24 July 2015

Getting ready for Botany 2015!

I'm heading off to Edmonton this weekend for Botany 2015. This conference is co-hosted by a handful of professional societies, a few for which I've maintained membership for several years (Botanical Society of America and American Society of Plant Taxonomists). I'm excited to be returning to this conference after attending Evolution for several years. My dance card is very full for this conference, but I'm looking forward to each part:
  1. Ecological niche modeling workshop: A few of my undergraduate researchers are pursuing some projects involving species distributions and niche modeling. Pam Soltis (who was on sabbatical at NESCent when I was there) is hosting a workshop on this very topic, and I'm elated to have some time to sit down and formally learn methods using QGIS, an open-source modeling tool.
  2. Oral presentation: I'll be presenting preliminary results from my characterization of transposable elements in Agavoideae (agave/tequila, yucca, etc). I'm really excited for this collaboration with two other early career scientists, Michael McKain and Alexandros Bousios, to finally come to fruition. You can read my presentation abstract here, and I'll be posted the slides to SlideShare after the presentation. 
  3. PLANTS mentor: The Botanical Society of America sponsors undergraduates from under-represented groups to attend their annual meeting, and I'll be acting as a mentor for one of these students during the conference. He'll be giving a talk on palm evolution that sounds great! I'm looking forward to meeting him this weekend.
  4. Student career luncheon: I've been invited to speak at a luncheon for student conference attendees about careers in botany. This sounds like a great way to share my experiences in my path to research and teaching. My short talk will be followed by "speed dating," where students will be able to interact with professionals.
  5. Professional society service: I have some additional obligations to serve a professional society during the conference, which makes me feel like a Very Adult Scientist but also will keep me pretty busy!

13 July 2015

Debriefing from TACC Summer Supercomputing Institute

I had such good intentions to blog more this summer during my reprieve from class planning, but that obviously didn't work out. A short blog post describing my adventure at the Texas Advanced Computing Center (TACC) Summer Supercomputing Institute (SSI) in Austin, Texas last week seemed a good way to reinforce my lessons learned (as well as provide a convenient way to ease back into a normal work schedule). Here are the highlights:

  • Students in the class were from a variety of disciplines, including applied math, physical science, and life science. Some folks were proficient programmers, others (like me) were familiar with running programs but had little experience writing their own compiled programs (consequently, I'm looking into taking more formal courses in CS).
  • The class covered a variety of topics, including parallel programming (OpenMP and MPI), debugging/optimization, data management, and data visualization. You can see some of TACC's previous course materials here (and I hope they'll add public access for materials used for our class, some of which were new!). I was especially enthralled with the session on data management, and am intrigued by exploring Hadoop for genomics.
  • Given that this class (as well as many of TACC's other training sessions) are geared towards folks with a background in programming, I spent some time talking to folks there about whether the resource is appropriate for entry-level folks (i.e., biologists rather than computer scientists). As it turns out, the marketing and education folks there are very interested in continuing to expand the user base, including folks who may not be very proficient at the command line. To that end, I've started a GitHub repo to develop materials for folks new to TACC (which I'll rely on heavily for my graduate-level bioinformatics class this fall). These materials might also be of use to folks who access HPC resources through XSEDE, which has a campus champions program that I'm thinking of joining. 
All in all, it was an intense but intellectually profitable week. Plus, I learned to make fancy figures! I don't know what it means, but the units are PARSECS! Super cool.

11 May 2015

Self evaluation for teaching an undergraduate bioinformatics course

I wrote several lengthy posts last fall and winter that reflected on my preparations to teach a new course this spring (bioinformatics lecture and lab for undergraduate biology majors, main post here). The logistics and intellectual drain of new course prep kept me from writing much about this course as it progressed. Now that the semester is nearing its end (finals are in a week and a half), I'm compelled to report back on how the class developed.

For a quick overview, check out this poster I put together for the UT Tyler teaching symposium, highlighting my experiences implementing this new course:

While each bullet point below could certainly warrant a post all by itself, I'm going ahead and outlining everything while it's still fresh in my memory, laundry-list style:

General course description:
  • Lecture met twice a week for an hour and a half on Tuesday/Thursday morning (three hours total per week)
  • Lab met once a week for three hours on Thursday night.
  • Eight students enrolled, all biology majors, mostly pre-professional.
  • Assessment consisted of weekly homework submitted via GitHub for lab and Blackboard for lecture. Students also completed a class project for lecture by researching and presenting on a topic we didn't cover in class.
  • Only pre-requisites were two semesters of introductory biology.
What worked well:
  • I tried to adopt a lecture style that minimized actual lecturing (I averaged 20 slides for an hour and a half lecture). I implemented class discussions and think-pair-share type activities, including drawing a concept map at the end of semester to summarize.
  • For lab, my students loved R, especially working in RStudio.
  • I explored the use of analogies to explain complicated concepts in genomics and bioinformatics.
  • I used signed pre- and post-class surveys and anonymous mid-semester evaluations to gauge how students felt about the class (this is mostly how I know things were working well!).
What I'll change for next time:
  • Establishing the computational infrastructure remains challenging. I can't require my students to have their own (personal) machine for installing software. I have a computer lab for students to use during lecture and lab, but university policy constrains my ability to use these machines (e.g., I can't install software myself). I also had students log on to a remote HPC resource through TACC, but about half my students had problems accessing it. 
  • Continued from the last point, I had students use Cygwin to learn Unix/shell/bash commands, but the installation on the class computers made the path names ridiculously awful to navigate. My students agreed this was their least favorite part (which is a shame, since shell scripting is my personal workhorse for research).
  • Students appreciated not having exams, but the workload (for them and for me grading) was a bit cumbersome (I had a lecture and lab to grade for each student almost every week). I will consider using alternative assignments (weekly online quizzes, and halving the number of lecture assignments) in the future.
With all of that in mind, I'm pretty happy with how the semester finished out, and am still excited to teach Bioinformatics for Research at the graduate level this fall.

24 April 2015

Under-appreciated Texas wildflowers

In my ongoing quest to balance the computational aspect of my work, I've been working with the East Texas Master Naturalists to continue developing their herbarium collection of local, native plants. It's been a great synergistic relationship: they teach me about native species, and I've been setting up their herbarium database as a series of spreadsheets and documents in Google Drive.

We met this morning out at The Nature Center, a Texas Parks & Wildlife facility that houses the herbarium and has meeting space. This very wet spring has led to an abundance of iconic Texas wildflowers, like bluebonnets and primroses. Much to my delight, I also found some of my favorite spiderworts growing nearby. It's been several years since I did serious plant collections, but I still managed to spot them on a roadside on my way from campus this morning. I apparently haven't lost my skill at picking out the flower color and growth habit from the multitudes of flowers blooming right now. This beauty (picture to right) is a great example of Tradescantia ohiensis, one of the very widespread species of erect Tradescantia. Each individual flower only lasts a day before deliquescing (melting), but the plant will keep blooming until next fall, as long as it doesn't get fried in the Texas heat.

While visiting with my old friend T. ohiensis, I took the opportunity to scratch another itch that's been in my mind for several weeks now. I've been absolutely awestruck by the thistles growing on the roadsides this spring. The picture (to the left) doesn't do it justice, but these plants are almost five feet tall, and covered in menacing, spiky leaves. There appear to be several species of Cirsium here in Texas, and I'm looking forward to seeing more examples of these monsters.

While perhaps not as charismatic as other wildflowers, these two examples get a thumbs up from me as particularly cool plant species.

30 March 2015

Data Carpentry hackathon for genomics

I'm pleased to report back from the Data Carpentry (DC) genomics hackathon, which I attended last week with ~26 other folks at Cold Spring Harbor Labs in New York. The goal of this meeting was to develop modules for a DC workshop focused on analysis of next-generation sequencing and other genomic data. The original DC lessons were designed for a very general audience using ecological data, so we were tasked with outlining, organizing, and starting to write materials for a two-day workshop specifically for genomics.

Each of the following points could be thoroughly explored in their own post, but here are a few highlights from this meeting:

  • Attendees were a great mix of biology researchers and educators from a range of institutions (research intensive, primarily undergraduate), computer scientists, and assessment specialists. This meant we were pulling from a broad range of skills, and incorporating multiple perspectives in planning.
  • The length of the meeting (2.5 days) allowed us to get a running start on actually developing materials (GitHub repos here prefaced with "genomics"). In addition to "intro to Unix" material that would largely remain constant from the original DC lesson, we started developing six modules that cover a general genomics workflow: setting up a project, getting to know your data, data wrangling (QC and alignment), analysis and visualization, and cloud/HPC. I personally found it remarkable and gratifying to see so much attention paid to the initial preparatory stages of a project.
  • Numerous folks emphasized the importance of understanding your target audience. Some of these discussions related to the assumed skill level (or pre-requisites) for workshop attendees. Other conversations related the need to accommodate particular cultural or gender issues while teaching to make the learning environment comfortable for everyone. 
  • What makes DC workshops special and distinct from other courses? In developing the modules described above, we talked about the distinction between Software Carpentry and Data Carpentry, as well as if and when instructors should be expected to teach about biology (rather than computing/data analysis). The general consensus is that the focus of DC on telling a narrative about data means we should be emphasizing "best practices" for improving productivity and reproducibility, rather than advocating for particular types of analyses. That being said, there is ample opportunity during lessons to model rigorous methods, as well as provide extra resources for students to improve their skills in experimental design and statistical reasoning.
  • A particularly challenging aspect of developing such resources is assessment of student improvement following a workshop. It's challenging to evaluate how much students will retain after such a short period of time (2 days), as well as whether these skills will transfer over to their research methods. One breakout group focused on developing a strategy for surveying students prior to and directly following a workshop to measure immediate learning, as well as 3-6 months following to measure long-term gains. We targeted question formats that would address student learning in terms of the following areas: declarative knowledge (Can you recall this fact?), skills (Can you write this code?), and attitude (Will you use this skill?).

I was initially on the fence about whether to apply for the hackathon. I'm a first year professor wallowing in the murky depths of teaching a new course, and my overtaxed brain was whispering that maybe it would cause too much stress. My gut, thankfully, doesn't always listen to my brain. Moreover, the class I'm piloting this semester is an undergraduate bioinformatics class focused on genomics, so the DC hackathon fit naturally into my preparation for the last few weeks of the semester. I'm looking forward to reporting back soon about my semester-long class is wrapping up, as well as my first teaching experience for Software Carpentry workshop in a few weeks.

18 December 2014

Formal address.

Right after finishing my PhD, I started preparations to move to North Carolina to begin a job as a postdoctoral researcher. My mother accompanied me on a preliminary scouting trip to find an apartment. I was baffled and a little amused when she made sure potential landlords and leasing agents knew I was "Dr." Kate Hertweck.

My title has never really felt comfortable to me. I certainly feel like I earned it, but I don't necessarily feel compelled for other folks to address me as such. I added "PhD" to my email signature, along with my affiliation, and that seemed to suit my electronic communication needs. It took over a year before I stopped laughing when people introduced me in person using it. Now that I'm a professor, I still don't introduce myself using that title. More often than not, however, I find myself needing to clarify to various folks on (and off) campus that I am, indeed, a Doctor of Philosophy.

I've taught classes as both a graduate student and postdoc, and until now I've been comfortable with students referring to me by my first name. As I'm writing the lab manual for my class next semester, though, I'm constantly second-guessing my choices in how to reference myself. The generic "your instructor" seems so sterile and unnecessary, given that I'm writing documents specifically about me and my class. But what is a better option?

Of course, I'm a resource junkie, so I took a few minutes to look at what other folks think about this topic. I grabbed blog posts from NeuroDojo and Small Pond Science and articles from Slate and Inside Higher Ed. I was really serious about learning things, so I even read the comments. Here are the options I've discovered for how students may choose to address me:
  1. Dr. Hertweck
  2. Professor Hertweck
  3. Doctor Professor Hertweck
  4. Dr. Kate
  5. Kate
  6. Dr. Hert (pronounced "hurt")
  7. Ma'am
  8. Ms. Hertweck
  9. Mrs. Hertweck
With such a plethora of options, I definitely feel like I need to at least narrow it down for students. I find the last two to be unacceptable, and #7 to be somewhat distasteful (although I often feel compelled to address other folks as such, and it's rather unavoidable here in the South). #3 has too many syllables, with #2 almost too many. #6 exists only to amuse me. But still, I'm straddling the fence over whether to prefer formal or informal names. I've even considered offering all remaining options to students, and keeping track on which they choose (I really do like collecting data). 

I recognize all the arguments for different forms of address. The argument from NeuroDojo resonates with my personal philosophy of science. However, I'm a young, early-career female, so I may need to impose more authority on students. There doesn't seem to be a clear standard in my department, either. Moreover, when my mom introduced me as a doctor when looking for apartments, it actually made a difference (my application fee was waived). I dislike using that type of privilege, but I need to admit that it does occur.

I suppose I've spent a lot of time thinking about this particular topic because it represents a very tangible manifestation of my uncertainty with my new job description. What's appropriate clothing for me to wear to work? How formal should my language be? Moreover, how do all of these considerations interact with my own personal preferences and sense of self? If any of this sounds familiar, it's because I pondered the same issues of personal feelings vs. perceived expectations in my last post. I suspect that this current post will also not be the last.

15 December 2014

Struggling with assessment.

I'm in the final throes of course design for my bioinformatics class next semester. I've already written a bit about planning the course, and a little about my problems convincing students to take it. I've spent a lot of time getting the computer lab up and running, and a lot of time preparing course materials. Although I still need a few more students to enroll, I'm fairly certain I'll actually get to teach the course (and hey, if it doesn't make this semester, there's always two years from now? *eye roll*).

Here's where I am in planning. I've got a lecture that meets for three hours a week on Tuesday and Thursday mornings, and a lab that meets for three hours Thursday evening. Lecture is about general theories and concepts, while lab is about implementation of that content in coding and data analysis. The course content is split into two sections: the first six weeks is what I call the Bioinformatics Framework, where we talk about bioinformatics as a field of research/applications, managing data, developing pipelines, and hypothesis testing. The second part of the class is Applied Bioinformatics, where we'll cover several "vignettes" of bioinformatics applications, like sequence alignment, clustering/phylogenetics, and genome assembly. I'm pretty comfortable with this plan, including how it relates to my objectives for student learning.

My last big hurdle in course preparation is finalizing how I will assess student performance (i.e., giving grades). Because the class is based on skills development but also incorporates interdisciplinary thinking (biology + computer science), I'll need to implement a variety of assignment formats. I'm planning at least one formative assessment for students to turn in each week to make sure everyone's on the same page. I'm also going to have each student do a class project: researching a type of bioinformatic analysis not covered in class (like protein structure/folding, network analysis, metabolomics, etc). They will present their findings on the major challenges, methods, and applications in that topic to the class, so we'll get a broader feeling for research topics than what I'll have time to cover.

My problem is that I need to be able to explain exactly what students learned during the semester (summative assessment). This is partly for my own ability to track student performance, but also for reporting to departmental and university groups. However, I appear to have developed an allergy to things called "exams" (the most common form of summative assessment). I get anxious just thinking about having to write, administer, and grade an exam.

Azuki beans. They are pretty,
but I am no bean (or point) counter.
(thanks Wikimedia Commons)
I met with a fantastic instructional designer (Leslie Lindsey) from the aptly-named Office of Instructional Design last week, and we talked about different approaches to evaluating student performance. She validated me in my belief that I can give a course that does not include exam-based assessment. She helped me realize that my aversion to exams seems to be a fear of the reductionism of simply counting points to assess student learning, which seems to be required to give a final letter grade in the class. What she said to me blew my mind: "Think of assessments as a way of collecting data about student performance."

Oh, the irony! I'm teaching a class on using computers to analyze biological data and I failed to realize that assessing student performance and assigning grades is just another data analysis problem. I was getting bogged down in my imagined obligations as a professor, and not thinking about this enough as a data analysis problem. My problem is largely semantic, and perhaps I just need to think about offering a different kind of exam, designed to emphasize the things I value as a professor. I value steady, consistent effort by students throughout the semester, even if it means I need to keep up with grading on a weekly basis. I value student comprehension that allows conceptual synthesis and connection between topics, but understand this may take more time than is allowed in a class period. I don't want my need for data collection to adversely affect student grades when there are other, better means of assessing their understanding.

Ultimately, I've decided to use an evaluation strategy based on "units of assessment" that are graded on a similar (but adjustable and specifiable) rubric. Weekly assignments in both lecture and lab will count as 1 unit each. Research projects for each class will have multiple parts, each of which counts as 1 unit. For lecture, I'll have a day each for both the first and second part of the course for students to perform summative assessments. These assessments will include two parts (one in-class, one out-of-class) which count as 1 unit each. That means the summative assessments are weighted as a bit more important than weekly assessments. I'll average the rubric scores throughout the semester and convert to a letter grade. This seems much more palatable to me than assigning absolute point values or weighted percentages to every type of assessment. Also, I'm hoping it will capture student performance much more authentically than grading based on exams that occur on a few days throughout the semester.

I don't know if this makes sense to anyone else, but it's starting to make sense in my head?