What lab reports can learn from literary analysis (Throwback Thursday)

Series note:  The following post is part of the Rutgers Graduate Student Blog Throwback Thursday blog series, in which we will repost one of our most popular blog posts from years past.

The lab report is a staple of introductory science classes, so anyone who’s taken such a class knows how it goes. There’s a hypothesis, then an experimental procedure, then some data, then a discussion of whether the data agrees with the hypothesis. While the spirit of the assignment is good — emphasizing the importance of empirical verification through an experiment — it perpetuates some key misunderstandings about how real science is done. Continue reading “What lab reports can learn from literary analysis (Throwback Thursday)”

A few things I wish someone had told me before I went to my first conference

As conference season approaches, I always have mixed feelings about going. I feel like I’m going to be missing a lot of work time and giving a big presentation can definitely be daunting. Honestly, however, attending conferences and presenting my work have been some of the most important factors in shaping my research.  After chatting with other conference goers and getting feedback on my talk, I came back from a conference last fall with an entirely new game plan for tackling my next research phase. There’s a couple of great previous posts on why to attend conferences and how to get the most out of going to conferences. Here’s my two cents on some things I wish someone had told me before I went to my first conference:

  1. There’s going to be A LOT of talks and posters to see: choose wisely, make a schedule. One of the first things you should do is open up the abstract booklet with the conference schedule (or download the online version). Make notes of which talks/posters to see and have a schedule for where to be and when.
  2. Go to some talks outside of your expertise. Find something that genuinely piques your interest. You never know where you might find inspiration or what you might learn from seeing how work is done outside of your personal research bubble.
  3. Bring business cards. Check out Rutgers Visual Identity website for a downloadable template for designing business cards. I got 250 cards printed at Kinko’s for cheap and they look good.
  4. Have an elevator speech ready for explaining your research. One of the most common ice breakers when you meet people is, “So what do you do?” Be ready to concisely explain what you do and why it’s important at the level of an educated person who has no idea what you’re talking about. Don’t use jargon. Make it quick; up to 30 seconds is fine and if they want to know more, they’ll ask.
  5. Dress nicely. Talk with people who’ve attended the conference before and ask about recommended attire. If in doubt, it doesn’t hurt to directly email the conference organizers. Always air on the side of dressing up than dressing down. You want to make a good impression – you’re probably presenting yourself and your work to almost everyone in your field.
  6. Make a summary of your conference experience. After you return home, go through the notes you took during talks and type them up. Reference the papers you meant to look up. Organize the business cards you got and follow-up with people you said you’d contact. Talk to your adviser about your experience and compare notes with any other fellow students who attended too.
  7. Try to see the city a little bit. You’re there to go to a conference, but why not plan ahead to see some sites while you’re there during break times? There’s typically group dinners organized at local restaurants, like for a school’s alumni or hosted by a sponsor company – check with your adviser on which ones they recommend you seek out. Maybe you could even extend the trip through the next weekend and do some touring!

March Mad-Scientist

It’s probably been too long since I wrote when I have trouble remembering my password to submit this post. There have been times during grad school when I could easily blame laziness as an excuse, but the past four weeks have been the most taxing and stressful of my academic career: finalizing my dissertation.

So here I am, writing this, in my possession a fully revised and edited document containing over 31,000 words thinking that while my defense is still ahead of me, do I feel much different than I did before sending my final draft to my committee? Okay, bad example, that e-mail had so many emotions tangled together before hitting that Send button.  Let’s go back an hour earlier to when I packaged my Word document into a .pdf and finally had time to exhale. Breath in……and…..out.

I was surprised at how little I felt. Now, maybe this isn’t the case for other people, but I had this preconceived notion that finishing your dissertation should feel like this monumental moment in your life, the culmination of 4+ years potentially ending in you never being labeled a “student” again.  That all those sleepless nights or worse, nights you slept and dreamt about your dissertation, were going to stand for something and you’d have this sense of pride and accomplishment. For me, nothing.

Through the process of writing, editing, yelling obscenities at Microsoft Word, editing, fixing graphs in Excel, and (still more) editing, I started to see places in my results that opened up not holes, but passages for future and additional work that could show critical information. Information that would allow our whole research group to make stronger conclusions about our respective individual projects and potentially what they could mean for the scientific community. So, despite not feeling any changes, those thoughts made me realize one thing. It was time for me to go and maybe that was THE difference.

3D Printing at Rutgers

Since I am going to be using 3D printing as part of my research, I’ve been on the lookout for places to print at Rutgers for quite some time. If you’re also interested to do some 3D printing for your research, or you just want to 3D print something for fun, then I have come across a number of options that might be useful for you. I’m sure there might be even more locations available. So, if you happen to know of any other locations that allow for open use of printers, please let me know.

  1. Douglass Library, Fordham Commons area Fablab, Douglass Campus: on the ground level of the library are two MakerBot Replicator 2’s and computers with design software. You can schedule an appointment to print your project and to get pricing estimates.
  2. Rutgers Makerspace, 35 Berrue Circle, Livingston Campus: MakerBot Replicators and other fun items, like a pool table, are available here. The Makerspace normally has regular drop in hours for printing or just hanging out. The space is run by Rick Andersen who has lots of experience in computers and electronics including web design, Arduino and soldering.
  3. Rutgers Mechanical Engineering Dept., Busch Campus: the department has a few options available for Rutgers affiliates to use, including a Stratasys Objet350 Connex and Stratasys uPrint SE. The contact person for setting up an appointment to get your projects printed and for pricing is John Petrowski (petrows@rci.rutgers.edu).
  4. FUBAR Labs, Highland Park, NJ: Fair Use Building and Research (FUBAR) Labs is a nonprofit that provides a local spot for people with common interests, usually in science and technology, to meet and collaborate. It’s an open community offering classes, workshops, study groups, and long term project collaboration. You can join as a member for 24/7 use of the space, or you can drop by for one of their events to check them out.

“Sabbaticals” for graduate students

Dynamic Ecology is a fantastic blog (written by a small group of contributors) on various topics in academic research and careers, especially in evolution and ecology.  They just featured a provocative new post advancing the idea of taking a “graduate student sabbatical” — when a grad student spends a long period of time somewhere outside of his/her home institution — to achieve research goals (e.g., forming a new collaboration, facilitating field work) or to accommodate family needs (e.g., a significant other with a job elsewhere).  Usually we only think of sabbaticals for faculty members, but grad students often do similar things all the time, even if we typically don’t call them sabbaticals.  It’s a fascinating angle, I recommend checking it out!

Conferences in Mathematics

Attending conferences is an important part of academic work. Conferences help us share our research with one another, find new collaborators and research topics, and keep up to date on our fields of interest.

I recently attended a bi-annual conference hosted by Integers (The Electronic Journal of Combinatorial Number Theory). I should say that my travel was generously supported by the conference organizers (i.e. the journal, via the NSF I believe) and my department & advisor, although I should say that one part of the conference experience is waiting with bated breath to get reimbursement forms processed. The government shutdown doesn’t help with that long wait either.

Rather than talk about the math, which isn’t really the point of this blog, I wanted to share some of the peripherals — the details of the conference, its format, what the experience is like. I have heard stories from other fields of study, and conferences seem to be very different from place to (figurative) place.

The departure is usually a bit of a rush of packing and preparing slides for presentations. Beamer (or equivalent) have become the de facto presentation method at math conferences, having (somewhat recently…) displaced the long-reigning overhead projector. After a day of travel, including a bit of a drive to Carrollton GA (home of UWG), I got some sleep before the first long day of presentations. I don’t travel much, and it is certainly stressful and tiring, but in the end I do enjoy it, especially driving.

Conference presentations are usually split into short (20 min) and long (50 min) talks, the later being given by specially designated (invited, plenary, keynote etc.) speakers of the conference. Most talks aim to communicate some new results, ideas, or insights into some type of research, and even for a specialized conference, there is a great deal of diversity in the subject matter. Some speakers speak to the general conference audience, while others speak to the very best experts in their slice of the research world. Many of the most interesting talks, to me at least, don’t probe into the depth of the subject, but give a gentle introduction or overview, and then outline or sketch the major new results or ideas. I’m more of a breadth-first guy.

The conference lasts for several days, as many conferences do, with talks back-to-back from about 9 to 5 every day. There are breaks for meals and coffee, and many conversations — professional and social — branch out from the main group during and after the sessions. Conferences are a great way to meet, re-meet, or quasi-meet people. I re-met Brian Hopkins, who has done some work related to my talk, and Bruce Landman, who has also worked in a related area (and is one of the conference organizers). Both of them (and several other audience members) had interesting questions and comments following my talk — one of the best parts of a conference is getting insightful feedback from colleagues. But I also met a few people more socially. I had a short chat about hockey with Cam Stewart after overhearing him talking about the sport, and sat at a table during the conference banquet with Steve Butler, Mel Nathanson, and Neil Hindman. Mel proposed an interesting problem at the conference that provided stimulating discussion and that I’ve found to be an interesting diversion even after the conference ended.

I also met other grad students like myself, many from closer to UWG (from schools like UGA, Georgia Tech, etc.), including Kate Thompson, whose advisor Jon Hanke  spoke here at Rutgers (by coincidence) only a few weeks after the conference (he was not at the conference). Making acquaintances can be quite beneficial — in this case, Kate and Jon know quite a bit about quadratic forms, which is something that is at least tangentially related to some long-term research ideas I’ve kicked around for a little while (but quadratic forms, on the whole, is a foreign subject to me). One day, if it comes up, I know somebody I can email if I stumble across questions or ideas I can’t wrap my head around.

Conferences in other fields can (apparently) be very different — my friends in the humanities tell me that conferences sometimes (often? always?) consist of reading papers aloud and asking prepared questions, while I have seen that some (many? most?) scientific conferences revolve around poster sessions and other such media. But for us in math, at least in my experience, it is a long sequence of presentations aimed (usually) at general information for the research-level audience, describing research ideas and perspectives and leaving technical details for the published page. I like this format, especially because it promotes dialog, discussion, and feedback — and helps people like me reach out a bit and meet others with similar interests and ideas in mathematics.

What lab reports can learn from literary analysis

The lab report is a staple of introductory science classes, so anyone who’s taken such a class knows how it goes. There’s a hypothesis, then an experimental procedure, then some data, then a discussion of whether the data agrees with the hypothesis. While the spirit of the assignment is good — emphasizing the importance of empirical verification through an experiment — it perpetuates some key misunderstandings about how real science is done.

As many commentators have previously complained, standard labs teach students that doing science means following a recipe (e.g., the instructions from your lab book), and there is a “right” way to do it and a “wrong” way to do it. (Of course, the “right” way results in data that agrees with the hypothesis.) Practicing scientists know that actual science looks nothing like this. You rarely start with a clearly-defined hypothesis and straightforward experiment to test it. Instead you usually just have some vague idea you want to investigate, and then you do some calculations, perform some experiments, whatever you can think of, but with no guarantee they will work or solve your problem. And often you end up addressing a problem different from the original one you were trying to solve (see my post about this here).

But I contend the lab report fails to teach another important aspect of science: how to craft a persuasive, evidence-based narrative. Real scientists almost never write anything that looks like a lab report. A lab report is, well, just a report: rigid, sterile, lacking any point of view. Reports are what police officers write after they investigate a crime. Scientists write papers for scholarly journals. And scientific papers, in my opinion, are much more like the literary analyses I used to write for humanities classes. They’re persuasive. They have a point of view. You start off with a thesis, which can be pretty specific and quantitative (“My model in equation 1 describes the data well”) or broad and qualitative (“Protein folding stability is the main determinant of protein evolution”). But just like in literary analysis, you’re advancing a point of view, and your job is to convince the reader that it’s valid. To support the thesis you build a narrative based on evidence — in literary analysis, this may be quotations from the work being analyzed or historical facts about the author, while in science the evidence is experimental data and calculations. One professor I had in college described scientists as “lawyers for the natural world.” Your paper describes your case. You are trying to make a persuasive case about some phenomenon in nature, convincing the readers (the jury) that your thesis is correct.

The cold, rigid nature of the lab report pretty much kills this aspect of doing science. To students the lab report mainly serves as proof that they did the experiment “correctly,” and any discussion of the data is perfunctory and merely reiterates what they think is obvious, that the data agrees with the hypothesis. We need to break free from the rigid structure of the lab report and allow students to see their write-ups as opportunities to craft convincing narratives in support of a (scientific) point of view, supported by evidence. We should select topics that allow students to form a non-obvious point of view that must be carefully justified with data and argument, rather than giving them experiments where the outcome is obvious and the data is self-evident. Not only would this teach a much richer and more accurate version of science, but it reveals a major place of harmony for the sciences and humanities: how to use evidence and logical argument to support an idea through writing.

What are community land trusts, anyway?

For the last five years, I’ve been reading, studying, and working with a form of tenureship called the community land trust (CLT).  I’ve become very personally involved, serving both on the research and policy development committee for the National Community Land Trust Network and as a board member for the Essex Community Land Trust in Essex County. But what are they, you might ask?

A CLT is a participatory, community-based nonprofit organization that owns and holds land in trust for the common good. It leases that land to households that purchase the improvements (houses and whatnot) located on the trust’s land. When these households sign the ground lease, they are granted all the rights of more traditional homeownership. The main limitation in the lease comes with the resale of the home. They can only realize a certain percentage of any increase in the home’s value (usually between 10-15%), and can only sell the home to a household that falls within a certain income range. This allows them to realize a certain amount of equity while keeping the home affordable for the next low- to moderate-income household.

It was originally created in the late 1960s as a means for black farmers in rural Georgia to gain and control land. While it remained on the fringe of the affordable housing scene for a few decades after that, its star has been on the rise for the last ten years or so. It has attracted the attention of HUD, the Ford Foundation, and a few other major players on the community development scene. Why did I get interested in it? After spending time walking through neighborhoods in Essex County that had been hit hard by the housing/foreclosure/credit crisis, I became interested in forms of tenureship that would prevent housing from being entwined in the volatility of finance markets and speculative ownership. CLTs and another form of tenureship called limited equity cooperatives caught my eye, and the rest is history. My research is currently focusing on how CLTs are handling their emerging popularity and whether or not their radical ideological heritage as the means to fundamentally altering property relationships will survive the attempt at making them a viable alternative to traditional homeownership.

Any questions? Feel free to leave a comment! I love talking about this stuff.

Research in Mathematics

Working in mathematics, I’ve found myself often asked the question “What do you do?” Sometimes the expected response is my “elevator pitch” (the short blurb about my area of expertise). But sometimes the question is more basic: “What is it you do, though? Do you just sit all day and think?”

Thinking [cc]

Now, to a large extent, many people in research spend all day thinking. However, mathematics is not simply the art of staring at a problem until the solution materializes in one’s head. (It’s worth a try, but often solutions do not come from epiphany alone.) I would like to discuss a few of the ways in which research is conducted in mathematics, with emphasis on the parallels and similarities that may exist between mathematics and other fields, perhaps to somewhat debunk that notion there may not be any such similarities.

Nature [cc]

Mathematics research revolves around proving new theorems — mathematical statements that can be deduced from the fundamental axioms of mathematics and from preexisting theorems. Generally, though, the procedure is not to make a big pile of the existing statements and to try to string them together randomly until one forms a coherent deduction that results in something meaningful. That would be pretty rough sailing! Mathematics relies on conjectures, put forth as believed to be true and hopefully proven by someone at some later time. While there are conjectures (e.g. Goldbach’s conjecture) which remain unsolved for long periods of time (sometimes resulting in notoriety), most theorems start out as rough ideas or propositions that are developed with increasing structure and refinement until they are proven. In addition to proving new theorems, other steps forward in research include constructing examples of mathematical structures and verifying theorems by re-proving them in new ways. Computational work is also done to improve theorems in the case that a theorem is quantitative (or sometimes, to prove that a quantitative result is best-possible).

El. J. Comb. logo

Know the literature: As in most fields, the mathematical literature is vast, and perhaps especially in mathematics, it is easily accessible. Increasingly, mathematics journals are available online — not just through the library system, but free for instant download on the Web. Having less concern for the preservation of intellectual property, many editorial boards have shifted to such open/free publication. (Indeed, I myself have a publication in The Electronic Journal of Combinatorics, which is precisely such a journal.) There is also the arXiv (the X is pronounced sort of like χ, the Greek chi), which hosts preprints of papers and other works of mathematics (and many other fields).

Being familiar with the body of literature, both seminal papers and other older works as well as the current cutting-edge work (as it appears first, usually on the arXiv), is an important part of conducting research in mathematics. Jacob Fox came to Rutgers in 2009, when he was at Princeton, to speak at a seminar. He noted during his talk the importance of being familiar with the literature, mentioning in particular how his knowledge of a certain publication helped him and his coauthors solve a problem.

Mathematics [cc]

Crafting and Proving Good Conjectures: One of the more important questions is where to start — if we’re going to prove a statement is true, what is that statement? Generating good conjectures is not a matter of guesswork or divine inspiration, at least not entirely (although the former may have helped from time to time, and the latter is open to some debate at least). Increasingly, experimentation is a common way to generate conjectures. It is also often useful to test conjectures in small, typical, or special cases (where “small,” “typical,” and “special” depend on the problem at hand). Usually a conjecture applies to too many cases to test them all (sometimes, infinitely many cases), so this methodology is often used to verify that the conjecture is sometimes true, but not to verify the conjecture exhaustively. (Conversely, experimentation may lead to a disproof of a conjecture by identifying, constructing, or otherwise elucidating a counterexample.) Experimentation may also help unearth components of the proof of the conjecture at hand.

It is also crucial to have a firm understanding of the big picture in the field where these questions are being asked. There is a substantial amount of context and content that guides someone to the right kinds of conjectures and the proofs of those conjectures. Mathematics is a field in which the objects of study are highly structured, and knowing these structures helps eliminate some of the technical clutter that can obfuscate the underlying truths that one wishes to prove and the bits and pieces that go into proving them. Many proof techniques can be adapted to different situations, so in some sense theorems may be proved by matching a generalized proof to a statement you would like to prove specifically.

Proof [cc]

Building Theories and Solving Problems: Tim Gowers is famously credited for roughly dividing mathematicians into the two categories problem-solvers and theory-builders (or rather, he is credited for noting this division in his oft-quoted The Two Cultures of Mathematics). I won’t discuss this dichotomy, but these two activities characterize much of the research done in mathematics. Proving single, unrelated theorems one-by-one is not usually how research goes. Rather, the enterprise involves longer strands of investigation — a dozen theorems sometimes collapses into a single stronger and better statement after enough exploration and refinement. Meanwhile, single ideas branch into many avenues of investigation. But generally, the aim is not to knock down one theorem, then turn around π radians and start over, but to work on larger-scale investigations. I could make a metaphor about bowling pins or dominoes, but I think the idea is clear. An important aspect here is also collaboration, which is a major element of research for many mathematicians. Working on papers is one part of collaboration, but other important activities include seminars & conferences (as participants and as organizers), expository writing, editorial work, and many other collaborative activities.

Structure [cc]

So the venture is to find good lines of research and establish some clear, path along that line. There are the two approaches. The first is to identify important problems and build up theory to solve them. One famous example is Fermat’s last theorem, which conjectured centuries ago and recently proved by Andrew Wiles. During those centuries, large swaths of mathematics were developed in large part as attempts to prove this conjecture (including Schur’s theorem, one of my personal favorites). This “problem solver” work weaves what might be the leading strands of the theory, loose and rough but pushing outwards farther than neighboring strands. Such work often moves mathematics in innovative or interdisciplinary directions, building bridges between fields of mathematics, and may also connect with work in applied mathematics. The “theory builders” weave strength and cohesion into the fabric (to extend the metaphor). To this end, they focus their research on developing and enriching the theory. They may work to classify all types of a particular structure, for example. Such work includes that of several Rutgers faculty in classifying the finite simple groups. This theory-building reinforces others’ work as they develop and solve conjectures, as it makes the underlying theory more robust.

Images used in this entry are used under fair-use and/or under licensing guidelines set forth by the copyright holder that allow use in this blog, as presented for educational or critical commentary. Images are copyright their respective holders and credit or source is indicated in each caption or in the text of this entry, as applicable. Thanks to Yusra Naqvi for her helpful comments and suggestions.

Finding the needle in a haystack

Research methodology in the sciences can either make you jump up and down like you’ve won the lottery, or sit and cry (you already have enough of a headache, so banging your head on a table isn’t an option). There are those methods that are fail-safe and easy—the ones you don’t mind doing because you know they can’t go wrong. However, there are those methods that can be anything but right. The ones that make you cringe when you see the results, or become flabbergasted because you just don’t know what went wrong.

Sometimes it’s user error: maybe you added too much of this, didn’t add that, grabbed the wrong thing, pushed the incorrect button, broke something (it happens), the list goes on. Other times, it’s instrumentation: maybe it’s not been calibrated in a while or a sensor is malfunctioning. And at other times, it could simply be your sample. Either way, when things don’t go as you expect it, it becomes a game of cat-and-mouse, finding a needle in a haystack, whatever you have it. You have to hunt for what could be the root of this problem, this pure speck of evil that is getting in the way of you and your research.

However, let’s not overlook the beauty that research methodology has provided us. Yes, most of us complain about how tedious some of the work is, how long of an incubation time we have, how many problems we have with instrumentation. But let’s go back in time to before these techniques were discovered, before instruments were created. We would not be able to complete a fraction of all that science (as well as other fields) has offered. We would not be able to run DNA samples on a gel using electrophoresis in one hour, or extract RNA in half a day. We would not be able to perform all our animal studies, or measure blood samples. So while you’re banging your head (figuratively) on your desk, buried in frustration, think about all the good things research methodologies gives us—without it, you’d only be able to complete a fraction of all that you will accomplish during your time here at Rutgers.

Randomly Walking through Research

From reading papers, it’s easy to gain the following picture of what the research process looks like: someone starts at point A, a known point in the space of knowledge, then directly proceeds through various arguments and data to one’s conclusion at the previously unknown point B.  However, thinking that research actually works this way based on what you see in a paper is like thinking that Michael Jordan just awoke one day and suddenly starting dunking from the free throw line.

No, MJ almost certainly traveled a long road to get that much air.  The same goes for research.  The real research process more resembles the famous physics concept of a “random walk” (or more colorfully, the “drunkard’s walk”).  In a random walk, some process is imagined as an object, perhaps an inebriated human, taking a step in a random direction at regular time intervals [1].  This idea is used to model everything from chemical reactions to stock markets.

The random walk provides an interesting visualization of the research process as well.  Uri Alon, a scientist at the Weizmann Institute in Israel (whose outstanding set of resources for “Nurturing Scientists” will be a topic for future posts), has described the process as the following [2].  You indeed start out at A, headed for B.  (See figure below.)  But instead of a nice straight route, you embark on an irregular trajectory with many detours, barriers, and delays.  Often you are eventually forced to abandon B altogether: B was already discovered by some Russian guys in the 1970s, or maybe it’s impossible, or perhaps it just can’t be reached if you hope to graduate within the current decade.Image

At this point your random walk enters a limbo state that Uri Alon calls “the cloud”: you know you can’t go to B anymore, but you don’t know where else to go.  Being stuck in the cloud is probably the most difficult part of doing research.  But the key is recognizing this is a natural and inevitable part of the process.  If you persevere, you will leave the cloud by eventually finding a new place to go: point C.  In fact, often C is much more interesting than B would have been anyway — the unexpected almost always is.  Of course, sometimes C also fails to work out, too, in which case you redirect to D, E, F, etc.  (Hopefully you don’t run out of letters!)  The point is that research is less like a direct path from A to B and more like a random walk with an unknown trajectory and an unknown destination.  But after all, it is this journey into the unknown that makes research so exciting and so important.

[1]  Mlodinow L.  (2008)  The Drunkard’s Walk: How Randomness Rules Our Lives.  Pantheon, New York.
[2]  Alon U.  (2009)  “How To Choose a Good Scientific Problem.”  Molecular Cell 35: 726-728

Research Methodologies in Laboratory Sciences- The Joys of Analytical Instrumentation

Obtaining a graduate degree would be so much easier if the analytical instrumentation would just work…For those of you would don’t have to run various chromatography instruments (ICs, HPLCs, GCs), thermo-cyclers, spectrophotometers, or any of the other numerous finicky pieces of laboratory equipment, I envy you.  You haven’t had to start your day thinking you would be able to run 100+ samples and get another figure for your thesis, only to spend not just a day but a whole week troubleshooting a mysterious problem, eventually determining you’ll have to order a part that will be delivered in three more weeks just to determine the concentration of your chemical of interest.  This of course holds up all the other experiments you had planned to set up.  I welcome you all to the joys of basic wet science research.

When I find myself in these situations I take a deep breath and think of all the reading I’ll be able to get done while I wait.  In my experience these situations usually arise from a few common problems and are a major part of the experimental process.  First, make sure you really read the instrument manual before you attempt to use anything or try to fix it.  Many times an instrument isn’t working because someone else, who had no idea what they were doing, decided to make a “repair.”  This is one reason it is important for senior members of the lab to instruct the new lab members on proper usage.  Secondly, remember to perform routine maintenance, as neglected instruments are like high maintenance boyfriends and girlfriends.  They will not work solely out of spite if ignored for too long.  Instruments work best when used and maintained on a regular basis. Third, always remember that this is part of the “learning” process.  You never really understand how something works until you have taken it apart and put it back together a million times.   Now not only are you an expert on the instrument, but you can also understand and interpret your data better since you know the limitations of the measurement. Your advisor and other graduate students will agree that this is a large part of the experimental process.

Lastly, if all else fails, blame an undergraduate and take a long weekend or a mental health day.  Delays are only to be expected when relying on group used equipment and if you are lucky someone else will have fixed it by the time you get back.  Plus working this hard makes obtaining the data that much sweeter.  So the next time an instrument, computer, or your “favorite” piece of equipment gives you a strange error message remember that you are not alone and that this is all part of the process.

Educational Research

In the field of education, there are many opportunities for research using a variety of methods. As part of the doctoral program, all students are required to take 4 courses in research methods divided between qualitative and quantitative methods. Depending on the research interest of the student, they may select either methodology, or a combination of both. Quantitative research is research that uses numerical data analysis to support a hypothesis. This type of research is done when conducting program evaluation or when looking for statistical support for a position. Qualitative research is done when the researcher is looking to explain a particular phenomenon. This includes case studies, ethnographies, narrative descriptions, etc.

As part of the research sequence, many doctoral students in the field of education conduct a pilot study. These studies, although they are conducted as part of the qualitative methods course, tend to combine qualitative and quantitative methods to some extent. The pilot study allows students to go into a setting similar to that in which they hope to conduct their dissertation research and get a first-hand sense of what conducting qualitative research is like. In this study, students may take field notes, conduct interviews, analyze documents, survey individuals, and practice any other techniques that they may find useful in their future research. Overall, the research methodologies sequence at the Graduate School of Education is extremely useful in identifying the methods that will be most helpful in conducting dissertation research.

I can just Google it.

Despite having several thoughtful blog entries “in the works,” I thought I’d make my first post about something perhaps slightly amusing and somewhat observational. We live in an age well past the dawn of the internet. Indeed, I would not call it the age of information, but rather, the age of data. Social media, bulk email, Youtube, cell phones, smart phones, Twitter, the blog-o-sphere, and everything else — we are highly connected to media. I was struck recently when a group of undergraduates was somewhat shocked that I knew some basic theorems of mathematics off the top of my head. And while that was surprising to them, they were thoroughly confounded when I identified an arachnid as something other than a spider  — not only that it was possible to identify such things by their physiology, but that one could do so without aids or notes (some were also unaware that such creatures exist at all). Indeed, my observations and conclusions were checked on Wikipedia as soon as they got to a computer.

I don’t know if there are technical definitions of the terms data and information (and whether those definitions vary in whatever fields they find use), but to me, they have sharply different connotations. I believe the superhighway of the Internet, and all of its major repositories of (mostly) text-based media, are not conductors of information, but rather, of data – data of varying types, formats, detail, and reliability. And for that reason, significant research is being done to distill and interpret large sets of data, in myriad formats and structures and scenarios (which is not the topic of this blog post).

What I wanted to discuss is how “looking it up” has become such a pervasive technique for the acquisition of information, and why — with so much data around — it is important to know precisely what this process really means. In the end, I think there is an important distinction between looking up information and hearing it from an authority (in a lecture, discussion, conversation, correspondence, or however else). In person or by some personal medium of communication, knowledge and insight can be expressed and even transferred. The ideas are filtered and interpreted carefully, especially in a dialogue or discussion, and the information is contextualized and is explained with greater depth and breadth than a Google search or a Wikipedia article might provide.

Indeed, I believe various tools like Google, Wikipedia, or Wolfram Alpha (for those of us who are mathematically inclined) have all changed the nature of our interactions (be we students, teachers, or those outside of the university setting) with information and data. Painful anecdotes circulate about students who complain that no sources exist for their term paper because Google can’t find any, or who complain that a math problem cannot be solved because Wolfram Alpha can’t solve it. If only research were so easy!

That misunderstanding, which may be a more subconscious sort of convolution of bad habits and lack of information about better practices, really limits students. And these same misconceptions bleed over into the younger generation in academics and the workforce — and even into older generations as well. Bad habits are hard to break, but surely, they are somewhat easy to adopt. What used to be somewhat novel has become the go-to method for trying to find information, but is it the best first-line for that process? If not, when are alternatives more appropriate?

The reason it is so easy to adopt this model — that all information and knowledge can be obtained by reasonable computer search, and thus does not need to be known or understood beforehand — is that for trivial or logistical information, it has become increasingly valid. Indeed, I am blessed with a relatively uncommon name, and the number one Google search terms going to my website are things like “Kellen Myers Rutgers office hours” or “Kellen TA Math” or the like. Students who need logistical information about my office hours, course policies, etc. can find my website and find all that information there. But if they need help with the course, they should not Google “Kellen Myers calculus homework answers.” I doubt this would be useful, and at the very least, I don’t recommend looking up homework answers online to any students.

Once, in particular, I was sorely disappointed to find students asking (many of them repeatedly) when office hours were. Finally, when one student asked by email for the second time (having forgotten my previous response?), I responded that this information can be found by a Google search or by visiting my website — the student was very displeased, and even accused me of disrespect and dereliction of my duty as a TA for not answering the question directly. This stance, in addition to being somewhat hyperbolic, is an unfortunate passing over of the resources and information at hand. Students can often find a wealth of information about their courses’ logistical information, about their instructors’ availability, library hours, school policies, etc. etc. There is a huge amount of information out there! Here, by the way, is an important note for those who provide this information — doing it correctly, effectively, and clearly is an important part of the administrative side of instruction. Five minutes putting together a clear, concise webpage for a course may save hours of emails, confusion, etc.

But information like times, dates, locations, birth-dates, and so forth, seem to be easily accessible and, if the context is understood, a precise online search would yield this sort of information easily. There is no problem discerning from a search what data are valid and which can be trusted to give the correct, valid, desired piece of information. For example, if I needed to know offhand what year the Magna Carta was issued, without the Internet I could (very cautiously) ballpark it as 1100-1400, but a Google search brings it up immediately (the first hit being Wikipedia, which has the information right there in the first paragraph). But knowing the context helps, as a similar search attempting to find the year Marie Antoinette gave her famous “Let them eat cake.” speech brings up several news stories about Mitt Romney, various complicated historical accounts of how she never actually said such a thing, and much more data (related and unrelated) than I would have liked. More knowledge and context might help me sift through that information, but here Google does seem to fail to deliver precisely the datum I was expecting to find.

In the long run, this issue has an impact on how we teach students to find information, be it informally (that is, day-to-day stuff) or in some formal setting (e.g. term paper). This generation of undergraduates has, after all, never used an actual card catalog. Everything is an electronic search, but knowing how to search effectively and what to expect from various search tools is important, and this might be something students (and scholars, and others) lack. We may not have knowledge of the tools at hand, nor of the results one can obtain when using such tools (or how to use such results responsibly).

Indeed, upper level math courses in particular become a bit tough when planning homework. Is this problem solved online somewhere? Will my students find it through Google? It’s a pretty good argument against posting solutions when often, standard or important exercises would be rendered ineffective by having solutions available prematurely. Perhaps this is another piece of good practice for instructors, in both keeping solution sets off the internet and learning to adapt when such information becomes ubiquitous. It may be challenging, finding ways of still giving effective problem sets without running afoul of these online solutions, but I  would say it is usually possible.

The question becomes more complex when computers can solve problems too. In algebra and calculus, students can make use of Wolfram Alpha to solve problems (now with steps provided explicitly, which makes cheating-detection quite difficult). And this isn’t confined to homework or take-home assignments. Indeed, I have heard of students whose phones have been confiscated during exams, with the Wolfram Alpha App wide open with a solution to an exam question on the display. But is Wolfram Alpha the enemy? Let’s hope not — it’s an enemy we can’t fight! It won’t go away, and surely no one can believe such a service could be blocked, censored, or limited in some way.

Like Google or Wikipedia, Wolfram Alpha and any other such site will be there to provide students with access to various data, and how students use that data is something to which we must respond well, but also for which we can prepare. (And here, perhaps, I disagree with Wolfram’s description of its product as a “knowledge engine. I would consider it a data engine, and that usually it could be considered reliable enough to provide information, but not knowledge. To me, knowledge of a calculus problem is the ability to understand the methodology of the solution and solve it without an outside aid of that sort. I realize all three of these terms I have used without definition, and I am not brave enough to venture some postulated set of definitions for the terms despite using them freely.)

But, if students are taught how to effectively utilize searching resources, including things like library catalogs and journal resources, they will have access to a better base of data. If we prepare them to filter and interpret that data, we can mediate the problems created by the influx of data that might overwhelm someone searching on the Web. And if we prepare resources (mainly, websites) that provide important and essential information through search engines effectively, students will find the right information right away, using the resources that they have come to primarily rely on for acquiring data. And for times when this is not the right way to find data, we can help students learn to use other resources — which may, in this generation, be new to them (up until college, Google and Wikipedia may have sufficed entirely). Eventually, we can hope they will not be reliant on these resources for all data, as research, writing, learning, and other experiences should impart knowledge and information. And we ourselves, as faculty, graduate students, undergraduate students, or anyone else, can learn to better use these resources. Search engines and other online data/information resources can supplement instruction and research, and are incredible tools for data acquisition, but knowing when and how to use them is crucial — not only to prevent misuse or over-reliance on these resources, but to also make use of them as important and increasingly abundant tools for gathering and refining information.

“Desktop Faculty Development” — the Tomorrow’s Professor Mailing List

One of the best online resources for graduate students, especially those aspiring to academic careers in research or teaching, has to be the Tomorrow’s Professor (TP) mailing list:

http://www.stanford.edu/dept/CTL/Tomprof/index.shtml

Managed by Rick Reis, a professor of engineering at Stanford, the list produces entries twice weekly on a wide variety of topics relevant to graduate students, postdocs, and faculty members, many of which are excerpted from recent journals or books.

I first learned about TP through a colleague who forwarded one of their e-mails regarding teaching. Since then I have been actively reading most of the postings, which I’ve found to be both an outstanding source of advice and a great way to keep abreast of the latest issues in higher education and education research.

Anyone can subscribe to the list to receive the regular e-mails (subscription instructions available on the above URL), although the website contains a full archive of previous posts. There is also a blog that regularly reposts the content:

http://derekbruff.org/blogs/tomprof/