial and leisure activities. In their study of discretionarytime use by children (8–18) with ASD, Mazurek et al.(2012) found that participants spent on average 4.5 h perday using screen-based media (i.e., video games and television) compared to 2.8 h per day in non-screen activities(including playing with friends, engaging in sports andreading). Focusing specifically on adolescents’ media useand using 2009 data, Kuo et al. (2014) found that 98 % ofthe 92 participants with ASD surveyed used computersapproximately 5 h per day to watch cartoons and playgames. These studies confirm earlier findings from anationwide survey of transition age youth with disabilities(using 2001 data) that revealed heavy screen-based mediause of high school students with ASD (Mazurek et al.2012).123
3808ActivityThe key linkage for using technology to support the dailyactivities for individuals with ASD is the match betweenthe individualized goal(s) for youth with ASD and the dailyactivities in which these goals will be addressed. Becauseof the broad spectrum of characteristics for youth withASD (e.g., having vs. not having an intellectual disability),the variety of goals is large. The learning needs, and thusthe individual learning goals may include communication(Loucas et al. 2008), social competence (Walton and Ingersoll 2013), personal independence (Hume et al. 2009),challenging behavior (Matson et al. 2010), academics(Estes et al. 2011), and/or transition to work and community (Lee and Carter 2012). High school students work onthese goals in a variety of contexts like school classes,transitions within or between classes, school activities(e.g., lunch, clubs), the workplace in the community, and/or in the home.TechnologyThe technological device or application is the third factorin this conceptual framework. The forms of technology areas broad as the goals themselves, and some of these formsare more available and acceptable with the rapid spread oftechnology. In their review of technology designed forindividuals with ASD, Keintz et al. (2013) identified eightinteractive technology platforms: personal computers, useof the web, mobile devices, shared active surfaces, virtualreality, sensor and wearable technologies, robotics, andnatural user interfaces.A variety of examples of these platforms have appearedin the intervention literature. For students who are nonverbal or need augmentative assistance for communication,specialized speech generating devices have been developed(Ganz et al. 2012). More recently the advances in tablettechnology include speech generating device applicationsthat operate on commercially available equipment (Kagohara et al. 2013). Service providers and researchers initiallyused personal digital assistants (PDAs) to support independent performance of individuals with ASD (Gentryet al. 2011), but now smartphones, iPod touches, and MP3players can accomplish similar functions (Mechling 2011).In the early studies of video modeling, traditional videocassette recorder (VCR) and monitor technology providedvideo demonstrations (Gelbar et al. 2012). Service providers and researchers now use more portable devices suchas tablets and smartphones to collect and design videoexamples for video modeling interventions as well as todeliver the intervention (Plavnick 2012). Computer-assisted instruction was an early application to support learningof individuals with ASD and other disabilities (Hofmeister123J Autism Dev Disord (2015) 45:3805–3819and Friedman 1986), and it continues to support a varietyof learner outcomes such as academic skills (Ramdosset al. 2011b) and social competence (Reed et al. 2011). Inaddition, researchers have explored virtual reality systemsin which youth with ASD may participate in social or otheractivities with an avatar (Hopkins et al. 2011), as well asthe use of robotics to simulate facial expressions andinteractive engagement (Kim et al. 2013). Bluetooth andother audio telemetry may allow the traditionally clinic andlab-based covert auditory coaching or ‘‘bug in the ear’’practice to be extended to the school and community (Allenet al. 2012). Further, the availability of telecommunicationsystems, such as Skype, FaceTime, and telemedicine, isallowing service providers to deliver interventions to clients in remote locations (Vismara et al. 2009). As Keintzet al. (2013) observed, these platforms exist and the number is growing, but not all have evidence of efficacy.ContextsAs noted, the selection and use of technology occurs inmultiple contexts. Advocates of Persuasive Technologypropose that technology use should extend beyond theschool and into the home (Mintz et al. 2012), in which casefamily members are also potential users and supporters oftechnology in the home and community. Similarly, adolescents with ASD often participate in job training, community living skills training, and/or recreation in thecommunity. Features of these contexts influence theactivity or goal and type of technology selected, as well asthe critical situations in which the technology may be mosthelpful (i.e., the Kairos).The OverlapThe conceptual model proposed here suggests that threefactors affect the successful use of technology by adolescents with ASD. These factors are characteristics of theindividual (e.g., ASD proclivity for visual display, typicaltechnology interest and use by most adolescents), theactivity or purpose for which the adolescent uses thetechnology (e.g., support for making transitions betweenclasses in a school), and the device itself (e.g., ease andreliability of using a smartphone to provide prompts duringclass transitions). The overlap of these variables in theVenn diagram in Fig. 1 represents the intersection ofinformation that a teacher, parent, or student should use tomake the decision about technology use. Leaving out anyof these sources of information may lead to an ill-informeddecision.The CSESA conceptual framework for technology is alsouseful for organizing information from the empirical intervention literature about technology-assisted interventions.
J Autism Dev Disord (2015) 45:3805–38193809Descriptions of the potential use of technology for individuals with disabilities have appeared in the professionalliterature in recent years (DiGennaro Reed et al. 2011;Grynszpan et al. 2014; Keintz et al. 2013; Knight et al.2013; Mechling 2011; Pennington 2010; Ploog et al. 2013;Ramdoss et al. 2011a, b, 2012; Wainer and Ingersoll 2011).However, as Bennett et al. (2013) noted, a question mayexist about whether potential applications meet the currentstandards for evidence-based practice proposed for the field(Gersten et al. 2005; Horner et al. 2005). Recalling thepoint by Shane et al. (2012), it is essential that the efficacyof interventions incorporating technology be systematicallyevaluated, so that empirical support as well as enthusiasmguides selection of intervention and instructional practices.To date, a comprehensive review of intervention practicesinvolving technology use specifically for adolescents withASD has not appeared in the literature. The purpose of thisreview is to summarize this research base, focusing on theusers of technology, the activity/goals addressed, the typeof technology employed, and the contexts in which intervention practices are employed for adolescents with ASD.assisted intervention and instruction’’ or ‘‘video modeling’’were selected for inclusion in the current review. To beincluded, participants in a study had to be between the agesof 13 and 22 years or supporting youth of that age, and themajority of the participants had to be identified as havingan ASD (e.g., autism disorder, Asperger syndrome).The Wong et al. (2014) review covered literature up to2011. The authors of the current review conducted an additional computer and hand search of the literature for studiespublished between 2011 and the end of 2013. For this lattercomputer search, the systematic library search tools includedAMC Digital Library, Academic Search Complete, CINAHL, ERIC, IEEE Xplore, PsychINFO, Social WorkAbstracts, Medline, and Sociological Abstracts. It incorporated a variety of keyword terms: autism, Asperger, Pervasive Developmental Disorder, intervention, treatment,practice, strategy, therapy, program, procedure, approach,iPad, iPod, technology, computer, computer-assistedinstruction, device, personal digital assistant, computerbased instruction, application, app, virtual, and electronic.In addition, authors examined recent reviews of the literatureon selected technology interventions to obtain articles thatthe searches may have missed (Gardner and Wolfe 2013;Grynszpan et al. 2014; Irish 2013; Knight et al. 2013; Keintzet al. 2013; Mason et al. 2012; Ploog et al. 2013; Ramdosset al. 2011a, b, 2012; Stephenson and Limbrick 2013; Wainerand Ingersoll 2011). The authors used the same inclusioncriteria rubric to evaluate each of the articles generated bythis supplemental review.Previous Literature Search and Selection ProceduresGeneral Description of StudiesRecently, Wong et al. (2014) conducted a comprehensivereview of the intervention literature for children and youthwith ASD published between 1990 and 2011. An initialcomputer search yielded 29,501 articles, which the reviewteam reduced to 1,090 through a stepwise screening process. The review team trained a pool of 154 externalevaluators to use a quality indicator rubric for group andsingle case design to evaluate the methodological acceptability of each article. The methodological criteria werebased on the quality indicators established by the Councilfor Exceptional Children Division for Research (Gerstenet al. 2005; Horner et al. 2005). For example, indicatorsexamined the match between research question anddependent variable, comparability of groups for groupdesign, appropriateness of the statistical analysis (for groupdesign), and adequate demonstrations of experimentalcontrol (for single case design). Inter-rater agreement wascalculated and acceptable (i.e., 92 % for single case design,84 % for group designs). For the current paper, a subset ofthe articles that Wong et al. categorized as ‘‘technologyA total of 30 articles (7 group design and 23 SCD studies)met the inclusion criteria as technology interventions foradolescents and young adults with ASD. Table 1 containssummarized information from the studies. Using the conceptual framework presented earlier, information andfindings will be reported for user characteristics, activity,and type of technology.In the review of the literature in the subsequent section, theauthors provide information about the three primary CSESAframework variables as well as the broader ecological contexts in which research occurred.Review of the LiteratureUser CharacteristicsA total of 238 individuals with ASD and three individualssupporting teens with ASD participated in the reviewedSCD (n 58) and group design (n 183) studies,respectively. Eighty-four percent of the participants in SCDand 88 % in the group design studies were male. One groupdesign study (n 22) did not note gender. In the generalASD population approximately 75–80 % are male (Baio2014), which suggests that girls are somewhat under-represented in these studies. Seventeen studies included participants with co-occurring conditions. The most frequently123
3810J Autism Dev Disord (2015) 45:3805–3819Table 1 Summary of technology articlesName of rventionContextDesignOutcomesAllen et al.(2012)VocationalN 3Laptop and audiocueing device(head set pairedwith cell phone)VM andCACCommunitySVideo modelingresulted in little to nochange in behaviors,skills increased tocriterion for allparticipants followingaudio cueingVideotape model ofskill in vocationaltaskVMCommunitySCovert audiocoaching device(two way radiowith head set)CACCommunitySMet criterion forengaging in multiplebehaviors invocational task(advertisementcharacter for store)Increased frequencyand accuracy of shirtfolding task incommunity worksiteVideo prompting oniPhoneVPSchoolSIncreased independentcompletion of steps inusing a washingmachine, makingnoodles, and using thecopy machineVideo self-modelingon iPad ofcompletion ofmathematics storyproblemVMSchoolSIncreased independentcompletion ofmathematics storyproblems andmaintained across 6novel problem typesVideo promptingcompared to videomodeling forteaching dailyliving skillsVPSchoolSVideo prompting wasmore effective thanvideo modeling forwashing clothes anddishes tasksSpecific facetraining softwaredeveloped usingblack and whitephotos, AdobePhotoshop andMicrosoftPowerPointSTCommunityGShowed greatersensitivity to facialdetails but did notdiffer on holisticfacial processing fromcontrol group.Interactivemultimediapresentation ofsocial competencetrainingSTHome andCommunityGIncreased recognition ofcomplex emotionsMale 2CLD 0CoC ID(N 3); seizuredisorder(N 1)Allen et al.(2010)VocationalN 3Male 3CLD NRCoC ID(N 1)Bennett et al.(2013)VocationalN 3Male 2CLD 1AACoC NRBereznak et al.(2012)IndependenceN 3Male 3CLD 2AA, AsACoC NRBurton et al.(2013)AcademicN 3 (withASD; totalparticipantsN 4)Male 3CLD NRCoC NRCannellaMalone et al.(2011)IndependenceN 2Male 1CLD NRCoC ID andHearing LossN 1Faja et al.(2008)SocialN 10Male 10CLD NRCoC NRGolan andBaronCohen(2006)SocialN 41 and 27Male 31 and 23(Two studies)CLD NRCoC NR123
J Autism Dev Disord (2015) 45:3805–38193811Table 1 continuedName of rventionContextDesignOutcomesHaring et al.(1995)IndependenceN 6Videotape model ofpurchasing skillspaired with in vivotrainingVMCommunitySVideo-modeling andin vivo instructiontogether producedacquisition ofpurchasing skillsVideo self-model oniPadVMSchoolSIncreased correctunprompted responsesduring small groupscience instructionComputer-basedinteractivesoftware usingavatar assistantsSTSchoolGImprovements in facialand emotionalrecognition, socialinteractionsVideo prompting oniPod to teach foodpreparation skillsVPSchoolSIncreased independentcompletion of steps infood preparationacross three mealsiPod used as speechgenerating deviceSGDSchoolSIncreasedcommunicationVideo model oniPodVMCommunitySIncrease steps ofvocational tasks (e.g.,cleaning, takinginventory) completedindependentlyVideo model onpersonal digitalassistant versuslaptopVMSchoolSIncreased completion offine motor tasks inboth conditions;however, much higherlevel of correctcompletion withmodel provided vialaptopComparison ofcommercial videoprompting versuscustom-madevideo prompting;both delivered onlaptopVPSchoolSBoth modes of videoprompting resulted inincreased independentcompletion of foodpreparation, howevercustom-made videoprompting resulted inhigher level ofaccuracy for allparticipantsMale 5CLD NRCoC IDHart andWhalon(2012)AcademicN 1Male 1CLD NRCoC ModerateIDHopkins et al.(2011)SocialN 49Male 44CLD 14AA 13Other 1CoC IDN 25Johnson et al.(2013)IndependenceN 1 (withASD; totalparticipantsN 2)Male 1CLD NRCoC IDKagoharaet al. (2010)CommunicationN 1Male 1CLD NRCoC OCD,ADHDKellems andMorningstar(2012)VocationalN 4Male 4CLD NRCoC NRMechling andAyres(2012)VocationalN 4Male 4CLD NRCoC Mild andmoderate IDMechlinget al. (2013)IndependenceN 3 (withASD; totalparticipantsN 4)Male 3CLD NRCoC ID(N 3);WilliamsSyndrome(N 1)123
3812J Autism Dev Disord (2015) 45:3805–3819Table 1 continuedName of techstudyActivityMechlinget al. ntionContextDesignOutcomesN 2Video recording ofchoice selectionVPSchoolSReduced taskcompletion timeVideo and pictureprompts onCyranoCommunicatorPDAVPSchoolSStudents able to adjustprompt levels used onthe PDA and use toindependentlycomplete cookingrecipesPicture prompts onCyranoCommunicatorPDAVPSchoolSIncreased completion ofnovel tasks andindependenttransitioning withinand between tasksPDA withprogramming toprompt homeworkrecordingVPSchoolSIncreased homeworkcompletionBluetoothtechnology used torecord number ofprompts given tostudent with ASDand to give in vivofeedback to staffCACCommunitySReduction in number ofprompts provided tostudents with ASDVideotapedperformancefeedback duringsocial skills groupPFSchool andClinicGIncreased performanceon Theory of MindTasks and ratings ofsocial behaviorMultimediapresentationVPSchoolSIncreased knowledge ofadult outcomes andopportunitiesEmotion TrainersoftwareSTSchoolGImprovedunderstanding ofemotions andemotional expressionDesktop computerused to manageself-monitoringSMSchoolSIncreased taskcompletion anddecrease in tantrumsJobTIPS onlineprogram andVenuGen4 virtualreality interviewspace with avatarsSTCommunityGSignificant positiveeffects on interviewcontent skills.Nonsignificantimprovement oninterview deliveryskillsMale 2CLD NRCoC Mild andmoderate IDMechlinget al. (2009)IndependenceN 3Male 1CLD NRCoC ModerateIDMechling andSavidge(2011)IndependenceN 3Male 2CLD NRCoC ModerateIDMyles et al.(2007)IndependenceN 1Male 1CLD NRCoC NRNepo (2011)IndependenceN 3 (staffworking withadolescents withASD)Male 3CLD NRCoC NROzonoff andMiller(1995)SocialRichter andTest (2011)TransitionN 9Male 9CLD NRCoC NRN 3Male 2CLD NRCoC Moderateto Severe IDSilver andOakes(2001)SocialSoares et al.(2009)Academic andBehaviorN 22Male NRCLD NRCoC NRN 1Male 1CLD NRCoC SelfinjuryStricklandet al. (2013)VocationalN 22Male 22CLD NRCoC NR123
J Autism Dev Disord (2015) 45:3805–38193813Table 1 continuedName of techstudyActivityStromer et rventionContextDesignOutcomesN 1Desktop computerand softwaresupportingspelling programSTSchoolSIncreased spellingperformanceVibrating pagerSTSchool andCommunitySIncreased ability toproduce card to seekassistance in responseto pa
Samuel L. Odom Julie L. Thompson Susan Hedges Brian A. Boyd Jessica R. Dykstra Michelle A. Duda Kathrine L. Szidon Leann E. Smith Aimee Bord Published online: 3 December 2014 Springer Science Business Media New York 2014 Abstract The use of technology in intervention and instruction for adolescents with autism .
CAD/CAM Computer-Aided Design/Computer-Aided Manufacturing CADD Computer-Aided Design and Drafting CADDS Computer-Aided Design and Drafting System CADE Computer-Aided Design Equipment CADEX Computer Adjunct Data Evaluator-X CADIS Communication Architecture for Distributed Interactive Simulation CADMAT Computer-Aided Design, Manufacture and Test
Aided Machining became an acronym for Computer Aided Manufacturing (CAM). Earlier Computer Aided Manufacturing used to denote computer use in part-programming only. Today it means any non design function of manufacturing that is computer aided. In figure 2, CNC welding machine assisting in casting products. 1.3. Computer Aided Process Planning .
The computer aided technologies investigated are; computer aided design, computer aided manufacturing, computer integrated manufacturing, electronic data interchange, electronic point of sale, and internet. Findings from the analysis show that the applications were not equally . to higher value-added specialised textiles and fashion garments .
integrating Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM). Consequently, a great deal of research has been devoted for developing Computer-Aided Process Planning (CAPP) systems that can automatically perform the task of process planning. A CAPP system, depending on the
A. Computer-aided welding fixture design workflow and step Various computer-aided fixture design (CAFD) systems have been developed through the years to assist the designer during the various stages of fixture design. A welding fixture is a fixture [7-10]. There are several main stages within the computer-aided welding
(CAM), material requirements planning (MRP), and computer-aided planning (CAP) Computer-aided process planning (CAPP) is the use of computer technology to aid in the process planning of a part or product, in manufacturing CAPP is the link between CAD and CAM in that it provides for the planning of the process to be used in
Department of Civil Engineering AUST CE 102: Computer Aided Drafting Page 1 Chapter 1 Computer Aided Drafting (CAD) The use of computer technology for the process of design documentation (i.e. drafting). CAD may be used to draft in 2D or 3D space. 2D User Interface (AutoCAD 2007) Figure 1.1: 2D User Interface Drop Down Menu
3 Lorsqu’un additif présent dans un arôme, un additif ou une enzyme alimentaire a une fonction technologique dans la denrée alimentaire à laquelle il est adjoint, il est considéré comme additif de cette denrée alimentaire, et non de l’arôme, de l’additif ou de l’enzyme alimentaire ajouté et doit dès lors remplir les conditions d’emploi définies pour la denrée en question .
