Search Results (17)

The views and practices of teaching mathematics are significantly influenced by the cultural and social contexts, resulting in differences in teaching traditions among countries. Thus, when evaluating the effectiveness of professional development (PD) programs, it is crucial to consider differences in teaching traditions between PD participants and providers. There is limited research that examines PD participants’ acculturation strategies in such circumstances. This case study examines the influence of the PD program that introduced current teaching traditions in American mathematics education to Kazakhstan’s university mathematics faculty on their perceptions and practices of teaching discrete mathematics to aspiring mathematics teachers. The PD program focused on connecting abstract mathematical concepts to real-life applications, and integrating technology and STEM applications using inquiry-based strategies. The study findings indicate that, while PD enhanced faculty knowledge and attitudes toward technology integration, it did not significantly alter their views on teaching practices. A traditional teacher-centered approach persisted even when technology was incorporated, highlighting the deeply ingrained nature of educational traditions and their resilience to change. This underscores the importance of considering the cultural context and addressing deeply held beliefs in professional development initiatives, especially when aiming for substantial shifts in teaching practices. Full article

Accounts of revelation and contemporary views of these are based on beliefs and historical citations. These accounts shall not be limited to the understanding and interpreting of historical and other events within writings but must present the possibility of an objective analysis of the nature of revelation as a phenomenon, an object of our sensory and mental conscious experiences. This paper approaches the act or phenomenon of revelation regardless of the revealer and its nature. Can we abstract the revealer and the revealed from revelation and have an ontological account of revelation solely focusing on the occurrence itself? The central part of the discussion is based on the object/property pair as ontological categories through which the means are analyzed. A comparative method is used where Scripture, musical writings, and mathematical/physical formulae (as potential means of revelation) are scrutinized. As a result, without any need to determine the revealer, revelation can be based on and described through pure properties (not tropes) in human experience, intellect, and understanding. The possibility of revelation beyond Scripture and Jesus Christ—following a type of liberal and general theory of revelation—presents itself in arts and sciences. The “true” of a musical work, when found and experienced during musical performances and scientific truths represented by the formulae, which describe the world and a meta domain, can be derived from the chains of signs and symbols as it is through Scripture. Human cognitive faculties present a universal natural limit to our direct experiencing of the transcendent, of the supernatural. A new dualist conception of logos as a metaphysical category marks the domain bridging the non-transcendent with the transcendent. Full article

The concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch of Artificial Intelligence (AI) and develops algorithms that can learn from data and generalize their judgment to new observations by exploiting primarily statistical methods. The new millennium has seen the proliferation of Artificial Neural Networks (ANNs), a formalism able to reach extraordinary achievements in complex problems such as computer vision and natural language recognition. In particular, designers claim that this formalism has a strong resemblance to the way the biological neurons operate. This work argues that although ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science, at least from a methodological perspective. The main reason is that ML algorithms have notable prediction power although they cannot necessarily provide a causal explanation about the achieved predictions. For example, an ANN could be trained on a large dataset of consumer financial information to predict creditworthiness. The model takes into account various factors like income, credit history, debt, spending patterns, and more. It then outputs a credit score or a decision on credit approval. However, the complex and multi-layered nature of the neural network makes it almost impossible to understand which specific factors or combinations of factors the model is using to arrive at its decision. This lack of transparency can be problematic, especially if the model denies credit and the applicant wants to know the specific reasons for the denial. The model’s “black box” nature means it cannot provide a clear explanation or breakdown of how it weighed the various factors in its decision-making process. Secondly, this work rejects the belief that a machine can simply learn from data, either in supervised or unsupervised mode, just by applying statistical methods. The process of learning is much more complex, as it requires the full comprehension of a learned ability or skill. In this sense, further ML advancements, such as reinforcement learning and imitation learning denote encouraging similarities to similar cognitive skills used in human learning. Full article

The discussion about first-year engineering students’ difficulties in mathematics is continuous in the fields of engineering, mathematics and higher education. The present study aimed to examine the initial barriers academic math teachers need to have in mind if they want to improve students’ performance in engineering math courses through appropriate teaching practices in order to face their initial interindividual differences. During the first phase of the study, we examined first year engineering students’ initial beliefs about the nature of mathematics, their self-efficacy beliefs about mathematics and their basic mathematical knowledge. The math school grade was used for their previous mathematical performance. Results indicated the predominant role of the previous mathematical knowledge and the important role of the formalistic disposition toward mathematics. The lack of experience of using mathematics for problem-solving situations within an engineering framework prevented students from recognizing and appreciating the value of mathematical courses during the engineering studies. The second phase of the study examined, through an interview with a group of students, their perceptions of the teaching practices which were introduced after their teacher attended a training program. The discussion concentrates on how academics can use teaching processes for equity and not equality in order to motivate their students. Full article

Nature is composed of elements at various spatial scales, ranging from the atomic to the astronomical level. In general, human sensory experience is limited to the mid-range of these spatial scales, in that the scales which represent the world of the very small or very large are generally apart from our sensory experiences. Furthermore, the complexities of Nature and its underlying elements are not tractable nor easily recognized by the traditional forms of human reasoning. Instead, the natural and mathematical sciences have emerged to model the complexities of Nature, leading to knowledge of the physical world. This level of predictiveness far exceeds any mere visual representations as naively formed in the Mind. In particular, geometry has served an outsized role in the mathematical representations of Nature, such as in the explanation of the movement of planets across the night sky. Geometry not only provides a framework for knowledge of the myriad of natural processes, but also as a mechanism for the theoretical understanding of those natural processes not yet observed, leading to visualization, abstraction, and models with insight and explanatory power. Without these tools, human experience would be limited to sensory feedback, which reflects a very small fraction of the properties of objects that exist in the natural world. As a consequence, as taught during the times of antiquity, geometry is essential for forming knowledge and differentiating opinion from true belief. It not only provides a framework for understanding astronomy, classical mechanics, and relativistic physics, but also the morphological evolution of living organisms, along with the complexities of the cognitive systems. Geometry also has a role in the information sciences, where it has explanatory power in visualizing the flow, structure, and organization of information in a system. This role further impacts the explanations of the internals of deep learning systems as developed in the fields of computer science and engineering. Full article

To help prepare students to address future challenges in Science, Technology, Engineering, and Mathematics (STEM), they need to develop 21st-century skills. These skills are mediated by their beliefs about the nature of scientific knowledge and practices, or epistemological beliefs. One approach shown to support students’ development of these beliefs and skills is problem-based instruction (PBI), which encourages collaborative self-directed learning while working on open-ended problems. We used a mixed-method qualitative approach to examine how implementing PBI in a physics course taught at a Dutch university affected students’ beliefs about physics and learning physics. Analysis of the responses to the course surveys (41–74% response rates) from the first implementation indicated students appreciated opportunities for social interactions with peers and use of scientific equipment with PBI but found difficulties connecting to the Internet given the COVID-19 restrictions. The Colorado Learning Attitudes towards Science Survey (CLASS), a validated survey on epistemological beliefs about physics and learning physics, was completed by a second cohort of students in a subsequent implementation of PBI for the same course; analysis of the students’ pre- and post-responses (28% response rate) showed a slight shift towards more expert-like perspectives despite challenges (e.g., access to lab). Findings from this study may inform teachers with an interest in supporting the development of students’ epistemological beliefs about STEM and the implementation of PBI in undergraduate STEM courses. Full article

The spatial differences in the distribution of mosques reflect to a certain extent the diversity of the interaction between natural and human elements and Islamic beliefs in different geographic spaces. The Kashgar region of Xinjiang is one of the most developed regions of Islamic culture in China, its dominant religion is Islam, and the survival of Islamic culture in the region has a long history. The development of Islam in the region, after the founding of the People’s Republic of China, was influenced by the religious policy of Chinese Socialism, and the spatial distribution of mosques in the region has changed significantly. However, the distribution pattern of mosques in the spatial features of the region that had been especially indicated by the transformations in religious practice on the development of Islam impacted by geographical conditions and social factors has been less explored. Based on the Chinese Religious Digital Map dataset provided by the China Information Center at the University of Michigan, mathematical statistics and spatial analyses are used to analyze the spatial distribution pattern of mosques in the Kashgar region from 1955 to 2004, and the causes of the pattern characteristics in the context of the historical background of the study period. The results show that, during the study period, the spatial clustering of mosques occurred mainly in the northwestern and central parts of the Kashgar region. In all districts and counties, the number of mosques had increased and there was a growing gap in the number of mosques. Islam in the area had been well developed and the trend of spatially concentrated distribution of mosques had been increasing. The mosques in the region are mostly clustered in areas with gentle terrain, rivers and a dense population. In terms of the causes affecting the spatial distribution pattern of mosques in the Kashgar region, geographical conditions and population were the underlying factors that set the basic pattern for the location of mosques. In addition, the different effects of social factors, such as the improvement of productivity, the administrative system, religious management policies, and the historical background on the development of Islam in the area had led to a variation in the development of Islam, thus causing changes in the spatial distribution pattern of mosques in the area. In the period from 1976 to 1992, for example, the end of the Cultural Revolution and the shift in China’s foreign policy had a very major impact on Islam so that during this period the spatial distribution pattern of mosques varied the most in the area. This research has implications for learning about the spread of Islam in the Kashgar region after the founding of the People’s Republic of China, and the changes in the spatial distribution of mosques, and the causes of such variations. Full article

Recently there has been an ongoing discussion about energy security. This has been caused by tensions affecting international relations, and the emergence of new geopolitical threats. As one of the main sources of primary energy, natural gas is an obvious subject of interest in this discussion. In Europe, the natural gas market is rapidly evolving, which has resulted in a lack of clarity regarding who is responsible for the security of the gas supply. It is not clear now how to measure the security of the gas supply in economic estimates and by whom that security should be financed. In this paper, the authors present an approach which can be used for valuation of energy security concerning the security of natural gas storage using stochastic modelling based on the mathematical model of the “Newsvendor problem”. The valuation is made from the point of view of countries and considers their individual attitudes to the risk of disruption of deliveries, which is a novel approach to the problem. The authors believe that the current level of storage capacities, as compared to the demand for natural gas, can show the attitude of each country to the risk and potential cost of stockout. In line with this belief, the target value in the model is not the optimal level of inventory, but the cost of stockout. The results show significant variations in the assessment of the risk. The future of natural gas as an important fuel and source of primary energy in Europe is not clear and unfavorable changes have been sped up by the COVID-19 pandemic. Gas (energy) companies in Europe are turning to decarbonization and green energy, and the pandemic has accelerated these changes. European energy companies used to see the use of natural gas as a transition fuel and a key component of their long-term decarbonization strategies, but now they are switching to multi-energy models through massive investments in renewables and hydrogen. Experts expect that gas will remain an important part of Europe’s energy supply, but it may be gradually replaced by hydrogen and renewables. Full article

Due to the dynamic nature of the agricultural industry, educators and their institutions face difficult challenges as they try to keep pace with future demands for knowledge and skilled workers. On the other hand, computational thinking (CT) has drawn increasing attention in the field of science, technology, engineering, and mathematics (STEM) education at present and, as advanced technologies and tools emerge, it is imperative for such innovations to be sustained with knowledge and skill among STEM educators and practitioners. The present case study aims to explore the relation between CT, STEM and agricultural education training (AET) in a Greek vocational training institute (IEK), the Agriculture IEK of Metamorfosis city (IEKMC), which is active in agriculture education. The research methodology is utilized according the positivist philosophical approach through data acquisition employing a questionnaire and the quantitative (statistical) analysis of data collected. The sample consists of IEKMC educators and students selected based on simple random sampling. Based on the participants belief that CT and STEM philosophy add value in the learning process, it focuses on the application of knowledge in the real world (students) and problem solving using new technologies (educators). Educators consider “experiments” as the most significant educational tool for problem solving in teaching practice. Students rate Greek Agriculture Education and Training (GAET) higher than educators. However, the participants evaluate GAET very low due to the lack of new innovative teaching methods being introduced. Finally, there is great interest in the implementation of CT and STEM in the European Union (EU) by students and educators. Full article

Beliefs have been conceived as a hidden variable in mathematics education. It is important to know teachers’ beliefs as they can inform the way that teachers teach mathematics, make decisions in the classroom, and form opinions about the abilities of students. In Costa Rica, studies about beliefs have been conducted with in-service teachers, but there is no research on pre-service teachers and the beliefs they bring to the classroom from their teacher education programs (TEPs). This research aims to describe the beliefs held by 76 pre-service teachers and 19 teacher educators from four Costa Rican public universities, using the Teacher Education and Development Study in Mathematics (TEDS-M) questionnaire. The results suggest that both pre-service teachers and teacher educators believe in a constructivist orientation focused on the learner. Both groups support the view of mathematics as a process of inquiry and active learning and agree that mathematical skills are not fixed or associated with gender or culture. In the literature, the beliefs manifested by the participants are associated with positive results regarding student outcomes and teaching practices. Therefore, policymakers should be concerned with providing environments that allow and encourage teachers to continue with these belief orientations when they start teaching. Full article

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures. Full article

Preparation for outbreaks of emerging infectious diseases is often predicated on beliefs that we will be able to understand the epidemiological nature of an outbreak early into its inception. However, since many rare emerging diseases exhibit different epidemiological behaviors from outbreak to outbreak, early and accurate estimation of the epidemiological situation may not be straightforward in all cases. Previous studies have proposed considering the role of active asymptomatic infections co-emerging and co-circulating as part of the process of emergence of a novel pathogen. Thus far, consideration of the role of asymptomatic infections in emerging disease dynamics have usually avoided considering some important sets of influences. In this paper, we present and analyze a mathematical model to explore the hypothetical scenario that some (re)emerging diseases may actually be able to maintain stable, endemic circulation successfully in an entirely asymptomatic state. We argue that an understanding of this potential mechanism for diversity in observed epidemiological dynamics may be of considerable importance in understanding and preparing for outbreaks of novel and/or emerging diseases. Full article

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM_2.5 concentrations. The other one contains real images, PM_2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy. Full article

The pervasive and often uncritical acceptance of materialistic philosophical commitments within exercise science is deeply problematic. This commitment to materialism is wrong for several reasons. Among the most important are that it ushers in fallacious metaphysical assumptions regarding the nature of causation and the nature of human beings. These mistaken philosophical commitments are key because the belief that only matter is real severely impedes the exercise scientist’s ability to accurately understand or deal with human beings, whether as subjects of study or as data points to be interpreted. One example of materialist metaphysics is the assertion that all causation is physical- one lever moving another lever, one atom striking another atom, one brain state leading to another (Kretchmer, 2005). In such a world, human life is reduced to action and reaction, stimulus and response and as a result, the human being disappears. As such, a deterministic philosophy is detrimental to kinesiologists’ attempts to interpret and understand human behavior, for a materialistic philosophy, must ignore or explain away human motivation, human freedom and ultimately culture itself. In showing how mistaken these philosophic commitments are, I will focus on the sub-discipline of sport psychology for most examples, as that is the field of exercise science of which I am paradigmatically most familiar. It is also the field, when rightly understood that straddles the “two cultures” in kinesiology (i.e., the sciences and the humanities). In referencing the dangers of the materialistic conception of human beings for sport psychology, I will propose, that the materialist’s account of the natural world, causation and human beings stems from the unjustified and unnecessary rejection by the founders of modern science of the Aristotelian picture of the world (Feser, 2012). One reason that this mechanistic point of view, concerning human reality has gained ground in kinesiology is as a result of a previous philosophic commitment to quantification. As philosopher Doug Anderson (2002) has pointed out, many kinesiologists believe that shifting the discipline in the direction of mathematics and science would result in enhanced academic credibility. Moreover, given the dominance of the scientific narrative in our culture it makes it very difficult for us not to conform to it. That is, as Twietmeyer (2015) argued, kinesiologists do not just reject non-materialistic philosophic conceptions of the field, we are oblivious to their possibility. Therefore, I will propose two things; first, Aristotelian philosophy is a viable alternative to materialistic accounts of nature and causation and second, that Aristotle’s holistic anthropology is an important way to wake kinesiologists from their self-imposed philosophic slumber. Full article

The writings of J.G. Ballard respond to the sciences in multiple ways; as such his (early) writing may productively be discussed as science fiction. However, the theoretical discipline to which he publicly signalled most allegiance, psychoanalysis, is one whose status in relation to science is highly contested and complex. In the 1960s Ballard signalled publicly in his non-fiction writing a belief in psychoanalysis as a science, a position in keeping with psychoanalysis’ contemporary status as the predominant psychological paradigm. Various early Ballard stories enact psychoanalytic theories, while the novel usually read as his serious debut, The Drowned World, aligns itself allusively with an oft-cited depiction by Freud of the revelatory and paradigm-changing nature of the psychoanalytic project. Ballard’s enthusiastic embrace of psychoanalysis in his early 1960s fiction mutated into a fascinatingly delirious vision in some of his most experimental work of the late 1960s and early 1970s of a fusion of psychoanalysis with the mathematical sciences. This paper explores how this ‘Marriage of Freud and Euclid’ is played out in its most systematic form in The Atrocity Exhibition and its successor Crash. By his late career Ballard was acknowledging problems raised over psychoanalysis’ scientific status in the positivist critique of Karl Popper and the work of various combatants in the ‘Freud Wars’ of the 1990s; Ballard at this stage seemed to move towards agreement with interpretations of Freud as a literary or philosophical figure. However, despite making pronouncements reflecting changes in dominant cultural appraisals of Freud, Ballard continued in his later writings to extrapolate the fictive and interpretative possibilities of Freudian and post-Freudian ideas. This article attempts to develop a deeper understanding of Ballard’s ‘scientific’ deployment of psychoanalysis in The Atrocity Exhibition and Crash within the context of a more fully culturally-situated understanding of psychoanalysis’ relationship to science, and thereby to create new possibilities for understanding the meanings of Ballard’s writing within culture at large. Full article