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

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

Instance-based learning following physician reasoning for assistance during medical consultation

Esta tesis de maestría presenta un sistema automático que modela el conocimiento clínico para seguir el razonamiento médico durante una consulta ambulatoria. Se aplica un método de aprendizaje basado en instancias para proporcionar sugerencias durante el registro en una historia clínica electrónica. El método de aprendizaje propuesto tiene en cuenta la base de conocimiento clínico de cada médico, para presentar sugerencias basadas en tipos de casos clínicos previamente definidos, y deducidos según una métrica de similitud específicamente diseñada. El sistema se valida en un escenario de uso real, con la participación de estudiantes avanzados de medicina de un curso de informática médica de la Universidad de la República, Uruguay. Los resultados demuestran que el sistema propuesto es 2.5x más eficiente que una herramienta empírica de referencia para sugerencias, y dos órdenes de magnitud más rápido que un método de aprendizaje Bayesiano, considerando un marco de referencia de 250 tipos de casos clínicos. Los resultados también demuestran que el método de aprendizaje es capaz de producir sugerencias en tiempos razonables, incluso cuando se procesan grandes volúmenes de datos. Una encuesta realizada a estudiantes avanzados de medicina destaca que el enfoque propuesto se considera apropiado para la práctica médica. Esta investigación introduce una estructura formal para representar con precisión el conocimiento clínico, que apoya a los principales flujos que ocurren durante las consultas médicas. También se proporciona un marco que permite implementar un sistema en tiempo real capaz de asistir a los médicos durante sus consultas, y que además ayuda a reducir el tiempo de registro.This Master Thesis presents an automatic system for modeling clinical knowledge to follow physicians reasoning in medical consultation. Instance-based learning is applied to provide suggestions when recording electronicmedical records. The proposed learning method takes into account the clinical knowledge base of a physician, in order to present suggestions based on previously-defined clinical case types, and deduced according to an ad-hoc similarity metric. The system is validated on a real case study involving advanced medical students of a Medical Informatics course at Universidad de la República, Uruguay. Results show that the proposed system is 2.5× more efficient than a base-line empirical tool for suggestions, and two orders of magnitude faster than a Bayesian learning method, when processing a testbed of 250 clinical casetypes. Results also demostrate that the learning method is able to produce suggestions in a reasonable time frame, even when processing large volumes of data. A survey performed on advanced medical students highlights that the proposed approach is considered appropriate for medical practices. The research introduces a formal structure to accurately represent clinical knowledge, supporting the main flows of medical consultations. A frame for implementing a real-time system for assisting physicians during medical consultations is also provided, which helps reducing the time needed to register medical consultations

Marshall Space Flight Center Research and Technology Report 2019

Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come

Self-beliefs in the introductory programming lab and game-based fantasy role-play

This thesis was submitted for the degree of Doctor of philosophy and awarded by Brunel University LondonIt is important for students to engage in adequate deliberate practice in order to develop programming expertise. However, students often encounter anxiety when they begin to learn. This can present a challenge to educators because such anxiety can influence practice behaviour. This thesis situates this challenge within the Control- Value Theory of Achievement Emotions, emphasising a need for domain-specific research and presenting new research tools which can be used to investigate the area. Analysis of data collected from three cohorts of introductory programming students on web programming (2011-12) and robot programming (2012-13 and 2013-14) courses show that programming self-concept and programming aptitude mindset can predict programming anxiety and that programming anxiety is negatively correlated with programming practice. However, levels of anxiety remained consistently high across this period. A method to enrich these psychological constructs through a multimedia-rich learning environment is proposed. Drawing upon the interplay between narrative reinforcement and procedural rhetoric that can be achieved in a fantasy role-play, students' self-concept can be enhanced. A double-blind randomised controlled trial demonstrates promising results, however small effect sizes suggest further research is needed

Development of a novel virtual environment for assessing cognitive function. Design, Development and Evaluation of a Novel Virtual Environment to Investigate Cognitive Function and Discriminate between Mild Cognitive Impairment and Healthy Elderly.

Alzheimer's disease (AD) is neurodegenerative disorder that causes memory loss and cognitive dysfunction. It affects one in five people over the age of 80 and is distressing for both sufferers and their families. A transitional stage between normal ageing and dementia including AD is termed a mild cognitive impairment (MCI). Recent studies have shown that people with MCI may convert to AD over time although not all MCI cases progress to AD. Much research is now focussing on early detection of AD and diagnosing an MCI that will progress to AD to allow prompt treatment and disease management before the neurons degenerate to a stage beyond repair. Hence, the ability to obtain a method of identifying MCI is of great importance. Virtual reality plays an important role in healthcare and offers opportunities for detection of MCI. There are various studies that have focused on detection of early AD using virtual environments, although results remain limited. One significant drawback of these studies has been their limited capacity to incorporate levels of difficulty to challenge users' capability. Furthermore, at best, these studies have only been able to discriminate between early AD and healthy elderly with about 80% of overall accuracy. As a result, a novel virtual simulation called Virtual Reality for Early Detection of Alzheimer's Disease (VREAD) was developed. VREAD is a quick, easy and friendly tool that aims to investigate cognitive functioning in a group of healthy elderly participants and those with MCI. It focuses on the task of following a route, since Topographical Disorientation (TD) is common in AD. An investigation was set up with two cohorts: non-elderly and elderly participants. The findings with regard to the non-elderly are important as they represent a first step towards implementation with elderly people. The results with elderly participants indicate that this simulation based assessment could provide a method for the detection of MCI since significant correlations between the virtual simulation and existing neuropsychological tests were found. In addition, the results proved that VREAD is comparable with well-known neuropsychological tests, such as Cambridge Neuropsychological Automated Test Battery, Paired Associate Learning (CANTAB PAL) and Graded Naming Test (GNT). Furthermore, analysis through the use of machine learning techniques with regard to the prediction of MCI also obtained encouraging results. This novel simulation was able to predict with about 90% overall accuracy using weighting function proposed to discriminate between MCI and healthy elderly.Ministry of Higher Education, Malaysia and University Sultan Zainal Abidin, Malaysia (UNisZa