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10 tips to mentoring undergraduate researchers

“It takes time to be a good mentor… and not just to be a good mentor, but to get good results.”

By: Jessica Ernakovich

Morning after morning, you are up before the sun. You hop out of bed, throw coffee in your face, and you head into the lab. On the bike ride in you think, “Man, if I just had an undergrad work for me, I’d be sleeping right now.” Then you think of all of the other potential benefits of mentoring an undergraduate researcher, including:

  • Free/cheap labor
  • An extra set of hands collecting data
  • Teaching/ mentoring experience to put on your CV or for your broader impacts statement to NSF
  • Training an undergrad early to work with you throughout your masters/ PhD/ postdoc position
  • “Giving back” to the broader community… likely, some poor sap once mentored you when you were a pup!

So, you solicit for help and have teams of upbeat and excitable undergrads apply. You pick the most engaged seeming one of the gang. Let the work begin! You tell them to hop in there, show them how to do something, and walk away with the expectation that soon enough, data will be pouring onto your desk.

Does it really work out that way? No! Well, not for me or the friends I’ve known who have mentored undergrads in research. Why do many of the partnerships between mentors and undergrads falter? This is just my opinion, but I think it comes down to four things:

  • Lack of communication about your expectations,
  • Lack of continued explanations about the context of the project and how the undergraduate fits in the scope,
  • Not explaining what the data should look like, and
  • An underestimation of the time it takes to be a good mentor.

If you really want to get something out of your undergrad lab researcher while simultaneously teaching them the most, you’ll need to prepare a bit and put in some time with them. To help you along, I’ve summarized 10 simple steps to better mentoring as well as my perspective on commonly asked questions.


10 Steps to Better Mentoring

Step 1: Structure the project to take a finite amount of time (semester, summer) and define clear objectives for the research.

  • Even though your project is big, the student’s project needs to be more defined.
  • Think outside the box- they don’t have to collect new data. They can do data mining, synthesis, or entry.

Step 2: Explain the context/background of the project and its relevance. Continually revisit the context of the experiment throughout the research project.

  • Give them some literature and then explain how to find more
  • Explain campus resources (like Web of Science and subject librarians)
  • Tell them about journals that are prominent in your field and teach them how to sign up for RSS feeds/ emails for table of contents alerts

Step 3: Explain how what they are doing fits in the bigger picture (of your thesis or in the PI’s lab). Talk about how data will be used to address bigger questions you are answering.

  • Undergrads understand that they are not doing a huge project for you and that they are likely one in a succession of students collecting data on a long-term project, but they want to understand why the data they are collecting is important.

Step 4: Define your expectations regarding the time that they’ll need to work on the project, how you expect data organization to look, when and how they should communicate with you, etc.

  • If you don’t communicate your expectations from the start and continuously throughout the semester, you’ll feel frustrated with both yourself and the student if things aren’t going well. Naturally, conversations where you might have to say, “You are not meeting my expectations” or “No, that was not the correct way to collect that data” are hard, but ultimately the student will learn more and you will get better data.
  • Many mentors find it helpful to have a contract or rubric.
  • Provide an evaluation midway- they can evaluate you as well!

Step 5: Provide bounds for the expected values in the data they collect and go over analyses shortly after they are done

Step 6: Provide time/resources for data analysis rather than only focusing on data generation.

  • When students can move beyond data collection to actually analyzing their results, they can start to address the hypothesis that you laid out. (Even if their data is just a small part, it’s important that they start to understand what the data is saying.) This is a critical step in the development of the student as a scientist, and it will help you evaluate whether the student understands the context of the experiment and how the data they are collecting will help answer your questions.

Step 7: Don’t talk too much… ask questions and don’t take the “yeah, I get it” attitude for an answer.

  • Require them to explain the hypothesis, data collection, anticipated figures, etc.

Step 8: Be accessible, but not TOO accessible

  • It’s important for students to problem solve. But ultimately, if they are working on your experiment, you should at least be in the building so they can ask questions if something is unusual.

Step 9: Be patient!

  • Students are learning. If they mess up, you just need to explain what went wrong and let them start again.

Step 10: Have regular meetings with your student, and/or invite them to your group’s lab meetings.

  • Having a regular meeting with your student is a great way to keep coming back to the concepts AND to talk about all the technical details that might be on their mind.

Mentoring FAQ

How much time will it take to be a mentor?

In the beginning of their first semester, I would expect to put in an hour and a half for every three hours they work (in which time, they will produce what you could in 1.5 to 2 hours). Remember, this is a long-term time investment. Plus, you are getting other things out of this (mentoring skills, broader impacts, etc). Over time, they will become more familiar with the tasks at hand, and will produce more results per time invested.

It takes time to be a good mentor… and not just to be a good mentor, but to get good results. You need to put in time up front to explain the context of the project, and you should provide literature and help the student to understand it. You’ll need to train students about the technical details of the project that they’ll be performing. Within that, you’ll need to explain not only how to collect data, but the bounds of the data (expected values). Towards the end of the semester or project, you’ll need to help the student to analyze the data. Obviously, they can do this on their own time, but you’ll need to meet and talk about what figures should look like and what the results mean.

How do I to pick an undergraduate researcher?

I base most of my hiring decisions on (1) evidence of enthusiasm, (2) previous experience, and (3) scheduling.

(1)  If the student is excited about the project, this will generally translate to the work.

(2)  You are going to train the student, but it’s always good to have some previous experience. For example, if they’ll be doing lab work, it would probably be good if they’ve taken a course with a lab before. Or if they’ll be doing data mining and/or synthesis, it’s probably good if they have some concept of basic statistical methods. You can train on the tough stuff, but basic knowledge is nice.

(3)  Students who want to participate in research are often the types of people that take on lots of responsibility. They will likely be working for you around a busy course load. Even someone really good isn’t good if they can never come in while you are at work. It’s okay to pick someone based on schedules.

  • At first, I underestimated how critical timing and schedules are. I thought that we could just “work it out” as the semester progressed. You can’t! You’ll be bummed to be there in the lab with the student late at night once you realize that’s the only time they can be there. And in the first semester, it’s not fair (or maybe even safe) for them to have to be alone in the building unguided.

What if I have to fire an undergrad?

Dread. You’ve done all you can. You’ve had meetings about context. You’ve explained your expectations. And yet, you are not happy with the data or their timeliness or something. And then you realize you have to fire them, but you don’t even pay them. That feels bad. I know. How can you fire someone and when should you do it? Immediately after the initial negative thought creeps in, remind the student of your expectations. You are their boss, not their friend. They need to learn from you and your time together. Once that warning conversation has been done, if they are still not meeting your expectations, how do you fire them? I recommend, rip it off like a Band-Aid! And get someone new!

Shameless advertising for mentoring students from the SUPER program:

Students involved in SUPER are being trained on general research methodology. We are looking for mentors for research projects in the spring. Students would like to start working with mentors now to learn the background of the projects. Next semester while you are mentoring them, I’ll be meeting with them to probe their understanding of how to test your hypothesis. We have found that this helps them to keep on task over the semester, and helps you get good results in a short time.

Let’s chat about mentoring tips on Twitter! @JessErnakovich #EcoPress #mentoring

Lots of these ideas came out of a working group funded by a NREL program development grant where we explored how to train undergrads on how to become researchers. Some of the ideas are just my own from experience mentoring 16 undergrads over my PhD.

I’d like to continue this conversation further. Let’s talk about other tips and tricks!