From Theory to Intervention: Mapping Theoretically Derived Behavioural Determinants to Behaviour Change Techniques

Theory provides a helpful basis for designing interventions to change behaviour but offers little guidance on how to do this. This paper aims to illustrate methods for developing an extensive list of behaviour change techniques (with definitions) and for linking techniques to theoretical constructs. A list of techniques and definitions was generated from techniques published in two systematic reviews, supplemented by "brainstorming" and a systematic search of nine textbooks used in training applied psychologists. Inter-rater reliability of extracting the techniques and definitions from the textbooks was assessed. Four experts judged which techniques would be effective in changing 11 theoretical constructs associated with behaviour change. Thirty-five techniques identified in the reviews were extended to 53 by brainstorming and to 137 by consulting textbooks. Agreement for the 53 definitions was 74.7 per cent (15.4% cells completed and 59.3% cells empty for both raters). Agreement about the link between the 35 techniques and theoretical constructs was 71.7 per cent of 385 judgments (12.2% agreement that effective and 59.5% agreement that not effective). This preliminary work demonstrates the possibility of developing a comprehensive, reliable taxonomy of techniques linked to theory. Further refinement is needed to eliminate redundancies, resolve uncertainties, and complete technique definitions.Institute of Applied Health Sciences, Chief Scientist Office of the Scottish Government Health Directive, NHS NIHR Academic Unit Fundin

Developing and evaluating complex interventions: the new Medical Research Council guidance

<p><i>Evaluating complex interventions is complicated. The Medical Research Council's evaluation framework (2000) brought welcome clarity to the task. Now the council has updated its guidance</i></p> <p>Complex interventions are widely used in the health service, in public health practice, and in areas of social policy that have important health consequences, such as education, transport, and housing. They present various problems for evaluators, in addition to the practical and methodological difficulties that any successful evaluation must overcome. In 2000, the Medical Research Council (MRC) published a framework<sup>1</sup> to help researchers and research funders to recognise and adopt appropriate methods. The framework has been highly influential, and the accompanying BMJ paper is widely cited.<sup>2</sup> However, much valuable experience has since accumulated of both conventional and more innovative methods. This has now been incorporated in comprehensively revised and updated guidance recently released by the MRC (<a href="www.mrc.ac.uk/complexinterventionsguidance">www.mrc.ac.uk/complexinterventionsguidance</a>). In this article we summarise the issues that prompted the revision and the key messages of the new guidance. </p&gt

Adopting smart glasses responsibly: potential benefits, ethical, and privacy concerns with Ray-Ban stories

The adoption of innovative wearable technologies is potentially increasing as a new trend. Jumping into the augmented reality (AR) and Metaverse, Facebook (now known as Meta) launched smart glasses partnering with Ray-Ban sunglasses brand’s parent company EssilorLuxottica. Ray-Ban stories has several technical features for entertainment and socializing; more importantly, these features can be adopted in the future for more advanced wearable. However, these smart glasses also came with many ethical and privacy concerns along with their potential benefits. Furthermore, the unbridled deployment of these smart glasses brought several challenging questions for public social interaction when we will have more such devices in our lives. This short article has discussed the Ray-Ban stories’ ethical and privacy issues for social interaction and public places

Real-time hand interaction and self-directed machine learning agents in immersive learning environments

Integration of extended reality (XR) in education is becoming popular to transform the traditional classroom with immersive learning environments. The adoption of immersive learning is accelerating as an innovative approach for science and engineering subjects. With new powerful interaction techniques in XR and the latest developments in artificial intelligence, interactive and self-directed learning are becoming important. However, there is a lack of research exploring these emerging technologies research with kinesthetic learning or “hands-one learning" as a pedagogical approach using real-time hand interaction and agent-guided learning in immersive environments. This paper proposes a novel approach that uses machine learning agents to facilitate interactive kinesthetic learning in science and engineering education through real-time hand interaction in the virtual world. To implement the following approach, this paper uses a chemistry-related case study and presents a usability evaluation conducted with 15 expert reviewers and 2 subject experts. NASA task load index is used for cognitive workload measurement, and the technology acceptance model is used for measuring perceived ease of use and perceived usefulness in the evaluations. The evaluation with expert reviewers proposed self-directed learning using trained agents can help in the end-user training in learning technical topics and controller-free hand interaction for kinesthetic tasks can improve hands-on learning motivation in virtual laboratories. This success points to a novel research area where agents embodied in an immersive environment using machine learning techniques can forge a new pedagogical approach where they can act as both teacher and assessor

AGILEST approach: Using machine learning agents to facilitate kinesthetic learning in STEM education through real-time touchless hand interaction

There is an increasing interest in creating interactive learning applications using innovative interaction technologies, especially in STEM (Science, technology, engineering, and mathematics) subjects. Recent developments in machine learning have allowed for nearly perfect hand-tracking recognition, introducing a touchless modality for interaction within Augmented Reality (AR) environments. However, the research community has not explored the pedagogical approach of Kinesthetic Learning or “Learning by Doing”, hand tracking, and machine learning agents combined with Augmented Reality technology. Fundamentally, this exploration of touchless interaction technologies has taken on new importance in the new post-COVID world. Meanwhile, machine learning has gained attention for its ability to enhance personalized learning and play a vital new role as a virtual instructor. This paper proposes a novel approach called the AGILEST approach, which uses machine learning Agents to facilitate interactive kinesthetic learning in STEM education through touchless interaction. The first case study for this approach will be an AR learning application for chemistry. This application uses real-time touchless hand interaction for kinesthetic learning and uses a machine learning agent to act as both trainer and assessor of the user. The evaluation of this research has been conducted remotely through a usability study with expert reviewers, which includes 15 young researchers with peer-reviewed work in Human-Computer Interaction & AR and 2 subject experts STEM teachers at the secondary school level. The usability evaluation through NASA Task Load Index (NASA-TLX), Perceived Ease of Use (PUEU), and Perceived Usefulness (PU) with expert reviewers provide positive feedback about this approach for productive learning gain, engagement and interactiveness in learning STEM subjects

Resolution of the Abraham-Minkowski Controversy

The momentum of light inside ponderable media has an electromagnetic part and a mechanical part. The local and instantaneous density of the electromagnetic part of the momentum is given by the Poynting vector divided by the square of the speed of light in vacuum, irrespective of the nature of the electromagnetic fields or the local or global properties of the material media. The mechanical part of the momentum is associated with the action of the electromagnetic field on the atomic constituents of the media, as specified by the Lorentz law of force. Proper interpretation and application of the Maxwell-Lorentz equations within the material bodies as well as at their surfaces and interfaces is all that is needed to obtain a complete picture of the momentum of light, including detailed numerical values at each and every point in space and time. That the Abraham-Minkowski controversy surrounding the momentum of light inside material media has persisted for nearly a century is due perhaps to an insufficient appreciation for the completeness and consistency of the macroscopic Maxwell-Lorentz theory, inadequate treatment of the electromagnetic force and torque at the material boundaries, and an undue emphasis on the necessity of coupling the equations of electrodynamics to those of the theory of elasticity for proper treatment of mechanical momentum. The present paper reports the resolution of the Abraham-Minkowski controversy within the framework of the classical theory of electrodynamics, without resort to such complicating and ultimately unnecessary factors as pseudo-momentum, special surface forces, alternative energy-momentum tensors, and hidden momenta, that have caused so much confusion for such a long period of time.Comment: 13 pages, 4 figures, 13 equations, 59 reference

Current challenges and future research directions in augmented reality for education

The progression and adoption of innovative learning methodologies signify that a respective part of society is open to new technologies and ideas and thus is advancing. The latest innovation in teaching is the use of Augmented Reality (AR). Applications using this technology have been deployed successfully in STEM (Science, Technology, Engineering, and Mathematics) education for delivering the practical and creative parts of teaching. Since AR technology already has a large volume of published studies about education that reports advantages, limitations, effectiveness, and challenges, classifying these projects will allow for a review of the success in the different educational settings and discover current challenges and future research areas. Due to COVID-19, the landscape of technology-enhanced learning has shifted more toward blended learning, personalized learning spaces and user-centered approach with safety measures. The main findings of this paper include a review of the current literature, investigating the challenges, identifying future research areas, and finally, reporting on the development of two case studies that can highlight the first steps needed to address these research areas. The result of this research ultimately details the research gap required to facilitate real-time touchless hand interaction, kinesthetic learning, and machine learning agents with a remote learning pedagogy

The Student Movement Volume 107 Issue 16: Soul Lounge, Self-Care, and Stripple Breakfast Burritos: There\u27s Something for Everyone

HUMANS AU\u27s Favorite Classes, Solana Campbell Becoming Multilingual, Gloria Oh Greatest Gazebo Orders, Solana Campbell Interview with VP Nixon, Caryn Cruz ARTS & ENTERTAINMENT Currently: Babel, Terika Williams That 90\u27s Love: BSCF Soul Lounge, Skyler Campbell The Therapist, Marcel Mattox NEWS Experience Andrews University\u27s Community Adult Education, Gloria Oh Joyful Resilience: An Art Experience at AU, Solana Campbell Students Share Their African Heritage at Short Course, Andrew Francis IDEAS The Future of Self-Care, Katie Davis The Straw that Breaks the Camel\u27s Back? Beyoncé and the 2023 Grammys, Alexander J. Hess Death Toll Passes 41,000: Turkey and Syria Earthquakes, Abby Shim PULSE A Conversation with an NBA Physician, Reagan McCain Nick Bishop and Honduras, Interviewed by Abraham Bravo LAST WORD Intelligence, Artificial and Otherwise: A Reflection on Extended Cognition, ChatGPT, and Neurodivergence, Lily Burkehttps://digitalcommons.andrews.edu/sm-107/1015/thumbnail.jp