Disability-aware adaptive and personalised learning for students with multiple disabilities

Purpose The purpose of this paper is to address how virtual learning environments (VLEs) can be designed to include the needs of learners with multiple disabilities. Specifically, it employs AI to show how specific learning materials from a huge repository of learning materials can be recommended to learners with various disabilities. This is made possible through employing semantic web technology to model the learner and their needs. Design/methodology/approach The paper reviews personalised learning for students with disabilities, revealing the shortcomings of existing e-learning environments with respect to students with multiple disabilities. It then proceeds to show how the needs of a student with multiple disabilities can be analysed and then simple logical operators and knowledge-based rules used to personalise learning materials in order to meet the needs of such students. Findings It has been acknowledged in literature that designing for cases of multiple disabilities is difficult. This paper shows that existing learning environments do not consider the needs of students with multiple disabilities. As they are not flexibly designed and hence not adaptable, they cannot meet the needs of such students. Nevertheless, it is possible to anticipate that students with multiple disabilities would use learning environments, and then design learning environments to meet their needs. Practical implications This paper, by presenting various combination rules to present specific learning materials to students with multiple disabilities, lays the foundation for the design and development of learning environments that are inclusive of all learners, regardless of their abilities or disabilities. This could potentially stimulate designers of such systems to produce such inclusive environments. Hopefully, future learning environments will be adaptive enough to meet the needs of learners with multiple disabilities. Social implications This paper, by proposing a solution towards developing inclusive learning environments, is a step towards inclusion of students with multiple disabilities in VLEs. When these students are able to access these environments with little or no barrier, they will be included in the learning community and also make valuable contributions. Originality/value So far, no study has proposed a solution to the difficulties faced by students with multiple disabilities in existing learning environments. This study is the first to raise this issue and propose a solution to designing for multiple disabilities. This will hopefully encourage other researchers to delve into researching the educational needs of students with multiple disabilities

Ontology-based personalisation of e-learning resources for disabled students

Students with disabilities are often expected to use e-learning systems to access learning materials but most systems do not provide appropriate adaptation or personalisation to meet their needs.The difficulties related to inadaptability of current learning environments can now be resolved using semantic web technologies such as web ontologies which have been successfully used to drive e-learning personalisation. Nevertheless, e-learning personalisation for students with disabilities has mainly targeted those with single disabilities such as dyslexia or visual impairment, often neglecting those with multiple disabilities due to the difficulty of designing for a combination of disabilities.This thesis argues that it is possible to personalise learning materials for learners with disabilities, including those with multiple disabilities. This is achieved by developing a model that allows the learning environment to present the student with learning materials in suitable formats while considering their disability and learning needs through an ontology-driven and disability-aware personalised e-learning system model (ONTODAPS). A disability ontology known as the Abilities and Disabilities Ontology for Online LEarning and Services (ADOOLES) is developed and used to drive this model. To test the above hypothesis, some case studies are employed to show how the model functions for various individuals with and without disabilities and then the implemented visual interface is experimentally evaluated by eighteen students with disabilities and heuristically by ten lecturers. The results are collected and statistically analysed.The results obtained confirm the above hypothesis and suggest that ONTODAPS can be effectively employed to personalise learning and to manage learning resources. The student participants found that ONTODAPS could aid their learning experience and all agreed that they would like to use this functionality in an existing learning environment. The results also suggest that ONTODAPS provides a platform where students with disabilities can have equivalent learning experience with their peers without disabilities. For the results to be generalised, this study could be extended through further experiments with more diverse groups of students with disabilities and across multiple educational institutions

Facilitating learning resource retrieval for students with disabilities through an ontology-driven and disability-aware Virtual Learning Environment

Existing virtual learning environments VLEs in educational institutions are not designed with the expectation that students with disabilities will use them. Consequently, retrieving relevant information by some students with disabilities is a challenging task. The focus of this study was to propose the design of VLEs to incorporate ontologies that facilitate information retrieval by students with disabilities in their learning, thus serving as a semantic web-based assistive technology in education. An Ontology-Driven Disability-Aware Personalised E-Learning System ONTODAPS was designed and then used to recommend specific learning materials to learners based on their learning goal and disability type. Preliminary results of the evaluation of ONTODAPS, by 30 students with disabilities, indicate that 70% of the participants found ONTODAPS to offer a better personalisation, better access to learning materials 68% and is easier to use 63% in retrieving learning materials than Sakai. Thus ONTODAPS serves as an assistive tool in their education through retrieval of relevant learning materials in a suitable format which is compatible with their disability

Ontology‐driven disability‐aware e‐learning personalisation with ONTODAPS

Purpose- The purpose of this paper is to show how personalisation of learning resources and services can be achieved for students with and without disabilities, particularly responding to the needs of those with multiple disabilities in e-learning systems. The paper aims to introduce the ONTODAPS e-learning system which has the mechanism for such personalisation. Design/methodology/approach- This paper reviews current e-learning systems that provide personalisation for students, including their strengths and weaknesses. The paper presents personalisation and its techniques and then presents ONTODAPS which is an ontology-driven and disability-aware e-learning system which personalises learning resources and services to students. Three case studies are considered to show how personalisation is achieved using ONTODAPS. Findings- This paper shows that it is possible to use automated ontology-based agents intercommunicating to provide an effective personalisation for disabled students. The results reveal that ONTODAPS is flexible enough to provide enough control and freedom to drive their learning. The results also suggest that ONTODAPS has the ability to provide appropriate levels of learner control by allowing them to self-direct learning through personalising learning resources and then allowing them to choose which resources they wish to access. This thus gives them a sense of ownership and control. Research limitations/implications- This research reveals that it is possible for e-learning systems to personalise learning for users with multiple disabilities. Thus, by considering the needs of such users and consulting them in the design and development process, developers of e-learning systems can produce systems that are both accessible and usable to students with disabilities. Practical implications- The inclusion of multiple formats of learning resources and personalisation of their presentation to students means students will have increased access to such resources with the potential of consuming and assimilating the information. This also has the potential of improving understanding and hence and improvement in results. Originality/value- Although personalisation has been applied in e-commerce systems making them very successful, such personalisation is still a wish for e-learning systems which struggle to catch up. This research proposes a solution in the e-learning domain and its novelty is in its application to disabled students including those with multiple disabilities

Flexible Ontology-Driven Educational Apps and Social Media for Learners with a Disability

This paper explores how to build ontology-driven learning systems from a flexible disability-aware mentality and augment them into a learning blend that embraces social media. The approach emphasizes the use of user centered flexible software in a blended approach to learning. The paper starts with a discussion of how learners learn, including their recent fascination with Apps and social media. The need to provide disability-aware personalization of the educational Apps that are developed is discussed. The emphasis is on designing learning systems for learners with disabilities rather than providing for them as an afterthought. The paper introduces social media as a way of facilitating and supporting e-learning. It notes the recent changes that have taken place in the use of social media. Taking e-learning as a case study the paper demonstrates how various models of e-learning, emphasizing flexible learning, can be enhanced linking back the whole while of integrating into disability-aware information systems. Some practical approaches to modeling the learner with ontologies are provided. Finally, caution is noted on how we have to use social media. We detail some of the potential problems and pitfalls that may be a contemporary consequence of using this media, then offer some suggestions and work rounds

Ontology-based e-learning personalisation for disabled students in higher education

The number of students with disabilities in UK higher education institutions increases every year. Delivering education online is becoming increasingly challenging as institutions encounter some disabilities requiring adjustments of learning environments. The law requires that people with disabilities be given equivalent learning experiences to their non-disabled peers through "reasonable adjustments". Educational institutions have thus utilised assistive technologies to assist disabled students in their learning, but some of these technologies are incompatible with some learning environments, hence excluding some disabled students and resulting in a disability divide. To solve this problem, amongst other solutions, e-learning personalisation has been used and more recently, this is also achieved using Semantic Web technologies such as ontologies. Nevertheless, as ontologies are incorporated into learning environments little seems to be done to personalise learning for some disabled students. This study, in order to bridge the gap, proposes a personalisation approach based on a disability ontology containing information on various disabilities encountered in higher education, which can be used to present disabled students with learning resources relevant and suitable for their specific needs

Machine learning model (RG-DMML) and ensemble algorithm for prediction of students’ retention and graduation in education

Automated prediction of students' retention and graduation in education using advanced analytical methods such as artificial intelligence (AI), has recently attracted the attention of educators, both in theory and in practice. Whereas invaluable insights and theories for measuring and testing the topic have been proposed, most of the existing methods do not technically highlight the non-trivial factors behind the renowned challenges and attrition. To this effect, by making use of two categories of data collected in a higher education setting about students (i) retention (n = 52262) and (ii) graduation (n = 53639); this study proposes a machine learning model - RG-DMML (retention and graduation data mining and machine learning) and ensemble algorithm for prediction of students' retention and graduation status in education. This was done by training and testing key features that are technically deemed suitable for measuring the constructs (retention and graduation), such as (i) the Average grade of the previous high school, and (ii) the Entry/admission score. The proposed model (RG-DMML) is designed based on the cross industry standard process for data mining (CRISP-DM) methodology, implemented using supervised machine learning technique such as K-Nearest Neighbor (KNN), and validated using the k-fold cross-validation method. The results show that the executed model and algorithm based on the Bagging method and 10-fold cross-validation are efficient and effective for predicting the student's retention and graduation status, with Precision (retention = 0.909, graduation = 0.822), Recall (retention = 1.000, graduation = 0.957), Accuracy (retention = 0.909, graduation = 0.817), F1-Score (retention = 0.952, graduation = 0.885) showing significant high accuracy levels or performance rate, and low Error-rate (retention = 0.090, graduation = 0.182), respectively. In addition, by considering the individual features selected through the Wrapper method in predicting the outputs, the proposed model proved more effective for predicting the students' retention status in comparison to the graduation data. The implications of the models' output and factors that impact the effective prediction or identification of at-risk students, e.g., for timely intervention, counselling, decision-making, and sustainable educational practice are empirically discussed in the study

Describing and assessing image descriptions for visually impaired web users with IDAT

People with visual impairments, particularly blind people face alot of difficulties browsing the web with assistive technologies such as screen readers, when websites do not conform to accessibility standards and are thus inaccessible. HTML is the basic language for website design but its ALT attribute on the IMG element does not adequately capture comprehensive image semantics and description in a way that can be accurately interpreted by screen readers, hence blind people do not usually get the complete description of the image. Most of the problems however arise from web designers and developers not including a description of an image or not comprehensively describing these images to people with visual impairments. In this paper, we propose the use of the Image Description Assessment Tool (IDAT), a Java-based tool containing some proposed heuristics for assessing how well an image description matches the real content of the image on the web. The tool also contains a speech interface which can enable a visually impaired individual to listen to the description of an image that has been uploaded unto the system