Do Not Overgeneralize Our Own Experiences

I am motivated to write this blog by several things. These are mostly part of random conversations with friends or short moments of self-reflection. They are not very cohesive but I want to write these pieces out here.  I hope I can convey the idea that it is good to be mindful and reflective. I argue that we should avoid overgeneralizing our own experiences.

First, I held a review session for my undergraduate students before their lecture exam. During the review session, we spent a lot of time on exponential growth and logistic growth model. Understanding how parameters in the model influence population size, growth rate and per capita growth rate seems to be quite challenging to many students. This is a little surprising to me as I thought these models are quite intuitive and easy to understand. We used to have a lab on simulating these models. I always thought it is not a very useful lab. I did not realize my wrong perception until this review session. I start to think that what I thought is easy and intuitive may not be as easy and intuitive to many other people. The contents I thought I should spend more time on teaching students may not be best suited to help my students. What I thought as useless may actually be quite useful.

Second, I have a random talk with a fellow TA a few weeks ago. He told me that one student in the his class missed a few labs and finally emailed him about it. The student has some metal health problems. He/she was hesitant to contact the TA because he/she had been viewed as just making drama when he/she explained his/her difficulty to the instructor in a previous class. While I as a TA tend to be annoyed when my students just don’t show up in the lab, this reminds me that other people may have challenges I don’t face. What I thought as a easy thing, such as just show up in class or hand in homework on time, may be difficult when one faces other challenges. I may cause extra unnecessary burden to other people if I don’t care to understand their situation.

Third, I had an inspiring discussion with my statistics advisor Dan Hall on good research practices. During our discussion, we also discussed how variable grades for the statistics master qualifying exams sometimes are. The statistics master qualifying exam has a data analysis part and it is graded by several faculty members. Sometimes we see quite variable grades. One person may give a grade higher than 90 while the other person may give the same exam around 60. It is unexpected that opinions vary this much for a somewhat standardized problem. One reason for such inconsistency is that professors emphasize different aspects of data analysis. While one may value the recognition of the experimental design as the most important aspect of data analysis, the other may think exploratory data analysis and good data visualization as the crucial component of a good analysis. If what one believes to be the most important aspect in data analysis has such a strong influence in a qualifying exams as standardized as the statistics one, I cannot help thinking how much more influence this has on our ecology written exams. I think it is critical to have a serious discussion on the contents we want to cover and the standards we use to evaluate these exams. As researchers, we are very strongly influenced by our own interests and expertise. What we think a student in ecology should know may be be highly guided by our own research. What we view as a good discussion may not be viewed as good in another person’s eyes. To this end, I believe that conversations among professors and graduate students are critical. Leaving one  to design questions based on what he/she believe people should know, and grade it based on what he/she recognizes as a good discussion is prune to bias caused by personal research interest and expertise. This might not be the best way to ensure that we guide students to be well-rounded ecologists.

Fourth, I recently had a lot of discussions with a few other graduate students about the best way of mentoring undergraduate students. I have always thought that helping undergraduate students with their ideas is what I should focus on. Most people will figure out how to correctly carry out the project fairly easily. But this is obviously not the case for many undergraduate students I have worked with. I need to do some hand holding on the operation of experiment. I was led to this thoughts partly due to my own undergraduate experience. When I started undergraduate research, my major struggle was to make my ideas concrete and operational. I am relatively good at figuring out the methods and do the experiments successfully. But a large part of me being comfortable with the operation of experiment is due to the way I was trained. When I was  a undergraduate student, I only have five other classmates in the same major. The classes we took were very small and extremely hands on. We had specific courses on field methods, lab methods and operation of instruments. We had access to the real instruments and participated in research projects very early on. This is only possible due to the very tiny cohort. Certainly, if we have 100 students in the cohort, we will not have enough resources to do the same thing. I realize that the way I was trained is unique. Most undergraduate students were not trained that way. If I generalize my experience to other people, I will most likely not do a good job in helping undergraduate students succeed in their research.

I am not arguing that everything should be personalized or everything should be standardized. What I want to remind myself is that everyone is unique. We tend to be more influenced by our own experience and thus ignore the challenges others may face. Our own experience of success and failure may not be applicable to other people. It is helpful to give up some ego and stubbornness. It is good to be a person who listens, reflects and understands.