A Framework for Students Profile Detection

Some of the biggest problems tackling Higher Education Institutions are students’ drop-out and academic disengagement. Physical or psychological disabilities, social-economic or academic marginalization, and emotional and affective problems, are some of the factors that can lead to it. This problematic is worsened by the shortage of educational resources, that can bridge the communication gap between the faculty staff and the affective needs of these students. This dissertation focus in the development of a framework, capable of collecting analytic data, from an array of emotions, affects and behaviours, acquired either by human observations, like a teacher in a classroom or a psychologist, or by electronic sensors and automatic analysis software, such as eye tracking devices, emotion detection through facial expression recognition software, automatic gait and posture detection, and others. The framework establishes the guidance to compile the gathered data in an ontology, to enable the extraction of patterns outliers via machine learning, which assist the profiling of students in critical situations, like disengagement, attention deficit, drop-out, and other sociological issues. Consequently, it is possible to set real-time alerts when these profiles conditions are detected, so that appropriate experts could verify the situation and employ effective procedures. The goal is that, by providing insightful real-time cognitive data and facilitating the profiling of the students’ problems, a faster personalized response to help the student is enabled, allowing academic performance improvements

Artistic value and spectators’ emotions in dance performances

Our experiences of art are framed by the context and the rules that apply to it. Not only the theatre where performing arts are enjoyed, but also art museums displaying paintings, sculptures, installations and other artistic objects, dictate the distance to the pieces and the type of sensory perception permitted to the visitors. How art is presented to us reflects traditional established settings that determine the conditions of our appreciation and that have become part of each art’s ontology. Moreover, what we are allowed to do in those public contexts, when confronted with art works, reflects the artistic value attached to them. But, in some cases, there seems to be a tension between the borders erected by categories of artistic value and the affective reactions by the perceivers. In this paper I will discuss contexts (original, transplanted and mediated) and perceptual conventions for dance art, and how these define spectators’ roles and impact on their emotional responses to dance performances. In particular, I will focus on negative reactions to dance art to argue that the use of moving human bodies presents specific affective challenges to audiences

What is resilience and how can it be assessed and enhanced in social workers?

A report submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of Philosophy by Published WorksThe outputs chosen for inclusion for this PhD by publication comprise seven articles published in peer reviewed journals, two book chapters, one research paper and two resource guides commissioned by professional bodies. These outputs explore two major themes. The first concerns the nature of resilience in social workers and identifies the inter- and intra-individual competencies associated with the concept. The second concerns how resilience and its underpinning competencies can be enhanced in social work education, both pre and post qualification. The report begins by contextualising the research within the existing literature, outlining my epistemological and methodological position and highlighting the importance of a pragmatic mixed-methods approach to research design, data collection and analysis. A critique of the outputs is subsequently provided together with a discussion of how I developed as a social work academic and a researcher during the research programme. Finally, the significance of the contribution to the body of social work knowledge provided by these outputs is demonstrated by identifying how the research has enhanced understanding of improving wellbeing in social workers though the development of a tool box of strategies to manage stress and foster resilience in social work training and practice

Developing an ontology of mechanisms of action in behaviour change interventions

Background: Behaviour change interventions can influence behaviours central to health and sustainability. To design better interventions, a strong evidence base about ‘why’ interventions work is needed, i.e., their mechanisms of action (MoAs). MoAs are often labelled and defined inconsistently across intervention reports, creating challenges for understanding interventions and synthesising evidence. An ontology can address this problem by providing a classification system that labels and defines classes for MoAs and their relationships. Aims: To develop an ontology of MoAs in behaviour change interventions, and to explore challenges in understanding MoAs and their links to behaviour change techniques (BCTs) Methods: Behavioural scientists’ challenges to understanding MoAs and BCT-MoA links were investigated using a thematic analysis (Study 1 [S1]). To help better understand MoAs, Studies 2-7 developed the MoA Ontology: (S2) Identifying and grouping MoAs from 83 behavioural theories; (S3) Converting the groupings into an ontology by drawing on relevant ontologies; (S4) Restructuring the ontology to be more usable and ontologically correct; (S5) Applying and refining the ontology to code MoAs in 135 intervention reports; (S6) Nine behavioural scientists reviewing the ontology’s comprehensiveness and clarity, informing revisions; (S7) Investigating the inter-rater reliability of researchers double-coding MoAs in reports using the ontology, informing changes to the ontology. Results: Study 1 suggested challenges to understanding broad and underspecified MoAs. To form the basis of a detailed ontology, Study 2 identified 1062 MoAs and formed 104 MoA groups. Building on these groups, Studies 3-7 created the MoA Ontology, which had 261 classes (e.g., ‘belief’) on seven hierarchical levels. Inter-rater reliability was ‘acceptable’ for researchers familiar with the ontology but lower for researchers unfamiliar with the ontology (Study 7). Conclusions: The developed ontology captured MoAs with greater detail than previous classification systems. With its clear class labels and definitions, the ontology provides a controlled vocabulary for MoAs

Pedagogies of presence : contemplative education across the disciplines in Aotearoa New Zealand : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Education at Massey University, Manawatū, New Zealand

Listed in 2020 Dean's List of Exceptional ThesesFigures are re-used with permission or in the public domain.This study investigated contemplative pedagogy and practice within New Zealand universities, in the form of both mindfulness interventions targeting wellness and connection, and classroom pedagogy fostering attentional, critical, and creative thinking. Little previous research had been undertaken on the topic in this country. The integrated research design developed for the project - Critical Realist Mixed Methods Sequential Explanatory Design (CRMMSED) - included two phases, an extensive exploratory survey phase (n = 258), and an intensive, in-depth interview phase (n = 22). Critical Realist abductive and dialectical analyses took place alongside statistical and thematic analyses. The findings show that educators incorporate contemplative methods to address pressing issues ranging from student stress to climate change. Most contemplative teaching takes place within extant disciplinary framings. Key entry points into academia are through reflective practice in the contexts of professional education, critical social justice teaching, and creative projects. The study suggests that contemplative education arises in response to complex social factors involving several disconnects - with nature, people, the self, and the capacity for self-transcendence. This emergence is an outworking of historical forces and a response to research showing the potential of contemplative education for ameliorating difficult problems

Affective Computing for Emotion Detection using Vision and Wearable Sensors

The research explores the opportunities, challenges, limitations, and presents advancements in computing that relates to, arises from, or deliberately influences emotions (Picard, 1997). The field is referred to as Affective Computing (AC) and is expected to play a major role in the engineering and development of computationally and cognitively intelligent systems, processors and applications in the future. Today the field of AC is bolstered by the emergence of multiple sources of affective data and is fuelled on by developments under various Internet of Things (IoTs) projects and the fusion potential of multiple sensory affective data streams. The core focus of this thesis involves investigation into whether the sensitivity and specificity (predictive performance) of AC, based on the fusion of multi-sensor data streams, is fit for purpose? Can such AC powered technologies and techniques truly deliver increasingly accurate emotion predictions of subjects in the real world? The thesis begins by presenting a number of research justifications and AC research questions that are used to formulate the original thesis hypothesis and thesis objectives. As part of the research conducted, a detailed state of the art investigations explored many aspects of AC from both a scientific and technological perspective. The complexity of AC as a multi-sensor, multi-modality, data fusion problem unfolded during the state of the art research and this ultimately led to novel thinking and origination in the form of the creation of an AC conceptualised architecture that will act as a practical and theoretical foundation for the engineering of future AC platforms and solutions. The AC conceptual architecture developed as a result of this research, was applied to the engineering of a series of software artifacts that were combined to create a prototypical AC multi-sensor platform known as the Emotion Fusion Server (EFS) to be used in the thesis hypothesis AC experimentation phases of the research. The thesis research used the EFS platform to conduct a detailed series of AC experiments to investigate if the fusion of multiple sensory sources of affective data from sensory devices can significantly increase the accuracy of emotion prediction by computationally intelligent means. The research involved conducting numerous controlled experiments along with the statistical analysis of the performance of sensors for the purposes of AC, the findings of which serve to assess the feasibility of AC in various domains and points to future directions for the AC field. The AC experiments data investigations conducted in relation to the thesis hypothesis used applied statistical methods and techniques, and the results, analytics and evaluations are presented throughout the two thesis research volumes. The thesis concludes by providing a detailed set of formal findings, conclusions and decisions in relation to the overarching research hypothesis on the sensitivity and specificity of the fusion of vision and wearables sensor modalities and offers foresights and guidance into the many problems, challenges and projections for the AC field into the future

Using Student Mood And Task Performance To Train Classifier Algorithms To Select Effective Coaching Strategies Within Intelligent Tutoring Systems (its)

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System\u27s (ITS) coaching strategy based on the student\u27s mood. As a step toward this goal, this study evaluated the relationships between each student\u27s mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student\u27s performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student\u27s affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student\u27s interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student\u27s mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle\u27s (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank\u27s (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables