The advent of new educational technologies has stimulated interest in using online videos to deliver content in university courses. We examined student engagement with 78 online videos that we created and were incorporated into a one-semester flipped introductory mechanics course at the Georgia Institute of Technology. We found that students were more engaged with videos that supported laboratory activities than with videos that presented lecture content. In particular, the percentage of students accessing laboratory videos was consistently greater than 80% throughout the semester. On the other hand, the percentage of students accessing lecture videos dropped to less than 40% by the end of the term. Moreover, the fraction of students accessing the entirety of a video decreases when videos become longer in length, and this trend is more prominent for the lecture videos than the laboratory videos. The results suggest that students may access videos based on perceived value: students appear to consider the laboratory videos as essential for successfully completing the laboratories while they appear to consider the lecture videos as something more akin to supplemental material. In this study, we also found that there was little correlation between student engagement with the videos and their incoming background. There was also little correlation found between student engagement with the videos and their performance in the course. An examination of the in-video content suggests that students engaged more with concrete information that is explicitly required for assignment completion (e.g., actions required to complete laboratory work, or formulas or mathematical expressions needed to solve particular problems) and less with content that is considered more conceptual in nature. It was also found that students' in-video accesses usually increased toward the embedded interaction points. However, students did not necessarily access the follow-up discussion of these interaction points. The results of the study suggest ways in which instructors may revise courses to better support student learning. For example, external intervention that helps students see the value of accessing videos may be required in order for this resource to be put to more effective use. In addition, students may benefit more from a clicker question that reiterates important concepts within the question itself, rather than a clicker question that leaves some important concepts to be addressed only in the discussion afterwards.
Recently, the National Research Council's framework for next generation science standards highlighted "computational thinking" as one of its "fundamental practices". 9 th Grade students taking a physics course that employed the Modeling Instruction curriculum were taught to construct computational models of physical systems. Student computational thinking was assessed using a proctored programming assignment, written essay, and a series of think-aloud interviews, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Roughly a third of the students in the study were successful in completing the programming assignment. Student success on this assessment was tied to how students synthesized their knowledge of physics and computation. On the essay and interview assessments, students displayed unique views of the relationship between force and motion; those who spoke of this relationship in causal (rather than observational) terms tended to have more success in the programming exercise.
Numerical computation (the use of a computer to solve, simulate, or visualize a physical problem) has fundamentally changed the way scientific research is done. Systems that are too difficult to solve in closed form are probed using computation. Experiments that are impossible to perform in the laboratory are studied numerically. Consequently, in modern science and engineering, computation is widely considered to be as important as theory and experiment. Unfortunately, most high school students today are never introduced to computation's problem-solving powers. Computer usage is widespread in high school STEM courses (e.g., obtaining lab data using computer acquisition hardware/software), but such usage rarely involves students constructing a computational representation of a science problem. The lack of computation in domain--specific STEM courses is not addressed in most high school computer science courses, which typically focus on programming and procedural abstractions rather than solving science problems. In recognition of these shortcomings, the recently published National Research Council's (NRC) framework for next--generation K--12 science standards lists "computational thinking" as one of the fundamental "practices" that should be incorporated into future K--12 science curricula. 1 The framework acknowledges that experience with computational thinking is crucially important, not only for developing future scientists and engineers, but also for providing all citizens with general insight into the science behind proposed solutions to technically complex social problems. In this article, we describe a way to introduce physics high school students with no background in programming to computational problem--solving experiences. Our approach builds on the Modeling Instruction curriculum, which is currently used in approximately 10% of U.S. high school physics classrooms. 2 The Modeling Instruction approach Modeling Instruction treats each force and motion model as distinct, but the common thread of predicting motion using Newton's 2 nd law and kinematics unifies them. The computational algorithm used to predict motion likewise retains the distinctions between the force and motion models, but highlights the commonality among them: namely, that such models differ only in the net force exerted on the system and in their particular initial conditions. Given knowledge of the system's initial position and velocity, as well as the net force on the system, the algorithm for predicting motion can be described as a set of rules applied locally in space and time: (1) At a given instant in time t, compute the net force, Fnet, acting on the system, (2) For a short time ∆t later, compute the new velocity of the system using Newton's 2nd law, (3) At the same new time (t + ∆t), compute the new position of the object using this updated velocity, and (4) Repeat Steps (1)--(3) starting at the updated time t + ∆t. Formally, the iterative application of Steps (1)--(3) is, in effect, explicit (Euler--Cromer) numerical integration ...
In the current work, we report on simulations of double-stranded DNA (dsDNA) ejection from bacteriophage φ29 into a bacterial cell. The ejection was studied with a coarse-grained model, in which viral dsDNA was represented by beads on a torsionless string. The bacteriophage’s capsid and the bacterial cell were defined by sets of spherical constraints. To account for the effects of the viscous medium inside the bacterial cell, the simulations were carried out using a Langevin Dynamics protocol. Our simplest simulations (involving constant viscosity and no external biasing forces) produced results compatible with the push-pull model of DNA ejection, with an ejection rate significantly higher in the first part of ejection than in the latter parts. Additionally, we performed more complicated simulations, in which we included additional factors such as external forces, osmotic pressure, condensing agents, and ejection-dependent viscosity. The effects of these factors (independently and in combination) on the thermodynamics and kinetics of DNA ejection were studied. We found that, in general, the dependency of ejection forces and ejection rates on the amount of DNA ejected becomes more complex if the ejection is modeled with a broader, more realistic set of parameters and influences (such as variation in the solvent’s viscosity and the application of an external force). However, certain combinations of factors and numerical parameters led to the opposition of some ejection-driving and ejection-inhibiting influences, ultimately causing an apparent simplification of the ejection profiles.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.