Bridging the accessibility gap in Open Educational Resources

Open Educational Resources (OER) are being fostered as a global movement for providing educational opportunities to all. However, people with disabilities are still excluded from full participation because of the lack of accessibility of OER websites, as well as of the resources themselves. This work presents a proposal for the design of OER websites that would enable equitable access for all users. This design aims to bridge the accessibility gap through the personalization of the whole OER environment to facilitate an accessible User Experience (UX) based on a user profile that includes the self-identification of disability status. This profile configures not only the “look and feel” of the interface but also the delivery of educational resources suitable for this user profile. To achieve this purpose, the design goes beyond compliance with the ISO/IEC 40500 W3C Web Content Accessibility Guidelines (WCAG) 2.0, since it includes the personalization of the accessible experience through usability considerations and adaptations of educational resources. The delivery of educational resources matches the user’s profile with regard to their sensory abilities, cognitive faculties and their requirements of functionality control, display layout and language. As a proof of concept, we have developed an OER website based on this design and have conducted a set of UX tests that include users with different disabilities. The test results confirm the feasibility and suitability of our design regarding accessible UX. Finally, the contribution of this document arises from the explicit recognition of the particular needs associated with the disability profiles to establish the response of the entire OER system which enables a truly inclusive experience by exempting the user from performing configuration tasks.     

User sensitive research in e-learning: exploring the role of individual user characteristics

The increasing need for active and accessible learning in the inclusive knowledge society drives the demand for e-learning that engages users much more effectively than ever before. In this context, it is crucial to conduct research that embraces innovation in user sensitive design, or else influential individual user differences may be overlooked. The objective of this paper is to explore the creation of successful e-learning systems that are able to increase users’ learning performance and enhance their personal learning experiences. The paper reports two converging and complimentary approaches, namely case studies and experimentation. First, case studies are used to explore the extent to which effective e-learning systems comply with eight specific factors. Of the eight, accessibility, individual differences and student modeling turn out to be the weakest points in current practice. Second, an empirical study investigates the influences of user individual user differences on users’ learning outcomes in an e-learning environment. The experiment found that individual differences in motivation to learn and expectations about e-learning significantly impacted users’ learning achievements. Third, based on these studies, improvements in research methodology are identified towards greater consideration of user sensitive research issues, thus enabling us to outline improved experimental procedures. Further experiment results should provide us with better insights into the arguments needed to carefully assess benefits of developing and involving a user model in an e-learning application. Consequently, evaluation and justification could now encompass both system performance as well as user performance.     

A recommender agent based on learning styles for better virtual collaborative learning experiences

Almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Based on a survey related to how students find additional bibliographical resources for university courses, we concluded there is a strong need for recommended learning materials, for specialized online search and for personalized learning tools. As a result, we developed an educational collaborative filtering recommender agent, with an integrated learning style finder. The agent produces two types of recommendations: suggestions and shortcuts for learning materials and learning tools, helping the learner to better navigate through educational resources. Shortcuts are created taking into account only the user’s profile, while suggestions are created using the choices made by the learners with similar learning styles. The learning style finder assigns to each user a profile model, taking into account an index of learning styles, as well as patterns discovered in the virtual behavior of the user. The current study presents the agent itself, as well as its integration to a virtual collaborative learning environment and its success and limitations, based on users’ feedback.     

User‐centred design and educational data mining support during the recommendations elicitation process in social online learning environments

Social online learning environments provide new recommendation opportunities to meet users' needs. However, current educational recommender systems do not usually take advantage of these opportunities. To progress on this issue, we have proposed a knowledge engineering approach based on human–computer interaction (i.e. user‐centred design as defined by the standard ISO 9241‐210:2010) and artificial intelligence techniques (i.e. data mining) that involve educators in the process of eliciting educational oriented recommendations. To date, this approach differs from most recommenders in education in focusing on identifying relevant actions to be recommended on e‐learning services from a user‐centric perspective, thus widening the range of recommendation types. This approach has been used to identify 32 recommendations that consider several types of actions, which focus on promoting active participation of learners and on strengthening the sharing of experiences among peers through the usage of the social services provided by the learning environment. The paper describes where data mining techniques have been applied to complement the user‐centred design methods to produce social oriented recommendations in online learning environments.     

Non-visual access to mobile devices: A survey of touchscreen accessibility for users who are visually impaired

Accessibility revolves around building products, including electronic devices and digital content, so that diverse users can conveniently utilize them, irrespective of their capabilities. In recent years, the concept of touchscreen accessibility has gained a remarkable attention, especially within the considerable reliance on mobile touchscreen devices (MTDs) for information acquisition and dissemination as we witness nowadays. For users who are visually impaired, MTDs unlock different opportunities for independence and functioning. Thus, with the increasing ubiquity of MTDs and their potential extensive utility for all demographics, it becomes paramount to ensure that these devices and the content delivered on them are accessible. And while it might seem straightforward to achieve accessibility on MTDs, attaining this outcome is governed by an interplay between different elements. These involve platform (i.e., operating system) built-in support of accessibility features, content rendering modalities and structures pertaining to user needs and the peculiarities of MTDs as informed in standard accessibility guidelines, user studies uncovering preferences and best practices while interacting with MTDs, national legislations and policies, and the use of third-party devices such as assistive technologies. In this paper, mobile touchscreen accessibility for users who are visually impaired is surveyed with focus on three aspects: (1) the existing built-in accessibility features within popular mobile platforms; (2) the nature of non-visual interaction and how users who are visually impaired access, navigate, and create content on MTDs; and (3) the studies that tackled different issues pertaining to touchscreen accessibility, such as extraction of user needs and interaction preferences, identification of most critical accessibility problems encountered on MTDs, integrating mobile accessibility in standard accessibility guidelines, and investigation of existing guidelines in terms of sufficiency and appropriateness.     

The value of attention aware systems in educational settings

Research on human attention has provided designers with a wealth of information on conditions that promote effective information delivery. Attention aware systems attempt to apply those principles during computer-delivered experiences. These systems can track user interactions with computer presentations, dynamically updating interfaces and content to facilitate user focus on important material. Because the resources necessary to design and implement attention aware systems are not trivial, it is critical to outline the utility of these systems for applicable domains. In this article, the implications of such systems for educational settings are discussed. Attention aware systems may prove useful for teaching diverse learners, assessing student performance, providing feedback during curriculum development, and adding value to computer-assisted teaching methodologies. The implications described in this article are intended as a starting point for collaborative work on the application of attention aware systems to everyday educational settings.     

Evaluating and improving adaptive educational systems with learning curves

Personalised environments such as adaptive educational systems can be evaluated and compared using performance curves. Such summative studies are useful for determining whether or not new modifications enhance or degrade performance. Performance curves also have the potential to be utilised in formative studies that can shape adaptive model design at a much finer level of granularity. We describe the use of learning curves for evaluating personalised educational systems and outline some of the potential pitfalls and how they may be overcome. We then describe three studies in which we demonstrate how learning curves can be used to drive changes in the user model. First, we show how using learning curves for subsets of the domain model can yield insight into the appropriateness of the model’s structure. In the second study we use this method to experiment with model granularity. Finally, we use learning curves to analyse a large volume of user data to explore the feasibility of using them as a reliable method for fine-tuning a system’s model. The results of these experiments demonstrate the successful use of performance curves in formative studies of adaptive educational systems.     

Accessible e-learning material: a no-frills avenue for didactical experts

Different organizations adopt accessibility for various and diverse reasons. Interesting and inspiring as such reasons may be, it is important to ensure that the motivation for supporting accessibility is that it is the right thing to do, i.e. a commitment to the provision of equal opportunities for accessing resources for people with special needs. Among the various efforts for supporting the development of accessible e-learning material, most of them propose guidelines that prevalently address technical accessibility issues (such as the format and navigation of learning material) with little or no consideration for the didactical experts, and thus their didactical experience, in developing learning material. Moreover, the aforementioned guidelines tend to provide generic indications on alternative forms of didactical content for equivalent access to it. None the less, the sole provision of equivalent forms does not guarantee the retention of desirable user interface aspects and may therefore have a negative impact on learning effectiveness. While this paper acknowledges the role of such guidelines, it does propose that the didactical experts be provided with a non-technical recourse, improving their development of accessible e-learning content. By tapping into the experience of the didactical experts, this work provides them with an avenue leading to enhance the accessibility of e-learning material.     

Repositories and communities at cross-purposes: issues in sharing and reuse of digital learning resources

Abstract   Globally, institutions are setting up digital repositories to support the sharing of resources for teaching and learning. The educational rationale is that a single set of resources can be restructured to support different pedagogical approaches and to achieve economies of scale. A key problem is that these learning object repositories (LORs), like many other learning technology innovations, are often designed to exploit the capabilities of technology rather than to meet learners' needs. The premise of this study is that the nature and organization of a community is likely to impact the way an LOR is used. Key dimensions of repositories and communities are discussed. Three repository systems are analysed to identify how communities use repositories. Guided by Activity Theory, the LORs and user communities are analysed as activity systems. These activity systems are compared and contradictions between them are identified. From the analysis, barriers and enablers for the use of LORs to support learning are discussed and a framework for addressing the key issues in the repository development stage is proposed.     

Design of a delegable SCORM conformant learning management system

Owing to the emergence and flourishing of the Internet, traditional teaching and educational activities are increasingly relying on the convenient and global‐reaching networking technology. To boost up the development of e‐learning technology and the interoperability of learning materials, the Advanced Distributed Learning Initiative defined a data‐sharing standard, called the Sharable Content Object Reference Model (SCORM). In SCORM, because all learning materials (either SCOs or assets) are sharable, users cannot share (or delegate) parts or all of their teaching materials at will. This paper proposed an access control and delegation model for SCORM conformant learning management systems. Also, a prototype was developed to demonstrate the feasibility of the proposed model.     

Learning with personalized recommender systems: A psychological view

This paper explores the potentials of recommender systems for learning from a psychological point of view. It is argued that main features of recommender systems (collective responsibility, collective intelligence, user control, guidance, personalization) fit very well to principles in the learning sciences. However, recommender systems should not be transferred from commercial to educational contexts on a one-to-one basis, but rather need adaptations in order to facilitate learning. Potential adaptations are discussed both with regard to learners as recipients of information and learners as producers of data. Moreover, it is distinguished between system-centered adaptations that enable proper functioning in educational contexts, and social adaptations that address typical information processing biases. Implications for the design of educational recommender systems and for research on educational recommender systems are discussed.     

An AI-based open recommender system for personalized labor market driven education

Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements; (2) decomposes skills into learning topics; (3) collects a variety of open online educational resources that address those topics; (4) checks the quality of those resources and topic relevance with three intelligent prediction models; (5) helps learners to set their learning goals towards their desired job-related skills; (6) recommends personalized learning pathways and learning content based on individual learning goals; and (7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by means of a pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal recommendations provided by eDoer to acquire knowledge of basic statistics, attained higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported.     

A Framework for the Generation of Adaptive Courses Based on Semantic Metadata

This approach proposes the creation and management of adaptive learning systems by combining component technology, semantic metadata, and adaptation rules. A component model allows interaction among components that share consistent assumptions about what each provides and each requires of the other. It allows indexing, using, reusing, and coupling of components in different contexts powering adaptation. Our claim is that semantic metadata are required to allow a real reusing and assembling of educational component. Finally, a rule language is used to define strategies to rewrite user query and user model. The former allows searching components developing concepts not appearing in the user query but related with user goals, whereas the last allow inferring user knowledge that is not explicit in user model.John Freddy Duitama received his M.Sc. degree in system engineering from the University of Antioquia -Colombia (South America). He is currently a doctoral candidate in the GET – Institut National des Télécommunications, Evry France. This work is sponsored by the University of Antioquia, where he is assistant professor.His research interest includes semantic web and web-based learning systems, educational metadata and learning objects.Bruno Defude received his Ph.D. in Computer Science from the University of Grenoble (I.N.P.G) in 1986. He is currently Professor in the Department of Computer Science at the GET - Institut National des Télécommunications, Evry France where he leads the SIMBAD project (Semantic Interoperability for MoBile and ADaptive applications).His major field of research interest is databases and semantic web, specifically personalized data access, adaptive systems, metadata, interoperability and semantic Peer-to-peer systems with elearning as a privileged application area.He is a member of ACM SIGMOD.Amel Bouzeghoub received a degree of Ph.D. in Computer Sciences at Pierre et Marie Curie University, France.In 2000, she joined the Computer Sciences Department of GET-INT (Institut National des Telecommunications) at Evry (France) as an associate professor.Her research interests include topics related to Web-based Learning Systems, Semantic Metadata for learning resources, Adaptive Learning Systems and Intelligent Tutoring Systems.Claire Lecocq received an Engineer Degree and a Ph.D. in Computer Sciences respectively in 1994 and 1999. In 1997, she joined the Computer Sciences Department at GET-INT (Institut National des Télécommunications) of Evry, France, as an associate professor. Her first research interests included spatial databases and visual query languages. She is now working on adaptive learning systems, particularly on semantic metadata and user models.     

From SCORM to Common Cartridge: A step forward

Shareable Content Object Reference Model (SCORM) was proposed as a standard for sharable learning object packaging, delivering and sequencing. Several years later, Common Cartridge (CC) is proposed as an enhancement of SCORM offering more flexibility and addressing needs not originally envisioned, namely assessment and web 2.0 standards, content authorization, collaborative forums, outcomes reporting, accessibility. Educational policy makers, specialists responsible for learning systems deployment, educational content authors and teachers committed to the learning object paradigm must opt for or coexist with two different, partially overlapping proposals for content packaging. While SCORM was conceived for self-paced computer based learning, Common Cartridge attempts at providing support for all forms of teaching and learning with a stress on interactive and collaborative environments. Variety of content, distributed content, discussion forums, assessment, student’s tracking, interaction with external tools and authorization to access resources are listed as its main enhancements. This article reviews and compares SCORM and Common Cartridge from an educational perspective, seeking to provide some ground and guidelines on how to stand before these proposals. A simple process for authoring a Common Cartridge is described, as well as testing and conversion from SCORM. Suggestions are made to education practitioners on learning objects standards adoption in the most common scenarios.     

User-Centric Learning and Evaluation of Interactive Segmentation Systems

Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user??a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.     

Contributions of Dichotomic View of plasticity to seamlessly embed accessibility and adaptivity support in user interfaces

Recent legal changes have increased the need for developing accessible user interfaces in computer-based systems. In this sense, previously existing user interfaces are intended to be modified and new user interfaces are intended to be designed taking accessibility guidelines into account. Typically, model-based approaches have been used when developing accessible user interfaces or redefining existing ones. But the use of static models leads to the development of not dynamically adaptable user interfaces. Dynamic adaptation in accessible user interfaces is important due to the fact that interaction difficulties on people with disabilities may change through use. In this paper, we present some contributions that can be obtained from the application of the Dichotomic View of plasticity in the personalization of user interfaces. With the double perspective defined in this approach, it is intended to go further from a mere adaptation to certain user stereotypes, offering also a dynamic support to real limitations or difficulties users can encounter during the use of the UI. This goal is achieved analyzing user logs by an inference engine that dynamically infers modifications in the user interface to adjust it to varying user needs. A case study is presented in order to show how the guidelines and software support defined in the Dichotomic View of plasticity can be applied to develop a component for a particular system aimed at performing dynamic user interface adaptations with accessibility purposes. This approach includes some innovations that make it different from conventional adaptable mechanisms applied to accessibility in some important aspects.     

User attitude as a mediator of learning performance improvement in an interactive multimedia environment: an empirical investigation of the degree of interactivity and learning styles

Multimedia technology-based interactive learning/training programs have recently emerged as major tools for learning in educational settings (schools), at home and for training in corporations. Multimedia aspects and an ability to interact with the programs are claimed to enhance the learning experiences. A central thesis of this study is that such “interactive multimedia learning systems” would significantly improve users' attitudes, and that this, in turn, would enhance their learning achievement. An additional thesis of the study is that the “learning style” of the users will moderate the relationship between interactivity and attitude. This article reports the findings of a controlled quasi-experimental study of the influences of “interactivity” on six different dimensions of user attitude (content, format, user-control, feedback, ease of use and motivation) and performance improvement (achievement-gain). The results indicate that interactivity positively influences user attitude, and some dimensions of attitude translate into enhanced user performance. The study finds some interesting support for the moderating effects of learning styles. Implications and future research directions are discussed.