Thinking Process Profile of Senior High School Students in Physics Problem-Solving: Terms of Differences in Emotional Intelligence Levels and Gender

This literature study aims to obtain the thinking process profile of senior high school students in physics problem-solving based on differences in emotional intelligence levels and gender. In this study used literature study method with phenomenological analysis, functionalism analysis, and critical analysis. The sources used to compile this article include some literature on emotional intelligence, gender effect, thinking process, problem-solving, and several empirical study on students in physics problem-solving, especially in senior high school. The authors summed up the conclusions based on the results of the literature study as follows: (1) The profile of the thinking process is influenced by emotional intelligence level and gender; (2) Emotional intelligence is directly proportional to the thinking process of students in the process of solving physics problems; (3) Students who have high emotional intelligence are more likely to have high physics problem-solving skills; (4) Gender differences (male and female) in solving physics problems, generally, are still debatable, in which sense is it better? Then it further research needs that is valid and reliable to answer this question. (5) There is a strong tendency that the male gender is more resilient in dealing with and solving complex physics problems; (6) The gender of female students is superior in solving physics problems that require very high detail. The main implication of this article is to obtain the thinking process profile of senior high school students in physics problem-solving based on terms of differences in emotional intelligence levels and gender.

Two useful Python tools and their application in Physics

Python has become a popular programming language among physicists and students/researchers of other fields as well. However, Python still needs improvement to provide ease of use in physical problems. One such case is Python’s list array which is more powerful than conventional arrays in other languages like C, C++, Fortran, or Java, but, it becomes tedious and complicated to construct a list array of larger dimensions in Python in certain physical problems. Another such case may be — reading column-wise data from a data-file consisting of multiple columns - which may be simplified with the introduction of a Python class in a package. This article discusses the difference and limitations of array variables in various languages and introduces a new Python tool to make the construction of N-dimensional list arrays easier. This also introduces a way to handle data-file in Python in a simpler way and with some analytical features. It is also shown – how these two tools may improve our experience in dealing with Python in physics problems.

Analisis Pemahaman Konsep, Multirepresentasi, dan Kosistensi Jawaban Siswa SMA pada Konsep Hukum III Newton

Newton's third law is a fundamental concept of Physics that is still not reached by students. The tendency of students who do not understand the concept well can be observed by measuring the ability of multi-representation and solutions when solving Physics problems. Therefore, this study aims to determine the concept of Newton's third law understood by students, the multi-representation students have, and the consistency of students' answers in answering physics questions. The participants numbered 28 students from the second semester of high school, selected using the purposive sampling technique, who had just completed discussions about Newton's Law and were willing to participate in this study. Participants were given 6 two-tier question items that managed the R-FCI questions. The results of the answers were analyzed quantitatively to determine the initial percentage description and then analyzed qualitatively to determine understanding, multi-representation ability, and consistency in depth. Most of the participants had low conceptual understanding and answered the questions consistent-wrong. However, based on the analysis of the answer choices and the reasons given, most students understand the meaning of the representation of the answers given

New Horizons for Ferroelectrics

Since the discovery of ferroelectricity in 1920, dielectric research has provided a variety of fundamental physics problems and sustainable applications. Advances in synthesis and nanoscale characterization, along with theoretical innovations, have made ferroelectrics more versatile. In this perspective, we discuss several directions for future ferroelectric research in terms of flexoelectricity, ferroelectric topology, and lattice defects, as well as cooperation with associated fields.

From Asymptotic Series to Self-Similar Approximants

The review presents the development of an approach of constructing approximate solutions to complicated physics problems, starting from asymptotic series, through optimized perturbation theory, to self-similar approximation theory. The close interrelation of underlying ideas of these theories is emphasized. Applications of the developed approach are illustrated by typical examples demonstrating that it combines simplicity with good accuracy.

A machine learning approach for efficient multi-dimensional integration

Many physics problems involve integration in multi-dimensional space whose analytic solution is not available. The integrals can be evaluated using numerical integration methods, but it requires a large computational cost in some cases, so an efficient algorithm plays an important role in solving the physics problems. We propose a novel numerical multi-dimensional integration algorithm using machine learning (ML). After training a ML regression model to mimic a target integrand, the regression model is used to evaluate an approximation of the integral. Then, the difference between the approximation and the true answer is calculated to correct the bias in the approximation of the integral induced by ML prediction errors. Because of the bias correction, the final estimate of the integral is unbiased and has a statistically correct error estimation. Three ML models of multi-layer perceptron, gradient boosting decision tree, and Gaussian process regression algorithms are investigated. The performance of the proposed algorithm is demonstrated on six different families of integrands that typically appear in physics problems at various dimensions and integrand difficulties. The results show that, for the same total number of integrand evaluations, the new algorithm provides integral estimates with more than an order of magnitude smaller uncertainties than those of the VEGAS algorithm in most of the test cases.

Detailed Emotional Profile of Secondary Education Students Toward Learning Physics and Chemistry

The present research arises from the need to identify the emotions that K-7 to K-10 students experience toward the learning of Physics and Chemistry, since it is a fact that there is a decrease in the number of students choosing itineraries related to Science. Different blocks of contents have been considered in each subject in order to identify emotions toward each one of them. The considered sample consisted of 149 K-8 students, 152 K-9 students and 130 K-10 students from several middle and high schools in Badajoz (Spain) during the 2014–2015 school year. Students experienced more positive emotions toward the content of Chemistry than toward those of Physics. A decrease was detected in the mean frequency of positive emotions such as joy, fun, and tranquility from K-8 to K-10, as well as an increase in negative emotions such as boredom, anxiety, disgust, fear, nervousness, worry, and sadness. It has also been found that positive emotions toward Chemistry contents are mainly related to teachers’ methods and attitudes, while negative emotions toward Physics contents are related to the exclusive use of the textbook, solving Physics problems, or giving oral presentations of the topics in class.