Preparing students to work with computers

As I gear up to teach bioinformatics again (lecture + lab, to undergraduates), I've been thinking about ways to ease the pain of introducing novice learners to coding and data management. I feel like I'm pretty good at helping students overcome challenges similar to those I personally faced as a newbie, but I'm also realizing that undergrads (at least at my institution) are increasingly ill-prepared with basic computer skills. In fact, some of my students access course reading materials (ebooks and PDFs) and submit homework (through Blackboard, our learning management system) exclusively through a mobile device (phone/tablet), and rarely (if ever) use a desktop computer or laptop. 

To make sure everyone is on the same page, I've decided to spend a bit of time in class the first week on basic computer skills. I've consulted the Wikiversity Computer Skills page and added a few ideas of my own, and here's what I've come up with:

• Operating systems: Windows vs Mac vs Linux, since most students are familiar with the first two but we'll be working on Linux using a Virtual Machine
• Computer organization: User folder, Desktop, Documents, Downloads, etc
• Keyboards/typing: Location of special characters on keyboard (control, alt, etc), importance of capitalization/quotes/spaces
• Internet: remote vs. local computing (since most students don't seem to know where data used on their phone is stored, or where data processing occurs), locating/unzipping downloaded files
• Word processing: Microsoft Word vs. text editors

Granted, some of these ideas will need to be reinforced as we actually start coding (like "Don't write computer code in MS Word), but I'd like to introduce the concepts early. What am I missing? Are there other basic computer concepts we take for granted, that don't really fall into the "computer science" realm, but are necessary to be functional bioinformaticists? Please share in the comments!

Why I sign reviews.

I have been signing my manuscript reviews since I joined NESCent as a postdoc in late 2011. That organization upheld a strong commitment to open science (as evidenced by their data archival efforts since closing their doors last year), and it was that community of scientists that both introduced me to signed reviews and helped me feel comfortable adopting the practice as my default.

There are multitudes of blog posts and articles about signing reviews. Two sources I find particularly useful include multiple viewpoints from different scientists (from The Molecular Ecologist) and interesting data about frequency of signing in different subdisciplines (from publons).

Many of the arguments in favor of signing reviews refer to improving the quality of the scientific assessment in the review, as well as keeping the tone of the review civil and constructive (as opposed to possibly derogatory). I don't believe the content of my reviews differ based on whether I sign my name to them, because I try hard to be rigorous (and not a jerk). Instead, here are the reasons that resonate with me:

1. Signed reviews are easier for authors to interpret. When I received my first signed review as an author of a manuscript, I realized how much easier it was to understand the reviewer's recommendations. By signing my reviews, I'm offering authors additional context into the comments I've provided. I include my name, title, department, university affiliation, and email, which makes me easily google-able (they could even find this blog post!).
2. Sometimes it's pretty obvious who the reviewer is anyway. On occasion, I've received reviews as an author that are almost laughably transparent as to the identity of the "anonymous" reviewer. The science world isn't so big! If you're a taxonomic expert in a particular group of organisms, it's easy to guess that it's you catching spelling mistakes in names of subfamilies (or suggesting someone cite your papers).
3. It's gratifying to be explicitly included in the process of improving a manuscript. I work hard to be a thorough reviewer, and I hope that my effort may be rewarded by a reputation for fairness and helpful insight. I appreciate my name being included in the acknowledgements (although this did once cause some consternation with a few peers, as it wasn't clear I was a reviewer and they thought I was collaborating with their competitors). In fact, I particularly enjoy reviewing for PeerJ, as my review is published with the early version of the manuscript.

Here are a few things to keep in mind about signing your reviews:

1. Be consistent. It's easy to sign a glowing review of a manuscript that you're accepting without recommendations. It's harder to sign a review of a manuscript you're rejecting, but (for the reasons I mention above), is arguably more important for both you and the author.
2. Ask journals before agreeing to review. Here's a tweet exchange I had with @rmflight about corresponding with journals (pure genius!):

@k8hert that is why I ask the editor first before agreeing to review, and make it a condition of reviewing

— Robert M Flight (@rmflight) January 15, 2016

3. You get used to it quickly. Once I committed to my decision to sign all reviews (for papers I accept AND reject), it only took a few reviews to feel comfortable with it. Nothing disastrous has happened, and reviewing papers feels less like working for free.
4. You can do it! Anonymous peer review is still the default in most areas of academia. In most cases, though, signing is a choice. I've heard early career scientists warned away from the practice, but it hopefully helps you to know that there is, in fact, a community of folks who make transparency in science a priority, and they exist in all career stages.

In short, I've signed ALL my reviews for about five years now. In that time, I've published my own papers, applied for grants, and even landed a tenure track faculty job I'm still quite happy with my decision.

(Thanks to @MusselDS for asking the question and prompting me to finally write this post.)

Classroom informatics: Managing students, information, and communication.

One of my biggest challenges as an educator is helping students manage information. I don't believe it's my job to impart information to students, but to help them learn to think about information and integrate it into their knowledge structures more effectively. Example: I'm not trying to teach my students how genome assembly algorithms work, I'm trying to teach them how to use the instruction manuals that come with assembly software to analyze their data. Layperson example: I'm trying to teach them how to use a recipe book, rather than memorize all the recipes.

This means that I rarely give my students a list of things they need to remember. More often I give students lists of resources that they might be able to use to help themselves find an answer or solution. The end result is that students are confronted with a huge amount of information, not that they're expected to memorize in minute detail, but rather learn to filter and search to find the facts and strategies necessary to complete a task. As someone who's memory is complete rubbish, but who is quite efficient at managing information, I find this a much easier task. For students who have been trained in most contemporary classrooms, they may consider this task monumental.

Essentially, I'm teaching students informatics strategies. This is certainly appropriate, given my research and teaching specialty is bioinformatics. The trouble is that it takes more explanation to describe how to use information, than to simply tell students what information they need to remember. Students then become overwhelmed at the amount of information to which they are given access, because they are trying to process the information in the same way they have managed facts given to them in previous classes. Instead of seeing information as a resource that may possibly be used, they see it as a mountain that needs to be climbed.

How do you help students think about information as a resource to reference, rather data to retain? This seems to be a logical extension of the commonly-touted plight of science educators: we need to focus less on memorization and more on critical (scientific) thinking. We need to teach processes and ways of thinking, rather than factoids and things to remember. To me, the amount of information I give my students seems like overkill, that I'm making the assignment too easy. In truth, it's actually the opposite: I'm requiring my students to use appropriate information filtering methods, and to help themselves.

In practice, I spend a lot of time trying to remind students of the bigger picture, and pointing them towards materials that are already available that may answer questions for them. This is actually an essential but overlooked skill, not just in bioinformatics. Professors often complain about students who don't read the syllabus, which suggests that we're not doing a very good job teaching students how to help themselves. I'm starting to believe this is the real key to contemporary education: helping students utilize the sheer amount of information available to explore and innovate.

Bioinformatics "Purity Test": Need more questions!

I'm prepping for the new class I'm teaching this semester, Bioinformatics for Research, and I need help! It's a graduate level class that I'm designing to introduce our Master's students to the basic computational skills they will require to perform common research tasks.

Here are the general skills we'll be covering:

• data/metadata organization and management
• automation of tasks with the Unix shell
• introductory R scripting (data parsing, simple statistics, visualizations)
• executing command-line programs on remote HPC resources
• version control with Git
• small projects in genome assembly/annotation and phylogenetics (common questions for which previous grad students have asked for help)

As a part of motivating students to learn these skills, I'm developing a Bioinformatics "Purity Test," similar to the ones I remember being all the rage when I was in high-school and college. Students will be presented with circumstances and they will calculate the percentage of scenarios they've encountered. Here are the survey questions so far:

-----

Have you ever:

1. Tried to open a file and found it corrupted
2. Had to recreate a file because it wasn't backed up
3. Couldn't find a file because you forgot what it was called or where you put it 
4. Had a computer crash lose your unsaved work
5. Had to redo work because you (or someone else) decided it needed to be done differently
6. Couldn't redo your work because you forgot how you did it
7. Spent hours performing the same task over and over
8. Read a scientific paper and wondered, “How did they make those number things happen?”
9. Created a graph/diagram in Microsoft Excel (or Powerpoint)

Calculate your score: 1 point for every "yes" answer, divide by total number of questions.

-----

My question for you, dear blog readers: what other questions should I include?

UPDATES:
Permanently deleted your work on accident (from @thatdnaguy)
Frequently copy/paste to reformat documents (from @PaulBlischak)
Found two versions of the same file, but didn't know how they differ (from @nmatasci)
Forgotten your abbreviations for samples, dates, etc in an analysis (from @nmatasci)
Been unable to use software because you can't find a computer that can run it (from @nmatasci)
Been unable to determine the required input format for a program (from @dwbapst)
Been unable to install software dependencies (from @BrownJosephW)
Had errors related to Windows vs Unix line endings (from @BrownJosephW)

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!

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.

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:

Developing an undergraduate bioinformatics course from Kate Hertweck

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.

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.

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.

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.

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?

Departmental holiday parties, then and now.

My PhD advisor, Chris Pires, sent me this picture via email a few days ago. It's the two of us at our first holiday party at University of Missouri, during my first year of graduate school in 2005. I think we won trivia or something, and got an awesome grab bag of "prizes" that included slightly broken garden shears?

Picture courtesy of Melody Kroll

My first UT Tyler Dept of Biology holiday party was today at lunch. I must not have really changed much over the last decade, because I still took leftover food (mainly dessert, because priorities). I'm also dressed a bit better than in the picture above, not necessarily because my fashion sense has changed, but so I can go to commencement tonight. I haven't participated in commencement since I graduated from high school (although I did attend my big brother's Ph.D graduation last spring). Onwards and upwards!

Now if only I could finish my class plan for bioinformatics next semester...

Geeking out about acorns on the local news.

My university has a pretty decent relationship with the local news. Reporters fairly frequently contact the public relations office, seeking out an authority (i.e., professor) on the topic of the story they're preparing. A few folks in my department appear in news stories on a semi-regular basis, mostly offering facts and opinions on issues tangentially related to their areas of expertise.

I wasn't entirely surprised, therefore, when I got a phone call from the public relations office a few days ago, asking if I'd be willing to talk to a reporter. I was initially apprehensive, as I'm still trying to get my bearings in my new home state and wasn't sure I wanted to be put on the spot if asked about something controversial. I had no need for fear, though: I'd been recommended as someone who could talk about why it was there seemed to be such a large acorn crop this year.

"Heck yeah!" I thought, and calmly agreed to meet with the reporter that afternoon. I spent a few minutes doing a quick literature search. As I suspected, there's a decent amount of literature on that very topic. I made a list of talking points, including a few general notes about plant biology/ecology:

1. Acorns are the fruit of the oak tree, created by the fertilization of a female flower by pollen from a male flower.
2. Multiple factors can affect the development of an acorn, including how many flowers of each type are produced, effectiveness of pollination, and whether the tree has resources to dedicate to developing lots of fruits.
3. The factors above are, in turn, dependent on temperature, amount of sunlight, and levels of precipitation.
4. Acorn production (masting) also varies temporally (through years), spatially (across geography), and by species (some oak species produce more/larger acorns).
5. This summer has been particularly cool and wet compared to the past (I checked average temperature and precipitation for the month of July for 2011-2014), so perhaps those conditions favor more acorn production.
6. Trees are large and can't move, so resource allocations from previous years can affect acorn production in subsequent years.

The interview took about 15 minutes. We went outside and stood near some conveniently located oaks near my building. Of course, the talking points I had planned were shuffled around and reframed depending on the questions he asked, but I think I covered everything listed above and more (acorns are food for wildlife, they do eventually grow into trees, etc). Then the reporter grabbed a totally awkward shot of me walking down a path looking at leaves.

It's totally cringe worthy, but I know at least my dad will want the link, so you can see how my talking points above translated into the final story here: KLTV News, Why so many acorns this fall?

Lessons learned: keep makeup and a blazer in my office, to prepare for next time. Be a more careful about preparing appropriate sound-bites. I was also surprised to learn how much trouble the reporter had getting someone to talk to him about this; a number of arborists in town completely blew him off. I thought it was super cool! That's probably a good thing, because the public relations representative said at the end of the interview, "Great! I can add you to the list to talk about plant stuff!"

Great, indeed. Let's call this "service to the university." 

A catalysis meeting on long term experimental evolution.

Travel makes it easy to let blog posts slip away without being fully formed, written, and posted. Now that the semester is winding down, I'm going to try and follow through with writing the backlog of posts that have been piling up from my adventures over the last few weeks. Today's report is about the first part of my trip back to NESCent and North Carolina the week before Thanksgiving.

The catalysis meeting I attended on long term experimental evolution (you can read a little more about the meeting and participants here) was not only fantastic but also the last NESCent will host (more on this in a later post). Although the topic is outside of my main research interests, I answered the solicitation to participate in this meeting because of some research I've been doing with Joe Graves, Michael Rose and colleagues on experimentally evolved Drosophila populations. Moreover, there seem to be some really interesting opportunities to explore robustness of analytical methods using experimental evolution data, which is of particular interest to me.

Here are the things I found compelling about this meeting:

1. Meeting organizers set the tone. Rob Lanfear, the main organizer, put together a fantastic webpage and started a Mendeley group so we could share literature beforehand.
2. The participants were diverse. Forty four percent of attendees were female. There were graduate students, postdocs, early career scientists, and senior researchers present, and folks came from all over the world. Model systems included microbes, invertebrates, fish and trees. 
3. We capitalized on the group's diversity. As an early career scientist, I was pleased to develop relationships with a number of other folks starting faculty jobs at similar institutions. As a group, I was gratified to hear well-respected, senior scientists describing junior scientists' research as "brilliant." There were multiple types of interactions incorporated into the meetings such that folks who were hesitant to speak in full-group discussions could still contribute ideas. In short, this meeting exhibited many aspects of scientific discourse that are overlooked, but which I value deeply.
4. Attendees were invested in the meeting itself. Part of the meeting was structured (or perhaps more accurately, unstructured) as an "unconference," with participants determining topics for talks and group discussions on the fly. Despite this free-form format, folks in this group were very interested in talking about broader research ideas, rather than pushing their own agendas.

I left the meeting with a much wider and deeper understanding of experimental evolution as an active field of research, as well as a better grasp on different ways of thinking about the process of science and its limitations. I was also grateful to participate in one last meeting of this type at NESCent...stay tuned for my next post to hear more about that!

How did I get here? Learning to love research.

I grew up in southern Indiana, in an area stuck mid-way between small town and big city (Evansville). I thought biology and music were both pretty cool in high school. I applied to colleges in a haphazard way, auditioning on flute for some schools, and checking out biology programs at others. I eventually ended up at Western Kentucky University, mostly because of the generous academic scholarship they offered me and the WKU Forensics (Speech and Debate) team. I had competed in high school, and had lots of friends who were also on the team, so it seemed like fun thing to do. After spending so much time my first semester practicing, traveling, and competing for speech, I declared my major in my second semester as something like "corporate and organizational communications," although I really lacked an understanding of what a job in such a field would entail. I stuck with a minor in biology, since I'd already taken a semester of introductory classes. My reasoning for this adjustment was that, although biology was interesting, I didn't want to be a doctor. Moreover, I literally couldn't imagine spending years of my life working on the same biological research question. 

That spring, I went to a departmental seminar for biology because I thought it sounded interesting. My professor for introductory biology, Larry Alice, saw me there and suggested I start working for him doing research. Not one to balk at offered opportunities, I relented and started learning how to sequence DNA to determine the evolutionary relationships among species of grass. It only took a semester of actually performing research to realize how gratifying it can be. I switched my major to biology within a few months, this time keeping communications (and also history) as minors.

Casting a wide net.

I was fortunate as a post-doc at NESCent to have a huge community of like-minded scholars to help me develop intellectually. I'm still very fortunate to be surrounded by folks doing awesome research, but I was hired specifically to fill a missing niche (bioinformatics) in the department. That means I need to work extra hard to find ways to connect with my new students and colleagues. While my skills are definitely desired and I have lots to contribute, many things I'm doing simply haven't been done here before, so I'm thinking creatively about how to fit in on campus. I'm doing my best to think broadly (cast a wide net), while at the same time focusing my time and energy on tasks that will have an impact (and hopefully catch a few big fish).

The benefit of working as a bioinformaticist is that I can work with anyone who has data (hint: that means pretty much everyone). My specialty as a genomicist also makes me well suited for the emerging interests of other folks on campus. I've been sitting down to talk to lots of folks about opportunities for collaboration on such projects. It's incredibly interesting to learn about different model systems, and gratifying to know that I can contribute to such a breadth of projects. At the very least, I can save folks time by providing a bit of information in current genome assembly methods, for instance.

It's easy enough to work with folks in other science departments, but I've been casting an even wider net. I was delighted when a friend from the history department came over for a chat about filtering data. He had a large digital dataset of documents and was looking through them for a particular type of data. Luckily, that type of data was always described with a particular string of text. Three lines of bash scripting later, and we managed to save him days of work. I've long been interested in these broad approaches to academia, and even attended a THATCamp meeting at NCState several months back. My brain works best when building connections between seemingly disparate ideas, so a little bit of my time in pursuing small projects like that helps keep me happy.

The unexpected returns are also nice: getting to know folks over in nursing, for example, let me know about better ways to teach in ways for which they are distinguished: applied methods (for which bioinformatics certainly applies) as well as online classes. At the risk of extending the metaphor too far, casting a wide net is making the fishing expedition of research and academia more appealing to me.

Value judgements and scientific results.

As scientists, we like to think that we are objective in the interpretation of data. As humans, our personal value-based judgments creep into these assessments far more than we might realize. I often think of these biases in my career, both as a researcher (when writing papers or reviewing manuscripts) but also as an educator.

I mentioned at the beginning of the semester that I'm obligated to watch student presentations for a class on science communication. As the semester progresses, I'm getting a better handle on how students (at least in our department) are starting to think about scientific arguments. A few students have made what are, at least to me, surprising statements about the strengths and weaknesses of the primary literature they discuss: they relate that failure of results to support a hypothesis is a weakness of the paper (also vice versa, that "supporting the hypothesis" is a strength).

One of the first lessons I learned while doing scientific research is that (more often than not) unexpected results and questions can emerge from our experiments. You can learn something from any experiment, even if it's simply a better way to perform the experiment in the future. Unfortunately, research on science research (I know, so meta) indicates that results which fail to support the hypothesis are often discarded by researchers as "useless" or "no good" (you can read more about this phenomenon in association with the formation of the Journal of Negative Results in Biomedicine here). Albeit unfortunate, this behavior makes sense in the face of the competitive realm of science research, where a nice, concise story can make the difference between publishing or wallowing in academic purgatory.

It gives me pause, however, when students indicate such a value preference for results that support a hypothesis. In my mind, results aren't "good" or "bad," they simply...are. Is devaluation of negative results innate in our educational mindset? On the other hand, students vocalizing negative results as a weakness of the paper may be more semantic than scientific; for example, students aren't nuancing their argument to discuss particular experimental drawbacks, which is a completely justifiable concern. Alternatively, they might just be saying what they think we (the instructors) want to hear, and hoping we'll give them points for covering all the required topics listed on the rubric.

Regardless of the direct causes of such reasoning, I'm going to be keeping an eye (ear?) on such comments as I continue developing classes. When operating in isolation, these judgements about the value of results can be a starting point to some interesting discussions about epistemology and hypothesis testing. More often, though, these mindsets work in concert with other scientific misconceptions, which can result in huge problems for me as an educator. Fortunately, there are sections in my bioinformatics class that I'm explicitly developing so we can have discussions about whether the results we're obtaining are accurate and meaningful. I'm looking forward to embedding these higher-level reasoning skills into the regular content of the class.

The art of asking in science.

I posted some love for Neil Gaiman's spoken words a few weeks back, so it only seems fitting that I follow up with inspiration from his partner, Amanda Palmer. She did a TED talk (the video is slightly NSFW at 10:49) last year that explored the relationship between artist and fan, emphasizing that we need to think less about how to make people pay for music and more about how to let them. She relates that asking people to help us is hard because it makes us feel vulnerable and shameful, but that asking for help can also create a profound connection with other people that allows a mutually beneficial transaction to occur. But watch the TED talk...she says it much better than I ever could.

I am not a professional artist or musician, but life in the ivory tower of academia does not shield me from the need to reach out to other people. I'm lucky that I don't have to ask people for financial help to pay my bills, but there are plenty of other circumstances in which my professional success rests on my ability to appeal to other people for assistance.

Learning to ask other people for help was a major stepping stone for me during graduate school, as I realized the need for assistance in troubleshooting lab techniques, designing experiments, and proofreading papers. As an assistant professor, asking for help is becoming even more of an art. I wouldn't be able to navigate the logistical issues of university bureaucracy without running to the office next door every few days. I belong to a small department, so connecting with other academic units on campus and at other schools is important to have access to resources for teaching and research. An important part of my job right now is applying for grants, which is really just another way of saying "asking for money to fund my research." Some scientists are taking an even more Amanda-style approach to paying for research by launching crowdfunding campaigns for particular projects. Moreover, my job as a bioinformaticist means I rely on other people for data, so asking for research opportunities is essential. I knew my job would require me to engage in asking, and even persuading, my peers to help me. I'm only starting to realize, however, what it means to ask students for help.

Am I really asking students to help me? That seems counter-intuitive to the stereotypical role of a professor, but indeed, we rely on students to take our classes and do research with us. As a newbie, I can't rely on my reputation to attract students to my class or lab. I have to ask students to consider it (hence my post from a few days ago: Student motivation, AKA please take my class). Sometimes that requires educating students about what I have to offer, like the ability to obtain marketable skills. Moreover, it makes me consider another question Amanda ponders: "Is this fair?" For my job, that means asking whether this student has the capacity to succeed, and whether it will be beneficial for them to spend the time, energy, and money to do so.

Framing my professional, scientific interactions as "asking for help" appeals to me on a fundamental level. It gives me the responsibility of finding the things I need. Moreover, it allows choice and freedom on the part of the audience, whether it's a student wanting to work with me or a funding agency deciding to award a grant. We pursue science because we think the work is interesting, but we're fooling ourselves if we think everyone will innately feel the same. Making an art of asking is a way of starting a conversation, and opens the door to persuade without being overbearing.

There is also a benefit in the act of asking a question. I wouldn't've ended up in graduate school if one of my professors hadn't asked me to consider it, and you certainly have to ask (i.e., apply) to receive a grant. I'm practicing asking questions as way to start a conversation about obtaining the things I need.

Can you fund my research?
Would you like to work on a project together?
Have you thought about going to graduate school?
Are you interested in taking my class?

When a computational biologist yearns for the field/lab/greenhouse.

A darling little rose bush.

I'm a sucker for cute little plants on sale at the grocery store. I saw these delightful miniature rose bushes when I first started shopping, and waffled for the next 15 minutes as I circulated the store about whether to take one home with me. I ultimately relented, because I decided that it was a small price to pay to feel more connected to my new home city (Tyler has a thing for roses, including a pretty nice rose garden!).

My compulsion to surround myself with plants started during my undergraduate education after taking plant taxonomy and beginning to work in a molecular systematics of plants lab. I enjoyed working in the field, but realized during graduate school I was more suited to computational work. After three years as a NESCent postdoc, during which my work was exclusively computer-based, I found myself yearning to physically get my hands on some live organisms (hence the compulsive purchases of houseplants).

Happy Commelinaceae in the greenhouse.

The problem is that I've been hired as the resident bioinformatics/genomics person, which brings with it certain expectations about how I spend my time (mostly, analyzing data that other people collect). As a scientist, though, I think it's important to still maintain a connection to my study organisms. My plants a source of inspiration and wonder, as well as a resource for future research questions, and I'm loathe to permanently pigeonhole myself as a "computer person." How do I balance these opposing expectations?

My research mindset right now is one of nearly infinite possibility. I want (and need) to be productive as a scientist, but it's up to my discretion exactly how to make that happen. I work at a small, regional university, which means I may need to be creative (financially and with other resources) about how to set up research projects for my students in the future. I have some of my Commelinaceae living collection growing quite happily in the greenhouse here in town, and access to growth chambers on campus if I want to do hybridization or selection experiments.  Even though I don't have immediate research plans for my plant and DNA collections, I'll keep them as long as I can to keep my options open.

From a teaching perspective, I'm really excited about designing courses which capitalize on either computers or live organisms. I've already written about the bioinformatics course I'll be teaching next semester, and I'm considering offering a class on plants of Texas (taxonomy and systematics). I helped teach plant systematics as a graduate student, and find myself really excited at the prospect of getting back into the business of instructing students about the local flora.

Ultimately, I know trying to balance these opposing forces are making my life at least a little harder. It's more work to figure out effective pedagogy for classes based in the field and on computers. Oddly enough, my educational and work experience has set me up for precisely these tasks (see references to my experiences above), and there are quite a few other academics who successfully split responsibilities between different projects. What's the main reason that I remain committed to being a jack-of-(plant and computer)-trades? It makes me happy, of course.

Student motivation, AKA Please take my class.

My current homework for Software Carpentry instructor training is to think about a time when I lost my motivation to learn. I posted my response to the SWC blog, where you can also poke around a see other folks' stories (click on the "Motivation" tag).

The other part of our homework is to work on a three-minute pitch to motivate students to learn a particular topic. The timing of this assignment is fortuitous, as I'm also promoting the bioinformatics class I'm teaching next spring as well as an undergraduate minor in bioinformatics and genomics. I took to twitter with my attempts at persuasion:

Disclaimer: I am not planning on physically harming students if they have a different shell preference than bash, and I don't particularly dislike perl. I'm really just rather fond of word games.

A few other folks chipped in with their own token words of wisdom:

It's not surprising that my motivation for getting students to take bioinformatics differs from their reasons for enrolling. I am personally passionate about teaching next semester because I think I can help students be better scientists and thinkers. I'm hoping to convince them that it will help them be more marketable (taking additional biology and computer science classes will certainly accomplish that). 

At times, these persuasive attempts seem like fighting a rather uphill battle. Convincing students to take extra classes that bridge boundaries between different types of knowledge is difficult, especially when students who might be interested are already quite overwhelmed by courses required for their major. I come across lots of folks who are intimidated by large datasets or using a command line interface. 

I spend a lot of time talking to folks about my experiences, and how I'm planning on teaching. 

Here are my talking points in encouraging folks to step up to the plate and learn some bioinformatics, from the perspective of a biology student who has little computation experience: 

1. You can do this. Not too long ago, I was in your shoes. I didn't know a lot about computers, how they worked, or how I could use them to answer questions. I don't have a ton of formal training in computer science, and my degrees are all in biology. Writing computer scripts may seem really different from other things you've studied, but...
2. Learning a little can be very powerful. Learning to work on the command line and write computer scripts will take work. You will be surprised, however, at how many tedious, mundane tasks you can accomplish much more quickly and efficiently with a little bit of shell scripting. Better yet...
3. These skills are transferrable. You may not end up working in a job where you need to assemble genomes or build phylogenetic trees. It is possible, though, that you'll need to manage large numbers of files or answer questions about large data sets. You can apply these skills to lots of other practical tasks, but in addition...
4. The topics are interesting. Technological advances are producing genomic and other large-scale biological datasets at an unprecedented rate. The applications of these data include empirical research, agriculture, and medicine. 

At the very least, I hope to convince students the first point is true. Nothing is more frustrating than hearing students declare, "I can't do that." A student saying "That's too hard" is a student who's hit a motivational brick wall, and can't even ask themselves whether it might be beneficial for them (or, heaven forbid, that they might enjoy it!). If they can do that, hopefully one of the last three points will be appealing.

On the other hand, I'm still trying to figure out effective ways to appeal to computer science students. The fourth point above definitely still applies, and they can arguably improve (or at least broaden) their job prospects by gaining some understanding of biology. More importantly, they can learn to answer hypothesis-driven questions, which seems to be less of a focus in their curriculum than in biology. 

As always, this is a work in progress. What am I missing? I'm planning a few mini-workshops on for students (graduate and undergraduate) on campus, which will certainly allow me more opportunity to pinpoint more effective pitches. These students are not motivated by the same factors that convinced me to pursue higher education, and bioinformatics for research. I need to find out what they need. However, I struggle with how much I should cater to student interests. That, however, is part of a broader discussion about the purview of higher education, and is perhaps left to another post.