Adaptive and Context-Aware Hypermedia Model for Users with Communication Disabilities

Gea, María Magdalena; Rodrí­guez, Marí­a José; Rodríguez, María Luisa; Medina-Medina, Nuria; López-Cózar, Ramón; Paderewski, Patricia; Gutiérrez, Francisco

doi:10.1007/1-4020-4205-1_2

Cited by 5 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the reported systems are pictogram-oriented, or imageoriented. Gea et al [2006] describe the development of a rule-based, location-aware AAC device for children with autism. In this approach, teachers or rehabilitation professionals are required to create semantic networks (domain model) connecting general communication concepts (e.g., food), basic concepts (e.g., apple, fish), and locations (e.g., home or restaurant).…”

Section: Location-aware Aac Systemsmentioning

confidence: 99%

Measuring the Performance of a Location-Aware Text Prediction System

Garcia

Oliveira

Matos

2015

ACM Trans. Access. Comput.

View full text Add to dashboard Cite

In recent years, some works have discussed the conception of location-aware Augmentative and Alternative Communication (AAC) systems with very positive feedback from participants. However, in most cases, complementary quantitative evaluations have not been carried out to confirm those results. To contribute to clarifying the validity of these approaches, our study quantitatively evaluated the effect of using language models with location knowledge on the efficiency of a word and sentence prediction system. Using corpora collected for three different locations (classroom, school cafeteria, home), location-specific language models were trained with sentences from each location and compared with a traditional all-purpose language model, trained on all corpora. User tests showed a modest mean improvement of 2.4% and 1.3% for Words Per Minute (WPM) and Keystroke Saving Rate (KSR), respectively, but the differences were not statistically significant. Since our text prediction system relies on the concept of sentence reuse, we ran a set of simulations with language models having different sentence knowledge levels (0%, 25%, 50%, 75%, 100%). We also introduced in the comparison a second location-aware strategy that combines the location-specific approach with the allpurpose approach (mixed approach). The mixed language models performed better under low sentence-reuse conditions (0%, 25%, 50%) with 1.0%, 1.3%, and 1.2% KSR improvements, respectively. The location-specific language models performed better under high sentence-reuse conditions (75%, 100%) with 1.7% and 1.5% KSR improvements, respectively.

show abstract

Section: Location-aware Aac Systemsmentioning

confidence: 99%

Measuring the Performance of a Location-Aware Text Prediction System

Garcia

Oliveira

Matos

2015

ACM Trans. Access. Comput.

View full text Add to dashboard Cite

show abstract

“…Although several works have demonstrated that different word prediction techniques improve communication rates significantly in AAC (Venkatagiri 1993;Anson et al 2006;Trnka et al 2007), only a few instances can be found in the literature where some kind of prediction method has been applied to pictograms. Gea et al (2006) describe a location-aware Augmentative and Alternative Communication tool for children with autism. Symbols are presented in the interface according to a set of rules that take into account user location and semantic networks created by teachers and rehabilitation professionals.…”

Section: Prediction Techniques In Augmentative and Alternative Communication Systemsmentioning

confidence: 99%

Predictive composition of pictogram messages for users with autism

Hervás

Bautista

Méndez

et al. 2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

Communication is a basic need for every person. However, there are many people who present disabilities that prevent communication through natural language. Augmentative and Alternative Communication (AAC) systems, including those based on pictograms, attempt to facilitate the communication for people with this kind of difficulties. In this paper we present PictoEditor, an Augmentative and Alternative Communication application for the composition of pictogram messages for users with autism that incorporates prediction functionalities. Although such functionalities have been widely studied in text-based Augmentative and Alternative Communication tools, they have not been applied to pictogram based ones. The results show that prediction based on frequency of use of specific pictograms improves the immediate availability of the desired pictograms, but the improvement with pre-The work presented in this paper has been funded by the projects IDiLyCo: Digital Inclusion, Language and Communication, Grant. No.

show abstract

“…Panselina (2002) implemented a system based on sign language to teach geography to deaf students. Mezak and Hoic-Bozic (2006) focused their studies on students with communication difficulties. They created a hypermedia adaptive system that adjusts to these students' special requirements.…”

Section: Related Workmentioning

confidence: 99%

Content adaptation for students with learning difficulties: design and case study

Lancheros-Cuesta

Carrillo-Ramos

Pavlich-Mariscal

2014

International Journal of Web Information Systems

View full text Add to dashboard Cite

Purpose – This article aims to propose an adaptation algorithm that combines the analytical hierarchy process (AHP), a rule-based system, and a k-means clustering algorithm. Informatic tools are very useful to enhance the learning process in the classroom. The large variety of these tools require advanced decision-making techniques to select parameters, such as student profiles and preferences, to adjust content and information display, according to specific characteristics and necessities of students. They are part of the Kamachiy–Idukay (KI), a platform to offer adaptative educational services to students with learning difficulties or disabilities. Design and Methodology – The design and implementation of the adaptation algorithm comprises the following phases: utilization of the AHP to determine the most important student parameters, parameter to take into account in the adaptation process, such as preferences, learning styles, performance in language, attention and memory aspects and disabilities; designing the first part of the adaptation algorithm, based on a rule-based system; designing the second part of the adaptation algorithm, based on k-means clustering; integration of the adaptation algorithm to KI; and validation of the approach in a primary school in Bogotá (Colombia). Approach – The main approach is the application of computational techniques, namely, rule-based systems and k-means clustering, plus an AHP prioritization at design time to yield a system to support the teaching–learning process for students with disabilities or learning difficulties. Findings – The algorithm found several groups of students with specific learning difficulties that required adapted activities. The algorithm also prioritized activities according to learning style and preferences. The results of the application of this system in a real classroom yielded positive results. Limitations of the research – The algorithm performs adaptation for students with mild disabilities or learning difficulties (language, attention and memory). The algorithm does not address severe disabilities that could greatly affect cognitive abilities. Contributions – The main contribution of this paper is an adaptation algorithm with the following distinctive characteristics, namely, designed utilizing the AHP, which ensures a proper prioritization of the student characteristics in the adaptation process, and utilizes a rule-based system to identify different adaptation scenarios and k-means clustering to group students with similar adaptation requirements.

show abstract

Adaptive and Context-Aware Hypermedia Model for Users with Communication Disabilities

Cited by 5 publications

References 6 publications

Measuring the Performance of a Location-Aware Text Prediction System

Measuring the Performance of a Location-Aware Text Prediction System

Predictive composition of pictogram messages for users with autism

Content adaptation for students with learning difficulties: design and case study

Contact Info

Product

Resources

About