Related papers: Using machine learning to understand physics gradu…
Joining a research group is one of the most important events on a graduate student's path to earning a PhD, but the ways students go about searching for a group remain largely unstudied. It is therefore crucial to investigate whether…
Researchers have pinpointed recognition from others as one of the most important dimensions of students' science and engineering identity. Studies, however, have found gender biases in students' recognition of their peers, with inconsistent…
Historically, physics has been a predominantly male field, with previous literature showing there is little diversity amongst U.S. physics students at the undergraduate and graduate levels. Recent research indicates that the lack of…
We discuss and analyze the fact that physics is generally losing its ability to captivate students who may possess the potential to enhance the quality of our future in this age of technology. We have tried to investigate the reasons behind…
On their way to an academic career in physics, Ph.D. students have to overcome difficulties at many levels. Beyond the intellectual challenge, there are also psychological, social and economic barriers. We studied the difficulties…
Computation is a central aspect of 21st century physics practice; it is used to model complicated systems, to simulate impossible experiments, and to analyze mountains of data. Physics departments and their faculty are increasingly…
Perceptions of disciplinary recognition and identity can be shaped by various forms of feedback and experiences. Here we focus on the potential effects of course grades on the perceievd recognition and physics identity of students. We…
With the recent implementation of the K to 12 Program, academic institutions, specifically, Colleges and Universities in the Philippines have been faced with difficulties in determining projected freshmen enrollees vis-a-vis decision-making…
The persistent underrepresentation of women and other gender minorities in physical science fields has been an ongoing concern. This study investigates gender dynamics in introductory physics laboratory courses, focusing on whether students…
Despite nation efforts to promote diversity and inclusion, women and gender minorities remain underrepresented in physics. One common approach to studying gender in physics contexts treats gender as a categorical identity variable (e.g.…
The importance of science beliefs such as self-efficacy, interest, identity, sense of belonging, perceived recognition and effectiveness of peer interaction in science education has been increasingly recognized in recent years. Here, we use…
Diversity has an important, multifaceted role in the success of education. I have used a corpus analysis of the first ten years of Physical Review Physics Education Research to investigate the diversity, broadly defined, of physics…
Initiatives to increase the number, persistence, and success of women in physics in the US reach pre-teen girls through senior women. Programs exist at both the local and national levels. In addition, researchers have investigated issues…
In this study, we adapt prior identity framework to investigate the effect of learning environment (including perceived recognition, peer interaction and sense of belonging) on students' physics self-efficacy, interest and identity by…
It is well known that women are underrepresented in the academic systems of many countries. Gender discrimination is one of the factors that could contribute to this phenomenon. This study considers a recent national academic recruitment…
In this paper, we study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. We consider affirmative action policies that seek to increase the number of…
Gender imbalance among German physicists persists, with fewer women in advanced degrees and research leadership roles. Although female enrollment in Physics programs increased slightly until 2022, potentially influenced by COVID-19 the…
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on…
The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced,…
Graduate school admission committees consider many factors for admission into a mathematics PhD program, and aspiring applicants often wonder which factors are more important. Applicants discerning where to best dedicate their time may ask…