Physics

Simple experiments for which differential equations cannot be solved analytically can be addressed using an effective model that satisfactorily reproduces the experimental data. In this work, the one-dimensional kinematics of a remote-control model (toy) car was studied experimentally and its dynamical equation modelled. In the experiment, maximum power was applied to the car, initially at rest, until it reached its terminal velocity. Digital video recording was used to obtain the relevant kinematic variables that enabled to plot trajectories in the phase space. A dynamical equation of motion was proposed in which the overall frictional force was modelled as an effective force proportional to the velocity raised to the power of a real number. Since such an equation could not be solved analytically, a dynamical model was developed and the system parameters were calculated by non-linear fitting. Finally, the resulting values were substituted in the motion equation and the numerical results thus obtained were compared with the experimental data, corroborating the accuracy of the model.

This article is derived from the development of educational activities in astronomy, with a view to encouraging the training of future researchers. The goal is to present how the participation of students from EEEM Dr.Silva Mello took place in the Mostra de Astronomia do ES (MAES), and how that promoted their involvement and participation in the three types of education, formal, informal and non-formal, highlighting the articulation between them. In order to train citizens with interests for development of scientific research on astronomical themes, developing a taste for the search for knowledge and to motivate this action, we form groups to present works at MAES, used as a non-formal education space . The event included the presentation of works, lectures and prizes. As a result, one of the works presented by the students was awarded. Informal education has gained special relevance, when students discussed science, bringing it to their daily lives

Mathematical reasoning skills are a desired outcome of many introductory physics courses, particularly calculus-based physics courses. Positive and negative quantities are ubiquitous in physics, and the sign carries important and varied meanings. Novices can struggle to understand the many roles signed numbers play in physics contexts, and recent evidence shows that unresolved struggle can carry over to subsequent physics courses. The mathematics education research literature documents the cognitive challenge of conceptualizing negative numbers as mathematical objects--both for experts, historically, and for novices as they learn. We contribute to the small but growing body of research in physics contexts that examines student reasoning about signed quantities and reasoning about the use and interpretation of signs in mathematical models. In this paper we present a framework for categorizing various meanings and interpretations of the negative sign in physics contexts, inspired by established work in algebra contexts from the mathematics education research community. Such a framework can support innovation that can catalyze deeper mathematical conceptualizations of signed quantities in the introductory courses and beyond.

Scientific knowledge has been accepted as the main driver of development, allowing for longer, healthier, and more comfortable lives. Still, public support to scientific research is wavering, with large numbers of people being uninterested or even hostile towards science. This is having serious social consequences, from the anti-vaccination community to the recent "post-truth" movement. Such lack of trust and appreciation for science was first justified as lack of knowledge, leading to the "Deficit Model" \cite{Durant:1989, Bauer:2007}. As an increase in scientific information did not necessarily lead to a greater appreciation, this model was largely rejected, giving rise to "Public Engagement Models" \cite{Miller:2001}. These try to offer more nuanced, two-way, communication pipelines between experts and the general public, strongly respecting non-expert knowledge, possibly even leading to an undervaluing of science. Therefore, we still lack an encompassing theory that can explain public understanding of science, allowing for more targeted and informed approaches. Here, we use a large dataset from the Science and Technology Eurobarometer surveys, over 25 years in 34 countries \cite{Bauer:2012}, and find evidence that a combination of confidence and knowledge is a good predictor of attitudes towards science. This is contrary to current views, that place knowledge as secondary, and in line with findings in behavioral psychology, particularly the Dunning-Kruger effect, as negative attitudes peak at intermediate levels of knowledge, where confidence is largest. We propose a new model, based on the superposition of the Deficit and Dunning-Kruger models and discuss how this can inform science communication.

The lack of diversity and the under-performance of underrepresented students in STEM courses have been the focus of researchers in the last decade. In particular, many hypotheses have been put forth for the reasons for the under-representation and under-performance of women in physics. Here, we present a framework for helping all students learn in science courses that takes into account four factors: 1) characteristics of instruction and learning tools, 2) implementation of instruction and learning tools, 3) student characteristics, and 4) students' environments. While there has been much research on factor 1 (characteristics of instruction and learning tools), there has been less focus on factor 2 (students' characteristics, and in particular, motivational factors). Here, we focus on the baseline motivational characteristics of introductory physics students obtained from survey data to inform factor 2 of the framework. A longitudinal analysis of students' motivational characteristics in two-semester introductory physics courses was performed by administering pre- and post-surveys that evaluated students' self-efficacy, grit, fascination with physics, value associated with physics, intelligence mindset, and physics epistemology. Female students reported lower self-efficacy, fascination and value, and had a more "fixed" view of intelligence in the context of physics compared to male students. Grit was the only factor on which female students reported averages that were equal to or higher than male students throughout introductory physics courses. These gender differences can at least partly be attributed to the societal stereotypes and biases about who belongs in physics and can excel in it. The findings inform the framework and have implications for the development and implementation of effective pedagogies and learning tools to help all students learn.

We developed a simple, flexible, low-cost, and computer-controlled cryogenic temperature measurement system for undergraduate instructional laboratories. An Arduino microcontroller board measures the voltage across a silicon diode to calculate its temperature. Resistors and a voltage regulator provide constant current into the silicon diode. We present a graphical user interface based on the open-source Processing language. The cost of the complete temperature measurement system is thus only a small fraction of any highly-developed commercial system. Our performance test shows that the system works at a reasonable accuracy from 297.15 K (typical room temperature) down to 77 K (liquid nitrogen temperature).

There is a significant underrepresentation of women in many Science, Technology, Engineering, and Mathematics (STEM) majors and careers. Prior research has shown that self-efficacy can be a critical factor in student learning, and that there is a tendency for women to have lower self-efficacy than men in STEM disciplines. This study investigates gender differences in the relationship between engineering students' self-efficacy and course grades in foundational courses. By focusing on engineering students, we examined these gender differences simultaneously in four STEM disciplines (mathematics, engineering, physics, and chemistry) among the same population. Using survey data collected longitudinally at three time points and course grade data from five cohorts of engineering students at a large US-based research university, effect sizes of gender differences are calculated using Cohen's d on two measures: responses to survey items on discipline-specific self-efficacy and course grades in all first-year foundational courses and second-year mathematics courses. In engineering, physics, and mathematics courses, we find sizeable discrepancies between self-efficacy and performance, with men appearing significantly more confident than women despite small or reverse direction differences in grades. In chemistry, women earn higher grades and have higher self-efficacy. The patterns are consistent across courses within each discipline. All self-efficacy gender differences close by the fourth year except physics self-efficacy. The disconnect between self-efficacy and course grades across subjects provides useful clues for targeted interventions to promote equitable learning environments. The most extreme disconnect occurs in physics and may help explain the severe underrepresentation of women in "physics-heavy" engineering disciplines, highlighting the importance of such interventions.

The traditional approach to studying student understanding presents a question and uses the student answers to make inferences about their knowledge. However, this method does not capture the range of possible alternative ideas available to students. We use a new approach, asking students to generate a plausible explanation for every choice of a multiple-choice question, to capture a range of explanations that students can generate in answering physics questions. Asking 16 students to provide explanations in this way revealed alternative possibilities for student thinking that would not have been captured if they only provided one solution. The findings show two ways these alternatives can be productive for learning physics: (i) even students who ultimately chose the wrong answer could often generate the correct explanation and (ii) many incorrect explanations contained elements of correct physical reasoning. We discuss the instructional implications of this multiple-choice questioning approach and of student alternative ideas.

Engineering Thermodynamics has been the core course of many science and engineering majors around the world, including energy and power, mechanical engineering, civil engineering, aerospace, cryogenic refrigeration, food engineering, chemical engineering, and environmental engineering, among which gas power cycle is one of the important contents. However, many Engineering Thermodynamics textbooks focus only on evaluating the thermal efficiency of gas power cycle, while the important concept of specific cycle work is ignored. Based on the generalized temperature-entropy diagram for the gas power cycles proposed by the authors, an ideal Otto cycle and an ideal Miller-Diesel cycle are taking as examples for the thermodynamic analyses of gas power cycles. The optimum compression ratio (or the pressure ratio) for the maximum specific cycle work or the maximum mean effective pressure is analyzed and determined. The ideal Otto and the ideal Miller-Diesel cycles, and also other gas power cycles for movable applications, are concluded that the operation under the maximum specific cycle work or the maximum mean effective pressure, instead of under the higher efficiency, is more economic and more reasonable. We concluded that the very important concept, i.e., the optimum compression (or pressure) ratio for the gas power cycles, should be emphasized in the Engineering Thermodynamics teaching process and in the latter revised or the newly edited textbooks, in order to better guide the engineering applications.

The value of multiple choice questions (MCQs) in seeking large-scale, high-stakes, educational assessment is widely established. Students' responses to test items with a multiple-choice question format enable assess the extent of students' understanding and also help make valuable decisions about the quality of questions that make robust assessments possible. The use of discrimination index (DI) to analyse MCQs is also widely prevalent in literature Kelly(1939). This paper makes a case for using a novel approach to analyzing data using the DI. The case for novelty is argued through an empirical, comparative analysis on three sets of data: conjecture data, data from an exam for screening talented students for a competitive event (two examples), and data from an international competitive academic event. The scheme is developed to handle the data gathered from different question formats such as MCQs, Long answer questions (LAQs) and a combination of these two question formats. A code has been developed for carrying out computational analysis on large data sets. A comparison with the conventional approach to data analysis establishes the worthiness of ideas proposed for making meaningful inferences and simultaneously renders it possible to attend to nuances that are greatly compromised while analyzing huge data-sets. The paper brings a critical value-addition to the body of analytical knowledge building.