Innovative Learning Environments in STEM Higher Education

As explored in this open access book, higher education in STEM fields is influenced by many factors, including education research, government and school policies, financial considerations, technology limitations, and acceptance of innovations by faculty and students. In 2018, Drs. Ryoo and Winkelmann explored the opportunities, challenges, and future research initiatives of innovative learning environments (ILEs) in higher education STEM disciplines in their pioneering project: eXploring the Future of Innovative Learning Environments (X-FILEs). Workshop participants evaluated four main ILE categories: personalized and adaptive learning, multimodal learning formats, cross/extended reality (XR), and artificial intelligence (AI) and machine learning (ML). This open access book gathers the perspectives expressed during the X-FILEs workshop and its follow-up activities. It is designed to help inform education policy makers, researchers, developers, and practitioners about the adoption and implementation of ILEs in higher education

Temporal multimodal video and lifelog retrieval

The past decades have seen exponential growth of both consumption and production of data, with multimedia such as images and videos contributing significantly to said growth. The widespread proliferation of smartphones has provided everyday users with the ability to consume and produce such content easily. As the complexity and diversity of multimedia data has grown, so has the need for more complex retrieval models which address the information needs of users. Finding relevant multimedia content is central in many scenarios, from internet search engines and medical retrieval to querying one's personal multimedia archive, also called lifelog. Traditional retrieval models have often focused on queries targeting small units of retrieval, yet users usually remember temporal context and expect results to include this. However, there is little research into enabling these information needs in interactive multimedia retrieval. In this thesis, we aim to close this research gap by making several contributions to multimedia retrieval with a focus on two scenarios, namely video and lifelog retrieval. We provide a retrieval model for complex information needs with temporal components, including a data model for multimedia retrieval, a query model for complex information needs, and a modular and adaptable query execution model which includes novel algorithms for result fusion. The concepts and models are implemented in vitrivr, an open-source multimodal multimedia retrieval system, which covers all aspects from extraction to query formulation and browsing. vitrivr has proven its usefulness in evaluation campaigns and is now used in two large-scale interdisciplinary research projects. We show the feasibility and effectiveness of our contributions in two ways: firstly, through results from user-centric evaluations which pit different user-system combinations against one another. Secondly, we perform a system-centric evaluation by creating a new dataset for temporal information needs in video and lifelog retrieval with which we quantitatively evaluate our models. The results show significant benefits for systems that enable users to specify more complex information needs with temporal components. Participation in interactive retrieval evaluation campaigns over multiple years provides insight into possible future developments and challenges of such campaigns

XR, music and neurodiversity: design and application of new mixed reality technologies that facilitate musical intervention for children with autism spectrum conditions

This thesis, accompanied by the practice outputs,investigates sensory integration, social interaction and creativity through a newly developed VR-musical interface designed exclusively for children with a high-functioning autism spectrum condition (ASC).The results aim to contribute to the limited expanse of literature and research surrounding Virtual Reality (VR) musical interventions and Immersive Virtual Environments (IVEs) designed to support individuals with neurodevelopmental conditions. The author has developed bespoke hardware, software and a new methodology to conduct field investigations. These outputs include a Virtual Immersive Musical Reality Intervention (ViMRI) protocol, a Supplemental Personalised, immersive Musical Experience(SPiME) programme, the Assisted Real-time Three-dimensional Immersive Musical Intervention System’ (ARTIMIS) and a bespoke (and fully configurable) ‘Creative immersive interactive Musical Software’ application (CiiMS). The outputs are each implemented within a series of institutional investigations of 18 autistic child participants. Four groups are evaluated using newly developed virtual assessment and scoring mechanisms devised exclusively from long-established rating scales. Key quantitative indicators from the datasets demonstrate consistent findings and significant improvements for individual preferences (likes), fear reduction efficacy, and social interaction. Six individual case studies present positive qualitative results demonstrating improved decision-making and sensorimotor processing. The preliminary research trials further indicate that using this virtual-reality music technology system and newly developed protocols produces notable improvements for participants with an ASC. More significantly, there is evidence that the supplemental technology facilitates a reduction in psychological anxiety and improvements in dexterity. The virtual music composition and improvisation system presented here require further extensive testing in different spheres for proof of concept

Conceptual Framework for Designing Virtual Field Trip Games

This thesis aimed to provide designing models to explore an alternative solution for a field trip when it becomes impossible for several reasons such as the limitation of cost and time. Virtual field trip games are relatively new means to create virtual field trips in game environments through adding game aspects to learning aspects to enhance the learning experience. The simple combining of game and learning aspects will not guarantee the desired effect of virtual field trips. Theoretical and logical connections should be established to form interweave between both aspects. This thesis proposes a designing framework by establishing three links between game design aspects and learning aspects. The three links are constructed by modelling: the experiential learning theory (ELT), the gameplay, and the game world. ELT modelling quantifies the theory into the internal economy mechanic and balances the levels of game task difficulty with the player’s ability through game machinations, game modelling links the learning process to gameplay, and world modelling connects field environment to game environment. The internal economy mechanic and its components (resources, internal mechanic, feedback loop), formulating equations to define generic player’s interactions and identify indicators to capture evidence of achievements via a mathematical (evaluation) model. The game modelling includes skill models to design two important high-order skills (decision-making and teamwork) and connects them to the evaluation model. The game world is modelled through defining its variables and relationships’ rules to connect both environments (game and field) expanding the evaluation model. The framework is supported by essential learning theories (ELT, task-based learning, some aspects of social learning) and pedagogical aspects (assessment, feedback, field-based structure, high-order skills) and connected to the key game elements (interaction, multimodal presentation, control of choice…etc) of field-based learning along with suitable game mechanics. The two research studies that were conducted as part of this thesis found that the designing framework is useful, usable, and provides connections between learning and game aspects and the designed VFTG based on the framework improved learning performance along with providing motivation and presence. This suggests the effectiveness of the framework

Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations

International audienc

An emotionally responsive AR art installation

In this paper, we describe a novel method of combining emotional input and an Augmented Reality (AR) tracking/display system to produce dynamic interactive art that responds to the perceived emotional content of viewer reactions and interactions. As part of the CALLAS project, our aim is to explore multimodal interaction in an Arts and Entertainment context. The approach we describe has been implemented as part of a prototype “showcase ” in collaboration with a digital artist designed to demonstrate how affective input from the audience of an interactive art installation can be used to enhance and enrich the aesthetic experience of the artistic work. We propose an affective model for combining emotionally-loaded participant input with aesthetic interpretations of interaction, together with a mapping which controls properties of dynamically generated digital art. 1

Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications