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

Attention modeling using inputs from a Brain Computer Interface and user-generated data in Second Life

A model of attention in computer-based assessment exercise in Second Life is presented. Attention is measured considering psychometric inputs based on Electro Encephalogram (EEG) readings using NeuroSky technology. The model of attention considers the readings and combines them with user-generated, performance data [1] (giving-up, answer correctness and time spent) to determine states of attention and trigger strategies to improve or sustain an optimal level of attention. The novelty of this approach is in using NeuroSky technology to read attention levels and in combining this input with user-generated data taken from interaction. This model of attention is based on the ARCS [2,3] model of motivation and can be later integrated into a model of motivation [4] for virtual worlds learning. The paper discusses the feasibility of using attention to complement existing models of motivation [4] and outlines work for the future

Economic behavior of households and their impact on the development model of the country

The article analyzes the motivation of the behavior of households as the most important factor that determines the choice of their strategy. Consequently, this choice has an impact both on the economy and global development. The results of the study determined that the world has two large economic models: European and Asian ones. The first one is a European model based on the paradigm of maximizing the well-being and the satisfaction of material goods, which is reflected in high consumption and low savings. The second one is an Asian model based on the understanding of the achievements of the welfare of households on the basis of prestige in the eyes of others through the prism of education, religiosity or moral principles of society, is inherent to a greater extent developing countries. As a result of this principle in Asian countries households have a high level of savings. However educated people tend to realize themselves in countries with the European model, which is more attractive for them. As a result of these contradictions, the world has created an imbalance, in which Asian countries with high saving rates are the suppliers of human resources and creditors for countries with the European model of development. System approach including comparative, intercountry, index and econometric ones is used in the research.This article is supported by the Urals Branch of the Russian Academy of Sciences, Project 15-14-7-2 “Forecast assessment of modernization priorities of the Ural industrial region for expanding the import substitution.

A flexible framework for metacognitive modelling and development

Research in eLearning and technology enhanced learning (TEL) has predominantly focused on the creation of learning materials in appropriate forms, such as learning objects, the assessment methods that can usefully be applied online, and the delivery mechanisms for these materials, particularly in virtual learning environments (VLEs). In more recent times, research has begun to focus on pedagogical issues, and in particular whether there is some specific model that applies explicitly to online learning situations. Through a number of projects over the last ten years the authors have considered issues of learning style, learning strategy, pedagogy, immersive environments, student engagement and motivation, games-based learning, adaptation and personalisation. Emerging from this work, and from extensive consideration of the existing research in this area, this paper argues a need to move not only to a different pedagogic model, but also to change the existing structural approach to learning to support the rising demand for online distance learning provision worldwide. Fundamental to this argument is a need to support a heutagogic model of student learning, which requires that the students involved are sufficiently educationally mature to take control of their own learning experience. Whilst within traditional teaching models in higher education there is an explicit aspiration that students will emerge as educationally mature, metacognitive graduates, this is often seen as an outcome of the learning process itself, rather than as a skillset which can be taught and assessed. The paper describes an approach to metacognitive assessment that has already been used to determine the level and skills displayed by students in making selections of learning materials online. Based on this approach, a structural model for online learning support is proposed, using an assessment, feedback and training loop to ensure that students have the level of metacognitive skills necessary to take effective control of their own online learning experience

Building Student Capacity to Lead Sustainability Transitions in the Food System through Farm-Based Authentic Research Modules in Sustainability Sciences (FARMS)

Undergraduate courses provide valuable opportunities to train and empower students with the knowledge, skills, and motivation to advance society in more sustainable directions. This article emphasizes the value of bridging primary scientific research with undergraduate education through the presentation of an integrated experiential learning and primary research model called Farm-based Authentic Research Modules in Sustainability Sciences (FARMS). FARMS are collaboratively designed with agricultural stakeholders through a community needs assessment on pressing food system issues and opportunities with the objective for faculty and students to jointly identify evidence-based management solutions. We illustrate the implementation of FARMS in an undergraduate course in Ecological Agriculture at Dartmouth College, NH where students assessed various agroecological solutions for managing plant vitality, weeds, soil quality, pests, pollinators, and biodiversity at the Dartmouth Organic Farm. Student reflections indicate that the FARMS course component was beneficial for understanding agroecological theories and concepts while also motivating involvement in sustainability sciences despite the challenges of primary research. Educator reflections noted that the FARMS pedagogical approach facilitated achieving course objectives to develop students’ ability for systems thinking, critical thinking, and interdisciplinarity while fostering students’ collaboration skills and overall motivation for creating change. Adopting the FARMS model should enable faculty in the sustainability sciences to serve as bridges between the learning, practicing, and scientific communities while supporting educational programming at student and community farms. Ultimately, it is expected that the implementation of FARMS will increase student capacity and prepare the next generation of leaders to address complex challenges of the food system using an evidence-based approach

Regression-free Blind Image Quality Assessment

Regression-based blind image quality assessment (IQA) models are susceptible to biased training samples, leading to a biased estimation of model parameters. To mitigate this issue, we propose a regression-free framework for image quality evaluation, which is founded upon retrieving similar instances by incorporating semantic and distortion features. The motivation behind this approach is rooted in the observation that the human visual system (HVS) has analogous visual responses to semantically similar image contents degraded by the same distortion. The proposed framework comprises two classification-based modules: semantic-based classification (SC) module and distortion-based classification (DC) module. Given a test image and an IQA database, the SC module retrieves multiple pristine images based on semantic similarity. The DC module then retrieves instances based on distortion similarity from the distorted images that correspond to each retrieved pristine image. Finally, the predicted quality score is derived by aggregating the subjective quality scores of multiple retrieved instances. Experimental results on four benchmark databases validate that the proposed model can remarkably outperform the state-of-the-art regression-based models.Comment: 11 pages, 7 figures, 50 conference