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Research topics

Learning research topics

  • 1. Development of interactive web platforms
    • 1.1. ChemEd X Data
    • 1.2. Models 360
    • 1.3. Dynamic simulations
    • 1.4. Games
  • 2. Exploring additional tools for communication in the class
    • 2.1. Using social technologies in the General Chemistry class
    • 2.2. The OLCC project
  • 3. Development of assessment tools
    • 3.1. Teaching Chemistry with question banks
    • 3.2. Learning analytics: development of BoSCO and SLICE

Computer simulations topics

    4. Molecular Simulation of Proton Channels: Proton Gating in Influenza M2 Protein

1.1. ChemEd X Data: Exposing students to open scientific data for higher­‐order thinking and self‐regulated learning.
http://chemdata.umr.umn.edu/chemedXdata/

“…we should teach them to judge between conflicting influences. That is the essence of our subject, for it is rare that a single property governs the outcome of a reaction. We need to train our students to judge the likely outcome of conflict”
P. Atkins. Pure Appl. Chem., Vol. 71, No. 6, pp. 927-929, 1999.

The above quote represents the essence of learning chemistry and it can be easily extrapolated to many other areas of knowledge. Identifying and considering the conflicting influences that decide the outcome of an event is one of the most challenging aspects to learn.  ChemEd X Data aims at helping students discover and judge between conflicting influences.





The availability of large amounts of online information and the omnipresence of computer-based learning environments (CBLE) has multiplied the potential for learning in STEM courses. Some problems may arise when the very nature of online materials such as their nonlinearity, nonsequentiality, and open-endedness has posed significant learning challenges for beginning students such as information overload and confusion due to lack of self-regulation and self-evaluation skills. Typically, textbooks may contain static pictures and other static charts with the purpose of exactly proving the scientific topic that is being covered. However, in order to achieve higher-order levels of thinking (analysis, application, and evaluation) instructors need to include more flexible, open-ended, and evidence-based learning resources. Higher-order thinking and learning autonomy is now a required skill in order to navigate through vast amounts of online information and succeed in this ever-changing dynamic society. As an attempt to address the problems listed above, ChemEd X Data has been designed; it is an open web platform that makes it easy to browse, represent, and compare physical and chemical information involving several hundred substances. This platform takes advantage of open scientific databases to allow students to navigate, choose, represent, and analyze real experimental data. At the same time, it allows instructors to build activities to empower students to build their own knowledge

See article here http://pubs.acs.org/doi/abs/10.1021/ed500316m

ChemEd X Data: Exposing Students to Open Scientific Data for Higher-Order Thinking and Self-Regulated Learning

1.2. Models 360: a visual collection of molecules http://www.chemeddl.org/resources/models360/

Models 360 enables users to investigate a collection of 3-D interactive models of organic and inorganic compounds, including extended-structure solids. Users can manipulate the models to examine structure and bonding and demonstrate molecular geometries, vibrations, symmetry, and orbitals. This resource has been built specifically to meet the needs of educators. The molecules and structures shown have been vetted as to their usefulness in teaching, and their accuracy. The structure of each molecule has been calculated using modern quantum mechanical software in order to obtain high-accuracy properties to be used in classrooms


1.3. Simulations of the molecular world

One of the main difficulties that chemistry students face is that they need to be able to think and convert between the macroscopic, molecular and symbolic levels of representation. The use of visualization and simulation software in chemistry tries to help students make the abstract structure and interaction of molecules more concrete (Suits and Sanger 2013).

  • Dynamic nature of molecules for reactions, intermolecular forces and phase change
    • http://chemdata.umr.umn.edu/chem2331/explosion/
    • http://chemdata.umr.umn.edu/chem2331/brownian/
    • http://chemdata.umr.umn.edu/chem2331/ionicmelt/
    • http://chemdata.umr.umn.edu/chem2331/waterphasechange
    • http://chemdata.umr.umn.edu/chem2331/hydrophobic/
    • http://chemdata.umr.umn.edu/chem2331/compressibility/
  • Quantum nature of matter (still dynamic, but à la quantum)
    • Hydrogen orbitals and their radial distribution functions http://chemdata.umr.umn.edu/chem2331/orbitals/h.html
      but this one is way better http://chemapps.stolaf.edu/jmol/orbitals/
    • The time-dependent wavefunction of the molecule H2 http://chemdata.umr.umn.edu/chem2331/h2/
    • HOMO and LUMO of H2 as a function of the bond distance http://chemdata.umr.umn.edu/chem2331/h2bond/
    • Comparing classical and quantum view of an electron http://chemdata.umr.umn.edu/chem2331/electron/

1.4. Development of game-like interfaces with the jQuery library

Introductory chemistry and other undergraduate first-year science courses involve some topics that need first to be mastered before students can address higher-order thinking problems. One could say that students need to first learn the language of that scientific discipline before they can express themselves in that  language. In chemistry, for example, it is necessary to master low-order skills such as chemistry nomenclature, common oxidation states, memorizing the amino  acids or identify the strength of common acids and bases before one can solve some higher-order problems. These kind of low-order skills are typically repetitive and students have a hard time being engaged and achieve the desired mastery level. A solution to this problem is the so-called gamification, that is, to design more engaging game-like activities to achieve the desired goal.
See presentation here: Brandon P. Eklund, Joseph W. Inhofer, Jason D. Greenwood, Omar Mohamed, Peter L. Larsen, Xavier Prat-Resina
Students designing online games for active learning sessions in chemistry courses
Proceedings of EDULEARN14 Conference. 7th-9th July 2014, Barcelona, Spain
ISBN: 978-84-617-0557-3 Full article click this link

2.1. Using social technologies in the General Chemistry class

The emergence of online collaborative tools, the so-called Web 2.0 tools, has been received by the community of educators some times with enthusiasm, other times with prudence or even resistance (Ajjan and Hartshorne 2008). Understandably, the potential of these tools to benefit learning is not yet clearly identified, therefore, when including them in the classroom one must be very careful at how, when and what social technologies can be integrated in educational contexts (Cole 2009).

  • The Reddit project
  • The Mediawiki project

2.2. Assessment of an Intercampus cheminformatics course

I am currently involved in the assessment of the NSF funded project (NSF TUES awarded #1140485 http://olcc.ccce.us/) “Cheminformatics OLCC”. This project uses Web 2.0 and social networking methods to create an OnLine Chemistry Course (OLCC) in the area of cheminformatics. See http://www.divched.org/winter2012/cheminformatics

3.1. Teaching Chemistry with question banks

3.2. Learning analytics: development of BoSCO and SLICE

See this page

4. Molecular Simulation of Proton Channels: Proton Gating in Influenza M2 Protein

 


   
 Proton channels are membrane bound proteins that selectively conduct protons across a cell membrane. Either according or against a concentration and electrical gradient, the mechanism by which the transfer occurs consists of the combination of water wires and titrable residues that pump a net number of protons from one side to other of the membrane. In most cases, atomistic detail of the proton transfer process will provide extremely useful information towards a better understanding of the mechanism and it may contribute to the design and control of proton channels.
One of the proton channels that have recently attracted a lot of attention is the M2 protein from influenza A virus, which is the target of influenza drugs amantidine and rimantidine. The M2 channel is a pH-activated and highly-selective proton pump that consists of a homotetramer of a very small protein (96 residues). The mechanism by which this small channel can selectively pump protons vs. other ions is still not clear.
In order to build a realistic computational model to study the reaction mechanism of such process, we need to take into account the flexibility of the tetramer bundle at a given temperature, the short and long range interactions between charged residues as well as the forming and breaking of chemical bonds during the proton pump process. Such atomistic model consists of a reactive core treated with Quantum Mechanics (QM) linked to a coarser description of atoms with Molecular Mechanics (MM) and finally embedded in a continuum electrostatic field to simulate the low dielectric medium of the phospholipidic membrane. We explore the configuration space of such model by Molecular Dynamics. Forcing the system to transfer a net charge across the channel allows us to compute the Potential Of Mean Force of the proton transport process and obtain a reliable estimation of its energetics.