Earlier this summer our education team visited the Khan Academy Discovery Lab, a summer camp where upper-elementary students spent one week learning about either math or robotics. During the visit, we saw students in the math-focused track working in small groups with paper containers and ping-pong balls to explore the relationship between shape and volume. In the robotics track, we saw students building robots from cheap electrical components and then modifying and programing them to navigate mazes. It was exciting to see students so engaged in educational activities during their summer break.

In my last blog, I discussed a theory that helps us make sense of motivation. According to that theory, a person’s motivation to achieve a goal has two distinct and critical components: the importance that a person places on the goal (subjective value), and the degree to which the person believes he is capable of achieving the goal (expectancy). My observations at the Discovery Lab highlight a number of general insights about how strategies like blended learning, mastery-based learning, and project-based learning can improve student motivation. The resources and constraints at the Discovery Lab may differ from those of your classroom or school, but the principles of motivation still apply.

When it comes to educational goals, the expectancy component of motivation depends heavily on students’ beliefs about their own academic competencies. Students will not be motivated if they don’t think they can succeed at the assignments and goals we place before them. The Discovery Lab addressed the competencies underlying expectancy by providing students with multiple resources to support their learning. In addition to teacher-led instruction, students were provided with tutors roaming the classroom, peer-to-peer learning, and online instruction from the Khan Academy website. These resources made it relatively simple for students to figure out what they needed to know to complete their tasks regardless of their skills and background knowledge coming into the program. Having these resources helped give students the confidence they needed to figure out how to successfully complete their projects and activities.

Another motivational benefit of the camp was that it presented students with problems that were right at the frontier of their capabilities and their curiosity. As Daniel Willingham illustrates in Why Don’t Students Like School, solving problems is an innately satisfying experience as long as the problems are challenging but not too difficult. If a problem is too easy, it will lack subjective value because it will not provide any sense of accomplishment. On the other hand, students lose their sense of efficacy when they confront problems that are too far beyond their abilities.

The Discovery Lab was also loaded with other sources of subjective value for students. First, by giving students agency over parts of their work, teachers saved themselves from some aspects of the difficult job of trying to figure out what students will value. Exploratory, self-guided learning allows students to self-align their work with their personal sources of subjective value. Project-based learning can also be used to tap social sources of subjective value. For example, students find great satisfaction when they work on their projects with their friends and when they have the chance to show-off their projects to teachers, parents, and peers.


Important caveats

As we look at instructional strategies through the lens of this motivation theory, one important fact to keep in mind is that it is not a theory of learning. It does not give us any insight into the effectiveness of particular strategies at producing learning. There are plenty of activities that students love to do but that do little to help them learn. That being said, motivation and learning are highly complementary. If we can find ways to align our instruction with goals and activities that students are motivated to tackle, their learning will be greatly enhanced. By using motivating instructional strategies we can dramatically accelerate learning.

As you apply this theory to your circumstances, you will likely find it difficult to get a good read on students’ expectancies and sources of subjective value. Unfortunately, the theory does not tell us what creates subjective value for our students. Nor does it tell us how to identify the capabilities and beliefs that make up students’ expectancies. Thus the theory cannot tell us what will motivate our students; it can only tell us why the motivational components we’ve chosen are or are not working.

Best practices in conjunction with trial and error can help us find instructional strategies that motivate many of our students most of the time. Nevertheless, to motivate each student you have to spend time and energy getting to know each individually. This is why motivating each and every student can be so difficult. Fortunately, mastery-based, online learning can help with this challenge. Online learning can free up some of the instructional demands we place on teachers so that they can spend more time building relationships with their students. These relationships help teachers to tap into students’ individual sources of subjective value in order to motivate them.


  • Thomas Arnett
    Thomas Arnett

    Thomas Arnett is a senior research fellow for the Clayton Christensen Institute. His work focuses on using the Theory of Disruptive Innovation to study innovative instructional models and their potential to scale student-centered learning in K–12 education. He also studies demand for innovative resources and practices across the K–12 education system using the Jobs to Be Done Theory.