Open Access Articles
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.
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.
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.
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.
For thousands of years, the compass-and-straightedge tools have dominated the learning and teaching of geometry. As such, these inherited, long-standing instruments have gained a lustre of naturalized pedagogical value. However, mounting evidence suggests that many learners and teachers struggle to efficiently, effectively and safely use compasses when constructing geometric figures. Compasses are difficult for learners to use, can lead to inaccurate drawings, and can be dangerous. Thus, there is value in reconsidering the role of the compass in the learning and teaching of geometric constructions and to offer better tools as alternatives. The purpose of this work is to address the aforementioned need by proposing an alternative tool to the compass that is safer, more efficient and more effective. We will argue that a circle arc template forms such an alternative tool, and we will illustrate how learners and teachers can add value to their classrooms by using it, in conjunction with a straightedge, to establish the well-known constructions seen in geometry curricula around the world.
Discussions about teaching area measurement in primary school have been ongoing over some decades. However, investigations that thoroughly examine the current research on conceptual understanding in area measuring in elementary schools are still lacking. The objective of this paper is to review whether conceptual knowledge in area measurement may support students to obtain better results in primary schools. This study is to gain insight into how conceptual knowledge in area measurement has been portrayed for primary school students, and reveal possible omissions and gaps in the synthesized literature on the subject. To gather information, two databases were used: Scopus and Web of Science. Primary searches pulled up many studies on the subject of investigation. After analyzing abstracts and eliminating duplicates, our systematic review indicates that there seems a direct link between conceptual understanding and area measurement in primary school mathematics. Hence, teaching children the principle of area measurement rather than a procedure for solving problems seems to be the most effective way of improving problem-solving skills and conceptual understanding for primary students.
Integration by parts can be applied in various ways for obtaining solutions for different types of integrations and hence it is taught in all calculus courses in the world. However, the coverage and discourse of various applications of integration by parts in most textbooks, often packed into one section, lack a cohesion of progression for solving different types of integrals. Students may be confused by such incohesive presentation of the method and applications in the textbooks. Based on the author's experiences and practices in teaching applied calculus for undergraduate engineering and education students since 2013, a streamlined approach in teaching integration by parts has been gradually developed to the current state and ready to be shared with the mathematics teaching and learning communities. This streamlined approach allows integration by parts to be applied to solve complicated and integrated problems in a progressive way so that students can improve efficacy in their use of integration by parts gradually. This approach also makes communications easier with students on particular problems involving integration by parts.
The redesign of national curricula across the Anglophone world since the 1990s is demonstrably shaped by common policy trends. Focusing on the profound and uncritiqued changes that have been implemented in New Zealand education, this paper provides a critical commentary on the characterising features of the current New Zealand mathematics curriculum, describing a context within which mathematics education at schools is severely compromised. Drawing on the evidence available from large-scale international indicators, such as PISA and TIMSS, to benchmark associated curriculum changes implemented by the New Zealand government, we hypothesise that the ongoing decline of student mathematical achievement is the result of four main interdependent features which characterise the New Zealand curriculum. The features are (1) its highly generic non-prescriptive nature, (2) a commitment to teacher autonomy in curriculum knowledge selection, (3) competency-based outcomes approach, and (4) a commitment to localisation in curriculum selection. Recognising socio-political forces and ideological and intellectual ideas associated with those forces, we discuss each characterising feature, in turn, to show how they contribute to and draw from the others to create a 'curriculum without content'. We conclude with explicit recommendations and a call for future studies to establish the extent to which each of these four features contributes to the decline of student achievement.
The integration of robotics education with science, technology, engineering, and mathematics (STEM) education has a great potential in future education. In recent years, numerous countries have hosted robotic competitions. This study uses a mixed research method to explore the coaches' views on student participation in the World Robot Olympiad (WRO) by incorporating the questionnaire surveys and interviews conducted at the 2019 WRO finals in Hungary. By quantitative and qualitative analyses, coaches generally agreed that participation in the WRO improved students' STEM learning skills and cultivated their patience and resilience in handling challenging tasks.
This study set out to evaluate an intervention that introduced a period of non-routine problem-solving into tertiary STEM lectures at four tertiary institutions in New Zealand for 683 students. The aim was twofold: to attempt to increase student engagement and to introduce them to the kind of domain-free abstract reasoning that involves critical, creative, and innovative thinking. This study was conducted using a mixed-methods approach, utilizing different types of instruments to gather data: comprehensive student pre- and post-test questionnaires, a content validation survey for the questionnaires, focus group interviews (student participants), open-ended questionnaire (lecturer participants), and naturalistic class observations. The main findings are as follows. Students' behavioural engagement was significantly greater during the intervention. Perceptions of the utility value of the activity improved at the end of the semester for all students. There were no significant changes in students' convergent thinking (problem-solving), intuition, or creativity (originality, fluency, and elaboration traits of the divergent thinking) during the course, probably due to the relatively short timescale of the intervention. However, overall, the results of the investigation suggest that with a relatively small effort, teachers can improve STEM student engagement by devoting a few minutes per lecture on non-routine problem-solving. This is something that can be easily implemented, even by those who primarily teach in a traditional lecturing style.
The goal of this paper is to examine the place of modelling in STEM education and teacher education. First, we introduce modelling as a cyclical process of generating, testing, and applying knowledge while highlighting the epistemological commonalities and differences between the STEM disciplines. Second, we build on the four well-known frameworks, to propose an Educational Framework for Modelling in STEM, which describes both teacher and student roles in the modelling cycle. Third, we use this framework to analyze how modelling is presented in the new mathematics and science school curricula in two Canadian provinces (Ontario and British Columbia), and how it could be implemented in teacher education. Fourth, we emphasize the epistemological aspects of the Educational Framework for Modelling in STEM, as disciplinary epistemological foundations may seem too abstract to both teacher educators and teachers of STEM school subjects. Yet, epistemologies are the driving forces within each discipline and must be considered while teaching STEM as a unified field. To nurture critical thinkers and innovators, it is critical to pay attention to what knowledge is and how it is created and tested. The Educational Framework for Modelling in STEM may be helpful in introducing students and future teachers to the process of modelling, regardless of if they teach it in a single- or a multi-discipline course, such as STEM. This paper will be of interest to teacher educators, teachers, researchers, and policy makers working within and between the STEM fields and interested in promoting STEM education and its epistemological foundations.
With the rise of the COVID-19 pandemic and its inevitable consequences in education, increased demand for robust online learning frameworks has occurred at all levels of the education system. Given the transformative power of Artificial Intelligence (AI) and machine learning algorithms, there have been determined attempts through the design and application of intelligent tools to overcome existing challenges in online learning platforms. Accordingly, educational providers and researchers are investigating and developing intelligent online learning environments which share greater commonalities with real-world classroom conditions in order to better meet learners' needs. However, short attention spans and the widespread use of smart devices and social media bring about new e-learning systems known as microlearning (ML). While there has been ample research investigating ML and developing micro-content, pedagogical challenges and a general lack of alternative frameworks, theories and practices still exist. The present models have little to say about the connections between social interaction, including learner–content, learner–instructor and learner–learner communication. This has prompted us to investigate the complementary aspects of Computer-supported Collaborative Learning (CSCL) as an interactive learning model, along with an embedded ML module in the design and development of a comprehensive learning platform. The purpose of this study is to explore the pedagogical frameworks and challenges with reference to interaction and retention in online learning environments, as well as the theoretical and pedagogical foundations of ML and its applications. In addition, we delve into the theories and principles behind CSCL, the main elements in CSCL, identifying the issues and challenges to be faced in improving the efficacy of collaboration processes and outcomes. In short, we aim to synthesize how microlearning and CSCL can be applied as effective modules within a comprehensive online learning platform, thereby offering STEM educators a relevant roadmap towards progress that has yet to be offered in previous studies.
The Laplace transform is a popular approach in solving ordinary differential equations (ODEs), particularly solving initial value problems (IVPs) of ODEs. Such stereotype may confuse students when they face a task of solving ODEs without explicit initial condition(s). In this paper, four case studies of solving ODEs by the Laplace transform are used to demonstrate that, firstly, how much influence of the stereotype of the Laplace transform was on student's perception of utilizing this method to solve ODEs under different initial conditions; secondly, how the generalization of the Laplace transform for solving linear ODEs with generic initial conditions can not only break down the stereotype but also broaden the applicability of the Laplace transform for solving constant-coefficient linear ODEs. These case studies also show that the Laplace transform is even more robust for obtaining the specific solutions directly from the general solution once the initial values are assigned later. This implies that the generic initial conditions in the general solution obtained by the Laplace transform could be used as a point of control for some dynamic systems.
Robot and programming education, as a key part of STEM education, is attracting more and more attention in the education industry. In this paper, a novel open-sourced educational robotic platform, Daran robot, is proposed with key features in terms of reconfigurable, powerful, and affordable. As an entry-level robotic platform, the Daran robot consists of three individual robots, which are a Mecanum-wheeled robot, a three-wheeled robot, and a 4-DoF robot arm. Both graphical and Python programming environments are developed for students with different entry levels. Thanks to the reconfigurability, four classic constructions of the Daran robot are presented with corresponding case studies, based on which the students can practically learn basic knowledge of sensing and control technologies.
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This commentary is an extension to the integrated S-T-E-M Quartet Instructional Framework that has been used to guide the design, implementation and evaluation of integrated STEM curriculum. In our discussion of the S-T-E-M Quartet, we have argued for the centrality of complex, persistent and extended problems to reflect the authenticity of real-world issues and hence, the need for integrated, as opposed to monodisciplinary, STEM education. Building upon this earlier work, we propose two additional variationsjsolution-centric and user-centric approachesjto the provision of integrated STEM curricular experiences to afford more opportunities that address the meta-knowledge and humanistic knowledge developments in 21st century learning. These variations to the S-T-E-M Quartet aims to expand the scope and utility of the framework in creating curriculum experiences for diverse profiles of learners, varied contextual conditions, and broad STEM education goals. Collectively, these three approachesjproblem-centric, solution-centric, and user-centricjcan afford more holistic outcomes of STEM education.
Industries that use fruits as raw materials must, at some point in the process, classify them to discard the unsuitable ones and thus ensure the quality of the final product. To produce mango nectar, it is necessary to ensure that the mango is mature enough to start the extraction of the nectar; however, sorting thousands of mangoes may require many people, who can easily lose attention and reduce the accuracy of the result. Such kind of decision can be supported by current Artificial Intelligence techniques. The theoretical details of the processing are presented, as well as the programming code of the neural network using SCILAB as a computer language; the code includes the color extraction from mango images. SCILAB programming is simple, efficient and does not require computers with large processing capacity. The classification was validated with 30 images (TIF format) of Manila variety mango; the mangoes were placed on a blue background to easily separate the background from the object of interest. Four and six mangoes were used to train the neural network. This application of neural networks is part of an undergraduate course on artificial intelligence, which shows the potential of these techniques for solving real and concrete problems.
STEM (science, technology, engineer, mathematics) education and engineering education are receiving an increasing amount of interest worldwide, but related research on the influence of STEM courses on students' engineering problem solving in China is scarce. Considering the rapid prototyping function of laser-cutting tools, this study was conducted to develop a STEM course based on laser cutting and to explore how the course affected high school students' engineering problem-solving abilities. A 9-week curriculum was implemented in a science, technology, and fabricating club of a high school in Zhejiang, China. The data were collected by pretest and posttest questionnaires and presentations of group assignments. The results were as follows. First, when presented with an engineering problem, the students demonstrated problem-solving abilities because they followed principles of engineering design, such as sketching, modeling and modifying. Second, while completing the assignment, the students proposed solutions with comprehensive factors in many aspects. They showed high-level thinking, such as consideration of the background, limiting conditions, and multidisciplinary knowledge, and they used technological tools to complete the task. However, some students ignored the assessment and redesign of their solutions. Further research could use a larger sample from different grades and explore how a STEM course combined with technology tools could influence students' high-level thinking skills.
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