Writing clearly, thinking clearly

Two weeks ago, for the second time in this semester, I taught a block course on “research skills” for students in our international master programs here at the faculty. Topics were how to develop a hypothesis, design experiments, do analysis, write it down, do graphs etc.

It believe these courses have been a quite a good benchmark for how well prepared students are to do research (or even think research) when coming into those programs, and a few things clearly stood out for me, some expected, some rather surprising (at least to me).

  • There was a clear lack of statistical and mathematical training; even those that had statistics in their undergrad degrees (by far not all) had a very hard time to apply this knowledge when on their own, the number of people who would have been able to design and analyze a very simple experiment without help was exactly zero.
  • The students clearly exceeded our expectations regarding their skills in oral and visual presentation
  • There was a major problem with logical thinking and, connected to that, clear writing. The problems went really through all aspects of writing, paragraph structure, sentence connection, fundamental logic

Now, problems with statistics are probably no surprise to many, but the problems of making a compelling logical argument was somewhat surprising to me. Of course students just ain’t what they (we) used to be ;), but this problem was so clear and went through all cultural backgrounds (including the native speakers) that I would say the problem must be system-inherent; there seems to be too little that prepares students for independent logical or scientific thinking in their undergrad degrees (by that I mean in the widest sense thinking about how I could show whether a certain hypothesis is true or not). Let’s take the typical statistics lecture as an example, where a lot of options for regressions and hypothesis tests pass by the students, but they are seldom just presented with a more complex problem they don’t know anything about such that they have to think for themselves (to potentially fail, which is important experience I think).

Is that a new problem? I don’t know, but it seems to me it is exacerbated by the recent European reforms in higher eduction which tend to encourage streamlining teaching towards the “smalled testable multiple-choice unit”. On the other hand, I believe already Humboldt said (my translation from memory): “the foundation of the German education system is much exalted, but there is no one standing on it”. Would be interested to hear about other people’s experience.

4 thoughts on “Writing clearly, thinking clearly

  1. Last semester we’ve been teaching bioinformatics for 2nd year undergraduate students majoring in Biology (in Switzerland). The course covers basics of searching, analyzing and visualizing molecular sequencing data. What has come as a big surprise to us is a very limited capacity of students to interact with and analyze the online content unless it’s presented in a visually intuitive way. They dealt much quicker with the information shown as self-explanatory, visually appealing pictures compared to the information shown as text. Furthemore, they had hard time when questions required re-analysis of a text-based web content (was not the case for pictures). Often and many failed to scroll down the web page and lost they attention when the page was too long. Overall, my personal impression is that the way new technologies (apps/devices) deliver information trains the way students handle and process it.
    We tried to get some feedback from students regarding the format of practices and lectures, as obviously they were not very happy with how we presented the course. To what extent we will be able to address it I am not quite sure:)

  2. Hi Florian,

    I’m currently winding up teaching a course for “Level 0” students on “Data Analysis and Presentation”. L0 students have come to our (British) university through different routes – but they all have in common a lack of the required high school grades for entry into L1 (the traditional 1st year of uni).

    I’m covering all the basics: continuous vs discrete data, scales of measurement, when to use different graph types, or a table, introduction to descriptive stats, hypothesis testing and simple inferential stats, which students are doing by hand.

    While it’s been an enormous workload this term, I’ve deliberately been setting the bar high (for this group), given the low level of statistical understanding I noticed when marking Master’s level coursework last term. There may be a reasonable ability to use statistical software, but the same level of ability was not obvious in the interpretation of the most basic software output, which suggests a lack of basic statistical competence. That is genuinely worrying at Master’s level – the majority of these students will be going out into the workplace upon completion, to local and national environment agencies etc, underprepared for carrying out proper scientific design, investigation and interpretation.

    I may be asked to teach essentially the same course to L2 students next year. In some ways I hope so, while in others it’s slightly shocking that I have to. Apologies for venting here, but I seem to be experiencing similar problems to you right now. Hopefully by starting early I can give the new students a head start which will properly prepare them for the future.

    • Hi Mike,

      thanks for sharing this … yes, this sounds familiar indeed 🙂

      I should say that nevertheless I enjoyed teaching those courses a lot, they allow for so much more creativity on the side of the students than in a usual course (as they were supposed to do their own small research project), it’s just that there was this big gap of knowledge in something I thought they should have learned how to do (at least on a basic level).

      Addressing this problem early seems the way to go, so good that your university is offering these courses (and that you put your energy in them)

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