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Peer Instruction (PI) and Continuous Assessment(CA) are two distinct educational techniques with extensive research demonstrating their effectiveness. The work herein combines PI and CA in a deliberate and novel manner to pair students…

Computers and Society · Computer Science 2024-07-26 Steve Geinitz

Computation, the use of a computer to solve, simulate, or visualize a physical problem, has revolutionized how physics research is done. Computation is used widely to model systems, to simulate experiments, and to analyze data. Yet, in most…

Physics Education · Physics 2015-11-18 Marcos D. Caballero

In computer simulation of the learning process is usually assumed that all elements of the training material are assimilated equally durable. But in practice, the knowledge, which a student uses in its operations, are remembered much…

Computers and Society · Computer Science 2013-12-20 Robert V Mayer

Project Based Learning (PBL), recognized as an active learning strategy, has been linked to self efficacy of student in prior studies, including those within Physics Education Research. Meanwhile, technological advancements have…

Physics Education · Physics 2026-02-17 Mutmainna , Edi Istiyono , Haryanto , Beta Wulan Febriana

Modeling Instruction (MI), an active-learning introductory physics curriculum, has been shown to improve student academic success. Peer-to-peer interactions play a salient role in the MI classroom. Their impact on student interest and…

Physics Education · Physics 2022-10-26 Remy Dou , Eric Brewe , Geoff Potvin , Justyna P. Zwolak , Zahra Hazari

One of the greatest research challenges of this century is to understand the neural basis for how behavior emerges in brain-body-environment systems. To this end, research has flourished along several directions but have predominantly…

Neurons and Cognition · Quantitative Biology 2021-06-10 Madhavun Candadai

Physics-informed deep learning has been developed as a novel paradigm for learning physical dynamics recently. While general physics-informed deep learning methods have shown early promise in learning fluid dynamics, they are difficult to…

Fluid Dynamics · Physics 2024-06-07 Jing Qiu , Jiancheng Huang , Xiangdong Zhang , Zeng Lin , Minglei Pan , Zengding Liu , Fen Miao

Gradient-based meta-learning algorithms have gained popularity for their ability to train models on new tasks using limited data. Empirical observations indicate that such algorithms are able to learn a shared representation across tasks,…

Machine Learning · Computer Science 2025-01-09 Hui Wang , Cho Tung Yip , Bo Li

Questions of participant understanding of the nature of an activity have been addressed in anthropology and sociolinguistics with the concepts of frames and framing. For example, a student may frame a learning activity as an opportunity for…

Physics Education · Physics 2007-12-04 Rachel E. Scherr , David Hammer

We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of the model on the current training…

Numerical Analysis · Mathematics 2024-07-01 Amanda Howard , Yucheng Fu , Panos Stinis

The objective of our study is to ascertain the present learning behaviors, driving forces, and assessment techniques as perceived by first-year students, and to examine them through the lens of the most recent developments (pandemic, shift…

Computers and Society · Computer Science 2024-07-16 Manuela Andreea Petrescu , Tudor Dan Mihoc

Physics Education Research (PER) has made significant progress in developing knowledge about teaching and learning as well as effective instructional strategies based on this knowledge. Disseminating knowledge and strategies to other…

Physics Education · Physics 2007-05-23 Charles Henderson , Andrea Beach , Michael Famiano

Accurately predicting fluid dynamics and evolution has been a long-standing challenge in physical sciences. Conventional deep learning methods often rely on the nonlinear modeling capabilities of neural networks to establish mappings…

Machine Learning · Computer Science 2025-04-09 Huaguan Chen , Yang Liu , Hao Sun

Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences. In this survey, we begin by providing an example of this with…

Machine Learning · Computer Science 2023-04-04 Artur P. Toshev , Ludger Paehler , Andrea Panizza , Nikolaus A. Adams

This paper describes the development of a new university physics course designed to integrate physics, education, research, and community partnerships. The coordinated system of activities links the new course to local community efforts in…

Physics Education · Physics 2007-05-23 N. D. Finkelstein

Many mathematical models of synaptic plasticity have been proposed to explain the diversity of plasticity phenomena observed in biological organisms. These models range from simple interpretations of Hebb's postulate, which suggests that…

Neurons and Cognition · Quantitative Biology 2025-08-05 Danil Tyulmankov

Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are sample efficient, and interpretable but often rely on rigid assumptions. Furthermore, direct numerical approximation…

Machine Learning · Computer Science 2023-03-02 Rui Wang , Rose Yu

Writing and argumentation are critical to both professional physics and physics education. However, the skill of making an extended argument in writing is often overlooked in physics classrooms, apart from certain practices like lab…

Physics Education · Physics 2020-06-19 Tor Ole B. Odden , John Burk

The technological and digital reality has broadly expanded, reaching the school. Therefore, this research aims to develop a didactic sequence, using as strategy a comic book, Figures, Gifs and videos, programs for activities applied to…

Physics Education · Physics 2020-08-12 Lucia Helena Horta Oliveira , Ayler Francisco Horta Oliveira

The modeling theory of instruction is widely applied and highly successful in high-school instruction, and seldom in university physics. One reason is lack of familiarity with models in the physics classroom. Ongoing curriculum development…

Physics Education · Physics 2007-05-23 Eric Brewe