Web accessibility has gained significant attention over the past decades due to the widespread use of the internet, which has urged web developers to address the needs and preferences of a variety of users. In e-learning contexts, learner profiles can be used to describe the needs and preferences of users and adapt the educational resources accordingly. We propose the use of ontologies to represent accessibility needs and preferences in learner profiles in order to structure the knowledge and to access the information for recommendations and adaptations in OpenCourseWare systems. In particular, we propose to use and extend the ACCESSIBLE ontology containing knowledge about disabilities and web accessibility standards. In this work, we extend the ACCESSIBLE ontology to represent accessibility knowledge and requirements for learning contexts with respect to the standards of the IMS Global Learning Consortium.
Accessibility is a key requirement for developing web applications, especially in
elearning contexts [
We propose the use of ontologies to describe the accessibility knowledge required to represent learner profiles and fostering the development of accessible OCW systems. Formally representing this knowledge by means of ontologies can ease sharing, integration, reuse, and reasoning as well as steer the adaptation of the OCW system and educational content.
In our previous work [
The remainder of this paper is organized as follows: Section 2 summarizes relevant background knowledge on web accessibility and learner profiles. Section 3 introduces the IMS AfA concepts and presents our extension of the ACCESSIBLE ontology. Section 4 outlines the envisioned personalization system, before the paper is concluded in Section 5. 2
As we are interested in how accessibility information can be integrated into learner profiles using ontologies, we examined existing standards for learner profiles and ontologies related to accessibility and learning objects. 2.1
Since we are focusing on the accessibility needs and preferences of learners, two
accessibility standards are most relevant: IEEE PAPI and IMS LIP. The “IEEE Standard for
Learning Technology – Public and Private Information for Learners (PAPI Learner)” [
The IMS Learner Information Package (LIP) specification [
We decided to base our work on IMS LIP because it explicitly defines web
accessibility concepts in accordance with the W3C WCAG standards and guidelines [
A number of ontologies have been developed to represent accessibility knowledge and
requirements. Some ontologies focus on the characteristics of disabilities, web
accessibility standards, and guidelines, while others define mappings of user preferences to
assistive devices [
Other ontologies have been developed specifically for e-learning contexts, and also
take learner profiles into account: The Learning Object Context Ontologies (LOCO) are
a group of ontologies developed for an e-learning framework [
We reuse and extend the ACCESSIBLE ontology to represent domain knowledge of disability types, characteristics, functional limitations, and their relations to devices and web accessibility standards. Our ultimate goal is to recommend and apply accessibility configurations with respect to the needs and preferences of individual learners, and let the OCW system adapt itself and/or the educational content accordingly. In particular, we use the “IMS Learner Information Package Accessibility” for the aforementioned LIP model (ACCLIP) to generate learner profiles based on the IMS concepts. In the IMS specifications, the learners are represented with respect to the assistive technologies they are using rather than with their disabilities.
One of the aims is to extend the “Access for All (AfA)” concept with additional properties and preferences to support the use of different resources depending on the situation and context. IMS AfA groups the user needs and preferences into three categories: 1) display, 2) control, and 3) content, as illustrated in Figure 1. The display 4 http://www.accessible-eu.org 5 http://www.who.int/classifications/icf/en/ category describes the user’s preferences for previewing and presenting information, whereas control defines the input devices preferred by the user. The content category contains preferences for the content format, such as a preference for audio or visual resources.
The IMS accessibility standards include two central concepts that are not contained in the ACCESSIBLE ontology: one containing properties of assistive technologies (e.g., the number of dots and cells of a braille device) and the other representing user characteristics (e.g., education level, language). These concepts can be directly added to the ACCESSIBLE ontology, since it already contains appropriate classes, as we will detail in the following. Figure 2 shows a part of the ACCESSIBLE ontology. For representing the assistive technologies defined by the IMS standards, we extended the Device class of the ontology. The Device class has a number of sub-classes (e.g., Braille, Screen Reader, and Magnifier) that have, however, only a limited number of properties, namely the type and name of the device. The class misses other properties and information about the devices. For instance, screen readers usually have a number of properties that can be configured in accordance with the preferences of the users, such as the speech rate or volume. The device properties are important for representing learner disabilities and preferences in accordance with IMS AfA. Adding the properties of the AfA information model (cf. Figure 1) to the Device class of the ACCESSIBLE ontology allows to capture the learner disabilities and preferences in more detail and aligns the ontology better with the IMS AfA specifications.
A benefit of representing accessibility knowledge in ontologies is that suitable devices and properties can be inferred from the type of disability. For instance, the ontologies can be used to recommend assistive devices and preferences to the learner based on SPARQL queries. Listing 1.1 shows a simple SPARQL query retrieving a list of devices that might be suitable for an autistic user. Similar SPARQL queries may be used to retrieve the properties and possible user preferences for the devices, after we added the information of IMS AfA to the ACCESSIBLE ontology. Since the device class is linked with concepts from web accessibility standards (e.g., WCAG 2.0), we can also use it to retrieve the required checklists and techniques. 1 PREFIX acc: <http://www.Acces[...]ogy.com/GenericOntology.owl#> 2 SELECT * 3 WHERE { 4 acc:Autism acc:Disability_has_Device ?device. 5 }
Listing 1.1. Simple SPARQL query retrieving assistive devices suitable for autistic users However, before we can retrieve information about suitable devices and properties, we need to collect information about the user’s disabilities. A possible dialog asking the user to enter this information is given in Figure 3, showing the needs and preferences entered by a color-blind user as an example. The extended version of the ACCESSIBLE ontology can be used to automatically generate such a form asking the user for input about disabilities, available devices, and personal preferences. Users could enter that information (or it could partly be automatically inferred from the context), and the ontology could be populated accordingly.
Furthermore, the ontology contains knowledge about user limitations and required web accessibility success criteria, which can be used not only to generate IMS AfA terms but also to personalize web content in OCW contexts. For example, in the given case of a color-blind user, success criterion 1.4.6 of WCAG 2.0 requires to check if the foreground and background color (or image) have a contrast ratio of at least 7:1. This could be automatically validated by the OCW system, and the colors could be adapted accordingly if they do not meet the requirements.
Finally, it must be considered that learners might have multiple disabilities. Thus, they must be enabled to enter all their impairments and the OCW system should adapt accordingly, which might require to perform some reasoning on the ontology to find the best combination of adaptations. A learner should also be able to define several profiles; for example, a person with visual impairments might prefer to use a braille device at work but a screen reader at home.
Figure 4 shows a part of the information model of IMS PNP, which contains the learner preferences in IMS AfA. We extracted this information from the XML schema that contains all AfA terms. Some properties can be directly added to the user class of the ACCESSIBLE ontology (e.g., educationLevelofAdaptation), whereas others can be inferred from the proposed user dialog, such as atInteroperable referring to the assistive technology used. Yet other properties are a combination of both, i.e., the recommendation resulting from querying the ontology and the preference selected by the user, such as accessModeRequired. As an example for a visually impaired user, by querying the ACCESSIBLE ontology, we can conclude that she might want to use a braille device, magnifier, or screen reader. We may recommend that the user should use textual or audio resources, while she decides for audio resources according to the preferences she stated in her profile.
At the end, all of these properties and preferences should be mapped to the PNP XML schema structure and terms, as it is a unified structure used by IMS AfA to represent and map the information of learner preferences to educational resources. 4
Our ultimate goal is to personalize the educational content and materials of OCW systems with respect to the accessibility needs and preferences given in ontology-based learner profiles. Figure 5 illustrates the architecture of the envisioned personalization system. It makes use of the extended ACCESSIBLE ontology, incorporating the IMS AFA specifications as described in the previous section.
In a first step, the ontology is used to feed the fields of the learner profile dialog with the related data and preferences, as described in Section 3.1. The learner creates a personal profile by selecting his or her preferences in the dialog, and the input is saved in an XML file. The XML file structure is based on the format specified by IMS PNP in order to use it for the mappings to the properties of the educational resources represented in the IMS DRD format.
The personalization system module takes the profile and the ontology as an input to capture the preferences, assistive technology requirements, accessibility guidelines and standards related to a learner. It adapts the OCW content to these requirements, needs, and preferences, and suggests appropriate educational resources to the learner. In this paper, we proposed using the ACCESSIBLE ontology to represent learner needs and preferences with respect to the accessibility requirements of the IMS AfA specifications. IMS AfA is concerned with annotating digital resources and learner preferences with terms that can be easily mapped. IMS AfA is mainly addressing visually and hearing impaired users. Combining it with the ACCESSIBLE ontology will make it more extendable and not limited to special types of disabilities. The combination of IMS AfA and the ACCESSIBLE ontology provides more detailed descriptions of disabilities, assistive technologies, and user preferences. Moreover, it allows to add descriptions of other disabilities, such as cognitive impairments, which are relevant in learning contexts, and suggests mappings to educational resources. In our future work, we plan to implement the presented approach in the OCW system SlideWiki, and evaluate it with disabled users.
Acknowledgments. This research has been supported by the EU project SlideWiki (grant no. 688095).