Hasil untuk "Education"

R. Nelson, E. Phelps

E. Kahn, L. Ramsey, R. Brownson et al.

P. Houts, C. Doak, L. G. Doak et al.

P. Jeffries

B. Clark

T. Schuller

Yi Lyu, Luke Dotson, Nic Draves et al.

In this paper, we take a close look at how CTF can be used in cybersecurity education. We divide the CTF competitions into four different categories, which are attack-based CTFs, defense-based CTFs, jeopardy CTFs and gamified and wargames CTFs. We start our analysis by summarizing the main characteristics of different CTF types. We then compare them with each other in both learning objectives and other aspects like accessibility. We conclude that combining all four CTF formats can help participants build one's cybersecurity knowledge. By doing that, we hope that our findings will provide some useful insights for future CTF educators.

Shweta Bahl, Ajay Sharma

Using nationally representative data for India, this paper examines the incidence of education occupation mismatch and returns to education and EOM for internal migrants while considering the heterogeneity among them. In particular, this study considers heterogeneity arising because of the reason to migrate, demographic characteristics, spatial factors, migration experience, and type of migration. The analysis reveals that there exists variation in the incidence and returns to EOM depending on the reason to migrate, demographic characteristics, and spatial factors. The study highlights the need of focusing on EOM to increase the productivity benefits of migration. It also provides the framework for minimizing migrants' likelihood of being mismatched while maximizing their returns to education.

Rubaina Khan, Tammy Mackenzie, Sreyoshi Bhaduri et al.

This autoethnographic study explores the need for interdisciplinary education spanning both technical and philosophical skills - as such, this study leverages whole-person education as a theoretical approach needed in AI engineering education to address the limitations of current paradigms that prioritize technical expertise over ethical and societal considerations. Drawing on a collaborative autoethnography approach of fourteen diverse stakeholders, the study identifies key motivations driving the call for change, including the need for global perspectives, bridging the gap between academia and industry, integrating ethics and societal impact, and fostering interdisciplinary collaboration. The findings challenge the myths of technological neutrality and technosaviourism, advocating for a future where AI engineers are equipped not only with technical skills but also with the ethical awareness, social responsibility, and interdisciplinary understanding necessary to navigate the complex challenges of AI development. The study provides valuable insights and recommendations for transforming AI engineering education to ensure the responsible development of AI technologies.

Yuhan He

This study examines the influence of employee education and health on firm-level Total Factor Productivity (TFP) in China, using panel data from A-share listed companies spanning from 2007 to 2022. The analysis shows that life expectancy and higher education have a significant impact on TFP. More optimal health conditions can result in increased productivity through decreased absenteeism and improved work efficiency. Similarly, higher levels of education can support technological adaptation, innovation, and managerial efficiency. Nevertheless, the correlation between health and higher education indicates that there may be a point where further improvements in health yield diminishing returns in terms of productivity for individuals with advanced education. These findings emphasise the importance of implementing comprehensive policies that improve both health and education, maximising their impact on productivity. This study adds to the current body of research by presenting empirical evidence at the firm-level in China. It also provides practical insights for policymakers and business leaders who want to improve economic growth and competitiveness. Future research should take into account wider datasets, more extensive health metrics, and delve into the mechanisms that contribute to the diminishing returns observed in the relationship between health and education.

Talha Enes Ayranci, Florent P. Audonnet, Gerardo Aragon-Camarasa et al.

Student engagement is one of the key challenges in robotics and artificial intelligence (AI) education. Tangible learning approaches, such as educational robots, provide an effective way to enhance engagement and learning by offering real-world applications to bridge the gap between theory and practice. However, existing platforms often face barriers such as high cost or limited capabilities. In this paper, we present Curio, a cost-effective, smartphone-integrated robotics platform designed to lower the entry barrier to robotics and AI education. With a retail price below $50, Curio is more affordable than similar platforms. By leveraging smartphones, Curio eliminates the need for onboard processing units, dedicated cameras, and additional sensors while maintaining the ability to perform AI-based tasks. To evaluate the impact of Curio on student engagement, we conducted a case study with 20 participants, where we examined usability, engagement, and potential for integrating into AI and robotics education. The results indicate high engagement and motivation levels across all participants. Additionally, 95% of participants reported an improvement in their understanding of robotics. Findings suggest that using a robotic system such as Curio can enhance engagement and hands-on learning in robotics and AI education. All resources and projects with Curio are available at trycurio.com.

W. Bang, J. T. Carlin, K. Kim et al.

<p>Winter precipitation types (WPTs) are controlled by many factors, including thermodynamic and microphysical processes. Therefore, realistically simulating interactions between precipitation particles and the atmosphere is important when diagnosing the WPT. In the present study, we analyze the performance of a modified version of the one-dimensional spectral bin model (SBM; version 1DSBM-19M) of Carlin and Ryzhkov (2019), which simulates the change in the physical characteristics of precipitation particles of various sizes as they fall from the cloud top to the ground and diagnoses surface WPTs. We compare the performance of the SBM and four other diagnostic methods that use the following variables: (1) atmospheric thickness, (2) wet-bulb temperature, (3) temperature and relative humidity, and (4) wet-bulb temperature and low-level lapse rate. Three reference WPTs (snow (SN), rain (RA), and RASN) are obtained from particle size velocity (PARSIVEL) disdrometer data using a newly proposed decision tree algorithm. The results show that the SBM has the highest overall hit rate for all cases among five diagnostic methods. In contrast, the hit rate of the SBM for each WPT shows lower performance for RA than for the other methods. These results indicate that the SBM simulations tend to underestimate melting compared to observations. We thus explore the effects of the SBM's microphysics scheme on the extent of melting in cases of misdiagnosed RA. An optimized SBM that uses the climatological snow density–diameter relationship for the Pyeongchang region produces an increased amount of melting and achieves improved skill scores compared to the current SBM, which uses a snow density–diameter relationship for the Colorado region.</p>

Xiangdong Xu, Lin Chen, Hewei Meng et al.

To explore the vibration transmission characteristics of jujube mechanical harvesting, and optimize the relationship between vibration input and dynamic response of jujube branches, the vibration characteristics simulation and layered vibration test of jujube branches were carried out. The jujube branch model was established by means of three-dimensional scanning and reverse reconstruction. The natural frequency and suitable vibration parameter range of the jujube branch model were obtained by simulation. Finally, the stratified vibration field experiment of jujube branch was carried out. The results show that there are multi-order natural frequencies of jujube branch in the range of 0–30 Hz. The typical vibration modes include the overall deformation of jujube branch, the deformation of unilateral branch and the deformation of the end of twigs. The resonance frequencies of the measuring points on different branches are mostly close, but the frequencies of the maximum peaks on different paths are different, which is often related to the branch path. The optimal working parameter combination under layered vibration is: the lower layer excitation frequency and amplitude are 5.80 Hz and 7.00 mm, the upper layer excitation frequency and amplitude are 15.60 Hz and 8.50 mm. Under this parameter combination, the acceleration of the measuring point on the fine branch is closest to the separation acceleration. Under this parameter combination, the average harvest rate is 88.74 %. The research can provide reference for the development of forest fruit vibration harvesting machinery.

S. K. Malick, V. Chavan, V. Chavan et al.

Geospatial technologies are rapidly emerging as pivotal tools for advancing sustainable urban and rural development through citizen empowerment in India and worldwide. This study systematically reviews peer-reviewed and grey literature to examine their integration with global frameworks, such as the SDGs, Paris Agreement, and Sendai Framework, while aligning with Indian initiatives like NAPCC, Smart Cities, Digital India, SVAMITVA, AMRUT, and the National Geospatial Policy 2022, with emphasis on the citizen as a crucial feedback factor. Employing thematic mapping and comparative analysis between the Global North and South, we evaluate applications in urban planning, mobility, energy, resilience, and health, highlighting platforms like PPGIS, VGI, Bhuvan, and 'Know Your DIGIPIN' for participatory data collection and decision-making.</p> <p>Our analysis reveals regional disparities in India, with the southern zone leading in innovation (35% adoption) and the eastern region focussing on disaster management (15%), along with global successes in disaster relief, welfare targeting, and immunisation tracking. Quantitative impacts include India's geospatial market growth to ₹63,000 crores by 2025 and AMRUT 2.0's rapid water and sewerage coverage expansion in many major cities. However, persistent challenges include technical knowledge gaps in academia, insufficient institutional support for geospatial startups, and barriers like low digital literacy and language limitations that restrict broader participation.</p> <p>We recommend enhanced geospatial education, open data policies, vernacular interfaces, and inclusive citizen science frameworks to bridge these gaps, foster equitable participation, and realise geospatial intelligence's full potential for resilient, data-driven sustainability.

Ruixue Zhang, Yunxiao Xie, Miao Zhang et al.

Abstract Myopia is a serious public health issue worldwide. Damage to retinal ganglion cells (RGCs) in the retina induces degeneration of the visual cortex, which is known as anterograde trans-synaptic degeneration (TSD). However, the role of TSD in myopia is still unknown. Here single-cell RNA-sequencing revealed the activation of RGC apoptotic signals in the retinal ganglion and the remodeling of synapses in the visual cortex in myopia. The thickness of the retinal nerve fiber layer was negatively correlated with the degree of damage to the visual cortex and damage to neurons in the visual pathway and to the synaptic structure and function of the visual cortex indicated the occurrence of anterograde TSD in the visual pathway. The knockdown of Fos, which inhibited retinal neuronal apoptosis, suppressed TSD, indicating that myopia can aggravate RGC apoptosis, induce anterograde TSD and thus aggravate synaptic remodeling. Our findings provide a new experimental basis for understanding the pathogenesis of myopia.

J. Sallis, T. McKenzie

Marlaine E. Lockheed

A. Gibb

Birthe Fritz, Dana Kube, Sonja Scherer et al.

Technology enhanced learning analytics has the potential to play a significant role in higher education in the future. Opinions and expectations towards technology and learning analytics, thus, are vital to consider for institutional developments in higher education institutions. The Sheila framework offers instruments to yield exploratory knowledge about stakeholder aspirations towards technology, such as learning analytics in higher education. The sample of the study consists of students (N = 1169) and teachers (N = 497) at a higher education institution in Germany. Using self-report questionnaires, we assessed students and teachers attitudes towards learning analytics in higher education teaching, comparing ideal and expected circumstances. We report results on the attitudes of students, teachers, as well as comparisons of the two groups and different disciplines. We discuss the results with regard to practical implications for the implementation and further developments of learning analytics in higher education.

Dauda Abdu, Almustapha Abdullahi Wakili, Lawan Nasiru et al.

Over a decade there has been a rapid growth in Nigerian educational system particularly higher education. Various institutions have come up both from public and private sector offering many of courses both under and post graduate students. Therefore, rates of students enroll for higher educational institutions in Nigeria have also increased. Hence it is very important to understand the roles play by data mining in analyzing the collected data of students and their academic progression. It is a concern for today's education system and this gap has to be identified and properly addressed to the learning community. Data Mining it helps in various ways to resolve issues face in predictions students and staff performances within Nigerian education system. This paperwork we discuss the roles of Data Mining tools and techniques which can be used effectively in resolving issues in some functional unit of Nigerian tertiary institutions.