May 2022 , Volume 2 , Issue 2
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Fluids′ viscous behavior is apparent in many everyday life situations, for example, in squeezing shampoo from a bottle or spooning honey from a jar. As a result, it is quite reasonable to assume that students develop (pre)conceptions to explain such phenomena even before they enter kindergarten or elementary school. As yet, however, empirical studies on children′s conceptions regarding the viscous behavior of fluids are remarkably scarce. The present study aims to address this research gap on an exploratory level. More precisely, we conducted a qualitative interview study in which we explored the conceptions about the viscous behavior of honey among N = 6 preschool children attending their final year in a kindergarten in Hamburg (Germany). For stimulating the conversation during the interviews, an easily noticeable phenomenon in which the viscous behavior of honey can be observed (dropping two identical spoons into a honey-filled and a water-filled glass) was demonstrated to the participating children. In summary, the analysis of the transcribed interviews revealed three distinguishable conceptions of the children about the viscous behavior of honey: (1) The viscous behavior of honey results from its stickiness, (2) from its additional physical characteristics, and (3) from its use in everyday life. In this Express Letter, we present the design and results of our study in detail. Recommendations for future research in science education are outlined at the end of this paper.
Motivation is a key factor for success in education and modern working life. Cross-cultural environment is a challenge to it and, if not taken into account, it can impair learning outcome and lead to high turnover rates in companies. We performed an ethnographic study in two Chinese companies expanded to Europe and observed what problems the organizations faced. Our finding is that main problems originate from cultural differences between Chinese and Western organizations, and that they are mostly explained by the different power distance in the two cultures. The host company has a steep hierarchy of the organization, and it did not delegate the decision making to the locals. This led to frustration, loss of motivation, and high turnover rate.
Spare-view CT imaging is advantageous to decrease the radiation exposure, acquisition time and computational cost, but suffers from severe streak noise in reconstruction if the classical filter back projection method is employed. Although a few compressed sensing based algorithms have recently been proposed to remedy the insufficiency of projections and have achieved remarkable improvement in reconstruction quality, they face computational challenges for large-scale CT images (e.g., larger than 2000℅2000 pixels). In this paper, we present a fast non-uniform Fourier transform based reconstruction method, targeting at under-sampling high resolution Synchrotron-based micro-CT imaging. The proposed method manipulates the Fourier slice theorem to avoid the involvement of large-scale system matrices, and the reconstruction process is performed in the Fourier domain. With a total variation penalty term, the proposed method can be formulated into an unconstrained minimization problem, which is able to be efficiently solved by the limited-memory BFGS algorithm. Moreover, direct non-uniform Fourier transform is computationally costly, so the developed NUFFT algorithm is adopted to approximate it with little loss of quality. Numerical simulation is implemented to compare the proposed method with some other competing approaches, and then real data obtained from the Australia Synchrotron facility are tested to demonstrate the practical applications of the proposed approach. In short, the significance of the proposed approach includes (1) that it can handle high-resolution CT images with millions of pixels while several other contemporary methods fail; (2) that it can achieve much better reconstruction quality than other methods when the projections are insufficient.
Tertiary education faced unprecedented disruption resulting from COVID-19 driven lockdowns around the world, leaving educators with little understanding of how the pandemic and consequential shift to online environments would impact students′ learning. Utilising the theoretical framework of a student′s affective field, this study aimed to investigate how student achievement, achievement-related affect, and self-perceived well-being contributed to predicting how their learning was impacted. Questionnaire responses and academic achievement measures from students (N = 208) in a New Zealand second-year, tertiary mathematics course were analysed. Despite a return to in-person teaching after eliminating community-transmission of the virus, students reported larger impacts of the disruption to semester on both their learning and well-being at the end of the term than during the lockdown. Hierarchical multiple regression revealed that gender, prior achievement, performance on low-stakes assessment, as well as exam-related self-efficacy and hope, made significant, independent contributions to explaining students′ perceived learning impact. Even when controlling for achievement and achievement-related affect, students′ perceived impact to their well-being made a significant and substantial contribution to the impact on their learning. The findings provide motivation to further investigate whether attempts to address student achievement-related affect can help mitigate the effects of major life disruptions on studying. We suggest that frequent, low-stakes assessment can identify students who are more likely to report greater negative impacts to their learning. We finally conclude that student well-being is paramount to how students perceive their own learning, even when controlling for actual measures of and about their achievement.
The COVID-19 pandemic has accelerated innovations for supporting learning and teaching online. However, online learning also means a reduction of opportunities in direct communication between teachers and students. Given the inevitable diversity in learning progress and achievements for individual online learners, it is difficult for teachers to give personalized guidance to a large number of students. The personalized guidance may cover many aspects, including recommending tailored exercises to a specific student according to the student′s knowledge gaps on a subject. In this paper, we propose a personalized exercise recommendation method named causal deep learning (CDL) based on the combination of causal inference and deep learning. Deep learning is used to train and generate initial feature representations for the students and the exercises, and intervention algorithms based on causal inference are then applied to further tune these feature representations. Afterwards, deep learning is again used to predict individual students′ score ratings on exercises, from which the Top-N ranked exercises are recommended to similar students who likely need enhancing of skills and understanding of the subject areas indicated by the chosen exercises. Experiments of CDL and four baseline methods on two real-world datasets demonstrate that CDL is superior to the existing methods in terms of capturing students′ knowledge gaps in learning and more accurately recommending appropriate exercises to individual students to help bridge their knowledge gaps.
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