Forget Motivation and Double Your Chances of Learning Success
What are motivation and implementation intention?
In the context of learning data science, we all have our own motivations. Some of us just love working with data, others may be looking for a more fulfilling career, or a bigger paycheck. These motivations are the “why” of our data science studies, the things that drive us to study in the first place. “Why are you learning data science?” The answer to that question is your motivation. If motivation is the “why,”
implementation intention is the “how.” It is a specific plan we’ve made for executing on our goal. If our goal is to learn data science, for example, our implementation intention might be to study for one hour starting every day at five o’clock, or to work through one Dataquest mission every evening before bed. “How are you going to learn data science?” The answer to that question should be your implementation intention, and it should be specific about what precisely you’re going to do and when you’re going to do it. Why does this matter? Because in the psychology literature, setting an implementation intention has a pretty significant impact on whether or not you actually achieve it. The basic idea, which a variety of studies have confirmed, is that setting specific, environmentally-triggered goals like “I’m going to do this every day at 5 PM” results in a higher rate of success than thinking about our motivation for doing something (like the higher salary we might eventually get as a data scientist).
An example: getting fit
To see this effect in action, let’s look at
a real study that was published in the British Journal of Health Psychology in 2002. In the study, researchers wanted to assess how motivation and implementation intention would affect the success rates of people who wanted to exercise consistently, at least once per week. So they split 248 adult research subjects who wanted to work out more into three groups. All three groups were asked to track their workouts over two weeks, and but each group was given some other tasks, as well: The results were pretty dramatic. In the control group, just 38 percent actually exercised once a week, as they had planned. In the “motivation” group, just 34 percent were successful. But the “intention” group, which got motivated and then made specific plans for executing, had an incredible 91 percent success rate. There are two major lessons here, and they crop up again and again in similar studies:
- Motivation on its own isn’t particularly effective
- Setting specific when-and-where goals—implementation intentions—actually works.
What’s really going on here?
Accomplishing a goal almost always means we need to execute regularly on some kind of behavior. Want to be a great guitarist? You need to get yourself to practice consistently over a long period of time. We need a “trigger” to make ourselves execute on that behavior. The problem with motivation as a behavioral trigger is that it’s not particularly reliable. We’re not going to be motivated enough to actually execute on any behavior every day. In using implementation intention to craft a specific where-and-when plan, we’re setting a different,
external trigger for the behavior. If I plan to study in my room every day at 9 PM, the trigger for my studying behavior is no longer my motivation level, it’s the clock striking 9 PM. Obviously, there’s still some level of will power involved. The clock striking nine will not, in and of itself, force you to start studying. But as studies like the one above have shown, simply making a specific, intentional plan does make you more likely to respond to the external trigger of the clock striking nine.
How to make your plan
Let’s return to the world of learning data science. How can we use this knowledge to make our own studies more effective? First, we can make a specific plan for when we’re going to study, where we’re going to study, and how much we’re going to study each week. Choose a time when you’re highly likely to be available, a place that’s likely to support your learning goals, and a time period that’s reasonable (it would be great to study for six hours every day, but most people won’t be able to keep that up for long). Next, we need to account for contingencies. No matter how well we plan, life is unpredictable, and we’re certain to encounter interruptions and last-minute schedule changes that interfere with our plan every now and then. The key to dealing with these is anticipating them, and planning if-this-then-that rules to address likely issues. For example: “if something comes up and I miss my 9 PM study session, I will make up for it with an extra hour of study on Sunday at 2 PM.” Or: if I am not at home, I will go to a coffee shop and study with headphones for an hour at 9 PM.”
If you want to improve your chances of meeting your data science study goals, you can’t count solely on motivation. Here’s what you need to do:
- Make a specific plan about how your goals will be accomplished that includes the details of where, when, and how you’ll study each week. Be sure that this plan is realistic and can be adapted into your daily schedule.
- Make if-this-then-that rules for the times when your plans go awry. “If I miss my 9PM study session, I’ll wake up an hour early the next morning to study.”
That won’t be enough to guarantee you success, of course, but the science suggests that simply doing those two things will significantly increase your chances. What have you got to lose?
Dive into a related question: does sharing your goals help or hurt your chances of success?
Charlie is a student of data science, and also a content marketer at Dataquest.