Archive for the ‘computers’ Category

Risks of Outsourcing Computer Software and Hardware support

January 13th, 2011 No comments

Outsourcing is when a company turns to external resources at other companies to do tasks they once did internally. Outsourcing may be done to draw on external expertise, such as hiring legal specialists or certified auditors. However, outsourcing is frequently done to save money by shifting work to companies that gain efficiencies from large scale by being specialized. This outsourcing creates risks for firms that outsource software and hardware support.

1. Loss of control

Server support is now determined by both the policies of the outsourced firm and by corporate policy. Employees of the outsourcing firm may follow their own policies first and your policies second. Outsourcing firms can dictate service level agreements and penalties if standards are not met, but they cannot control how work is performed. Companies that outsource work must also cede control of contractors to the managers who direct their activities.

2. Added feedback delays

Instead of calling an internal support staffer to resolve a problem, customers must call an outsourcing firm for help. This frequently results in calls being received by a first level technician following a script. If the problem requires advanced skill sets, there is additional delay in transferring the call to a subject matter expert or putting in a ticket for them to call a user back. The need to pass through an intermediary adds delays to customer support. If problem does require the subject matter expert, they may need to travel to the site to resolve the problem. This delay may not occur when in house staff can walk down the hall and solve the problem.

3. Loss of expertise

Employees of an outsourced firm are rarely as knowledgeable as internal staff who have worked with specific hardware or software applications. Even if the internal staff are available, they may be reassigned to other areas or may not be available when outsourcers come to answer calls. If the internal experts are transferred to the outsourcing firm, ether as part of a realignment or a reorganization, the risk that they will seek yet another employment opportunity arises. There is also the risk that experts who are sent to the outsourcing firm may be terminated by the new company once they have trained lower paid technicians to do the most common tasks they performed.

4. Loss of accountability

When an internal employee performs poorly, that individual can face internal corporate consequences. This can include reassignment or firing. When an outsourced technician performs poorly, the outsourcing firm can solve the problem by assigning a different technician as the first solution. Changing account managers may also be offered as a solution. The worst case scenario for the outsourcer is that you take back the IT functions that you outsourced, but for which you may have lost your key personnel or licenses. This causes a loss of accountability when outsourcers do not perform to prior service levels.

5. Loss of intellectual property

When data is processed by an outsourcer, there is an additional risk of data loss due to the extra processing steps or outside hands and eyes that perform the work. Another risk is that outsourcers may take processes learned from interacting with staff and transferring those lessons learned to their other customers or using those best practices themselves; this may not be a direct effort to steal intellectual property but the spread of best practices to the competition does not help the outsourcer. While outsourcing may save money, the cost can be higher than it seems.

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TRAINing to Read: A Cognitive Tutor for Children with Mental Retardation

June 24th, 2010 Comments off


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According to Kame’enui and Wallin (2006), defining features of children identified as having special needs include significant deficits in their reading development and performance. Approximately 78% of children with moderate mental retardation (IQs between 50-69) are able to learn to read, according to one study, and all children can learn to read when their IQs are 70 or above following the appropriate intervention (Mervis, 2009).

Unfortunately, due to state and nationwide budget cuts, special education classrooms for children with a wide range of abilities are overflowing, leaving overextended teachers unable to provide the one-on-one attention necessary in literacy development. For this reason, a specialized cognitive tutor, the TRAINing to Read (TTR) program should be developed to supplement traditional reading instruction. This new system follows three important caveats (1) ease of use; (2) scalability; and (3) cost effectiveness, providing applicability for both home and school use. Moreover, it will provide customization based on individual student ability and reduce the required student to teacher ratio in special education classrooms.


Why Develop a Computerized Literacy Program?

Although the adoption of computer technology for individuals with mental retardation has lagged behind its usage in business, government, and typically developing educational applications, computers are increasingly empowering individuals with mental retardation to enjoy more independently lives. The ability to read a list of tasks and self-regulate time management can be essential for individuals with mental retardation to hold meaningful employment. Davies et al. (2002) used a portable schedule prompting system loaded onto a Palm Pilot to determine the efficacy of scheduling software to increase independence. In this study, as well as the next by Davies et al. (2003), computer applications were significantly more effective in obtaining and maintaining independent living skills.

Software in Davies et al. (2003)’s study provided adults with mental retardation a means of tracking their personal checking accounts by storing and retrieving common payees, automatically balancing accounts, and posting checks to the register. Results from Davies et al. (2003)’s within-subjects experimental design indicated that using accounting software significantly reduced the errors by users with mental retardation when compared to traditional checking by hand (p < 001), (Davies et al. 2003). Perhaps the use of literacy software will also reduce error production by children with mental retardation without direct adult supervision.

Not only have computer programs been implicated in adult independent living skills, but they are also being adapted to special educational applications for children with mental retardation. In Ortega-Tudela et al. (2006)’s, two groups of children with Down syndrome were trained to perform mathematical skills; One group used multimedia software and the other performed traditional pencil and paper practice examples. While the authors suggest that simply working with a computer does not produce learning, individuals with Down syndrome in the computer program use group performed significantly better than their pencil and paper peers in the tested mathematical ability. Ortega-Tudela et al. (2006) propose that computer software provides the tools necessary to learn abstract concepts that were once thought impossible for individuals with Down syndrome to develop. Although evidence suggests that computer programs have been applied to teaching basic academic and independent living skills to individuals with mental retardation, the complex task of reading instruction has not been attempted in the available literature.

Computer applications provide unique methods to teach children who learn at individualized rates. Davies and Hastings (2003) argue that virtual environments are appropriate learning tools for individuals with mental retardation because of two of their inherent properties: (1) they provide opportunities for learners with mental retardation to make mistakes without fear of humiliation, and (2) programmers can manipulate virtual learning technologies in ways that they are unable to in the real world. Stories can literally come alive when teaching children to read when imagination is limited by the burdensome act of first sounding out individual words and conceptualizing them into a meaningful sentence.

Computers in the classroom offer a versatile instrument from which to introduce educational skills that are both compelling and entertaining to elementary school aged children of all cognitive abilities by operating colorful displays, interactive features, and immediate feedback. Programs like CompSkills provide self-pace, self-directed computer-generated prompting to guide a series of computer training tasks such as mouse operation and typing on a keyboard (Davies et. al, 2004). According to these findings, computer-based education for children with mental retardation provides a new frontier for intelligent tutors in the context of literacy learning. As such, CompSkills style training tasks will be an important facet to preparing children to learn early reading strategies on the computer. To perform well on the TTR module, children must be proficient in basic computer skills, many of which they have acquired through similar CompSkills programs or leisurely gaming.

Intelligent Tutors: Teaching Literacy Skills to Children with Mental Retardation

Now that we have evaluated the effectiveness of computer applications for teaching individuals with mental retardation, the preliminary overall structure of the proposed computer program should be established. The design must incorporate both model tracing, described by Koedinger et al. (1997) as a process in which student’s actions are continually compared against those that the model might generate, and knowledge tracing, which monitors student learning across all problems (Koedinger et al. 1997). An intelligent tutoring system would meet these criteria, as well as provide an external representation for students with mental retardation to demonstrate their reasoning steps in problem solving (Crowley et al. 2007) so that identification of mistake patterns could be recognized and corrected immediately. A computer program may offer corrections to mistake patterns on screen or alert a classroom teacher to the difficulty experienced by the student so that the teacher may intervene.

Moreover, Antshel et al. (2009), find that for children with hyperlexia, described as characteristically capable of decoding text but unable to comprehend its meaning, executive functioning skills are crucial to eventual success in reading. Cognitive based tutors provide children access to their own thought processes because they are able to trace the reasoning of their previous actions, fulfilling an important need among many children with mental retardation, not just those with a hyperlexia diagnoses. Awareness of metacognition will be beneficial across all fields for children with mental retardation, not just those particularly relevant to education.

A final deciding factor in the use of the computerized intelligent tutor design concerns the ease of use and intrinsically rewarding nature of the steps which serve to motivate previously uninterested learners. As a whole, cognitive tutors reduce frustration and provide children pride in a completed goal (Koedinger et al. 1997). Soares et al. (2009) determined that for children with autism, self-monitoring of their own activity completion reduced rates of maladaptive behaviors including self-injury and tantrums. Self-monitoring through means of a cognitive tutor can train children with mental retardation to recognize and track their own behavior.

Implicit learning through observation of one’s own behaviors is essential to successful learning by cognitive tutors, especially for a complex task such as reading. Much of what children with mental retardation or even children in general know about the world is learned implicitly by inducing ideas from experience rather than being explicitly taught. Learning by means of a cognitive tutor happens in much the same way, as rules for the construct being taught (in this case, reading skills) are often not overtly stated. Instead they are determined by the child through trial and error.

Research suggests that for many children with mental retardation, implicit learning skills are a strength. Don et al. (2003) discovered that when controlling for working memory and nonverbal intelligence, individuals with Williams syndrome performed equally as well on an artificial-grammar learning paradigm requiring implicit learning as their typically developing controls. Results from a study by Atwell et al. (2003) indicate that individuals with mental retardation perform poorly when compared to typically developing individuals on explicit learning activities but did just as well on implicit learning tasks. Atwell et al. (2003) suggest that acquiring knowledge about complex material is equivalent for individuals of varying cognitive abilities when accomplished through implicit learning. While this assumption may be a stretch, it is an important skill to tap in teaching new skills to individuals with mental retardation.

Considering the role of implicit learning in cognitive based tutors, as well as the motivation and metacognition skills taught through them, designing a computerized cognitive based tutor seems to be the best option to teach individuals with mental retardation how to read.

Why is Learning to Read so Important?

For children with mental retardation, reading opens the door to an independent life and the ability to further learn more complex academic skills. When children are able to read, they can to communicate with others through written language, create their own to-do lists, and shop for their own supplies. Writing offers a creative outlet for individuals with mental retardation, while reading provides a quiet recreation activity that can be enjoyed in solitude. For these and many other reasons, learning to read must be a priority in the education of all children, even those with mental retardation.

Phonics versus Whole-Word Literacy Training

After carefully considering the ease of implementation and value of a computer-based intelligent tutor to teach children with mental retardation, researchers must next identify the best mechanism for reading instruction: phonics or the more traditional sight word recognition method.

In the typically developing population, the process of reading is comprised of two distinct yet interrelated abilities: (1) word recognition and (2) reading comprehension (Macaruso et al. 2006). To identify a new sight word in reading, children must apply known rules to segment sounds into phonemes and then recombine these phonemes fluently. As a child becomes a more proficient and experienced reader, this decoding process becomes increasingly automatic, as he or she is able to consciously manipulate the components of the language in a construct known as metalinguistics (Mervis, 2009). In reading comprehension monitoring, a core component of metalinguistics, each child must think about what he or she has just read and determine if the mental translation from text is understandable. When the reading is not understood, a child must be prepared to employ cognitive tools to clarify the text such as looking for contextual cues (Mervis, 2009).

Children with mild to moderate mental retardation have historically been taught to memorize sight words rather than to “sound them out”, learning to segment words into distinct phonemes in the letter-sound pairing process of phonics (Laurice & Seery, 2004). Many reading experts are now endorsing phonetic instruction for children with developmental delays, but educators are hesitant to embrace this teaching technique. In a metanalysis conducted by Laurice and Seery (2004), comparisons between sight word learning, where children repeated words presented on flashcards, and phonics instruction, including a tactile kinesthetic task of children tracing letters over sandpaper while also saying the word, indicated improved learning through the phonics approach. Unfortunately, in the seven studies included in this metanaylsis, only one (Barbetta et al. 1993) specifically addressed teaching phonics through error correction (Laurice & Seery, 2004). Error correction procedures such as a teacher breaking down words into syllables following a mistake in phonetic “sounding out” should be a fundamental component of any intelligent tutor system as its importance has been empirically supported (Laurice & Seery, 2004).

An essential factor, the characteristic weaknesses in language abilities for specific diagnoses that also result in mental retardation, should not be overlooked in the design of this computer-based tutor for reading instruction. According to Klusek et al. (2009), nearly two thirds of all children with Down syndrome experience some degree of hearing loss, which could potentially compromise their ability to learn phonetically if unable to hear sounds accurately. The visual processing strengths of children with Down syndrome make learning to recognize whole words more natural; however, phonological memory was also able to predict variation in reading ability (Klusek et al. 2009). Moreover, Cupples and Iacono (2002) determined that children with Down syndrome who received phonological training generalized these methods to unknown words, whereas children who had learned solely to use whole words were not able to (Klusek et al. 2009). These findings indicate that phonological awareness must be integrated with whole word learning for any cognitive tutor built specifically for children with mental retardation.

Previous research suggests that performance on phonemic awareness tasks is actually a better predictor of reading achievement than IQ across various syndromes and special needs (Laurice and Seery, 2004). Macaruso et al. (2006) utilized a computer program for at-risk children to conclude that first graders eligible for Title 1 services can outperform their socioeconomically matched peers when they are taught to read using phonics rather than whole word strategies. Murphy (2009) indicated that learning through phonics was an appropriate method for teaching children with Turner’s syndrome, whereas Finestack et al. (2009) suggested that children with Fragile X have more difficulty with higher level phonological processing, but are able to both whole-word and phonetically decode less difficult words. Mervis (2009) determined that children with Williams syndrome benefit from phonetic instruction because of the strong relations between their phonological awareness and reading skills. Finally, Hatcher et al. (2004) as described by Macaruso et al. (2006) found that explicit phoneme training in reading instruction did not improve literacy skills in typically developing children, but was extremely beneficial for children with reading delays (Regtvoort and van der Leij, 2007). Obviously, there has not been sufficient and conclusive research regarding the use of phonics to teach children with mental retardation, but these strategies provided in a computer-based intelligent tutor could serve as platform to launch future research and a new tool in the educational market.

The TRAINing to Read Cognitive Tutor Design

To formulate an effective cognitive tutor that can be easily replicated to teach millions of children with mental retardation, an adaptable, visually stimulating, and interactive program must be designed where children can learn at varying speeds. Self-paced knowledge tracing will be an important facet of our design to prevent children from becoming bored or overly frustrated, in addition to the many characteristics described in the following paragraphs.

Input from an animated character, similar to Lexie the Lion employed by Macaruso and Walker (2008), will provide instructions for each module and scaffold hints to support student progress on the cognitive tutoring system. A Bayesian estimation procedure will be used to identify individual strength and weaknesses based on responses to questions posed in the model and used to select follow up questions that are in the appropriate range of difficulty for each student (Koedinger et al. 1997). Throughout a child’s operation of the computerized intelligent tutor, the program will trace all responses and return to areas frequently missed to provide hints and specific practice as necessary (Macaruso and Walker, 2008), while alerting classroom teachers and parents of these difficulties. In this way, our cognitive based tutor will provide the individualized support that has become increasingly important as children with a wide range of intellectual abilities are placed together in the same public school classrooms with one common instructor.

Koedinger et al. (1997) emphasize the importance of timely feedback in our cognitive tutor to provide adequate motivational benefits. Macaruso et al. (2006) substantiated that corrective feedback was crucial to the process of learning through cognitive tutors, citing programs like Daisy Quest and the Daisy’s Castle, precursors to our phonetic instruction model. Children must be informed of their mistakes so that they can modify their approach before the incorrect strategy becomes habitual or strongly encoded.

It will be important to constrain the problem space of our students’ responses similar to those described by Lesgold et al. (1992) in their SHERLOCK system, by allowing only actions that would be plausible for users with mental retardation to make. For example, mistakes resulting from inattention will be built into the problem space so that when errors predictive of distraction are made, the child’s attention is redirected back to the task quickly. Specific hints should also be given to the student based on his or her past performance on the task to ensure the appropriate level of feedback is given. The complexity of feedback should be personalized to each child’s individual cognitive ability so that lower functioning children are not given more information about their performance than they can adequately process and retain.

In a new computerized cognitive tutor design, TRAINing to Read (TTR), children with mental retardation will learn to segment, pronounce and blend phonics in an introduction to basic literacy skills. Downloadable from the internet, TTR can be mass distributed to children’s homes, schools, and libraries, where daily reports of activities and errors will be generated and emailed to educators and parents. Each child will be assigned a unique login name and password so that multiple students can share a single computer. Children can also access their TTR module from various computers, allowing for them to practice at home under parental supervision as well as at school, in the library, or in after-school care settings.

Upon loading the software, children will be administered a CompSkills style training (Meyers, 1988) to become comfortable with pointing and clicking their desired response with a computer mouse. I had originally considered developing a membrane keyboard overlay to label objects on the screen as was described by Meyers (1988) but decided that learning to move a mouse would be an easier skill for children to acquire. On the other hand, for children with visual-spatial processing difficulties, a keyboard substitution will also be made available.

Following computer skill training, children will be introduced to a realistic yet animated character, Bob the Train Conductor, who will lead them through the exercises and provide hints when they are requested. Trains will be a structural theme throughout the TTR program, as many children with mental retardation show interest in modes of transportation, particularly trains. Other theme structures could be built for additional levels in the program or for specific interests for various children with mental retardation and downloadable from the same internet website.

Bob, our train conductor, will walk across the screen, pause to introduce himself, and explain his mission while requesting help from the learner to achieve his assigned task. Bob has been asked to transport cars full of toys from the toy store to homes throughout the community where hundreds of little children are waiting to play with their gifts. Cars contain toys and their corresponding activities and are propelled along the track with the completion of each activity. After the learner has completed the activity in each car, another train car “pulls” into the screen view and its corresponding activity is launched.

The first car in Bob’s train asks participants to repeat a particular sound (written on the side of the train) and click on an item that begins with this same sound. For example a colorful “P” would be placed on the side of the train car and highlighted when the sound was produced by the conductor. The learner will next be expected to click on the puppy and drag its icon over to the top of the train car, where the mouse button would then be released and the puppy toy would fall inside the train car to await delivery. Once the toy was successfully dropped into the train car, the “P” on its exterior would be highlighted and the child would be asked to repeat the sound into a microphone headset where observed auditory waves are compared against the expected response to determine accuracy of pronunciation. If an error occurs, the child is prompted to try again. If not, the train’s whistle blows and the next car pulls forward to reveal the next letter-toy pair.

Another activity built into the design of the train program will be a fading task to enhance recall skills for children with mental retardation in literacy learning. Using Bob’s voice, a phoneme drawn on the side of a train car will be pronounced slowly, and the child will be encouraged to repeat this sound. The word will be broken down into phonemes and paired with a simple line drawing of a child’s toy. For example, the word “cat” will be written on the outside of the train car and individual sounds will light up as the conductor pronounces them. In the same fading strategy discussed by Hetzroni and Shalem (2005), a toy cat will be shown clearly on the top of the train car, pairing the toy with the letters and spoken sounds. As the train conductor repeats the word, the lines of the cat drawing will gradually fade, leaving only the words to represent the cat toy. The child will repeat the word “cat” into the microphone headset and the train car will roll off of screen and the next activity car will be presented. Each of the child’s verbalizations will be recorded in the computer and forwarded to the supervising teacher or parent who can also gauge the child’s progress throughout the exercise auditorily.

After the child has successfully completed three very difficult train car tasks in a row (or is displaying signs of fatigue), he or she will be presented words segmented into phonemes. A headshot of the train conductor will read the posted word complete with facial expression and moving lips, while sounding out each syllable and encouraging the child to repeat the word as loudly or softly as desired. This fast paced exercise should reinvigorate the student for learning new material while also making him or her feel accomplished when quickly completing an easier task. The integration of the conductor’s face in this screenshot is important to note, as it mirrors the findings by Massaro and Bosseler (2005), who discovered that children with autism learn significantly faster in the presence of a computer generated face. According to this same source, the human face provides visual information during speech production which may be helpful for individuals with mental retardation.

Throughout the TTR program, children will be reminded to click on any of the help modules to request assistance as needed. Information provided in the help bars will be based on the child’s current abilities and success in responding to previous train car activities, through a knowledge tracing process. Each child can request a spoken cue, one where the conductor enters the center of the screen and speaks directly to the learner, providing the first few sounds of a letter or other hints as appropriate according to the exercise. Other cues can be given through animated prompts or separation of syllables in a more difficult word. When a child has asked repeatedly for help through the help menu, an instant message will be immediately sent to the supervising teacher or parent via cellular phone or computer. This notification system will allow instructors to intervene before a child becomes too frustrated or to step in to correct a behavioral problem. An email log of each help request and incorrect response will be maintained and emailed to all applicable users (teachers, parents, clinicians, and others).

After all of the train tasks have been completed, the train will travel across the screen in a circular pattern on the tracks quickly as the conductor (and the student) repeat words from earlier within the lesson across the train cars.

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TRAINing to Read: A Cognitive Tutor for Children with Mental Retardation (Part 2)

June 24th, 2010 Comments off


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Bob the Conductor will be shown delivering all of the toys that each child learned about through the reading activities, and at the end of this sequence of graphics, children will be presented with a redemption code where they can ask a parent to help them enter online and chose one of the many prizes to be mailed to their home.

The physical construction of this downloadable computer program must be designed and tested on hundreds of children with mental retardation to determine the efficacy of the model with an appropriate power. Developers must first identify a school district similar to those involved in the testing of Betty’s Brain where cooperation and collaboration is beneficial to both parties and there exists a large population of high functioning individuals with mental retardation. In the initial test stages, TTR should first be deployed specifically for children with mental retardation with IQs greater than 70 because previous research suggests that these children are capable of learning to read. Once the effectiveness has been determined and revisions made to the program, children with lower IQs should be tested to ensure that children of all cognitive abilities can benefit from the TTR computer program. Modifications may be required to provide a more suitable learning environment for children with lower cognitive functioning, but this should be addressed after initial deployment and revisions have been made.

To ensure proper implementation and execution of the TTR literacy program, teachers must “buy into” the program, for lack of a better word. During the initial beta testing of the program, teachers from school districts participating in the first pilot studies will be invited to attend a free two-day seminar where they will learn about TTR’s development. Attendees will have the opportunity to test out the TTR software and make recommendations to the developers, as well as sign up for their classrooms to be one of the first to launch the program. Through these pre-release seminars, classroom teachers should feel ownership for the program and will thus be more likely to administer and supervise according to the original design. New modules in place of the train theme, additional hint instructions, and other improvements will be made based on teacher feedback before the final version is released. As TTR will be available for update on the internet, this software can be modified and downloaded throughout the life of the program to continually improve it as a literacy training resource.

Once TTR has been downloaded onto school computers and teachers have been trained to administer the program, an informational session will be held for parents at the school so that TTR may be successfully used in the home. Parents and teachers will be able to communicate with one another through the program’s messaging service to track student progress and indicate concern in specific areas of learning.

Should the initial implementation of TTR be successful, additional academic lessons for older children with mental retardation can be produced within a similar framework. In this way, children with intellectual and even physical disabilities could safely conduct their own science experiments using virtual Bunsen burners and volatile chemicals that would otherwise be unsafe in a special education classroom. Cognitive tutors can be expanded beyond reading and mathematics applications to encompass other courses that could benefit children with mental retardation. Based on the design and implementation process described, I predict great success in the use of TTR and other future cognitive tutor models to supplement traditional academic instruction.


Antshel, K. Marrinam, E. Kates, W. Fremont, W. & Shprintzen, R. (2009). Language and literacy development in individuals with Velo-cardio facial syndrome. Topics in Language Disorders, 29, 170-186.

Atwell, J. Conners, F. & Merrill, E. (2003). Implicit and explicit learning in young adults with mental retardation. American Journal of Mental Retardation, 108, 56-68.

Crowley, R. Legowski, E. Medvedeva, O. Tseytlin, E. Roth, E. & Jukic, D. (2007). Evaluation of an intelligent tutoring system in pathology: Effects of external representation on performance gains, metacognition, and acceptance. Journal of American Medical Informatics Association, 14, 182-190.

Davies, D. & Hastings, R. (2003). Computer technology in clinical psychology services for people with mental retardation: A review. Education and Training in Developmental Disabilities, 38, 341-352.

Davies, D. Stock, S. & Wehmeyer, M. (2002). Enhancing independent time-management skills of individuals with mental retardation using a palmtop personal computer. Mental Retardation, 40, 358-365.

Davies, D. Stock, S. & Wehmeyer, M. (2003). Utilization of computer technology to facilitate money management by individuals with mental retardation. Education and Training in Developmental Disabilities, 38, 106-112.

Davies, D. Stock, S. & Wehmeyer, M. (2004). Computer-mediated, self-directed computer training and skill assessment for individuals with mental retardation. Journal of Developmental and Physical Disabilities, 16, 95-105.

Don, A. Schellenberg, E. Reber, A. MiGirolamo, K. & Wang, P. (2003). Implicit learning in children and adults with Williams syndrome. Developmental Neuropsychology, 23, 201-225.

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TRAINing to Read: A Cognitive Tutor for Children with Mental Retardation (Part 3)

June 24th, 2010 Comments off


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Finestack, L. Richmond, E. & Abbeduto, L. (2009). Language development in individuals with Fragile X syndrome. Topics in Language Disorders, 29, 133-148.

Hetzroni, O. & Shalem, U. (2005). From Logos to orthographic symbols: a multilevel fading computer program for teaching nonverbal children with autism. Focus on Autism and Other Developmental Disabilities, 20, 201-212.

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Koedinger, K. Anderson, J. Hadley, W. & Mark, M. (1997). Intelligent tutors to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30-43.

Laurice, J. & Seery, M. (2004). Where is the phonics? A review of the literature on the use of phonetic analysis with students with mental retardation. Remedial and Special Education, 25, 88-94.

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Macaruso, P. Hook, P. McCabe, R. (2006). The efficacy of computer-based supplementary phonics programs for advancing reading skills in at-risk elementary students. Journal of Research in Reading, 29 (2), 162-172.

Macaruso, P. & Walker, A. (2008). The efficacy of computer-assisted instruction for advancing literacy skills in kindergarten children. Reading Psychology, 29, 226-287.

Martin, G. Klusek, J. Estigarribia, B. & Roberts, J. (2009). Language characteristics of individuals with Down syndrome. Topics in Language Disorders, 29, 112-132.

Massaro, D. & Bosseler, A. (2005). Read my lips: The importance of the face in a computer-animated tutor for vocabulary learning by children with autism. SAGE Publications and the National Autistic Society, 10, 495-510.

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Meyers, L. (1988) Using computers to teach children with Down syndrome spoken and written language skills. In Nadel, L. (Ed): The Psychology of Down Syndrome. New York: NDSS.

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Ortega-Tudela, J. & Gomez-Ariza, C. (2006). Computer-assisted teaching and mathematical learning in Down syndrome children. Journal of Computer Assisted Learning, 22, 298-307.

Regtvoort ,A. & van der Leij, A. (2007). Early intervention with children of dyslexic parents: effects of computer-based reading instruction at home on literacy acquisition. Learning and Individual Differences, 17, 35-53.

Soares, D. Vannest, K. & Harrison, J. (2009). Computer aided self-monitoring to increase academic production and reduce self-injurious behavior in a child with autism. Behavioral Interventions, 24, 171-183.

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