ChemEd X Data: Exposing Students to Open Scientific Data for Higher

Jul 15, 2014 - ChemEd X Data is an open web tool that collects and curates physical and chemical data of hundreds of substances. This tool allows stud...
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ChemEd X Data: Exposing Students to Open Scientific Data for Higher-Order Thinking and Self-Regulated Learning Brandon Eklund and Xavier Prat-Resina* Center for Learning Innovation, University of Minnesota Rochester, Rochester, Minnesota 55904, United States ABSTRACT: ChemEd X Data is an open web tool that collects and curates physical and chemical data of hundreds of substances. This tool allows students to navigate, select, and graphically represent data such as boiling and melting points, enthalpies of combustion, and heat capacities for hundreds of molecules. By doing so, students can independently identify correlations between magnitudes, laws, and outliers. One of the merits of this tool is that it may help students recognize situations when conflicting influences play a role (e.g., molecular weight, shape, and dipole effect on boiling point). Identifying conflicting influences empowers students with analytical skills that leads them to higher-order thinking and self-regulated learning. ChemEd X Data has been implemented in an undergraduate general chemistry course, and it has successfully allowed students identify laws that were not previously known by them. KEYWORDS: High School, Introductory Chemistry, First-Year Undergraduate, General, Chemoinformatics, Computer-Based Learning, Inquiry-Based, Discovery Learning, Internet, Web-Based Learning, Phases, Phase Transitions, Diagrams, Molecular Properties, Structure

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curation of data is the necessary maintenance for its long-term reuse and preservation. Our platform contains four different types of data, namely, organic, inorganic, reaction data, and chemical elements data. Currently, our database includes 366 organic compounds, 234 inorganic compounds, and 392 reaction data as well as data for most of the elements in the periodic table. The properties for organic molecular compounds that were extracted are normal boiling and melting points, enthalpies of vaporization and fusion, enthalpies of formation for liquid and gas, enthalpies of combustion for liquid and gas, and constant pressure heat capacities for liquid and gas as well as Henry’s law constant of solubility when available. For inorganic compounds, the melting and boiling point were collected, and the physical properties of elements were directly taken from the BODR repository. The database was completed with molecule names and other descriptors (InChI, molecular weight, InChIKey, formula and Chemical Abstracts Service, CAS index). These data were computed with Pybel/OpenBabel16 or retrieved from the CIR server. All data are available for download in JavaScript Object Notation (JSON) format in the “download” section of the interface. Other molecular properties are queried online or automatically calculated. For example, when clicking on the “View 3D!” button, the molecular coordinates are retrieved from the CIR server and displayed on the JSmol molecular viewer.17 JSmol automatically calculates other properties such as partial charges, dipoles, electrostatic maps, and point symmetry elements. This package uses the Merck Molecular Force Field MMFF94 charge model18 and calculates the dipole and electrostatic map as a system of point charges. Although this approach is good for qualitative properties of simple molecules, this may not be the

he 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.1−4 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, openended, 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.5−8 As an attempt to address the problems listed above, ChemEd X Data9 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.

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DEVELOPMENT OF THE TOOL ChemEd X Data relies on freely available data, and the web application is being released under Creative Commons10 BY-SA license. Collecting and Curating Data

The physical and chemical data for our current collection of molecules was taken from databases available to the public such as Wikipedia,11 BODR,12 NIST,13 ChEMBL,14 and CIR.15 The © 2014 American Chemical Society and Division of Chemical Education, Inc.

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Figure 1. Screenshot of ChemEd X Data showing the organic section.

the table rows that they are interested in plotting. The data are plotted on a scatter plot using the Flot plugin.22 The user can save the selection by clicking the “Save selection” button, which prompts a link with a stamp that does not expire. This functionality may also be useful to assign a task to students and ask them to submit the saved stamp. Upon the selection of compounds and clicking the “View 3D!” button, a new window pops up with as many JSmol molecular structures as compounds were selected. Each structure can display the atomic partial charges, the bond and molecular dipole, the molecular electrostatic map, and all of the elements of point group symmetry for that particular molecular structure. The purpose of the “3D representations” section is to compare molecular properties associated with the structure and how these properties can be used as an explanation for the trend seen in the 2D representations. For example, one may have plotted CH3F, CH2F2, CHF3, and CF4 in the 2D chart and see that the highest boiling point corresponds to CH2F2 and the lowest to CF4. An explanation for this case cannot be understood unless one takes into consideration structural properties such as charge distribution and molecular symmetry that gives the highest dipole moment to CH2F2. The inorganic section works in a similar fashion to the organic section. The main difference is that one can navigate the data by selecting groups and periods of the periodic table. This section contains both data for chemical elements (such as ionization energy, radius, and melting point) as well as inorganic compounds. The navigation of properties of chemical elements has a similar functionality to the chart section of the Periodic Table Live! developed by J.W. Moore et al.23 The inorganic compounds are classified in oxides, oxoacids, oxosalts, halides, and hydrides, and only melting and boiling points are available. Although the JSmol structures shown for covalent inorganic compounds are still valid, the structure for ionic inorganic compounds show the cation and anion separated at a random distance and in no crystallographic structure. Future

case for molecules containing postsecond period elements. In this case, the partial charges would have to be calculated by using quantum chemistry packages. This was the approach that was followed to build the Models360 collection of molecules.19 Given that the size of the database is not too large, it was not necessary to store the molecular data into a relational database. Therefore, all the information is read from JSON files stored on the server’s file-system that are being retrieved client-side with AJAX as needed. Graphical Interface

The ChemEd X Data web platform is built with Javascript and the jQuery library.20 Using Javascript interactive and graphing tools, unlike Java applets, may allow its access not only from desktop computers but also from phones and tablets. The representations in ChemEd X Data have three distinct areas, namely, organic, inorganic, and reactions. The organic section is designed for users to discover trends and relationships between molecular structure and activity by plotting a sequence of data (see Figure 1); for example, figuring out the effect of mass or the effect of chain length in properties such as heat of combustion or melting point (see below for more specific examples addressing specific learning objectives). One can navigate this section by choosing as many functional groups as desired. The tables are built with the Datatables plugin.21 Tables contain molecular data and they are automatically updated on the right side of the page as the user checks or unchecks the boxes in the functional groups panel or the radiobuttons in the properties panel. The dynamic tables display the common name, an image, molecular weight, number of carbons, and the chosen property. Each column on the table can be sorted according to the displayed values. In order to narrow down any selection, typing any keyword in the search box right above the table can also filter the table rows. The idea for flexible tables is that users can easily browse and find the molecule and property they are looking for. Users may click on 1502

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developments will include crystallographic information files (cif) for ionic compounds. This problem is solved in the elements section, where the crystallographic structures are taken from the Models360 API.24 Finally, the section with reaction data offers pK, Ksp, and Eored values for common acid/base, solubility, and redox reactions, respectively. Although it does not contain the powerful search capabilities that the organic and inorganic sections offer, it still allows students to see the wide range of equilibrium data for different types of reactions.

be used for both highly guided exercises as well as unguided and open-ended exercises.

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*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACTIVITIES IN THE CHEMISTRY CLASS ChemEd X Data was designed to be used in class, in the lab, or as homework. Its flexibility allows instructors to address different orders of thinking and different levels of student autonomy. Below, we list different examples of activities that have been used in an undergraduate general chemistry course. It is worth noting that in chemistry departments such as the authors’, where an “organic first” curriculum is implemented, the general chemistry students are already familiar with organic structures. The activities are sorted from highly guided questions (more scaffold) to less specific and more openended questions. The more open-ended the question is, the higher the order of the thinking and the higher the demand for self-regulated skills is.

REFERENCES

(1) Hmelo-Silver, C. Problem-Based Learning: What and How Do Students Learn? Educ. Psychol. Rev. 2004, 16, 235−266. (2) Moos, D. C.; Azevedo, R. Learning With Computer-Based Learning Environments: A Literature Review of Computer SelfEfficacy. Rev. Educ. Res. 2009, 79, 576−600. (3) Devolder, A.; van Braak, J.; Tondeur, J. Supporting Self-Regulated Learning in Computer-Based Learning Environments: Systematic Review of Effects of Scaffolding in the Domain of Science Education. J. Comput. Assist. Learn. 2012, 28, 557−573. (4) Moore, E. B.; Herzog, T. A.; Perkins, K. K. Interactive Simulations as Implicit Support for Guided-Inquiry. Chem. Educ. Res. Pract. 2013, 257−268. (5) Gerjets, P.; Scheiter, K.; Schuh, J. Information Comparisons in Example-Based Hypermedia Environments: Supporting Learners with Processing Prompts and an Interactive Comparison Tool. Educ. Technol. Res. Dev. 2008, 56, 73−92. (6) Zimmerman, B. J. Becoming a Self-Regulated Learner: An Overview. Theory Pract. 2002, 41, 64−72. (7) Dignath, C.; Buettner, G.; Langfeldt, H.-P. How Can Primary School Students Learn Self-Regulated Learning Strategies Most Effectively?: A Meta-Analysis on Self-Regulation Training Programmes. Educ. Res. Rev. 2008, 3, 101−129. (8) Sharma, P.; Hannafin, M. J. Scaffolding in Technology-Enhanced Learning Environments. Interact. Learn. Environ. 2007, 15, 27−46. (9) Prat-Resina, X.; Eklund, B. P. ChemEd X Data Web Platform. http://chemdata.r.umn.edu/chemedXdata/ (accessed Jun 2014). (10) Creative Commons 3.0. Attribution ShareAlike http:// creativecommons.org/licenses/by-sa/3.0/us/ (accessed Jun 2014). (11) API:Main page - MediaWiki http://www.mediawiki.org/wiki/ API:Main_page (accessed Jun 2014). (12) Guha, R.; Howard, M. T.; Hutchison, G. R.; Murray-Rust, P.; Rzepa, H.; Steinbeck, C.; Wegner, J.; Willighagen, E. L. The Blue ObeliskInteroperability in Chemical Informatics. J. Chem. Inf. Model. 2006, 46, 991−998. (13) NIST Chemistry WebBook http://webbook.nist.gov/ chemistry/ (accessed Jun 2014). (14) ChEMBL database https://www.ebi.ac.uk/chembl/ (accessed Jun 2014). (15) NIH: Chemical Identifier Resolver http://cactus.nci.nih.gov/ chemical/structure (accessed Jun 2014). (16) O’Boyle, N. M.; Morley, C.; Hutchison, G. R. Pybel: A Python Wrapper for the OpenBabel Cheminformatics Toolkit. Chem. Cent. J. 2008, 2, 5. (17) Hanson, R. M. JSmol http://sourceforge.net/projects/jsmol/ (accessed Jun 2014). (18) Halgren, T. A. Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization, and Performance of MMFF94. J. Comput. Chem. 1996, 17, 490−519. (19) Prat-Resina, X.; Holmes, J.; Moore, J. W. ChemEd DL Application: Models 360 http://www.chemeddl.org/resources/ models360/(accessed Jun 2014). (20) jQuery Javascript Library http://jquery.com/ (accessed Jun 2014). (21) DataTables https://github.com/DataTables/DataTables/ (accessed Jun 2014). (22) Flot: Attractive JavaScript plotting for jQuery http://www. flotcharts.org/ (accessed Jun 2014).

“Explain Why” Questions

• Plot the boiling points for the following molecules and provide an explanation that justifies the order: HOCH2CH2CH2OH, CH3CH2CH2OH, CH3(CH3)CHOH, CH3CH2OH, CH3OH. • Use JSmol representations to explain the boiling point trend in the following molecules CH3F, CH2F2, CHF3, and CF4. “Problem Solving” Questions

• Considering the fact that the general rule regarding trends in molar heat capacity is the heavier the molecule, the larger the heat capacity, why does the heat capacity decrease in the following set of data? CHOOH, CH3CH2OH, CH3(CO)CH3, CH3OCH2CH3. “Open-Ended” Questions

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• Choose a set of molecules that prove that dipole−dipole interactions are stronger than London dispersion forces but much weaker than hydrogen bonds. • Are there any exceptions to the statement above? Find one and provide and explanation for not following the general trend. • What molecular properties have an influence in boiling points? Include functional groups, molecular structure, and so forth. • Are the rules that you inferred for boiling points applicable to melting points? Provide examples.

CONCLUSIONS ChemEd X Data, an open web platform, is presented and can be used in an undergraduate chemistry course to allow students discover correlations and exceptions between physical properties. It is important to notice that the “open-ended questions” listed above cannot be completed without a flexible platform such as ChemEd X Data. The web platform presented here can 1503

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(23) Periodic Table Live! Charts. http://www.chemeddl.org/ resources/ptl/ (accessed Jun 2014). (24) Prat-Resina, X.; Holmes, J.; Moore, J. W. ChemEd DL: Web API. http://wiki.chemeddl.org/mediawiki/index.php/ Development:ChemEdDLAPI (accesssed Jun 2014).

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