October 23, 2007
Sleep and scholarship
I have been chronically sleep-deprived since college. Perhaps as a consequence, I have become interested in sleep research over the years (and I have been diligent about trying to teach my kids good sleep hygiene!).
Not a lot is known about the role of sleep for cognitive activities, but much more is known than a couple of decades ago. What does this have to do with scholarship? Many research studies indicate that long-term memory formation, learning, complex skill performance, and creativity are strongly affected by sleep patterns.
A good place to start learning about sleep research is Stanford Professor William Dement's The Promise of Sleep. He explains the basic physiology of the sleep cycle and summarizes the state of sleep research (as of about 2000), with interesting results on memory, reaction time, learning, etc.
A lengthy article in today's New York Times reports on research by Dement, recent work by Prof. Matthew Walker at Berkeley, and others, on the role of sleep in learning and memory. For example, there is a large body of evidence now that the period of deep sleep that occurs relatively early during a normal night of sleep is crucial for encoding and strengthening declarative memory (like memorized facts).
Stage 2 sleep, on the other hand, which mostly occurs during the second half of the night, seems critical for mastering motor tasks (like playing the piano).
A story on LiveScience.com reports on other research by Walker showing that emotional responses to negative stimuli dramatically intensify in the sleep-deprived.
Po Bronson wrote another lengthy journalistic article summarizing research on sleep and learning in New York Magazine (2007).
One piece of suggestive evidence that I find particularly compelling (because of my passion for playing the piano): In his famous studies on deliberate practice and expertise acquisition, K. Ericsson and co-authors reported that the best violinists got measurably more sleep than good violinists and teachers, and also took more naps (1993).
October 21, 2007
Drago Radev's skill list for Ph.D. students
My colleague Drago Radev (with help from his former student, not a graduate, Jahna Otterbacher), has compiled a list of skills Ph.D. students should develop before they complete their degree (some are specific to natural language processing or computational linguistics). As with many things Drago does, this rather takes my breath away, and I think I don't score well enough for him on many (despited being 21 years past my Ph.D.!)
The list is long, so...
Prof. Dragomir Radev's Advice for Ph.D. Students
(with contributions from Jahna Otterbacher...)
List of skills that a PhD student in NLP/CL should have by the time he or she gets a PhD.
A new student should be expected to score very low on most of these criteria while one about to graduate should get very high scores on almost all of them.
- ability to build evaluation pipelines and perform evaluations for new tasks
- ability to locate and read the relevant papers on a "new" problem
- ability to come up with "easy" and "reasonable" baselines
- ability to find, download, install, and run existing software from third parties
- familiarity with machine learning, graph theory, linear algebra, calculus, combinatorics, statistics, and text processing
- understanding of linguistic phenomena and annotation
- understanding the variability of human judgements
- ability to write good narratives of experiments
- ability to write good overviews of existing research
- ability to develop and give presentations
- ability to discuss research with other team members
- ability to see a problem or an approach from a very broad perspective
- ability to assess the feasibility of a problem or approach
- ability to plan a research project and execute it over time
- intuition to try alternative methods
- understanding of the relative advantages and drawbacks of general methods across problems
- ability to implement in code generic algorithms and to make appropriate modifications to them
- understanding of related sciences such as bioinformatics, artificial intelligence, etc.
- understanding of computational complexity
- understanding of the fundamental data structures and algorithms
- familiarity with the availability on the Web of relevant corpora, papers, and tools
- excellent understanding of UNIX, including process control, scripting, and backup
- ability to build web-based and local demonstration systems
- ability to describe one's research to others with different levels of overlap in backgrounds with the student's
- understanding of project management: CVS, documentation, modularization, portability of code
- knowledge of a number of programming languages: C/C++, Java, perl/python, matlab
- ability to plan one's time, esp. wrt. courses, travel, committees
- ability to read a paper and abstract its main points - both strenghts and weaknesses
- ability to draw charts, diagrams, screen snapshots, and other illustrations for papers
- ability to write quick scripts to convert data from one format to another
- ability to write quick scripts to test existing libraries or external software
- ability to write quick scripts to evaluate experiments
- ability to teach the introductory class, as well as plan it and grade it
- ability to relate one's work to similar problems in related research areas
- ability to store and retrieve data in a database systems
- ability to write interfaces to existing resources: both local and Web-based
- ability to network with colleagues
- ability to promote oneself
- ability to organize events: colloquia, external visits, etc.
- ability to build an end to end system
- ability to take initiative and to propose new projects
- ability to write proposals for funding
- ability to elicit assistance from advisers, fellow students, and others.
- ability to ask intelligent questions at talks
- ability to design and perform user studies
- ability to request and obtain IRB support for user studies
- knowledge of a range of research methods, and an ability to read and give feedback on colleagues' work (that is not necessarily in my own area of interest).
- ability to initiate collaboration with others.
- knowledge of people from whom he or she can ask and receive helpful feedback on my work.
- knowledge of research communities in which to become an active member, get good feedback on his or her work and get exposure of his or her work to others.
- awareness of his or her key strengths as a researcher and future teacher (for people with academic career aspirations). Learn how to emphasize his or her strengths and use them to have impact.
October 03, 2007
"Quick thinks" for active learning
A leading communication objective for scholars is to persuade; another is to inform. Often the goal of informing is to make the audience aware: I have a new result, it is interesting, go read the paper to learn the details (the typical 15 minute conference presentation, for example). Sometimes of course, we want our audience to learn more deeply.
Though I mostly intend this blog to focus on scholarly activities other than classroom teaching, the line between classroom teaching and communicating our research ideas is blurry, to say the least. Yesterday I read a good 1997 essay aimed at classroom teachers; I'm reporting it here because I don't want to forget its advice, and because techniques to increase active learning are useful in seminars, conferences, and even in written scholarly communication (though the techniques need modification for different contexts).
Suzanne Johnston and Jim Cooper wrote about "Quick-thinks: The Interactive Lecture" (in the Cooperative Learning and College Teaching newsletter Vol. 8, no. 1 (Fall 1997)). They offer a good summary of then current research on the importance of active learning in the classroom, for those not already familiar with the ideas and their empirical support. Then they offer eight "quick-think" strategies, particularly for large audiences for which it is difficult to engage in whole-group discussion or even to break out into small groups:
- Select the best response (multiple choice)
- Correct the (intentional) error
- Complete a sentence starter
- Compare or contrast (two important parallel concepts from the lesson)
- Support a statement
- Re-order the (jumbled) steps (when teaching a procedure)
- Reach a conclusion (from proposed facts, assumptions, opinions)
- Paraphrase the idea
Via the Tomorrow's Professor (SM) mailing list (2 Oct 2007); all entries are archived (with a two-week delay).
October 02, 2007
Should scholars rely on Wikipedia?
As soon as Wikipedia achieved much critical mass, students began citing to it, and professionals and other writers have followed suit. Should research scholars rely on Wikipedia?
Neil Waters, a professor in the Department of History at Middlebury College, thinks that Wikipedia is a good place to get ideas, to get an initial introduction to a topic, or to get leads on references to pursue. He thinks students and scholars should not rely on it, however (that is, in scholarly currency, should not cite to it as a reliable source). He has published a short, cogent essay presenting his argument in the Communications of the ACM.
I agree with Waters. Indeed, Wikipedia agrees with Waters. This is not an attack on Wikipedia: it is a long-standing and general principle about not relying on (or citing to) tertiary sources in scholarly research, which includes all encyclopedias (even the venerable Britannica). The problems posed by Wikipedia are special, and of special concern, especially for less popular topics, but the principle is general.
One of Wikipedia's principles is "no original research", and all fact assertions are supposed to be documented by citations to primary or secondary sources. The latter guideline is followed only partially, but it is one of the quite useful features of Wikipedia for scholars: get an introduction to a topic, and then start following the references to more reliable source material.