Purpose of this Digital Library
Information for Instructors
Contributions to this
collection are encouraged from the 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 at firstname.lastname@example.org. The highest priority for acceptance to this
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 which focus upon modern chemical topics are particularly
Content and Formatting:
Submissions to JCE Data-Driven will include four items,
formatted as follows:
- Abstract (submitted as a separate, ASCII
document): Title, author and author affiliation,
and three to ten sentences that describe the data-driven exercise, the
source of the data, and the intended audience (provide a recommendation of
where the exercise might be used in the overall curriculum). This statement will be abstracted by
- Data-Driven Exercise (submitted as a PDF or HTML
document): Details about the expected content and
organization of the exercise are given below. NOTE: if the exercise is based upon an
extended numerical data set, please provide that data in a separate
‘PLAIN’ .txt file (containing only ASCII style data without descriptors;
the file can be TAB delimited if the data is multi-columned).
- Completed copyright form: This
form can be found here.
- Five key words suitable for indexing the document in the Journal of Chemical Education index.
Suggested key words can be found here.
are encouraged to look through posted submissions for guidance about the
expected layout and content of JCE
Data-Driven exercises. The following general
criteria should be kept in mind when designing your exercise:
The data-driven exercise should be organized into the
following sections (in the following order):
- The exercise should be based upon real-world data, particularly
data that is valuable in terms of learning the concepts encountered
in a 'modern' chemistry curriculum.
When literature data is utilized, credit should be given to the
authors by referencing the source of the data. Synthesized data is not permitted.
- The exercise should be relatively short in duration (i.e. ideally, a
student should be able to complete the assignment in one or, at most, two
sessions). The theoretical background that precedes the exercised should
be brief and include only those concepts that are central to the
- The prerequisites should be clearly stated. Identify the topics that a student will
need to have encountered in class or through independent study prior to
attempting the exercise. Clearly
state whether the student will be expected to use a particular type of
quantitative analysis software.
Specifically, identify whether the assignment can be done with a
simple calculator, or whether the student needs to use a spreadsheet style
software package (like Excel) or a symbolic mathematical software package
(like MathCAD, Mathematica, or Maple)?
- To promote brevity and make exercises usable by the broadest range
of users, no attempt should be made to instruct students in the use of any
particular software environment or computer platform, or how to otherwise
accomplish common numerical methods.
The burden of providing this type of prerequisite training is
placed on the instructor.
You are welcome to contact
the editor/curator of JCE
Data-Driven, W. Tandy Grubbs, at email@example.com
with questions about the suitability and expected content and format of
- Author and Author Affiliation
– a few sentences that state the phenomena that will be addressed and the
scientific model(s) that will be employed during data analysis.
- Prerequisites - classroom concepts that should have been covered prior to
attempting the exercise.
- Resources you will need - specify the quantitative analysis methods
that will be employed (if any) and, therefore, the type of software that
will be required.
- Background - a concise, one or two page theoretical description that
summarizes and, if necessary, expands upon the prerequisite concepts
mentioned in (2) above. Also review any uncommon numerical methods
that may be utilized.
- Experimental Data - the data can be listed in tabular form or,
if it is an extended data-set, a link can be included to a .txt file
(make sure this .txt file is included as part of your submission).
Make sure to reference the data source.
- Exercise - outline a suggested protocol for analyzing the data and provide
questions that the student should address.