Motivation Modelling and Computation for Personalised Learning of People with Dyslexia

The increasing development of e-learning systems in recent decades has benefited ubiquitous computing and education by providing freedom of choice to satisfy various needs and preferences about learning places and paces. Automatic recognition of learners’ states is necessary for personalised services or intervention to be provided in e-learning environments. In current literature, assessment of learners’ motivation for personalised learning based on the motivational states is lacking. An effective learning environment needs to address learners’ motivational needs, particularly, for those with dyslexia. Dyslexia or other learning difficulties can cause young people not to engage fully with the education system or to drop out due to complex reasons: in addition to the learning difficulties related to reading, writing or spelling, psychological difficulties are more likely to be ignored such as lower academic self-worth and lack of learning motivation caused by the unavoidable learning difficulties. Associated with both cognitive processes and emotional states, motivation is a multi-facet concept that consequences in the continued intention to use an e-learning system and thus a better chance of learning effectiveness and success. It consists of factors from intrinsic motivation driven by learners’ inner feeling of interest or challenges and those from extrinsic motivation associated with external reward or compliments. These factors represent learners’ various motivational needs; thus, understanding this requires a multidisciplinary approach. Combining different perspectives of knowledge on psychological theories and technology acceptance models with the empirical findings from a qualitative study with dyslexic students conducted in the present research project, motivation modelling for people with dyslexia using a hybrid approach is the main focus of this thesis. Specifically, in addition to the contribution to the qualitative conceptual motivation model and ontology-based computational model that formally expresses the motivational factors affecting users’ continued intention to use e-learning systems, this thesis also conceives a quantitative approach to motivation modelling. A multi-item motivation questionnaire is designed and employed in a quantitative study with dyslexic students, and structural equation modelling techniques are used to quantify the influences of the motivational factors on continued use intention and their interrelationships in the model. In addition to the traditional approach to motivation computation that relies on learners’ self-reported data, this thesis also employs dynamic sensor data and develops classification models using logistic regression for real-time assessment of motivational states. The rule-based reasoning mechanism for personalising motivational strategies and a framework of motivationally personalised e-learning systems are introduced to apply the research findings to e-learning systems in real-world scenarios. The motivation model, sensor-based computation and rule-based personalisation have been applied to a practical scenario with an essential part incorporated in the prototype of a gaze-based learning application that can output personalised motivational strategies during the learning process according to the real-time assessment of learners’ motivational states based on both the eye-tracking data in addition to users’ self-reported data. Evaluation results have indicated the advantage of the application implemented compared to the traditional one without incorporating the present research findings for monitoring learners’ motivation states with gaze data and generating personalised feedback. In summary, the present research project has: 1) developed a conceptual motivation model for students with dyslexia defining the motivational factors that influence their continued intention to use e-learning systems based on both a qualitative empirical study and prior research and theories; 2) developed an ontology-based motivation model in which user profiles, factors in the motivation model and personalisation options are structured as a hierarchy of classes; 3) designed a multi-item questionnaire, conducted a quantitative empirical study, used structural equation modelling to further explore and confirm the quantified impacts of motivational factors on continued use intention and the quantified relationships between the factors; 4) conducted an experiment to exploit sensors for motivation computation, and developed classification models for real-time assessment of the motivational states pertaining to each factor in the motivation model based on empirical sensor data including eye gaze data and EEG data; 5) proposed a sensor-based motivation assessment system architecture with emphasis on the use of ontologies for a computational representation of the sensor features used for motivation assessment in addition to the representation of the motivation model, and described the semantic rule-based personalisation of motivational strategies; 6) proposed a framework of motivationally personalised e-learning systems based on the present research, with the prototype of a gaze-based learning application designed, implemented and evaluated to guide future work

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

Learning Languages and Complex Subjects with Memory Palaces

A memory palace is an ancient technique of using space as a way of organizing memories. It’s a powerful tool for learning, retaining, and recalling large amounts of complex information quickly and effectively. In the middle ages, these techniques were widely used to learn and compose large texts and works of literature. In this paper, we present the fundamental theory behind memory palaces as the foundation for the project Macunx - a VR platform for building memory palaces to learn huge amounts in short time and with full retention - as well as the initial stages of its development. The paper concludes with a discussion of the future stages over the testing of the package with end-users for its final refinement

E-teacher in Inclusive e-education for Students with Specific Learning Disabilities

AbstractThe concepts of e-learning and e-teaching play important roles in educational technology applied in different educational contexts. E-learning technology can promote the inclusion of students with various disabilities in education. We considered roles of e-teacher which are useful in e-education of students with disabilities. Usefulness of assistive technology and e-learning technology are also considered (project OI179026). The examples of implementation of e-learning/e-teaching components in education of students with specific learning disabilities supported perspectives of inclusive e-education and importance of teachers’ competence of e-teaching in inclusive education

Can virtual reality improve dyslexic English students’ reading fluency and their emotional valence towards reading?

Abstract. The purpose of this master’s thesis is to compare whether a text read in a virtual environment improves the reading of English students with dyslexia in terms of fluency compared with a text laid and read on a piece of paper. Furthermore, another interest of this study is to identify how the participants’ emotional valence was aroused while reading. This master’s thesis is done with the help of Lyfta Oy, an EdTech learning company focused on 360º photos and VR learning environment. Moreover, the thesis design is based on a Lyfta’s workshops, were one the participants read an entire passage in VR without difficulties being dyslexic. Two research questions are aimed to be answered during this research: (1) Is there a difference in fluency between reading in virtual reality (VR) and on a piece of paper? And (2) How positive and negative emotions were empathized while reading? The study was carried out in the UK, were 23 Year 7, 8 and 9 students took part of the data collection. During this face, the participants were asked to read two short passages, one in VR and the other on a piece of paper, being video recorded and, they were asked to fill in two questionnaires about their emotions while reading both texts. Afterwards, the number of errors, words read per minute and prosody were quantified based on the videos, to analyze the participants’ fluency (which comprises three elements: (1) accuracy, (2) rate and (3) prosody) and to answer the first research question. The results suggested that there is not enough data to draw statistical difference between VR and paper. To answer the second research question, the questionnaires’ answers were analyzed. The results suggested that there is a statistical difference in terms of prosody and emotional valence between VR and paper. This study could have some implications in school children having dyslexia, since it might boost positive emotional valence and hence boost their motivation to practice their reading skills. Moreover, educational companies might find a motivation to research more in depth in some aspects of this research and create educational products that can beneficiate dyslexic students’ academic achievement. Also, this research could not only have an impact in dyslexic students, but in general education and other students, since the current master’s thesis continues investigating and analyzing issues that are important in the school days and everyday life of students, such as the role of emotions in the classroom and how VR can affect their emotional valence

3D digital modelling, fabrication and installation for understanding space and place

Traditionally the teaching of history or theory on art and design courses often takes place in a lecture theatre. Space and place theory is integral to informing the practice led and practice-based experiences in architecture, interior and the built environment. The research team has investigated how digital modeling, fabrication and population tools can enhance the understanding of current theoretical debates surrounding space and place. The aim is to integrate inter-disciplinary practice allowing us to address key research questions relating to the emergence of digital fabrication and its potential impact upon art and design education. The purpose is to provide an engaging and informative situated display, offering an experiential and intuitive frame of reference for constructing and placing objects, activities or events into their spatial context. The research has potential to act as an integrative experiential framework through which we can learn more about different contexts or connections between themes or theories which provides a deeper understanding of space or place. In this new work with Taylor, Benincasa, and Unver evolve their practice through translating 3D research data for a series of new digital and physical experiments intended for enhancing or informing teaching and learning in art, design & architecture. The researchers experimented with a range of 3D software and the functionality of different tool parameters. Fabrication apps and 3D crowd simulation animation tools were used for the first time in this research to explore digital fabrication using cardboard in order to compose and construct 2D and 3D physical simulations of this well-known built environment in the landscape. The fabricated physical cardboard models we produced were located in studio spaces and 3D visual projection live drawing experiences were tested with students and staff working together. The 2D and 3D simulations that the team envisioned are both digital and real; and when installed facilitate a more kinesthetic experience of learning as students are able to create together, and interact with fabricated structures. This evolving research demonstrates how these 3D models, animations and fabrications have the potential to be used together as a catalyst to explore multiple projections of space, place identities, historical and cultural built environment concepts for art, design and architecture students at undergraduate and postgraduate level

A review of teacher evaluation beliefs

Teacher evaluation beliefs have received a substantial amount of attention in the educational literature, but comparatively little attention from the belief research topics specially. As the driving force, evaluation resembles belief mention but lack the systemic description. On the base of the student-centered and teacher-centered philosophy, in the present paper, we provide a literature review to explore the essential factors of teacher evaluation beliefs (why, what, who, when and how), followed by the key problems of Chinese New Curriculum Reform as “why-aim”, “what-content”, “who-student-teacher relationship”, “how-method” and “when- time”. In line with the discussion of five factors of evaluation beliefs, we proposed six perspectives to inform educational researchers for the further researches

Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu

Automatic speech recognition (ASR) is potentially helpful for children who suffer from dyslexia. Highly phonetically similar errors of dyslexic children‟s reading affect the accuracy of ASR. Thus, this study aims to evaluate acceptable accuracy of ASR using automatic transcription and phonetic labelling of dyslexic children‟s reading in BM. For that, three objectives have been set: first to produce manual transcription and phonetic labelling; second to construct automatic transcription and phonetic labelling using forced alignment; and third to compare between accuracy using automatic transcription and phonetic labelling and manual transcription and phonetic labelling. Therefore, to accomplish these goals methods have been used including manual speech labelling and segmentation, forced alignment, Hidden Markov Model (HMM) and Artificial Neural Network (ANN) for training, and for measure accuracy of ASR, Word Error Rate (WER) and False Alarm Rate (FAR) were used. A number of 585 speech files are used for manual transcription, forced alignment and training experiment. The recognition ASR engine using automatic transcription and phonetic labelling obtained optimum results is 76.04% with WER as low as 23.96% and FAR is 17.9%. These results are almost similar with ASR engine using manual transcription namely 76.26%, WER as low as 23.97% and FAR a 17.9%. As conclusion, the accuracy of automatic transcription and phonetic labelling is acceptable to use it for help dyslexic children learning using ASR in Bahasa Melayu (BM

Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems

E-Learning systems can support real-time monitoring of learners’ learning desires and effects, thus offering opportunities for enhanced personalized learning. Recognition of the determinants of dyslexic users’ motivation to use e-learning systems is important to help developers improve the design of e-learning systems and educators direct their efforts to relevant factors to enhance dyslexic students’ motivation. Existing research has rarely attempted to model dyslexic users’ motivation in e-learning context from a comprehensive perspective. The present work has conceived a hybrid approach, namely, combining the strengths of qualitative and quantitative analysis methods, to motivation modeling. It examines a variety of factors that affect dyslexic students’ motivation to engage in e-learning systems from psychological, behavioral, and technical perspectives, and establishes their interrelationships. Specifically, the study collects data from a multi-item Likert-style questionnaire to measure relevant factors for conceptual motivation modeling. It then applies both covariance-based (CB-SEM) and variance-based structural equation modeling (PLS-SEM) approaches to determine the quantitative mapping between dyslexic students’ continued use intention and motivational factors, followed by discussions about theoretical findings and design instructions according to our motivation model. Our research has led to a novel motivation model with new constructs of Learning Experience, Reading Experience, Perceived Control, and Perceived Privacy. From both the CB-SEM and PLS-SEM analyses, results on the total effects have indicated consistently that Visual Attractiveness, Reading Experience, and Feedback have the strongest effects on continued use intentio