Contemporary Cypriot video art: an investigation of artistic practice and its educational implications for the visual arts curriculum

This qualitative project concentrates on the creative research processes of contemporary Cypriot video artists and on their interrelation with the field of visual arts education, examined through the triple role of artist/researcher/teacher. The project contains evidence of the achievement of a tangible research product in the form of an Educational Guide, accompanied by a DVD collection as a creative outcome that presents, in 10 DVDs, the video profiles of 10 local artists with a selection of their video artwork. The project adopts a pluralistic research methodology, and identifies and presents multiple results that are extracted from the artists’ case studies, together with a self-study concerning artistic research approaches to video art-making. The results are transformed through a hermeneutical and semiotic approach into educational suggestions for the employment of video art as an art form, and video as a medium into the visual arts educational context. The body of knowledge presented contributes to three major areas: the documentation and accessibility of the artistic practices of contemporary living Cypriot artists, the understanding of their artistic research processes, and the attribution of pedagogical value to video art’s content and context through the creation of educational materials that consider the availability of the artists’ video works. The outcome of the project is intended for general and visual arts educators, artists, art historians and gallery and museum professionals who wish to study the insights of video art in Cyprus through an audio-visual presentation. The overall contribution of the project to professional practice is summarised in the bridging of the gap between the sister fields of visual arts and contemporary visual arts education, by transforming everyday artistic practice in appropriate material for pedagogical contexts

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

Analytics of self-regulated learning: a temporal and sequential approach

In educational settings, the increasingly sophisticated use of digital technology has provided students with greater agency over their learning. This has focused educational research on the metacognitive and cognitive activities with which students engage to manage their learning and the achievement of their learning goals. This field of research is articulated as self-regulated learning (SRL) and has seen the development of several key theoretical models. Despite key differences, these models are broadly defined by thematic variations of the same fundamental phases: i) a preparatory phase; ii) a performance phase, and; iii) an appraisal phase. Given the phasic nature of these models, the conceptualisation of SRL as a phenomenon that unfolds in temporal space has gained much traction. In acknowledging this dimension of SRL, researchers are bound to address the methodological demands of process, sequence, and temporality. Learning Analytics research, however, is largely characterised by the use of statistical models for data interrogation and analysis. Despite their value, several researchers posit that the use of statistical methods imposes ontological limitations with respect to the temporal and sequential nature of SRL. Another challenge is that while learner data are mostly collected at the micro level, (e.g., page access, video view, quiz attempt), SRL theory is defined at a macro level (e.g., planning, monitoring, evaluation), highlighting a need to bridge this gap in order to provide meaningful results. This thesis aims to explore the methodological opportunities and address the theoretical challenges presented in the area of temporally focused SRL learning analytics. First, the thesis explores the corpus of research in the area. As such, we present a systematic review of literature that analyses the findings of studies that explore SRL through the lenses of order and sequence, to provide insights into the temporal dynamics of SRL. Second, the thesis demonstrates the use of a novel process mining method to analyse how certain temporal activity traits relate to academic performance. We determined that more strategically minded activity, embodying aspects self-regulation, generally demonstrated to be more successful than less disciplined reactive behaviours. Third, the thesis presents a methodological framework designed to embed our analyses in a model of SRL. It comprises the use of: i) micro-level processing to transform raw trace data into SRL processes; and ii) first order Markov models to explore the temporal associations between SRL processes. We call this the “Trace-SRL” framework. Fourth, using the Trace-SRL framework, the thesis explores the deployment of multiple analytic methods and posits that richer insights can be gained through a combined methodological perspective. Fifth, building on this theme, the thesis presents a systematic analysis of four process mining algorithms, as deployed in the exploration of common SRL event data, concluding that the choice of algorithm and metric is of key importance in temporally-focused SRL research, and that combined metrics can provide deeper insights than those presented individually. Finally, the thesis concludes with a discussion of practical implications, the significance of the results, and future research directions

Students learning science: Representation construction in a digital environment

This thesis showed the viable digital delivery for a representation-focused approach for teaching science. This study has led to guidelines for a generative digital design and sequencing of representational tasks and resources. It has also illustrated students’ collaborative reasoning processes during a problem-solving task, reflective of an authentic scientific inquiry

Somatics, creativity, and choreography: creative cognition in somatics-based contemporary dance

This doctoral thesis will comprise an in-depth, multidisciplinary and mixed methods research project examining creativity in Somatics-based choreographic practices. The project draws on methodologies from phenomenology, ethnography, close reading, grounded theory, and thematic analysis. It involved data collected from three well-known Somatics practitioners who embody a professional hybridity as artists, authors, and Somatic Movement Educators—Sandra Reeve, Andrea Olsen, and Miranda Tufnell—and who each use their somatic practice as instrumental in their choreography. Each practitioner utilises different Somatics modalities (Move into Life, Authentic Movement, Embodied Anatomy, Alexander Technique, among others) in various settings (higher education, community arts, professional practice, etc.), which provides an international (US and UK) and cross modality scope to examine shared ideologies within Somatics. Data was collected in a semi-structured, open-ended interview process, participant observation of workshops and intensives delivered by the artists, and a close reading of their published texts. It was analysed for emergent shared themes. I posit that these themes identify connections between the identification, definition, and facilitation of creativity within Somatics-based choreographic practice and cognitive psychological theories of creativity. I identify shared elements of the pedagogical environment and argue that they facilitate the development of a refined perceptual ability. This perceptual expertise is presented as a change-agent in facilitating both novelty in movement generation and the generation of meaning, allowing for a discerning, selective retention of this movement material in giving form to that meaning choreographically. Situating the processes within the Interacting Cognitive Subsystems model and theories of embodied cognition, I then propose a philosophical audit-trace of the ways in which this meaning and expertise is developed cognitively in somatic practices, and how that expertise may allow for novelty and creativity in choreography. The research closes with a discussion of implications of my proposal, how understanding these pathways might be instrumental in shaping dance pedagogy to facilitate dancers’ creativity, and what directions this theory produces for future research

Facial expression recognition in the wild : from individual to group

The progress in computing technology has increased the demand for smart systems capable of understanding human affect and emotional manifestations. One of the crucial factors in designing systems equipped with such intelligence is to have accurate automatic Facial Expression Recognition (FER) methods. In computer vision, automatic facial expression analysis is an active field of research for over two decades now. However, there are still a lot of questions unanswered. The research presented in this thesis attempts to address some of the key issues of FER in challenging conditions mentioned as follows: 1) creating a facial expressions database representing real-world conditions; 2) devising Head Pose Normalisation (HPN) methods which are independent of facial parts location; 3) creating automatic methods for the analysis of mood of group of people. The central hypothesis of the thesis is that extracting close to real-world data from movies and performing facial expression analysis on movies is a stepping stone in the direction of moving the analysis of faces towards real-world, unconstrained condition. A temporal facial expressions database, Acted Facial Expressions in the Wild (AFEW) is proposed. The database is constructed and labelled using a semi-automatic process based on closed caption subtitle based keyword search. Currently, AFEW is the largest facial expressions database representing challenging conditions available to the research community. For providing a common platform to researchers in order to evaluate and extend their state-of-the-art FER methods, the first Emotion Recognition in the Wild (EmotiW) challenge based on AFEW is proposed. An image-only based facial expressions database Static Facial Expressions In The Wild (SFEW) extracted from AFEW is proposed. Furthermore, the thesis focuses on HPN for real-world images. Earlier methods were based on fiducial points. However, as fiducial points detection is an open problem for real-world images, HPN can be error-prone. A HPN method based on response maps generated from part-detectors is proposed. The proposed shape-constrained method does not require fiducial points and head pose information, which makes it suitable for real-world images. Data from movies and the internet, representing real-world conditions poses another major challenge of the presence of multiple subjects to the research community. This defines another focus of this thesis where a novel approach for modeling the perception of mood of a group of people in an image is presented. A new database is constructed from Flickr based on keywords related to social events. Three models are proposed: averaging based Group Expression Model (GEM), Weighted Group Expression Model (GEM_w) and Augmented Group Expression Model (GEM_LDA). GEM_w is based on social contextual attributes, which are used as weights on each person's contribution towards the overall group's mood. Further, GEM_LDA is based on topic model and feature augmentation. The proposed framework is applied to applications of group candid shot selection and event summarisation. The application of Structural SIMilarity (SSIM) index metric is explored for finding similar facial expressions. The proposed framework is applied to the problem of creating image albums based on facial expressions, finding corresponding expressions for training facial performance transfer algorithms

The student-produced electronic portfolio in craft education

The authors studied primary school students’ experiences of using an electronic portfolio in their craft education over four years. A stimulated recall interview was applied to collect user experiences and qualitative content analysis to analyse the collected data. The results indicate that the electronic portfolio was experienced as a multipurpose tool to support learning. It makes the learning process visible and in that way helps focus on and improves the quality of learning. © ISLS.Peer reviewe

Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs