This paper presents three qualitative models that were developed for the Stargazing Live! program. This program consists of a mobile planetarium that aims to inspire and motivate learners using real telescope data during the experience. To further consolidate the learning experience three lessons are available that teachers can use as follow up activities with their learners. The lessons implement a pedagogical approach that focuses on learning by creating qualitative models with the aim to have learners learn subject specific concepts as well as generic systems thinking skills. The three lessons form an ordered set with increasing complexity and were developed in close collaboration with domain experts.
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According to the critics of conventional sustainability models, particularly within the business context, it is questionable whether the objective of balancing the social, economic and environmental triad is feasible, and whether human equality and prosperity (as well as population growth) can be achieved with the present rate of natural degradation (Rees 2009). The current scale of human economic activity on Earth is already excessive; finding itself in a state of unsustainable ‘overshoot’ where consumption and dissipation of energy and material resources exceed the regenerative and assimilative capacity of supportive ecosystems (Rees 2012). Conceptualizing the current ‘politics of unsustainability’, reflected in mainstream sustainability debates, Blühdorn (2011) explores the paradox of wanting to ‘sustain the unsustainable, noting that the socio-cultural norms underpinning unsustainability support denial of the gravity of our planetary crises. This denial concerns anything from the imminence of mass extinctions to climate change. As Foster (2014) has phrased it: ‘There was a brief window of opportunity when the sustainability agenda might, at least in principle, have averted it’. That agenda, however, has failed. Not might fail, nor even is likely to fail – but has already failed. Yet, instead of acknowledging this failure and moving on from the realization of the catastrophe to the required radical measures, the optimists of sustainable development and ecological modernization continue to celebrate the purported ‘balance' between people, profit and planet. This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in "A Future Beyond Growth: Towards a Steady State Economy" on 4/14/16 ,available online: https://doi.org/10.4324/9781315667515 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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We present the Stargazing Live! program comprising a planetarium experience and supporting lesson activities for pre-university physics education. The mobile planetarium aims to inspire and motivate learners using real telescope data during the experience. Learners then consolidate their learning by creating conceptual models in the DynaLearn software. During development of the program, content experts and stakeholders were consulted. Three conceptual model lesson activities have been created: star properties, star states and the fusion-gravity balance. The present paper evaluates the planetarium experience plus the star properties lesson activity in nine grade 11 and 12 classes across three secondary schools in the Netherlands. Learners are very positive about the planetarium experience, but they are less able to link the topics in the planetarium to the curriculum. The conceptual modelling activity improves the learners understanding of the causal relationship between the various stellar properties. Future work includes classroom testing of the star states and fusion-gravity balance lessons.
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There are three volumes in this body of work. In volume one, we lay the foundation for a general theory of organizing. We propose that organizing is a continuous process of ongoing mutual or reciprocal influence between objects (e.g., human actors) in a field, whereby a field is infinite and connects all the objects in it much like electromagnetic fields influence atomic and molecular charged objects or gravity fields influence inanimate objects with mass such as planets and stars. We use field theory to build what we now call the Network Field Model. In this model, human actors are modeled as pointlike objects in the field. Influence between and investments in these point-like human objects are explained as energy exchanges (potential and kinetic) which can be described in terms of three different types of capital: financial (assets), human capital (the individual) and social (two or more humans in a network). This model is predicated on a field theoretical understanding about the world we live in. We use historical and contemporaneous examples of human activity and describe them in terms of the model. In volume two, we demonstrate how to apply the model. In volume 3, we use experimental data to prove the reliability of the model. These three volumes will persistently challenge the reader’s understanding of time, position and what it means to be part of an infinite field. http://dx.doi.org/10.5772/intechopen.99709
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In this workshop we present three lesson activities to teach core (astro)physics concepts at pre-university level which students find difficult to grasp with traditional interventions: star properties, star states and the fusion-gravity balance. In each activity, students construct and simulate a conceptual cause-effect model. An evaluation study in nine Dutch classrooms showed that the star properties lesson significantly increased students’ understanding of the underlying causal relationships. The lessons were created as part of the Stargazing Live! project, which inspires students with an interactive planetarium lesson incorporating real astrophysical data before triggering deep learning with the conceptual modelling activities.
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Stargazing Live! aims to capture the imagination of learners with a combination of live and interactive planetarium lessons, real astronomical data, and lessons built around interactive knowledge representations. The lessons were created using a co-creation model and tackle concepts in the pre-university (astro)physics which students find difficult to grasp with traditional interventions. An evaluation study in 9 Dutch classrooms showed that learners are inspired and engaged by the planetarium lessons but are not always able to link the content to the classroom. Pre- and post-tests showed that the accompanying star properties activity significantly increased learners’ understanding of the causal relationships between mass and other properties (such as luminosity, gravity, and temperature) in a main sequence star.
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As artificial intelligence (AI) reshapes hiring, organizations increasingly rely on AI-enhanced selection methods such as chatbot-led interviews and algorithmic resume screening. While AI offers efficiency and scalability, concerns persist regarding fairness, transparency, and trust. This qualitative study applies the Artificially Intelligent Device Use Acceptance (AIDUA) model to examine how job applicants perceive and respond to AI-driven hiring. Drawing on semi-structured interviews with 15 professionals, the study explores how social influence, anthropomorphism, and performance expectancy shape applicant acceptance, while concerns about transparency and fairness emerge as key barriers. Participants expressed a strong preference for hybrid AI-human hiring models, emphasizing the importance of explainability and human oversight. The study refines the AIDUA model in the recruitment context and offers practical recommendations for organizations seeking to implement AI ethically and effectively in selection processes.
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BackgroundA modified version of the Berg Balance Scale (mBBS) was developed for individuals with intellectual and visual disabilities (IVD). However, the concurrent and predictive validity has not yet been determined.AimThe purpose of the current study was to evaluate the concurrent and predictive validity of the mBBS for individuals with IVD.MethodFifty-four individuals with IVD and Gross Motor Functioning Classification System (GMFCS) Levels I and II participated in this study. The mBBS, the Centre of Gravity (COG), the Comfortable Walking Speed (CWS), and the Barthel Index (BI) were assessed during one session in order to determine the concurrent validity. The percentage of explained variance was determined by analyzing the squared multiple correlation between the mBBS and the BI, COG, CWS, GMFCS, and age, gender, level of intellectual disability, presence of epilepsy, level of visual impairment, and presence of hearing impairment. Furthermore, an overview of the degree of dependence between the mBBS, BI, CWS, and COG was obtained by graphic modelling. Predictive validity of mBBS was determined with respect to the number of falling incidents during 26 weeks and evaluated with Zero-inflated regression models using the explanatory variables of mBBS, BI, COG, CWS, and GMFCS.ResultsThe results demonstrated that two significant explanatory variables, the GMFCS Level and the BI, and one non-significant variable, the CWS, explained approximately 60% of the mBBS variance. Graphical modelling revealed that BI was the most important explanatory variable for mBBS moreso than COG and CWS. Zero-inflated regression on the frequency of falling incidents demonstrated that the mBBS was not predictive, however, COG and CWS were.ConclusionsThe results indicated that the concurrent validity as well as the predictive validity of mBBS were low for persons with IVD.
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An important performance determinant in wheelchair sports is the power exchanged between the athletewheelchair combination and the environment, in short, mechanical power. Inertial measurement units (IMUs) might be used to estimate the exchanged mechanical power during wheelchair sports practice. However, to validly apply IMUs for mechanical power assessment in wheelchair sports, a well-founded and unambiguous theoretical framework is required that follows the dynamics of manual wheelchair propulsion. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to demonstrate the use of the IMU-based power estimates during wheelchair propulsion based on experimental data. Mechanical power during straight-line wheelchair propulsion on a treadmill was estimated using a wheel mounted IMU and was subsequently compared to optical motion capture data serving as a reference. IMU-based power was calculated from rolling resistance (estimated from drag tests) and change in kinetic energy (estimated using wheelchair velocity and wheelchair acceleration). The results reveal no significant difference between reference power values and the proposed IMU-based power (1.8% mean difference, N.S.). As the estimated rolling resistance shows a 0.9–1.7% underestimation, over time, IMU-based power will be slightly underestimated as well. To conclude, the theoretical framework and the resulting IMU model seems to provide acceptable estimates of mechanical power during straight-line wheelchair propulsion in wheelchair (sports) practice, and it is an important first step towards feasible power estimations in all wheelchair sports situations.
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In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7◦ root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.
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