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Investigating the Impact of Metacognitive Supports on Students’ Mathematics Knowledge and Motivation in MATHia

The Project: Enhancing Math Learning through Metacognition

We are thrilled to be part of the Digital Learning Platforms Network (SEERNet) in collaboration with Carnegie Learning’s team to enhance how middle school students regulate their learning and experience math. Through this partnership, we will leverage ideas from cognitive science and evaluate and implement them at-scale in an ecologically valid way with Carnegie Learning’s digital learning platform, MATHia.

Math is foundational to children’s learning. And although the world of mathematics is incredibly rich and full of interesting problems to solve, students often struggle in math class or have the belief that they don’t have the ability to succeed in mathematics. So, what can we do to help students, particularly middle schoolers, learn math in school? 

We propose that one answer is directly teaching and giving students practice engaging in metacognitive skills. A growing body of research shows that directly teaching students to engage in metacognition benefits math learning. Metacognition is broadly defined as thinking about and controlling one’s cognitions. In particular, we focus on integrating metacognitive skill enhancements within MATHia. We target three skills: 

  • Planning, which involves understanding the goal of the problem and a path to reach that goal
  • Monitoring, which involves keeping track of one’s understanding and progress
  • Evaluating, which involves reflecting on one’s learning process. 

Engaging in these skills has shown to benefit both procedural and conceptual knowledge as well as student motivation for the subject.

However, the majority of math classes do not support these metacognitive skills. Teachers have a ton of pressure to cover a lot of content over a school year, and it is challenging to spend less time on specific math content in order to dive deeply into any non-content-specific areas. Further, teachers are rarely, if ever, supported in learning what metacognition entails and how to apply it within their classrooms. In fact, some teachers have biases that only higher-performing students can benefit from metacognitive instruction. As a result, students do not have the opportunity to learn how or why they should regulate their own learning. This can have consequences as the demands of school, including the complexities of the math they encounter, increase in higher grades. Thus, our approach of including metacognitive supports in MATHia will encourage educational equity by providing all students, including those that are low-performing, opportunities to receive these supports. Critically here, supporting metacognition is an equitable practice such that these supports boost lower-performing students to a greater extent than higher-performing students, but has benefits for all (e.g., Cardelle-Elawar, 1995). 

In partnership with Carnegie Learning, a SEERNet Network DLP, our research team is investigating ways to improve these metacognitive skills during mathematics lessons. We chose to work with Carnegie Learning and their platform, MATHia, as they have an extensive set of diverse middle school users, and they have thoughtfully designed modules that allow learners to develop their knowledge and skills at their own pace. This platform seemed like an ideal place to embed metacognitive instruction and prompts to hopefully improve students’ metacognitive skills, motivation, and mathematics performance. Carnegie Learning also has had previous successful projects with researchers and has the flexibility in their platform to try out new ideas and questions. 

For the current project, we will bring in evidence-based enhancements to support middle school students’ metacognitive skills across several experiments. More specifically, we plan to support students’ planning, monitoring, and evaluation metacognitive skills. Using our enhancements, students will be explicitly assisted in developing their planning how to solve a problem before beginning the problem, monitoring their progress towards completing the goals of the problem, and evaluating their learning experience. 

Research Questions

These enhancements are grounded in our past work that has shown supporting these skills benefits middle schoolers’ science learning and motivation (Zepeda et al., 2015). Our current study could provide similar evidence of the effectiveness of metacognitive approaches for math and pinpoint whether a particular set of skills or ordering of skills has the largest impact. We plan to answer the following research questions in a series of studies:

  1. Does adding instruction and practice with all three metacognitive skills benefit middle school students’ mathematics knowledge and motivation?
  2. To maximize the effectiveness of this direct instruction and practice, is it more beneficial for middle school students to receive the integrated support of all three metacognitive skills versus support for a particular metacognitive skill? Further, are there differential effects when supporting a particular metacognitive skill (e.g., are monitoring and evaluating more beneficial to mathematics outcomes than planning)?
  3. Is it better to interleave the direct instruction and practice of all the metacognitive skills or to focus on one skill at a time and then interleave them?
  4. Are there developmental differences between sixth- and eighth-graders on the effects of interleaving the instruction and practice of the metacognitive skills on mathematics knowledge and motivation? 

These studies will reveal ways that digital learning platforms can easily be adapted to enhance students’ self-regulated learning and content knowledge while also revealing the order and types of metacognitive skills that lead to the most robust learning outcomes. The findings from this work will not only contribute to our understanding of generalizability, but also allow us to build upon existing theories of self-regulated learning by evaluating whether specific supports for metacognitive skills can benefit specific learning outcomes and motivational constructs within mathematics. 

Incorporating these supports into a digital learning platform like MATHia allows us to maximize the effects of the metacognitive supports as we evaluate how to scale up and generalize to different groups of students in authentic classrooms. If any of the metacognitive supports are found to improve students’ metacognitive skills, mathematics knowledge, and/or motivation, they will be relatively easy for the team at MATHia to integrate into a broader set of lessons throughout their platform and would also be relatively easy for teachers to incorporate into their problem-solving lessons in their classrooms. Using MATHia may also offer some insight into potential ways to scale up these types of metacognitive supports in productive and wide-reaching ways in other digital learning platforms as well.

We are thankful for our Co-Investigator (Dr. Bethany Rittle-Johnson at Vanderbilt University), consultants (Dr. Jon Star at Harvard University and Dr. Matthew Bernacki at the University of North Carolina, Chapel Hill), graduate research assistant (Xinran “Wendy” Wang at Vanderbilt University), and the Carnegie Learning Team for sharing their knowledge, brilliance, and support for this project!

How it Came Together

Originally, our team had gotten together to learn about the different digital learning environments with the intent of conducting entirely different projects. Our PI was contemplating running an experiment with college students using Terracotta with her postdoc advisor and our Co-PI and Co-I were investigating whether a different experiment could be conducted in MATHia. However, we realized some constraints in these initial paths that ultimately led us to the current project. The first constraint was a practical one: tenure and promotion. While the project could be implemented in Terracotta, the PI needed to show independence from prior advisors. So, the project can still be picked up at a later time point, but now is not the time. The second constraint was that the other project intended to be conducted in MATHia was not implementable within MATHia’s system given the constraints of what we could and could not change in the system. However, during conversations with MATHia, our PI realized that this might be a great opportunity to test out whether a prior successful metacognitive intervention and results from an observational study would have similar effects in a new platform with the ability to scale and the potential to make greater impacts on student learners. Not only would the project (as described above) have practical impact, but it would have implications for theory as it tests which metacognitive skill is driving the relationships with outcomes and understanding whom the different implementations benefit. 

The take-home message is that learning about the different digital learning platforms and thinking imaginatively about what we could do with what we have done before allowed us flexibility in coming up with a project that was a great fit for our chosen platform. We are admittedly new to the digital learning platform space, but having familiarity with papers that had conducted research with the platforms and thinking about different types of data to capture student behaviors gave us a decent grasp on what is possible. Together, we took a chance on an idea and put a lot of work into thinking through the different intricacies and benefits of MATHia. As our project is currently underway, we have continued to learn invaluable information about how the system works and how other digital learning platforms operate. Although we have found challenges like figuring out how to write an IRB for these types of studies, learning what all goes into a memorandum of understanding, and working with constraints in the digital learning system, for us, the best part has been to co-create our intervention with the Carnegie Learning Team. We are beyond grateful for the collaboration with and encouragement from Steve Ritter (Founder and Chief Scientist), April Murphy (Director of Learning Engineering), Josh Fisher (Content Engineer), and many others working to support our project at Carnegie Learning. Our meetings are full of energy as we creatively work to implement and test our ideas. 

The SEERnet Network Support and Collaboration

In addition to working with the incredible members of the Carnegie Learning Team, we have been welcomed by the larger SEERnet Network through various meetings and opportunities. For example, we have started to think about whether a study implemented in one platform could be implemented in another and the constraints and affordances of the different platforms. From our exchanges, we have also learned how to think about data in different ways and are constantly learning about the new features that the digital learning platforms are innovating to make the pipeline between research and practice more accessible. In fact, part of the network recently collaborated to design a Learning@Scale Workshop focused on A/B Testing and Platform-Enabled Learning Research where our PI (Dr. Cristina Zepeda) and the PI (Dr. Avery Closser) from the other funded research team will be presenting keynotes this July. 
And to top off this experience, it has been amazing to get to work alongside the other funded research team and share experiences. Dr. Cristina Zepeda and Dr. Avery Closser have continued to learn from each other and elevate each other’s voices in various venues.