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Applying computational thinking to boost student engagement in middle school math

Midwest | September 06, 2022

A small group of grade 6 math teachers and math coaches from Milwaukee Public Schools

In late July, a small group of grade 6 math teachers and math coaches from Milwaukee Public Schools came together with REL Midwest to implement the first phase of activities under the ENgagement and Achievement through Computational Thinking (ENACT) partnership. Over three days, the group explored how computational thinking practices can be used to engage and connect with students in new ways. The group also began to plan how they would integrate computational thinking into their math instruction by using ENACT lesson plans and teacher resources. REL Midwest and Milwaukee Public Schools math coaches had collaborated to develop these materials in the first half of 2022.

The ENACT partnership formed in response to Milwaukee Public Schools’ interest in bringing computer science to all students while promoting deep mathematical learning to close achievement gaps. Given the partnership’s long-term goals to increase students’ engagement and sense of self-efficacy in math, the discussion at the training focused on developing strategies to foster engagement in ways that would be responsive to students’ needs as they return to the classroom.

Student engagement is critical to long-term success

As students progress into middle school, their engagement with and motivation for learning math may decrease as they confront more challenging concepts.1 To address this issue, middle school math teachers need to use intentional practices to boost student engagement and motivation, and the ENACT partnership is developing tools to support these efforts. Increasing student enthusiasm for math is especially important because student perceptions of their personal competence, teacher expectations, and peer support are key drivers of successful math learning.2

Middle school math serves as an important bridge to further math and science learning. Providing broad support for student engagement can improve educational experiences for all learners. Students furthest from opportunity, including those in Milwaukee Public Schools, historically have had limited access to education practices that boost engagement in math, such as enrichment opportunities.3 The ENACT approach thus has the potential to have a meaningful effect in closing math achievement gaps.

Core Practices of Computational Thinking
Core practice


Pattern Recognition

Looking for ways that problems or situations are similar or different to help develop strategies and solutions


Identifying and representing the important information in a problem or situation


Breaking a complex problem into smaller parts that are easier to address


Finding and fixing mistakes to improve one’s work


Developing and using systematic, step-by-step approaches to problems

ENACTing a model to support student engagement in math

Integrating computational thinking into math instruction places a focus on reasoning and sensemaking—two skills supported by strong student engagement (see table). At the same time, the computational thinking strategies that students use when approaching math problems not only foster a deeper understanding of math concepts but also help students apply math reasoning in their daily lives. Teachers participating in ENACT partnership activities will have the opportunity and support to explore how to implement lessons in a way that connects math problems to students’ lived experiences, making math learning more relevant and meaningful for students.

To support middle school math teachers in integrating computational thinking practices, the ENACT partnership is developing strategies to connect math lessons to students’ lives outside of school, thereby enabling students to map new math knowledge to things they already know and care about. By creating opportunities for students to use multiple entry points into problems and by affirming multiple ways to solve problems, teachers can encourage students to approach math problems in ways that make sense to them. In addition, teachers will build on student thinking to ensure that students know they are bringing valuable ideas and work to the classroom and to help students make their thinking—and problem solving—visible. A bonus is that the practices will help encourage students to collaborate with one another to strengthen their computational thinking skills.

Coming up next

As the school year gets underway in Milwaukee, ENACT teachers and coaches, in partnership with REL Midwest, will begin to use the ENACT teaching strategies in their classrooms. In October, ENACT teachers will coteach with their ENACT coaches to practice focusing on student engagement and the five computational thinking strategies. Teachers and coaches then will have opportunities to debrief the computational thinking lessons and to plan for how to further encourage and empower students in applying computational thinking skills to math.


1 Birgin et al., 2017; Collie et al., 2019; Ryan & Patrick, 2001.
2 Kiefer et al., 2015; Lumsden, 1994, 1999; Özkal, 2018; Ruiz, 2012.
3 Kennedy & Smolinsky, 2016; National Academy of Sciences et al., 2007.


Birgin, O., Mazman-Akar, S. G., Uzun, K., Göksu, B., Peker, E. S., & Gümüs, B. (2017). Investigation of factors affected to math engagement of middle school students. International Online Journal of Educational Sciences, 9(4).

Collie, R. J., Martin, A. J., Bobis, J., Way, J., & Anderson, J. (2019). How students switch on and switch off in math: Exploring patterns and predictors of (dis)engagement across middle school and high school. Educational Psychology, 39(4), 489–509.

Kennedy, E., & Smolinsky, L. (2016). Math circles: A tool for promoting engagement among middle school minority males. Eurasia Journal of Math, Science and Technology Education, 12(4), 717–732.

Kiefer, S. M., Alley, K. M., & Ellerbrock, C. R. (2015). Teacher and peer support for young adolescents’ motivation, engagement, and school belonging. RMLE Online, 38(8), 1–18.

Lumsden, L. S. (1994). Student motivation to learn. ERIC Digest, Number 92. ERIC Clearinghouse on Educational Management.

Lumsden, L. S. (1999). Student motivation: Cultivating a love of learning. ERIC Clearinghouse on Educational Management.

National Academy of Sciences, National Academy of Engineering, & Institute of Medicine. (2007). Rising above the gathering storm: Energizing and employing America for a brighter economic future. National Academies Press.

Özkal, N. (2018). Relationship between students’ math engagement and math teachers’ motivational support. Turkish Journal of Education, 7(2), 86–98.

Ruiz, E. C. (2012). Setting higher expectations: Motivating middle graders to succeed. Association for Middle Level Education.

Ryan, A. M., & Patrick, H. (2001). The classroom social environment and changes in adolescents’ motivation and engagement during middle school. American Educational Research Journal, 38(2), 437–460.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60(6), 565–568.


Laura Checovich

Laura Checovich

Iszy Licht

Iszy Licht

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