For the most part, the scientific revolution witnessed the end of the pure empiricists and rationalists. The scientific method that emerged involved a union of the two philosophies whereby one first makes observations about a particular phenomena (data collection), makes logical rationalizations about patterns observed in the data (mental construction of a hypothesis), and finally repeats data collection on a new system to either confirm or refute the hypothesis.

Are the empiricist’s and rationalist’s philosophies represented equally in the scientific method? Most modern scholars think not; while rationalism plays an important role, there is a general consensus that “doing science” primarily involves empiricism. Empiricism is certainly heavily emphasized in fields like chemistry and physics, where every theory is born of observation and would be meaningless absent the physical world. Only in the field of mathematics does the rationalist camp maintain a comparable weighting, since the relationships between numbers and other mathematical concepts can arguably be mentally abstracted without any reference to the physical world (5).

Was Einstein an empiricist or rationalist? On the surface, he could be viewed as the poster-child of modern rationalism because he rarely entered a laboratory. Instead, he preferred the “laboratory of the mind”, where he ultimately formulated abstract and mathematically laden theories like general relativity (6). But we must keep in mind that Einstein’s theoretical developments did not happen in a vacuum. Einstein entered his laboratory armed with a wealth of prior information about the behavior of mechanical and electromagnetic systems, discovered via empiricist’s methods by a host of previous scientists, including Newton and Maxwell.

Teaching Science

What does all this mean for the chemical educator? Foremost, educators should keep in mind that we are training students to “do science”, which arguably begins and ends with empiricism. Effective pedagogies should mimic how science is done and should encourage students to construct their own knowledge from real empirical observations. For this reason, data-driven methods of learning, which present real-world data and guide students to discover important scientific principles, have long been hailed as superior to fact-driven instruction, which are comparatively passive and often place an inappropriate emphasis on the rationalistic.

Data-driven methods force the student to play the scientist, to engage concepts in light of tangible phenomena, to make connections, and to thereby assimilate an understanding that is both personal and meaningful to a broader range of topics. These activities are even more rewarding when they introduce the student to new methods of data analysis (either conceptual or numerical), involve the consideration of errors, and ultimately encourage the student to contemplate the validity of a model.

While most educators are willing to accept the value of data-driven modes of instruction, we often become frustrated with the apparent lack of raw data that is readily available for designing data-driven activities. Scientists seldom publish raw data in today’s literature. Consequently, instructors are forced to use data from the early to mid-twentieth century that often have little bearing on the modern curriculum. While some data certainly exist in the recent literature, most chemistry instructors simply do not find it worth their time to carry out the search. Students do have access to a handful of data sets that are experimentally acquired during the traditional laboratory portion of their course. Yet, even the most ambitious lab program generates a minimal quantity of data; data that address only a few of the topics that should be covered in the overall curriculum.

The JCE Data-Driven Exercises Digital Archive

The JCE Data-Driven Exercises collection is devoted to the accumulation, distribution, and curricular use of guided-inquiry, data-centered exercises. Each exercise is based upon real data that have been obtained either from the literature or, in a few cases, collected by students. Exercises can be downloaded from the JCE Data-Driven Exercises collection and assigned in or out of class. Each provides supplemental exposure to select topics in the curriculum.

An attempt has been made to keep the exercises in this collection relatively brief so that students can carry out the tasks associated with the exercise within a three-hour working period. The theoretical background descriptions are concise and include only those concepts that are central to the assignment. Students should only attempt certain assignments after receiving a more thorough exposure to the prerequisite topics in class or through independent study. The current collection is focused on physical chemistry, but will ultimately encompass other curricular areas. Since most of the current postings involve analysis of data with respect to a mathematical model, students will need access to some type of quantitative analysis software (spreadsheet-style applications such as Excel are adequate for most exercises, but others may require symbolic mathematical applications such as MathCAD, Mathematica, or Maple). In formulating these exercises, no attempt has been made to instruct students in the use of any particular software environment or computer platform, nor have instructions been provided about how to accomplish common numerical methods. Consequently, students may need some prior software training before attempting certain exercises.

Submissions are arranged by topical area and include (i) a description of the goals, prerequisites, and resources that will be needed to complete the assignment, (ii) a brief description of the phenomena of interest and an explanation (or illustration) of the experiment that generated the experimental data, (iii) the raw data (appropriately referenced), and (iv) a suggested protocol of data analysis and questions that should be addressed.

Currently posted exercises include contributions from:

W. Tandy Grubbs, Stetson University

1. Determination of Virial Coefficients for Argon Gas at 323 K
2. Activities of Hydrogen Ion from pH Measurements
3. Osmotic Pressure and Polymer Molecular Weight Determination
4. Viscosities of Simple Liquids: Temperature Variation
5. Adsorption Isotherms: Methylene Blue on Activated Carbon
6. Standard Molar Entropy of Aluminum Oxide
7. Liquid–Vapor Equilibria: ∆H and ∆S for Vaporization
8. Analysis of the IR Spectrum of Carbon Monoxide
9. Fluorescence Lifetimes and Dynamic Quenching
10. Enzyme Kinetics: The Alcohol Dehydrogenase Catalyzed Oxidation of Ethanol

Arthur Ferguson, Worcester State College

11. Determination of Partial Molar Volumes in Aqueous Solutions of Ionic Compounds

Franklin M. C. Chen, University of Wisconsin–GreenBay

12. Bartender’s Conundrum: Partial Molar Volume in Water–Ethanol Mixtures
13. Strain Energy in Organic Compounds: Bomb Calorimetry

The ultimate goal of the JCE Data-Driven Exercises project is to grow a vast collection of exercises that address most of the important topics that would be encountered in a modern undergraduate chemistry curriculum. This type of growth will require the efforts of several contributors, with expertise over a wide range of areas. As such, additional contributions to this collection are encouraged from the broader chemical community within any curricular area where data-driven inquiry is deemed beneficial (including general, physical, analytical, inorganic, and biological chemistry). As the collection grows, submissions from different areas will be categorized accordingly.

Prospective JCE Data-Driven Exercises should be sent to the editor and curator of this collection, W. Tandy Grubbs. While currently posted exercises can be used as a model, more complete instructions for authors can be found at JCE Data-Driven Exercises Web site (7). Posted exercises will ultimately be separated into two categories: (i) those that have received minimal peer review will be housed in an open-access collection and (ii) those that have received considerable peer review and have been found to be of the highest quality will be housed in a collection that is restricted to JCE subscribers. The highest priority for acceptance to the restricted-access collection is given to exercises that provide ample opportunity for students to interact with the material and discover relevant chemical principles. Submissions that take advantage of more contemporary literature data and that focus upon modern chemical topics are particularly encouraged.

Brief summary descriptions of JCE Data-Driven Exercises submissions that are accepted to the restricted-access collection will periodically appear in the Information, Textbooks, Media, Resources section of this Journal. Each summary will list the title, author information, the URL of the exercise, and will explain the assignment and intended target audience. These summaries will be abstracted by Chemical Abstracts and will therefore provide publication credit to the author.

Acknowledgments

Special thanks to Theresa Zielinski for her helpful suggestions and encouragement regarding this project and also to Arthur Ferguson and Franklin M. C. Chen for their initial contributions to this new digital collection.

Literature Cited

1. Lastrucci, Carlo L. The Scientific Approach: Basic Principles of the Scientific Method; Schenkman Publishing Company, Inc.: Cambridge, MA, 1967; pp 29–51.
2. Van Fraassen, Bas C. The Empirical Stance; Yale University Press: New Haven, CT 2002.
3. Trigg, R. Rationality and Science: Can Science Explain Everything?; Blackwell Publishers: Oxford, 1993.
4. The Rationalists: Critical Essays on Descartes, Spinoza, and Leibniz; Pereboom, D., Ed.; Rowman & Littlefield Publishers, Inc.: New York, 1999.
5. Shapiro, S. Thinking about Mathematics: The Philosophy of Mathematics; Oxford University Press: New York, 2000.
6. Brown, J. R. The Laboratory of the Mind: Thought Experiments in the Natural Sciences; Routledge: New York, 1991; pp 99–125.
7. JCE Data-Driven Exercises Home Page (accessed May 2007).