GUIDELINES FOR ABSTRACTS

Submission may include a wide array of concepts, research at all levels of progression, programs, evaluations, and ventures that reflect achievements and thought leadership. The deadline for abstract submission is May 15, 2016.  Abstracts should be approximately 200 words and will undergo peer-review. Notifications will be distributed no later than June 15, 2016. For information on poster specifications, how to develop your poster, sample abstracts and posters, please review the various tutorials on this website. For abstract or poster consultation, please contact learningshowcase@franklin.edu.

Your abstract should include the following information:

  • Introductory sentence(s)
  • Statement of hypothesis, purpose or question of study
  • General methods/procedures used, i.e., interviews, research, prior course integration
  • Primary result(s)
  • Primary conclusion of the work
  • General statement of the significance of the research

An abstract is simply a summative paragraph about your research, concept, program or initiative, or innovation. Below is a topic list of sentences that should guide your audience and inform them of your work. Usually, abstracts should be no longer than about ten sentences.
Motivation: Why do we care about the problem and the results? If the problem isn't obviously "interesting" it might be better to put motivation first; but if your work is incremental progress on a problem that is widely recognized as important, then it is probably better to put the problem statement first to indicate which piece of the larger problem you are breaking off to work on. This section should include the importance of your work, the difficulty of the area, and the impact it might have if successful.
Problem statement or Question: What problem are you trying to solve? What is the scope of your work (a generalized approach, or for a specific situation)? Be careful not to use too much jargon. In some cases it is appropriate to put the problem statement before the motivation, but usually this only works if most readers already understand why the problem is important.
Prior Knowledge: This is important if you are conducting research or evaluating a program or initiative. What have others done that informed your work?
Approach: How did you go about solving or making progress on the problem? Did you use simulation, analytic models, prototype construction, or analysis of field data for an actual product? What was the extent of your work (did you look at one application program or a hundred programs in twenty different programming languages?) What important variables did you control, ignore, or measure?
Results: What's the answer? Specifically, most good computer architecture papers conclude that something is so many percent faster, cheaper, smaller, or otherwise better than something else. Put the result there, in numbers. Avoid vague, hand-waving results such as "very", "small", or "significant." If you must be vague, you are only given license to do so when you can talk about orders-of-magnitude improvement. There is a tension here in that you should not provide numbers that can be easily misinterpreted, but on the other hand you don't have room for all the caveats.
Conclusions: What are the implications of your answer? Is it going to change the world (unlikely), be a significant "win", be a nice hack, or simply serve as a road sign indicating that this path is a waste of time (all of the previous results are useful). Are your results general, potentially generalizable, or specific to a particular case?