Download Full Issue PDF
Introduction to Volume 5 Issue 1
Steven I. Gordonpp. 1–1
A brief introduction to this issue of the Journal of Computational Science Education from the editor.
Volume 5 Issue 1 — August 2014
Contents
Characterizing Ligand Interactions in Wild-type and Mutated HIV-1 Proteases
Leyte L. Winfield, Rosalind Gregory-Bass, Jordan Campbell, and Andy Watkinspp. 2–9
https://doi.org/10.22369/issn.2153-4136/5/1/1BibTeX
@article{jocse-5-1-1,
author={Leyte L. Winfield and Rosalind Gregory-Bass and Jordan Campbell and Andy Watkins},
title={Characterizing Ligand Interactions in Wild-type and Mutated HIV-1 Proteases},
journal={The Journal of Computational Science Education},
year=2014,
month=aug,
volume=5,
issue=1,
pages={2--9},
doi={https://doi.org/10.22369/issn.2153-4136/5/1/1}
}
Copied to clipboard!
A computational module has been developed in which students examine the binding interactions between indinavir and HIV-1 protease. The project is a component of the Medicinal Chemistry course offered to upper level chemistry, biochemistry, and biology majors. Students work with modeling and informatics tools utilized in drug development research while evaluating wild-type and mutated forms of the HIV-1 protease in complex with the inhibitor indinavir. By quantifying the molecular interactions within protease-inhibitor complexes, students can characterize the structural basis for reduced efficacy of indinavir.
Scaling and Visualization of N-Body Gravitational Dynamics with GalaxSeeHPC
David A. Joiner and James Walterspp. 10–22
https://doi.org/10.22369/issn.2153-4136/5/1/2BibTeX
@article{jocse-5-1-2,
author={David A. Joiner and James Walters},
title={Scaling and Visualization of N-Body Gravitational Dynamics with GalaxSeeHPC},
journal={The Journal of Computational Science Education},
year=2014,
month=aug,
volume=5,
issue=1,
pages={10--22},
doi={https://doi.org/10.22369/issn.2153-4136/5/1/2}
}
Copied to clipboard!
In this paper, we present GalaxSeeHPC, a new cluster-enabled gravitational N-Body program designed for educational use, along with two potential student experiences that illustrate what students might be able to investigate at larger N than available with earlier versions of GalaxSee. GalaxSeeHPC adds additional force calculation algorithms and input options to the previous clusterenabled version. GalaxSeeHPC lessons have been developed focusing on two key studies, the structure of rotating galaxies and the large scale structure of the universe. At large N, visualizing the results becomes a significant challenge, and tools for visualization are presented. The canonical lesson in the original version of GalaxSee is the rotation and flattening of a cluster with angular momentum. Model discrepancies that are not obvious at the range of N available in previous versions become quite obvious at large N, and changes to the initial mass and velocity distribution can be seen more readily. For the large scale structure models, while basic clearing and clustering can be seen at around N=5,000, N=50,000 allows for a much clearer visualization of the filamentary structure at large scale, and N=500,000 allows for a more detailed geometry of the knots formed as the filaments combine to form superclusters. For the galactic dynamics simulations, we found that while a flattening due to overall angular momentum can be explored with N=1,000 or smaller, formation of spiral structure requires not only a larger number of objects, typically on the order of 10,000, but also modifications to the default initial mass and velocity distributions used in older versions of GalaxSee.
Introducing Evolutionary Computing in Regression Analysis
Olcay Akmanpp. 23–27
https://doi.org/10.22369/issn.2153-4136/5/1/3BibTeX
@article{jocse-5-1-3,
author={Olcay Akman},
title={Introducing Evolutionary Computing in Regression Analysis},
journal={The Journal of Computational Science Education},
year=2014,
month=aug,
volume=5,
issue=1,
pages={23--27},
doi={https://doi.org/10.22369/issn.2153-4136/5/1/3}
}
Copied to clipboard!
A typical upper level undergraduate or first year graduate level regression course syllabus treats model selection with various stepwise regression methods. Here we implement evolutionary computing for subset model selection and accomplish two goals: i) introduce students to the powerful optimization method of genetic algorithms, and ii) transform a regression analysis course to a regression and modeling without requiring any additional time or software commitment.Furthermore we also employed Akaike Information Criterion (AIC) as a measure of model fitness instead of another commonly used measure of R-square. The model selection tool uses Excel which makes the procedure accessible to a very wide spectrum of interdisciplinary students with no specialized software requirement. An Excel macro, to be used as an instructional tool is freely available through the author's website.
Teaching Students to Program Using Visual Environments: Impetus for a Faulty Mental Model?
Edward Dillon, Monica Anderson-Herzog, and Marcus Brownpp. 28–43
https://doi.org/10.22369/issn.2153-4136/5/1/4BibTeX
@article{jocse-5-1-4,
author={Edward Dillon and Monica Anderson-Herzog and Marcus Brown},
title={Teaching Students to Program Using Visual Environments: Impetus for a Faulty Mental Model?},
journal={The Journal of Computational Science Education},
year=2014,
month=aug,
volume=5,
issue=1,
pages={28--43},
doi={https://doi.org/10.22369/issn.2153-4136/5/1/4}
}
Copied to clipboard!
When learning to program, students are typically exposed to either a visual or command line environment. Visual environments are usually adopted to help engage students with programming due to their user-friendly feature capabilities. This article explores the effect of using visual environments such as Integrated Development Environments and syntax-free tools to teach students how to program. Prior studies have shown that some visual environments can have a productive impact on a student's ability to learn and become engaged with programming. However, the functional behavior of visual environments may cause a student to develop a faulty mental model for programming. One possible reason is due to the fixed set of skills that a student acquires upon initial exposure to programming while using a visual environment. Two systematic studies were conducted for exposing students to programming in introductory courses using both visual and command line environments. From the first study, it was found that visual environments can initially impose a lower learning curve for students. However, the second study revealed that visual environments may present a challenge for students to directly transfer their acquired skills to other programming environments after initial exposure.