In Chapters 3 and 4, the author examined the effects of an instructional package comprised of an autonomous pedagogical agent, automatic speech
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recognition, text-to-speech capability, and constant time delay during the instruction of reading sight words aloud to young adults with ASD. The tutoring software was carefully constructed only based on proven techniques for individuals with ASD in order to optimally gain their attention (via the PA’s gaze, head orientation, and pointing gesture), motivate them through timely reinforcement, and promote their learning via contingent corrective feedback.
The findings indicate that the tutoring software was successful in teaching SWR to the participants. The participants also maintained performance for eight weeks, and generalized performance outside of the laboratory setting to written stimuli within a classroom setting. These findings are compelling in that the participants met criterion with a limited amount of time spent in receipt of instruction.
The novel application of autonomous instructional technology may offer many potential benefits to teachers of and students with ASD. First, participants made gains in the absence of a human instructional agent. This is critical in that many students with ASD and other disabilities may require higher teacher to student ratios than available in some educational contexts [314]. The use of a PA during periods of instruction may assist teachers in increasing these ratios. In addition, this technology may be used for instruction outside of classroom settings, whereas a trained teacher may not be available. Parents, community support personnel, and other providers might use the technology to teach new skills germane to their specific environments. Second, it is important to note that though the use of computer-based instruction for students with ASD is not new, previous
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technologies required students to use a keyboard, mouse, or switch to respond to software directives. In the current investigation, students responded to the PA queries by speaking their responses. This use of ASR technology may increase access to computer-based instruction by students with motor impairments or deficits in keyboarding/mouse skills.
The autonomous tutoring system was designed to resemble the participants’ naturalistic instructional settings. Commonly used discrete trial training procedures were employed that typically occurred within the participants’ educational program. Since the students’ teacher was female, the author programmed a female PA to deliver commonly-used instructions and praise statements. Furthermore, the PA exhibited typical body and hand gestures, head nods, and changing facial expressions. The use of instructional stimuli common to the natural environments has long been recommended as a way to promote
generalization [283], and may have contributed to students’ generalized
responding in the current study. Future research on automatic tutoring software might incorporate other generalization strategies, such as the use of multiple PAs, or thinning schedules of reinforcement to those present in natural environments.
The study conducted in Chapters 3 and 4 illustrates the promise of computer-assisted instruction, and PAs in particular, to supplement conventional techniques in teaching SWR to students with ASD. Several existing technologies were combined to create an effective package that reflects a new direction in automated instructional software. The autonomous software afforded extensive one-to-one practice with the capacity to collect student data with minimal teacher
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supervision. This innovation may help teachers increase their instructional efficiency and support students’ active engagement during literacy instruction or students may use the software to practice at home, at their own pace, and in the absence of potential negative feedback from peers.
The preliminary findings, although encouraging, should be viewed in the context of several limitations. First, a multiple-baseline design across a small number of participants was employed. This design allowed for a single demonstration of effect for each participant (i.e., inter-subject replication) and in light of small number of stimuli taught, the author acknowledges the limited generalizability of the findings. Furthermore, the participants in the current study had extensive experience in the SWR instruction using CTD. It may have been the case that students less experienced in discrete trial SWR instruction would not have performed as well. Finally, participants spent a limited time interacting with the PA. It is not known whether extended period of PA delivered instruction might enhance or negatively impact student motivation and ultimately responding.
Several other features of this innovative program warrant further discussion. Since the software was able to detect and react to different student vocal responses, it also has the capacity to collect data and monitor student progress. In the current study, the tutoring software was able to discriminate between correct and incorrect responses with high levels of reliability. This finding suggests that teachers may be able to rely on the tutoring software to collect data during similar instructional contexts. Furthermore, the software may be able to make decisions about whether a student has mastered a skill and subsequently introduce new
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stimuli. This automated process might reduce teacher errors around data-based decision making. Future research should assess the efficacy and feasibility of such automated data collection systems.