Comparing Science and Engineering Students Using the Force Concept Inventory in Introductory Physics Courses

This work is intended to analyze and compare the performance of two groups of students on the understanding of force and motion concepts using the Force Concept Inventory (FCI). The FCI test serves questions on basic Newtonian concepts where the answers inclyde the correct response and commonly misconceived alternatives. The FCI test was implemented twice as pre and post-tests for two introductory calculus-based physics courses offered at the Sultan Qaboos University (SQU) in Oman for students mainly from the Colleges of Sciences, Education and Agriculture and the students from the College of Enginerring in the Spring 2017 and Spring 2018 semesters. These courses cover the traditional first-year level kinematics and dynamics in translational and rotational motions based on the same syllabus and the same textbook. Hake's normalized gain, defined as the change in class averages divided by the maximum possible increase, was used to compare the students'performances. The normalized gains for both groups of students were in the low gain category. Female students in both courses performed better on the FCI in general, but the difference was only statistically significant in the course offered to Science students.

The Scourge of Online Solutions and an Academic Hertzsprung–Russell Diagram

We report on preliminary results of a statistical study of student performance in more than a decade of calculus-based introductory physics courses. Treating average homework and test grades as proxies for student effort and comprehension, respectively, we plot comprehension versus effort in an academic version of the astronomical Hertzsprung–Russell diagram (which plots stellar luminosity versus temperature). We study the evolution of this diagram with time, finding that the “academic main sequence” has begun to break down in recent years as student achievement on tests has become decoupled from homework grades. We present evidence that this breakdown is likely related to the emergence of easily accessible online solutions to most textbook problems, and discuss possible responses and strategies for maintaining and enhancing student learning in the online era.

Observing the epidemiological SIR model on COVID-19 pandemic data

This article shows that in the period January 22-June 04, 2020, the combined data set of cumulative recoveries and deaths from the current coronavirus COVID-19 pandemic falls on the Kermack and McKendrick approximated solution of the epidemiological {\sir} contagious disease model. Then, as an original contribution of this work, based on the knowledge of the infectious period of any epidemic, a methodology is presented that helps to find numerical solutions of the full {\sir} model that falls on the observed data of the epidemic in case it could be described by the {\sir} model. The methodology is first illustrated by finding a solution of the {\sir} model that falls on the epidemic data of the Bombay plague of 1905-06 analyzed by Kermack and McKendrick. After that, the methodology is applied on analyzing the previously considered coronavirus COVID-19 pandemic data set. Moreover, since the Kermack and McKendrick approximated solution of the {\sir} model comes from solving a Riccati type differential equation, commonly found when studying (in introductory physics courses) the vertical motion of objects on a resistive medium, enough details are given in the article so the epidemiological {\sir} model can be used as an additional example for enhancing and enriching the undergraduate curriculum Physics courses for Biology, Life Sciences, Medicine and/or Computational Modeling.