Identifying Dyscalculia Symptoms Related to Magnocellular Reasoning using Smartphones

This thesis presents a research that has developed and evaluated a software application for smartphones, called MagnoMath. MagnoMath has been developed for assisting diagnosis of dyscalculia, a learning disability in mathematics, and for identifying dyscalculia symptoms possibly related to magnocellular reasoning. Typical software aids developed for individuals with learning disabilities are focused on both assisting diagnosis and teaching the material. The software developed in this project however maintains a specific focus on the former, while attempting to capture alleged correlations between dyscalculia symptoms and possible underlying causes of the condition. This was achieved by applying k-Nearest Neighbor algorithm classifying five parameters used in evaluating users' mathematical and magnocellular reasoning. Evaluation results were then utilized to support diagnosis and measure correlations between performances across the two task categories. To test MagnoMath's validity, an experiment involving seven dyscalculic and four non-dyscalculic volunteers was conducted. Results show that the software was able to reveal dyscalculic behavior. A strong correlation between deficiencies in arithmetic and spatial reasoning was identified, revealing a possible dependency valuable for detecting early signs of developmental dyscalculia. Learning disability experts at Linköping University, Sweden, University of Oslo, Norway and Dyslexia Norway have found the application to be appropriate and valuable for its intended purposes. Thus, results prove that mobile software is a suitable and valuable tool for assisting dyscalculia diagnosis and identifying possible root causes of developing the condition. Additional evaluation would be necessary to further test mobile softwares' ability to confirm the theory of magnocellular reasoning's involvement in developing mathematical deficiencies.INFO390MASV-INF

Dyslexia and Second Language Learning. State of the art and future perspectives at Pisa University.

In this thesis, I focus my attention on the problems connected with English language learning by dyslexic students, concentrating on the university system and, in particular, on the present situation and on future perspectives at Pisa University. I further described the difficulties caused by the study of a foreign language, in particular English, through the answers given by five students of the University of Pisa to a questionnaire that I gave them to complete. As the answers to my questionnaire prove, English language learning is an extremely challenging task for a dyslexic student. They find it hard to apply the grammar rules, to write and comprehend written texts and to speak in English and exhibit problems in the memorization of lexicon and in the correct pronunciation of foreign words. The main objective of this thesis is to find adequate methods and didactic instruments to let dyslexic students reach a good level in second language learning, focusing the attention, in particular, on the university system

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

How do medical doctors with specific learning difficulties (SpLD) cope in a clinical setting?

There have been a small number of studies conducted with qualified nurses and nursing/medical students with Specific Learning Difficulties (SpLD), showing that SpLD may affect those in the medical profession in relation to clinical tasks, career choices and success. There has been an increase in the number of medical students with SpLD (Gibson and Leinster, 2011) entering the medical profession, yet there is very limited research available regarding doctors who have SpLD. This research aimed to explore how doctors with SpLD are coping in the workplace, the coping strategies used, any perceived possible areas of strength or weakness, and any practices for ensuring patient safety. It also explored the perceptions held by doctors with SpLD about the impact this has on their working life, and factors that could influence their career choice and success. These areas were also explored in relation to medical students. A mixed methods approach was used for this project, consisting of 3 studies incorporating quantitative questionnaires with 129 doctors (Study 1) and 74 medical students (Study 3) with and without SpLD and qualitative telephone interviews with 11 doctors with SpLD (Study 2). The results showed that the main difficulties participants with SpLD experienced were in relation to written work and associated tasks, such as spelling. These were often overcome through the use of coping strategies. The doctors rated their clinical skills highly and described strengths such as communication skills and empathy. The doctors were selective when deciding who to disclose their SpLD to. In conclusion this project showed that SpLD do have a number of effects on doctors in the workplace. Through the implementation of coping strategies and support doctors were able to successfully overcome any difficulties experienced, indicating that having SpLD did not prevent individuals from becoming successful doctors

Identifying Dyscalculia Symptoms Related to Magnocellular Reasoning using Smartphones

This thesis presents a research that has developed and evaluated a software application for smartphones, called MagnoMath. MagnoMath has been developed for assisting diagnosis of dyscalculia, a learning disability in mathematics, and for identifying dyscalculia symptoms possibly related to magnocellular reasoning. Typical software aids developed for individuals with learning disabilities are focused on both assisting diagnosis and teaching the material. The software developed in this project however maintains a specific focus on the former, while attempting to capture alleged correlations between dyscalculia symptoms and possible underlying causes of the condition. This was achieved by applying k-Nearest Neighbor algorithm classifying five parameters used in evaluating users' mathematical and magnocellular reasoning. Evaluation results were then utilized to support diagnosis and measure correlations between performances across the two task categories. To test MagnoMath's validity, an experiment involving seven dyscalculic and four non-dyscalculic volunteers was conducted. Results show that the software was able to reveal dyscalculic behavior. A strong correlation between deficiencies in arithmetic and spatial reasoning was identified, revealing a possible dependency valuable for detecting early signs of developmental dyscalculia. Learning disability experts at Linköping University, Sweden, University of Oslo, Norway and Dyslexia Norway have found the application to be appropriate and valuable for its intended purposes. Thus, results prove that mobile software is a suitable and valuable tool for assisting dyscalculia diagnosis and identifying possible root causes of developing the condition. Additional evaluation would be necessary to further test mobile softwares' ability to confirm the theory of magnocellular reasoning's involvement in developing mathematical deficiencies

Psychological Engagement in Choice and Judgment Under Risk and Uncertainty

Theories of choice and judgment assume that agents behave rationally, choose the higher expected value option, and evaluate the choice consistently (Expected Utility Theory, Von Neumann, & Morgenstern, 1947). However, researchers in decision-making showed that human behaviour is different in choice and judgement tasks (Slovic & Lichtenstein, 1968; 1971; 1973). In this research, we propose that psychological engagement and control deprivation predict behavioural inconsistencies and utilitarian performance with judgment and choice. Moreover, we explore the influences of engagement and control deprivation on agent’s behaviours, while manipulating content of utility (Kusev et al., 2011, Hertwig & Gigerenzer 1999, Tversky & Khaneman, 1996) and decision reward (Kusev et al, 2013, Shafir et al., 2002)