This is the third part of 7 part series discussing ideas presented in “How Learning Works: 7 Research-Based Principles for Smart Teaching”. It focuses on the role of motivation in the learning process. The motto:
Student’s motivation generates, directs, and sustains what they do to learn.
The authors start with presenting the stories of two teachers, who fail to motivate their students. The reason for this is that they make the assumption that students are motivated by the same factors as they (teachers) are. As a result, students either don’t see the value of learning about the particular topic or don’t expect to succeed and therefore give up before trying.
Motivation turns out to be quite a complex beast. Students are motivated by goals when they see the value and expect to succeed. They also need the supportive environment for best performance.
At first thought, it seems that learners who come to Carpentries workshops should be already very motivated. They come because they want to learn the skills they need to do their research. They are also (most often) young researchers and therefore very driven. All above is true, but that does not mean that the instructor cannot affect the motivation of learners. Below are my few thoughts about maximising learners motivation.
For scientists, the value is in how they can apply new skills to enhance their research. Therefore it is very important that learning material matches as much as possible their domain. This is especially visible in Data Carpentry lessons, but also in Software Carpentry with python-novice inflammation and gapminder lessons. Moreover, it’s very important that exercises are realistic and represent real word problems.
Even though students come to the workshop voluntarily, they are not necessarily equally interested in every lesson. As the result, they will not be motivated and will not learn much about the topic they think is irrelevant for them. This can be avoided by explaining at the beginning of each lesson why the material is useful and important and how it can be applied in particular research domains. I see this happening especially often with SQL lesson. Learners often assume it is not helpful in their domain and don’t expect the lesson to be of value to them. One the same note, I have been recently teaching at the workshop in Paris. Even though the workshop has been advertised as based on R, the survey pointed out that some people are coming to learn Python and they already know R. We could have told them they should have read the announcement more careful and not come, but since they were already there that would not be very productive. I’m pretty sure that would cause the negative attitude along the whole workshop. Moritz (@zormit) my co-instructor suggested to split the group in two and teach Python and R in parallel. I have to admit I was sceptical about this approach. I still think that should not be a common practice, but have to admit this really helped with very high engagement during the course. I even believe this engagement continued to other lessons, because the learners understood we are open and the goal of the workshop is to teach them new skills they can use in their research. I’m very grateful to Moritz for coming up with this and challenging me in the process. I am very curious what other people think about it.
I like very much the authors’ advice “Show Your Own Passion and Enthusiasm for the Discipline”. Instructors are usually very passionate about their research and the tools their using and most often don’t need to be reminded about expressing it.
I think it is difficult to overstate the importance of open and friendly environment during the workshop. It is super important that students feel accepted and feel free to ask questions and try out things. It also helps if they feel comfortable among their peers in the room.
Authors also described the concept of fixed and growth mindset and how it affects the learning process. If you interested in this topic, I advise reading “Mindset” by Carol S. Dweck in which she explains the concept in more detail.
Thank you for reading and I hope you learned something in the process. As always I’m very interested in your thoughts.