The Right Kind of Internal Motivation Can Improve Your Studies
You probably already know that internal motivation is more powerful than external motivation. But did you know that the type of internal motivation could matter when it comes to unlocking better performance?
Learning data science can be a slog. At Dataquest, we try to make it as easy and fun as possible, but there’s no magic wand. Becoming a data scientist takes sustained hard work, and sustaining hard work takes motivation.
Types of motivation
We’ve all felt the little bursts of inspiration that come from outside sources. You might feel fired up after listening to a particular song, or reading an inspiring quotation. But these external or extrinsic motivations are fleeting. There’s no song that’s going to keep you on track over weeks or months of study.
Internal motivation – motivation that comes from within – is far more effective when it comes to producing performance. Experiment after experiment has found that when it comes to positive outcomes and psychological happiness, internal motivation beats extrinsic motivation every time.
But internal motivation can be split further into two different subcategories:
- Intrinsic motivation, which refers to the things we naturally want to do because we enjoy them, i.e. “I’m studying data science because learning to manipulate data is fun!”
- Identified motivation, which refers to things we’re doing because we’ve identified them as important to accomplishing some goal we want to achieve, i.e. “I’m studying data science because I want to become a data scientist.”
While the difference in outcomes between internal and extrinsic motivations has been widely studied, the difference between intrinsic and identified motivations is less widely discussed. However, a fascinating 2006 study that took a close look at this produced some meaningful results.
The key section of the study looked at 241 grade-school-aged children in Canada. Near the beginning of the school year, each child was asked to answer a questionnaire that assessed their reasons for their behavior at school, including questions about both intrinsic and identified motivations. They also filled out questionnaires about their emotions.
After the first set of grades were handed down, researchers returned to the classrooms to have students assess their emotions again, and then did some hierarchical multiple regression analysis to look at how intrinsic motivation and identified motivation affected both psychological well-being and academic performance.
Researchers found that students’ level of intrinsic motivation (i.e., “learning is fun”) positively predicted their psychological well-being after seeing their grades. In other words, the kids who liked learning more tended to be happier, regardless of how their grades turned out. Students with more identified motivation were more affected by their grades, with a positive emotional impact for those who scored well, and a negative emotional impact for those who did not.
More interestingly, researchers also found that identified motivation (i.e. “I want to learn new things because I’ve identified that as an important thing to do”) was a strong predictor of academic success. In other words, the more identified motivation students had, the better they did in school.
In a follow-up experiment with a smaller number of college-aged students, researchers confirmed these findings and made another important one: after measuring time spent studying, they found that students who spent more time studying also tended to report positive changes in their life satisfaction.
How we can apply this to learning data science
Obviously this was just a single study, and a lot more research into this topic is called for. But that doesn’t mean we can’t draw some basic conclusions from the data we already have.
The study suggests that having intrinsic motivation helps keep us happy (which will make it easier to deal with setbacks and those moments where we get stuck), and having identified motivation correlates with more successful outcomes. So when it comes to learning data science, we really want a healthy dose of both.
As far as intrinsic motivation, the best way to foster this is to work on projects that actually interest you. Or, as Dataquest founder Vik Paruchuri put it in his ‘How to Actually Learn Data Science’ blog post, you should “take control of your learning by tailoring it to what you want to do, not the other way around.”
For example, if you really love movies, finding film-related datasets to work with and trying to answer questions about film with data analysis is going to help you keep the effort up even when the going gets tough, because you’re genuinely interested in the subject.
At Dataquest, we try to reinforce every major lesson with an interesting data science project, and there are lots of other resources for project-based data science learning. But don’t feel you need to stick rigidly to any “assigned” project if you can modify it in a way that makes it more intrinsically interesting to you, or simply create projects of your own! Kaggle has a lot of active and completed competitions you can look to find projects on topics you’re interested in.
Your goal should be to find projects you care about and enjoy doing, not to force yourself through a series of projects that don’t interest you.
Now for the good news: identified motivation, the one that’s correlated with improved performance, is likely to be something that you already have. Your identified motivation is the reason you’re reading this article. It’s your answer to the question: “Why learn data science?”
This answer will vary from person to person, but the more convincing your answer is to you, the more valuable it’s likely to be as a motivator. Whatever your answer is, reinforcing it and reflecting on it every now and then may be helpful.
If you don’t have an answer to that question, this study suggests you’re probably better off finding one before you dig into learning data science. Even if you love working with data (an intrinsic motivation), having a more tangible reason to study seems to correlate with better performance, whereas intrinsic motivation does not.
Dig deeper into science-based motivation:
Charlie is a student of data science, and also a content marketer at Dataquest.