Downside of Perseverance

Sustained effort in the face of difficulty is important to mathematics learning but not always sufficient. Students and teachers must be able to recognize when effort is not productive. For example, continued effort may not be helpful if students keep failing to arrive at correct solutions.

These students do not give up too quickly; rather, they keep doing the same thing over and over without advancing. While computer based learning systems promise to increase efficiency in homework completion and grading, they also have the potential to use students’ time unproductively and undermine effective learning—revealing the need for refining pedagogical theories of how to support productive struggle.

Logic model of hypothesized factors with the potential to contribute to whether and how a student persists in an ASSISTments Skill Builder.

our questions about perseverance

1. What are the key differences between unproductive persistence and the productive struggle and productive failure that are necessary to learn mathematics?
2. At what point in the learning process can we detect that further persistence is unlikely to be productive?
3. What happens to students’ motivation and learning when they engage in wheel-spinning?
4. What are appropriate instructional designs and interventions to prevent wheel-spinning and to promote productive persistence?

An alternative to unproductive perseverance?

Promoting productive persistence, in any learning environment requires a focus on the mindsets themselves, the self-regulatory aspects of good strategies, and how mathematics learning environments can provide supportive help. We aim to contribute theoretically sound and empirically validated guidance to help educators achieve an effective balance of these elements, whether or not they use technology in their classrooms.

Our research program uses complementary methods in a coherent progression to address three goals:

1. To develop automated detectors that can differentiate between wheel-spinning and productive persistence in real time. We will create models that can differentiate these modes of engagement, using educational data mining (EDM) techniques to develop automated detectors.
2. To explore what factors predict wheel-spinning and productive persistence. In a correlational study, across a broad and diverse sample, we will examine the student factors, teacher practices, and system features that may predict wheel-spinning. A second study will use think-aloud methodology for an in-depth investigation of cognitive and self-regulatory processes associated with persistence.
3. To design and test interventions to reduce wheel-spinning and promote productive persistence. Building on these findings and others in the research literature, we will design a set of interventions. We will co-design the interventions with a team of master teachers, with guidance from our advisory board of international experts. We will conduct a series of randomized controlled trials (RCTs) to test the impacts of the interventions on students’ persistence, learning, and affective experience.