Evaluating Assistive Technology in Early Childhood Education

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Early Childhood Educ J (2009) 37:5–12 DOI 10.1007/s10643-009-0319-y

Evaluating Assistive Technology in Early Childhood Education: The Use of a Concurrent Time Series Probe Approach
Howard P. Parette Æ Craig Blum Æ Nichole M. Boeckmann

Published online: 23 May 2009 Ó Springer Science+Business Media, LLC 2009

Abstract As assistive technology applications are increasingly implemented in early childhood settings for children who are at risk or who have disabilities, it is critical that teachers utilize observational approaches to determine whether targeted assistive technology-supported interventions make a difference in children’s learning. One structured strategy that employs observations and which has powerful child progress monitoring implications is the concurrent time series probe approach. Requiring multiple performance measures of a child engaged in a targeted task over time—both with and without a specific assistive technology device—the concurrent time series probe approach can be used to evaluate the effectiveness of assistive technology tools in supporting skill acquisition in the classroom. This approach is described in the context of a case study, with accompanying explanations of how to interpret data and make decisions regarding the effectiveness of the technology. Keywords Assistive technology Á Progress monitoring Á Assistive technology consideration Á Concurrent time series Á Assistive technology outcomes Á Classroom data management

H. P. Parette (&) Á C. Blum Department of Special Education, Illinois State University, P.O. Box 5910, Normal, IL 61790-5910, USA e-mail: [email protected] C. Blum e-mail: [email protected] N. M. Boeckmann Department of Communication Sciences and Disorders, Illinois State University, P.O. Box 4720, Normal, IL 61790-4720, USA e-mail: [email protected]

Since enactment of the No Child Left Behind Act of 2001 (NCLB), early childhood education professionals have increasingly recognized the need for ‘scientifically based research’ and progress monitoring of children’s attainment of educational skills (Grisham-Brown et al. 2005; Helm et al. 2007; Neuman and Dickinson 2001; Sindelar 2006). State and national standards (Copple and Bredekamp 2009; Division for Early Childhood 2007; Sandall et al. 2005) have been established in response to increasing demands of accountability regarding young children’s learning (Sindelar 2006). Such accountability assumes that in the absence of effective classroom monitoring approaches, teachers cannot make informed teaching decisions (Grisham-Brown et al. 2005). Use of scientifically based research and progress monitoring is particularly important for young children who are at-risk or who have disabilities, and who must have individual education programs (IEPs) developed for them (Individuals with Disabilities Education Improvement Act of 2004). Recent studies have consistently recognized that educational decision-making must be couched in assessment approaches to evaluate children’s learning (Odom et al. 2005; Sindelar 2006). The National Association for the Education of Young Children (NAEYC) and the National Association of Early Childhood Specialists in State Departments of Education (NAECSSDE 2004) developed a position statement noting numerous indicators of effective assessment practices. Among these are (a) the need for developmentally and educationally significant assessments, (b) use of assessment information to understand and improve learning, (c) gathering assessment information in naturalistic settings such that children’s actual performance is addressed, and (d) use of data gathered across time. While some early childhood education professionals may feel uncomfortable with the practice

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of incorporating both assessment and research based practices into their curricula, particularly data collection strategies, there are numerous practical approaches that can be easily implemented by most practitioners. More importantly, assessment and use of scientifically based approaches are both mandated by law (i.e., IDEIA) and are best practices in the field (NAEYC/NAECSSDE 2004).

Observational Data in AT Decision-Making Use of observations across time (Brassard and Boehm 2007) has consistently been recognized as the primary approach for assessing the learning needs and educational progress of young children with or at-risk of disability (Bagnato and Neisworth 1991; Cohen et al. 1997; Meisels, and Atkins-Burnett 2005). Including a method for recording information gained throughout the observational process is an important component of the data gathering approach (Watts et al. 2004). Of particular importance in AT decision-making is the need for collecting and recording data both before and after an AT device is implemented with any particular child (Parette et al. 2007). Simply making a decision to purchase a device without data examining whether it made any immediate impact on a child’s performance, or failing to examine data related to whether the AT device made any difference in the child’s learning across time, would be ineffectual educational practices. In either instance, observational data of child performance is needed to make decisions about the AT device and its use with a particular child.

Assistive Technology Consideration During IEP Development A wide array of assistive technology (AT) devices have been reported to support the learning and classroom participation of young children who are at risk or who have disabilities (Mistrett et al. 2005; Judge 2006). The federal government has defined AT devices as ‘‘any item, piece of equipment or product system, whether acquired commercially or off the shelf, modified, or customized, that is used to increase, maintain, or improve functional capabilities of individuals with disabilities’’ [Individuals with Disabilities Education Improvement Act of 2004 (IDEIA 2004), 20 U.S.C. § 1401(251)]. AT devices are compensatory and enable children to perform tasks that would not be possible without the devices at some expected level of performance (Parette 2006; Parette et al. 2007). These devices have been shown to compensate for difficulties exhibited by young children in numerous areas including mobility (Butler 1986); communication (Schepis et al. 1998); enhanced caregiving (Daniels et al. 1995); emergent literacy (Parette et al. 2008); access to computers (Lehrer et al. 1986); and play (Lane and Mistrett 1996). The IDEIA requires that AT be ‘considered’ [20 U.S.C. 1401 § 614(B)(v)] by the team developing an individual education program (IEP) for a particular child who is at risk or who has disabilities. This process includes examination of a child characteristics, as well as the tasks the child is expected to complete in the context of activities in the classroom setting (e.g., communicating with the teacher and others during Circle Time; creating a product during Art; eating during Snack Time; identifying beginning sounds during Literacy Time). Understanding what the child can and cannot do in the context of natural settings (i.e., activities and their embedded tasks to participate in them) allows the team to consider specific AT devices that help the child to successfully complete important educational tasks. While the consideration process is beyond the scope of this article for discussion, numerous resources have been reported to assist early childhood education professionals to better understand this decision-making process (Center for Technology in Education Technology, Media Division 2005; Judge and Parette 1998; Mistrett 2004; Parette and VanBiervliet 1991; Watts et al. 2004).

Role of Concurrent Time Series Probe Approach An emerging practice in early childhood education that can help teachers with AT outcomes documentation is use of a ‘concurrent time series probe’ classroom approach (Smith 2000). This practical, data-focused approach involves the teacher in collecting performance measures of a child completing a specific task—both with and without AT— over a reasonable period of time (Edyburn 2002; Parette et al. 2008, 2007; Smith 2000). Measures of the child’s performance with and without AT—both before a device is purchased and after it has been integrated into the child’s curriculum—provide performance lines for comparison to what the teacher expects of the child to successfully complete a targeted task within a classroom activity area (Parette et al. 2007). In the concurrent time series approach, probes are then used concurrently to assess a student’s performance with and without AT. Probes are the assessment of a behavior (academic, social, or life skill) on systemically selected occasions when there is no contingency or support in effect for that behavior (Kazdin 1982). The probe, or performance assessment, is considered concurrent because during the same day or time period the child is evaluated both with and without AT. A probe strategy is ideal for use in the early childhood classroom because the teacher does not have to continuously monitor both the target behavior (or desired outcome for AT support) and performance without AT support. This makes data collection much more efficient and practical for

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early childhood educators. However, frequent assessments should be made during the initial AT consideration process to gather necessary information about the effectiveness of the AT in enhancing the child’s performance and providing needed compensatory supports. After an AT device or support is selected (based on data collected that demonstrate effectiveness), additional data should be collected on a monthly basis to determine whether the AT remains effective across time. Regularly scheduled data collection ensures that the AT continues to positively impact the child’s performance on targeted curriculum tasks over time (Parette et al. 2007). Presented in Fig. 1 is a graph using the concurrent time series probe approach to assess the effectiveness of AT. In this example, data would be collected (a) 1 week before an AT device was tried (to determine performance levels for a child without the AT device; this is sometimes called baseline); (b) after the AT device was introduced during a second week; and (c) again during a third week. Of particular importance when using this approach is securing a ‘probe’ periodically (a concurrent performance measure) in which the child is asked to complete the targeted task without the device to gain a data point that is then compared to the child’s performance using the device for completion of the same task. The probe should not be conducted until there have been three to five data observations with AT support. For example, a child who is nonverbal is presented with questions regarding her preferences during Circle Time over the course of a week to collect baseline data (see Fig. 1). The teacher knows that the child has difficulty communicating choices to others based on this data, and expects all children to communicate five or more choices in response to questions. A simple communication board

containing pictures of options for the child is considered for integration in the curriculum, and systematically used in Circle Time for a week (Intervention; see Fig. 1). The teacher collects data on the child’s responses, given that now she can simply point to her choice using the communication board. Changes in the child’s ability to respond are noted by the data, and after 5 days, the teacher conducts a ‘probe’ in which questions are posed to the child without the communication board (i.e., the communication board is not available during the classroom activity), and data collected. If the data indicated that the communication board was previously effective, the teacher would see an immediate decline in the child’s ability to perform the task (i.e., indicate preferences) when the board was not available. The teacher would then reinstitute use of the AT device and continue to collect data on a regular basis.

Previous Usage of the Concurrent Time Series Approach Concurrent time series approaches have been reported in documenting the effectiveness of AT devices in schoolage education settings and have been advocated for use both by school psychologists and school-age special education teachers (Parette et al. 2006). Mulkey (1988) used a time series approach to measure student gains in reading achievement. She investigated whether grouping students requiring special education by education needs rather than disabling conditions increases student performance. Anderson and Lignugaris/Kraft (2006) used a time series approach to assess the effects of video-case instruction for teachers of students with problem behaviors in general and special education classrooms. This design made it possible to evaluate the effects of program instruction on the analytical skill of participants on several different occasions. They also added a control group to allow for comparisons of skill acquisition and skill generalization. Schermerhorn and McLaughlin (1997) similarly used a time series approach to evaluate the effects of a spelling program across two groups of students, finding that children’s test scores significantly increased while using the program. Such studies have provided strong support for use of a concurrent time series approach in early childhood settings.

An Example of Implementation in a Preschool Classroom Setting The following brief case example describes how the concurrent time series approach could be applied to AT decision making in the early childhood classroom.

Fig. 1 Sample graph of data using a concurrent time series probe approach

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Shanika is an African-American preschool student who has been identified as at risk and attending an early childhood education center funded by the state. She is a lively and energetic child with who enjoys conversation with her friends at a school, loves to share, and be part of the class. She is well liked by her peers, and her teachers. Sometimes she does have difficulty focusing and listening to the teacher. When doing small group lessons in the class she is easily distracted and needs frequent reminders from the teacher to follow directions and perform at a specified level expected of children in the classroom. Her teacher is noticing that she has difficulty listening, and learning skills related to hearing sounds. As part of their efforts to improve their emergency literacy program, the early childhood center has adopted the use of curriculum-based measures (CBMs) for universal screeners and systematic data collection. As indicated by Shanika’s performance on the CBMs, she is having difficulty with the phonological awareness skill of onset (beginning consonants and consonant clusters) and rime (vowel and remaining sounds that provide meaning, e.g., ‘at’ in ‘cat’ and ‘bat’). The Approach to AT Decision Making In order to address the case above, Shanika’s teacher decided to use a concurrent time series probe approach as a systematic problem-solving method to make decisions about several AT devices and whether they made a difference in Shanika’s classroom performance. Initially, the teacher made observations of children’s performance of targeted skills for each of 5 days while a lesson in phonological awareness was being taught in the classroom. In the curriculum at Shanika’s school they use puppets in conjunction with picture cards with animals on them to teach phonological skills. The puppets were also used to play rhyming games during Circle Time. Students are expected to learn to match initial sounds with words on picture cards using the puppets. After instruction was provided to all the children during the instructional setting, children were asked to identify sounds made by letters that were targeted in the lesson. As children responded, the teacher simply made tally marks to indicate correctness of children’s responses (see Fig. 2). As noted previously, this process of taking data before intervention takes place is called baseline. In an instructional setting, baseline is the natural occurrence of an academic, social, or life skills task or behavior prior to some new instruction and/or AT is presented (Alberto and Troutman 2009). Baseline data provides a benchmark against data collected using other

Fig. 2 Excerpt of teacher data recording chart used during baseline

interventions and enables the teacher to make comparisons of child performance. In Shanika’s case, the IEP team chose to try the IntelliToolsÒ Classroom Suite 4 IntellitoolsÒ (2007a)—a scientifically based AT tool (IntellitoolsÒ 2007b)—to compensate for Shanika’s difficulty with phonological awareness. The IntelliToolsÒ Classroom Suite 4 supports children’s mastery of content and related literacy skill acquisition by using a cadre of well-supported learning strategies and premade templates for literacy skill building, including auditory cues, pictures, movies, and manipulatives (Howell et al. 2000). Teachers also can incorporate individualized curriculum content into the activity, and use an expanded keyboard for child access and control over activities presented (cf. http://www.intellitools.com/imple mentation/archive.aspx for guides and tutorials and http:// aex.intellitools.com [using Windows Explorer]) (Fig. 3). The classroom task presented to Shanika using the IntelliToolsÒ Classroom Suite 4 required the teacher to use a teacher-developed template (downloaded from the Classroom Suite Activity Exchange at http://aex.intellitools. com/) and which was presented on a computer screen. Shanika used an expanded IntellitoolsÒ keyboard connected to the computer to view the template presentation and which allowed her to make choices in response to hearing the program say, ‘‘Click the picture to hear its name. Say the name of the picture out loud. Find the letter that spells the first word’’ (see Fig. 4). A series of 10 screen presentations were made to Shanika using the IntelliToolsÒ Classroom Suite 4 template, and her responses recorded on each of five subsequent days, with a probe also being implemented on the 5th day using the baseline classroom strategy (i.e., no AT). As reflected in the data (see Fig. 4), Shanika’s phonological awareness skills did not improve markedly using the IntelliToolsÒ Classroom Suite 4 intervention. In fact, when a probe was conducted, her performance without the AT intervention was only slightly less than performance using the IntelliToolsÒ Classroom Suite 4 intervention. The IEP team then decided that another intervention was needed. The teacher used a MicrosoftÒ PowerPointTMbased curriculum—Ready-to-Go—which had been reported in the literature as a research-based strategy that positively impacted children’s phonological awareness skill development (Blum and Watts 2008). Utilizing direct instruction strategies (Carnine et al. 1995; Rosenshine 1986), this

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Fig. 3 Sample screen presentation from IntelliToolsÒ Classroom Suite 4 activity used to instruct phonological awareness

Fig. 4 Graph of Shanika’s phonological awareness classroom performance across baseline and AT interventions

PowerPointTM curriculum included features of scientifically based early literacy instruction: (a) explicit modeling, (b) guided practice with explicit corrective feedback, (c) independent practice and evaluation with corrective feedback, and (d) positive consequences for success. Animation features within the PowerPointTM curriculum, coupled with high quality graphic elements, were deemed to be elements that might be engaging to Shanika. The curriculum provided a structured approach, including specific statements that the teacher was to say as each PowerPointTM slide was

presented and the expected student response. Another benefit of this program was that it could be utilized with the entire class and delivered using the classroom computer and LCD system, thus enabling a ‘big screen’ presentation. For example, in using the curriculum during the second intervention period, the teacher would show a picture of a cat, and emphasize the /k/ sound. Then as a cat appeared on the screen multiple times, she modeled the /k/ as each picture appeared. She also showed a slide containing several pictures from which Shanika (and other students) had to choose which one began with the /k/ sound (see Fig. 5). Once this intervention was correctly implemented, Shanika’s performance greatly improved, i.e., her performance exceeded both that noted in the group activity-based intervention (baseline) and when the IntelliToolsÒ Classroom Suite 4 intervention was initiated. Based on these data, the IEP team could then make an informed decision regarding which AT intervention made a substantive difference in Shanika’s educational program. In this particular instance for this child, the Ready-to-Go curriculum made a bigger difference in Shanika’s phonological awareness skill acquisition than the first AT solution. Having data upon which to make an informed decision, the IEP team included the Ready-to-Go curriculum in Shanika’s IEP, and noted the need to monitor her progress in using the curriculum across time to ensure that the AT solution remained effective.

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desired behavior. Failure to do this could lead early childhood professionals to make the erroneous conclusion that their intervention was making a difference. Finally, if the probe without AT support is well above baseline (indicating that the student can perform the outcome without AT well above baseline levels), the probe should be conducted for at least three observation days to ensure that the student can truly perform the targeted behavior without AT. Sometimes young children are able to perform an outcome without support on a single occasion. When making decisions about AT support, it is essential to make careful decisions that result in minimizing the support a child needs to be successful.

Discussion
Fig. 5 Sample PowerPointTM-based Ready-to-Go curriculum slide presented to teach phonological awareness

Special Graphing Considerations for Concurrent Time Series Probes When using concurrent time series probe approach the teacher should always graph data. Both MicrosoftÒ ExcelTM and MicrosoftÒ PowerPointTM have extremely useful graphing features. Barton et al. (2007) developed guidelines for early childhood educators when using the graphing features MicrosoftÒ PowerPointTM. Graphing data provides a powerful visual support to teachers when making data-based decisions about their teaching and AT considerations. However, visual inspection of line graphs can be tricky, and caution should be used when interpreting them. While it is beyond the scope of this article to discuss all of the issues, we will outline a few of the major points when using concurrent time series probe approaches. One of the most common problems that teachers may encounter is extreme ‘data variability.’ If, during baseline (typically three observations), the data is highly variable it can be difficult to interpret progress during the implementation of AT support. In this case, the teacher may want to extend baseline a few more sessions/days to see if the baseline will stabilize around a consistent level or performance. Another potential problem is ‘increasing or decreasing baseline.’ Given that young children are constantly learning (i.e., at the time the educational team decided to take baseline), the student may have started demonstrating the performance outcome during baseline data collection. If this happens, the teacher should continue baseline for a few more sessions/days, and it will help the team see if the progress was just temporary or represents a real trend in the

The concurrent time series probe approach is only one of many classroom assessment strategies that can assist early childhood education professionals to make informed decisions regarding the impact of AT interventions considered for children who are at risk or have disabilities. The IDEIA places responsibility on all education professionals working with these children to both develop an understanding of the AT consideration process, as well as helping choose and implement AT solutions to support young children’s participation in the curriculum. In the typical early childhood education setting, teachers are familiar with assessment of a range of daily skills, and use of the concurrent time series probe approach simply provides needed data upon which decision making is based for a particular child. The approach lends itself to a variety of data collected using checklists, rating scales, samples of the child’s work, electronic recordings, and other assessment strategies (Cook et al. 2008). Teachers have great flexibility to design their own data collection forms to record child performance; the important consideration is that data be collected. Otherwise, understanding whether a particular AT solution does indeed make a difference may not be evident to the education professional. Admittedly, such formal approaches for monitoring child progress often meet with opposition by classroom practitioners who may be managing large numbers of children and have limited time. Use of classroom assistants and/or volunteers to help with collecting data may be necessary in some instances. For example, these individuals may observe a child’s performance in the context of a group activity and make tally marks to document performance on some targeted measure (see Fig. 2), thus freeing the teacher to focus on instruction. However, even with perceived time constraints and challenges of implementing such formal data collection strategies, most early childhood education teachers have the creativity to develop unique

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11 Butler, C. (1986). Effects of powered mobility on self-initiated behaviors of very young children with locomotor disability. Developmental Medicine and Child Neurology, 28, 325–332. Carnine, D., Grossen, B., & Silbert, J. (1995). Direct instruction to accelerate cognitive growth. In J. Block, S. Everson, & T. Guskey (Eds.), Choosing research-based school improvement programs (pp. 129–152). New York: Scholastic. Center for Technology in Education Technology, Media Division. (2005). Considering the need for assistive technology within the individualized education program. Columbia, MD and Arlington, VA: Author. Cohen, D., Stern, V., & Balaban, N. (1997). Observing and recording the behavior of young children (4th ed.). New York: Teachers College Press. Coleman, M. R., Buysse, V., & Neitzel, J. (2006). Recognition and response: An early intervening system for young children at-risk for learning disabilities. Executive summary. Chapel Hill, NC: University of North Carolina at Chapel Hill. Cook, R. E., Klein, M. D., & Tessier, A. (2008). Adapting early childhood curricula for children in inclusive settings. Upper Saddle River, NJ: Pearson Merrill Prentice Hall. Copple, C., & Bredekamp, S. (2009). Developmentally appropriate practice in early childhood programs serving children from birth through age 8 (3rd ed.). Washington, DC: National Association for the Education of Young Children. Daniels, L. E., Sparling, J. W., Reilly, M., & Humphrey, R. (1995). Use of assistive technology with young children with severe and profound disabilities. Infant-Toddler Intervention, 5, 91–112. Division for Early Childhood. (2007). Promoting positive outcomes for children with disabilities: Recommendations for curriculum, assessment, and program evaluation. Missoula, MT: Author. Edyburn, D. (2002). Measuring assistive technology outcomes: Key concepts. Journal of Special Education Technology, 18, 1–14. Grisham-Brown, J., Hemmeter, M. L., & Pretti-Frontczak, K. (2005). Blended practices for teaching young children in inclusive settings. Baltimore: Brookes. Helm, J. H., Beneke, S., & Steinheimer, K. (2007). Windows on learning: Documenting young children’s work. New York: Teachers College Press. Howell, R. D., Erickson, K., Stanger, C., Wheaton, J. E. (2000). Evaluation of a computer-based program on the reading performance of first grade students with potential for reading failure. Journal of Special Education Technology, 15(4), Retrieved January 26, 2009, from http://www.intellimathics. com/pdf/research/Research_Literacy.pdf. Individuals with Disabilities Education Improvement Act. (2004). 20 U.S.C. §§ 1400 et seq. IntellitoolsÒ. (2007a). IntelliToolsÒ Classroom Suite 4 [computer software]. Petaluma, CA: Cambium Learning, Inc. IntellitoolsÒ. (2007b). The research basis for Intellitools products. Petaluma, CA: Cambium Learning, Inc. Judge, S. (2006). Constructing an assistive technology toolkit for young children: Views from the field. Journal of Special Education Technology, 21(4), 17–24. Judge, S. L., & Parette, H. P. (Eds.). (1998). Assistive technology for young children with disabilities: A guide to providing familycentered services. Cambridge, MA: Brookline. Kazdin, A. E. (1982). Single-case research designs: Methods for clinical and applied settings. New York, NY: Oxford University Press. Lane, S. J., & Mistrett, S. G. (1996). Play and assistive technology issues for infants and young children with disabilities: A preliminary examination. Focus on Autism and Other Developmental Disabilities, 11, 96–104. doi:10.1177/108835769601 100205.

forms for collecting data that complement both their instructional styles and time commitments for the delivery of instruction. As response to intervention (RTI) models become more prevalent in early childhood settings (Coleman et al. 2006), data collection will become an everyday part of the teaching repertories of early childhood professionals. The concurrent time series probe approach allows for practitioners to use data collected as part of an RTI process to be used for AT considerations. When the concurrent time series probe approach is properly implemented, it is a problem-solving model that uses data based decision making. Of particular importance, however, is that early childhood education professionals recognize that positive outcomes are possible when AT is used to compensate for disabilities exhibited by young children who are at risk or who have specific disabilities. Given that these children are being prepared to enter the public schools and experience success in the general education curriculum, critical developmental skills acquired in the early childhood setting provide the foundation upon which all future learning occurs as children move into academically oriented educational milieus. Ensuring that important foundational skills are developed should be a primary concern for education professionals. The concurrent time series probe approach provides an important tool for both documenting AT outcomes and making decisions about AT effectiveness both short term and over time.
Acknowledgments This article is supported through a grant from the Illinois Children’s Healthcare Foundation to the Special Education Assistive Technology (SEAT) Center at Illinois State University. Content presented is based on a presentation at the National Association for the Education of Young Children 2008 Annual Conference and Expo.

References
Alberto, P. A., & Troutman, A. C. (2009). Applied behavior analysis for teachers (8th ed.). Upper Saddle River, NJ: Pearson Education, Inc. Anderson, D., & Lignugaris/Kraft, B. (2006). Video-case instruction for teachers of students with problem behaviors in general and special education classrooms. Journal of Special Education Technology, 21(2), 31–45. Bagnato, S. J., & Neisworth, J. T. (1991). Assessment for early intervention: Best practices for professionals. New York: Guilford. Barton, E. E., Reichow, B., & Wolery, M. (2007). Guidelines for graphing data with MicrosoftÒ PowerPointTM. Journal of Early Intervention, 29, 320–336. doi:10.1177/105381510702900404. Blum, C., & Watts, E. H. (2008). Ready-to-go curriculum. Normal, IL: Illinois State University. Brassard, M. R., & Boehm, A. E. (2007). Preschool assessment. Principles and practices. New York: Guilford.

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12 Lehrer, R., Harckham, L., Archer, P., & Pruzek, R. (1986). Microcomputer-based instruction in special education. Journal of Educational Computing Research, 2, 337–355. Meisels, S. J., & Atkins-Burnett, S. (2005). Developmental screening in early childhood: A guide (5th ed.). Washington, DC: National Association for the Education of Young Children. Mistrett, S. (2004). Assistive technology helps young children with disabilities participate in daily activities. Technology in Action, 1(4), 1–8. Mistrett, S. G., Lane, S. J., & Ruffino, A. G. (2005). Growing and learning through technology: Birth to five. In D. Edyburn, K. Higgins, & R. Boone (Eds.), Handbook of special education technology research and practice (pp. 273–308). Whitefish Bay, WI: Knowledge by Design. Mulkey, L. M. (1988). Using two instruments to measure student gains in reading achievement when assessing the impact of educational programs. Evaluation Review, 12, 571–587. doi: 10.1177/0193841X8801200506. National Association for the Education of Young Children and the National Association of Early Childhood Specialists in State Departments of Education. (2004). Early childhood curriculum, assessment, and program evaluation. Building an effective, accountable system in programs for children birth through age 8. Washington, DC: Author. Neuman, S. B., & Dickinson, D. K. (Eds.). (2001). Handbook of early literacy research. New York: Guilford. No Child Left Behind Act. (2001). 20 U.S.C. §§ 6301 et seq. Odom, S. L., Brantlinger, E., Gersten, R., Horner, R. H., Thompson, B., & Harris, K. R. (2005). Research in special education: Scientific methods and evidence-based practices. Exceptional Children, 71, 137–148. Parette, P. (2006). Assessment for assistive technology. Workshop presented at the National Association of School Psychologists 2006 Annual Convention, Anaheim, CA. Parette, H. P., Blum, C., Meadan, H., Watts, E. (2008). Implementing and monitoring assistive technology: How to use concurrent time series designs and interpret outcomes. Poster presentation to the National Association for the Education of Young Children 2008 Annual Conference and Expo, Dallas, TX. Parette, H. P., Hourcade, J. J., Boeckmann, N. M., & Blum, C. (2008b). Using MicrosoftÒ PowerPointTM to support emergent

Early Childhood Educ J (2009) 37:5–12 literacy skill development for young children at-risk or who have disabilities. Early Childhood Education Journal, 36, 233–239. doi:10.1007/s10643-008-0275-y. Parette, H. P., Peterson-Karlan, G. R., Smith, S. J., Gray, T., & SilverPacuilla, H. (2006). The state of assistive technology: Themes from an outcomes summit. Assistive Technology Outcomes and Benefits, 3, 15–33. Parette, H. P., Peterson-Karlan, G. R., Wojcik, B. W., & Bardi, N. (2007). Monitor that progress! Interpreting data trends for assistive technology decision-making. Teaching Exceptional, 39(7), 22–29. Parette, H. P., & VanBiervliet, A. (1991). Assistive technology guide for young children with disabilities. Little Rock, AR: University of Arkansas at Little Rock. (ERIC Document Reproduction Service No. ED324888). Rosenshine, B. (1986). Synthesis of research on explicit teaching. Educational Leadership, 43, 60–69. Sandall, S., Hemmeter, M. L., Smith, B. J., & McLean, M. E. (2005). DEC recommended practices. A comprehensive guide for practical application in early intervention/early childhood special education. Missoula, MT: Division for Early Childhood. Schepis, M., Reid, D., Behrmann, M., & Sutton, K. (1998). Increasing communicative interactions of young children with autism using a voice output communication aid and naturalistic teaching. Journal of Applied Behavior Analysis, 31, 561–578. doi: 10.1901/jaba.1998.31-561. Schermerhorn, P. K., & McLaughlin, T. F. (1997). Effects of the adda-word spelling program on test accuracy, grades, and retention of spelling works with fifth and sixth grade regular education students. Child & Family Behavior Therapy, 19, 23–35. doi: 10.1300/J019v19n01_02. Sindelar, N. (2006). Using test data for student achievement. Lanham, MD: Rowman & Littlefield. Smith, R. O. (2000). Measuring assistive technology outcomes in education. Diagnostique, 25, 273–290. Watts, E. H., O’Brian, M., & Wojcik, B. W. (2004). Four models of assistive technology consideration: How do they compare to recommended educational assessment practices? Journal of Special Education Technology, 19, 43–56.

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