A growing number of studies seek to evaluate the impact of school closures during the ongoing COVID-19 pandemic. While most studies reported severe learning losses in students, some studies found positive effects of school closures on academic performance. However, it is still unclear which factors contribute to the differential effects observed in these studies. In this article, we examine the impact of assignment strategies for problem sets on the academic performance of students (n ≈ 16,000 from grades 4-10 who calculated ≈ 170,000 problem sets) in an online learning environment for mathematics, during the first and second period of pandemic-related school closures in Germany. We observed that, if teachers repeatedly assigned single problem sets (i.e., a small chunk of on average eight mathematical problems) to their class, students' performance increased significantly during both periods of school closures compared to the same periods in the previous year (without school closures). In contrast, our analyses also indicated that, if teachers assigned bundles of problem sets (i.e., large chunks) or when students self-selected problem sets, students' performance did not increase significantly. Moreover, students' performance was generally higher when single problem sets were assigned, compared to the other two assignment types. Taken together, our results imply that teachers' way of assigning problem sets in online learning environments can have a positive effect on students' performance in mathematics.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10155976 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0284868 | PLOS |
J Chem Theory Comput
January 2025
Technische Universitát Berlin, Institut für Chemie, Theoretische Chemie/Quantenchemie, Sekr. C7, Straße des 17. Juni 135, Berlin D-10623, Germany.
Local hybrid functionals (LHs) use a real-space position-dependent admixture of exact exchange (EXX), governed by a local mixing function (LMF). The systematic construction of LMFs has been hampered over the years by a lack of exact physical constraints on their valence behavior. Here, we exploit a data-driven approach and train a new type of "n-LMF" as a relatively shallow neural network.
View Article and Find Full Text PDFMayo Clin Proc Digit Health
December 2024
Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
View Article and Find Full Text PDFSci Rep
January 2025
Jiangxi Tellhow Power Technology Co., Ltd, Nanchang, 330031, China.
This paper presents a surrogate-assisted global and distributed local collaborative optimization (SGDLCO) algorithm for expensive constrained optimization problems where two surrogate optimization phases are executed collaboratively at each generation. As the complexity of optimization problems and the cost of solutions increase in practical applications, how to efficiently solve expensive constrained optimization problems with limited computational resources has become an important area of research. Traditional optimization algorithms often struggle to balance the efficiency of global and local searches, especially when dealing with high-dimensional and complex constraint conditions.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Mathematics, Harbin Institute of Technology, Weihai, China. Electronic address:
Nonsmooth nonconvex optimization problems are pivotal in engineering practice due to the inherent nonsmooth and nonconvex characteristics of many real-world complex systems and models. The nonsmoothness and nonconvexity of the objective and constraint functions bring great challenges to the design and convergence analysis of the optimization algorithms. This paper presents a smooth gradient approximation neural network for such optimization problems, in which a smooth approximation technique with time-varying control parameter is introduced for handling nonsmooth nonregular objective functions.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Xi'an Aerospace Chemical Propulsion Co., Ltd., Xi'an 710089, China.
In this paper, we propose an optimal parking path planning method based on numerical solving, which leverages the concept of the distance between convex sets. The obstacle avoidance constraints were transformed into continuous, smooth nonlinear constraints using the Lagrange dual function. This approach enables the determination of a globally optimal parking path while satisfying vehicular kinematic constraints.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!