Using Technology to Support Secondary Students with Mild Disabilities Susie Gronseth Department of Instructional Systems Technology Indiana University 2011 ISTE Conference June 27, 2011

Assistive Technology (AT) AT is defined in US legislation as technology used to “increase, maintain, or improve functional capabilities” of individuals with disabilities (Tech Act, 1998, §300.5). AT to support students with mild disabilities may include adapted furniture, large print materials, talking books, calculators, communication aids, computer-based tools, specialized software, hardware adaptations, and other supports.

Benefits of AT Academic support Behavioral support Social support

AT in US Schools Prevalence AT consideration mandate AT consideration and implementation models Challenges

Statement of the Problem Indications of limited AT use in practice Empirical evidence needed for significance of model variables when identifying and using AT with students with mild disabilities General educator perspectives needed

Purpose To gain a better understanding of AT use by students with mild disabilities and how teachers perceive the consideration and implementation of AT with this student population

Research Questions 1. What is the prevalence of AT use to support secondary students with mild disabilities in the US? 2. What factors are predictive of AT use to support secondary students with mild disabilities? 3. What contributes to the level of support teachers provide to students with mild disabilities who use AT in their classrooms?

Research Design Explanatory mixed methods (QUANT  qual; Creswell, 2005) The teacher interviews are intended to explain and expand upon the NLTS2 analysis. The selected design is appropriate for this study in that it affords more comprehensive data, facilitates the investigation of different types of research questions, and provides triangulation of findings.

Data Sources National Longitudinal Transition Study-2 (NLTS2) ◦ Purpose ◦ Nationally representative sample of secondary students with disabilities in US ◦ Data collected from multiple perspectives ◦ Present study utilized NLTS2 Teacher Survey data on students with SLD and EBD (n=720) *Note: Raw numbers reported from NLTS2 dataset have been rounded to the nearest 10.

NLTS2 Analytic Sample Descriptives Specific Learning Disability (SLD)66%(480) Emotional and/or Behavioral Disorder (EBD)34%(250) Male71%(510) Female28%(200) White69%(500) African American15%(110) Other15%(110) 14 years15%(110) 15 years19%(130) 16 years25%(180) 17 years28%(200) years12%(90)

Data Sources Teacher Interviews ◦ Eight currently practicing general and special education teachers ◦ Four high schools in Southeastern US ◦ Referral approach used ◦ Taught or co-taught 9-12 grade level language arts, mathematics, sciences, and social sciences

Interviewee Descriptives General/Speci al Subject(s)Years Experience Grade Level(s) School 1 – Rural; Very Large District; 41% Free/Reduced Lunch GeneralSocial sciences SpecialLanguage arts, Science19-12 School 2 – Rural; Medium District; 42% Free/Reduced Lunch GeneralLanguage arts59-12 SpecialLanguage arts, Science School 3 – Suburban; Large District; 29% Free/Reduced Lunch GeneralMath169 SpecialMath, Language arts79 School 4 – Urban; Medium District; 63% Free/Reduced Lunch GeneralMath79-12 SpecialMath, Science109

NLTS2 Variables Dependent variable ◦ Assistive technology use (AT_use) ◦ Dichotomous; created from responses to AT tool categories that are provided to the students as part of their IEP or 504 plans Blocks of independent variables ◦ Student ◦ Contextual ◦ Teacher

Instruments NLTS2 Teacher Survey ◦ Development ◦ Format ◦ Content Interview Protocol ◦ Development ◦ Format ◦ Content

Procedure NLTS2 Secondary Analysis ◦ Merging and preliminary analysis of datasets ◦ Creation and recoding of variables ◦ Missing data ◦ SPSS and WesVar Teacher Interviews ◦ Conducted 8 interviews in Oct.-Dec ◦ Transcriptions of interview recordings ◦ TAMS Analyzer

Data Analysis Research Questions Data SourcesData Analysis 1.Prevalence of AT NLTS2 teacher survey Descriptive statistics 2.Factors predictive of AT use NLTS2 teacher survey Sequential binary logistic regression 3.Teacher support perspectives Interview transcripts Emerging coding structure; constant comparative method

RQ1: Prevalence of AT Use

Discussion on AT Use Prevalence Higher expectations than prevalence observed Consistency with prior research Prevalence figures may be impacted by definitional variations Indications of low AT awareness among educational professionals Possible tool incompatibilities with classroom routines

RQ2: Significant AT Use Factors *p <.05

Discussion on AT Use Factors Mathematics ◦ 4x more likely to use AT ◦ Calculator use ◦ Possible limitations in AT awareness in other content areas Some whole class use of computers ◦ 2x more likely to use AT ◦ Overlap of educational technologies and AT ◦ Benefits of technology use

Discussion on AT Use Factors General educator training adequacy in teaching students with disabilities ◦ Half as likely to use AT ◦ Co-teaching considerations ◦ Challenges ◦ “Second-order barriers” (Ertmer, 1999) ◦ Limited support for AT as evidence-based practice

RQ3: Teacher Support Issues Defining assistive technology Assistive technology academic uses and benefits Challenges Co-teaching perspectives Increasing teacher awareness in assistive technology

Implications for Practice Survey instrument design Significance of contextual factors on AT use AT awareness of current educators AT awareness in teacher preparation

Further Research Defining AT Exploration of additional factors Relationship between general educator training in special education and AT use Technology use to support students with emotional and/or behavioral disorders (EBD) Increasing teacher AT knowledge and skill

Using Technology to Support Secondary Students with Mild Disabilities Susie Gronseth Department of Instructional Systems Technology Indiana University 2011 ISTE Conference June 27, 2011