Stories, Humor, Analogies, References and Pictures/Visuals — SHARPS — are anchors for the details, facts and figures of our presentation – they make our message relevant, entertaining, and memorable to our audience.
Quick report: I used Cliff Atkinson’s Beyond Bullet Points quite dogmatically to create a one hour forty minute first-day lecture for a master’s class this week. I was very pleased with the results: after going through all of his templating and storyboarding steps, I had a much better idea of the main points I wanted to convey, and gave a much more focused presentation.
I also think the presentation was more lively, provocative, and engaging: it was a lecture to about 70 students I don’t yet know, in a large room, so it wasn’t intimate, but I think it was much closer to active learning than my previous versions of this same lecture have been. They seemed to enjoy it, and my doctoral student teaching assistants said it was an especially good opening lecture.
That said: it took me an enormous amount of time to prepare. This is partly because I basically had to read the entire Atkinson book, and go through the learning curve — future lectures using this style will be faster. It also took a lot of time because, even though I’ve given this lecture several times in the past, I reconceived the entire thing from scratch, rewrote every sentence, found new graphics, changed the visual style, etc. So, even though I thought of this as refreshing a lecture, it was really more like writing a new lecture from scratch, which is and should be time-consuming.
The other caveat: I’m not convinced yet that the rigid storytelling style that is the core of Atkinson’s method will work as well for all classroom lectures, especially with more technical material. This, being a first lecture, was largely motivational and high-level concepts (I don’t waste time with administrative trivia — handouts take care of that): I had a few persuasive points I wanted to make, and telling them through a story make sense. But when I’m teaching skills and techniques, I expect the method will need some modification.
But overall, I think Atkinson’s main communication points, drawing on (and referenced to) cognitive psychology literature on how people learn from visual presentations, are on target, and worth learning through doing, to incorporate in most presentations, even if not developed using his storyboarding method.
|Cliff Atkinson, Beyond Bullet Points|
Rick Reis at Stanford’s Center for Teaching and Learning has been running a quite nice mailing list for some time, also now available as a blog (with commenting): Tomorrow’s Professor Blog. If you want the mailing list (old fashion RSS 🙂 you can subscribe. And a searchable archive is available.
Reis sends out about two mailings/postings a week. The focus is quite similar to this blog: the archive and blog are categorized as: Academy; Graduate Students and Postdocs; Academic Careers; Teaching and Learning; Research. Most of the postings are written by scholars and other experts for various forums; Reis gets permission and re-posts them. So, lots of viewpoints and multiple expertises. There are several hundred articles in the archives (including articles from a Chronicle of Higher Education column Reis writes, such as “Interdisciplinary Research and Your Scientific Career”, and “The Scientific Job Talk”).
I find quite a few of these stimulating (and many irrelevant to what I do). I’ll blog specific articles from time to time, but wanted to highlight the overall resource, too.
Scholars and their employers have long wanted metrics for measuring the importance or impact of a scholar’s research output. Citation counts have been used for years, often based on the citation indices published by ISI. Recently many have started doing citation counts using Google Scholar (GS). Judit Bar-Ilan wrote a scholarly article comparing ISI, GS and Citeseer.
Recently, there have been various attempts to create metrics that are more informative than merely counting citations. The current favorite seems to be the h-index, suggested in 2005 by Jorge E. Hirsch at the University of California, San Diego (An index to quantify an individual’s scientific research output, arXiv:physics/0508025 v5 29 Sep 2005). The Wikipedia article has a good summary. Two others are the g-index (Leo Egghe, Theory and practice of the g-index, Scientometrics, Vol. 69, No 1 (2006), pp. 131-152) which gives more weight to highly cited articles, and the contemporary h-index (Antonis Sidiropoulos, Dimitrios Katsarow, and Yannis Manolopoulos in their paper Generalized h-index for disclosing latent facts in citation networks, arXiv:cs.DL/0607066 v1 13 Jul 2006), which is parameterized to weight recent articles more heavily.
Here is a web based h-index calculator (using citations from GS as its database). Note that any calculation is subject to error if the scholar’s name is not unique; this tool provides a boolean keyword restrictor that offers an attempt to ameliorate this problem. And here you can download a software tool that calculates h-index, g-index and others. This tool reports all of the articles used for the count, so you can check to eliminate those by different authors.
Using both of these tools, my h-index is 24 (although two articles with 24 cites also appear with 1 more cite to a listing with a typo in the title: when combined, my h-index is 25, a small example of the errors automatic tools can make).