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Interdisciplinary Ideas

Integrating Computer Science to Enhance Remote Learning

Science Scope—November/December 2020 (Volume 44, Issue 2)

By Raja Ridgway

Making the transition to teaching remotely can feel challenging and often frustrating, especially for science teachers who thrive on facilitating student-led labs and investigations. When students are not with us in person, how do we engage them in inquiry-based instruction, probe their thinking, and support collaborative groupwork? One way, and it might not seem obvious at first, is to integrate computer science.

Now, you might be thinking, “Sounds interesting, but I literally cannot add one more thing to my list right now.” And that is totally understandable as you are likely managing many changes from teaching asynchronously to building relationships with your students. The great news is that integrating computer science can open both you and your students up to a world of benefits that directly address issues in remote learning: student motivation and building relationships.

Students’ perceptions of their experience in distance learning have been shown to be correlated to their levels of motivation (Malinovski et al. 2014), and students tend to be more cognitively engaged when they can build relationships with their teachers and peers (Louwrens and Hartnett 2015). While these two factors—motivation and relationships—might be built into a science teacher’s daily routines during in-person teaching, how can they be developed when students are engaging in asynchronous learning?

Supporting motivation

An individual’s motivation can be based on many factors including self-efficacy (Bandura 2000) and interest (Hidi and Renninger 2006). Research with elementary-age students has demonstrated that the integration of coding and computational thinking in content-area classes can support students’ agency (Gadanidis 2017)—their belief that they can not only accomplish a task, but also pursue that task independently. Moreover, the integration of coding and computational thinking can make instruction more inclusive by increasing access to all learners through differentiation (Gadanidis 2017).

When learning asynchronously, coding in a block-based environment like Scratch (see Resources) can help develop students’ self-efficacy and interest. Scratch was designed to be an exploratory experience where students could learn through trial and error facilitated by immediate and visual feedback (Maloney et al. 2010). Students do not need to see a teacher model to know what the blocks can do as they can simply experiment and iterate quickly.

Supporting relationships

Asynchronous student-to-teacher and student-to-student relationships can be built through consistent engagement (e.g., providing reminders) and support (e.g., providing timely positive and constructive feedback; Louwrens and Hartnett 2015). Although reminders can be provided through email and announcements on tools like Google Classroom, integrating coding can provide students with unparalleled levels of feedback (Maloney et al. 2010). In addition to the visual feedback provided by tools like Scratch, students can also engage in peer-to-peer feedback using rubrics. Multiple studies have found that students, when provided with rubrics, are able to both self-assess and peer assess their code similarly to a teacher (Sentence, Barendsen, and Schulte 2018).

In an asynchronous learning environment, teachers can also harness the efficiency of tools like Dr. Scratch (see Resources) to facilitate quick feedback for students. Dr. Scratch can be used by students, peers, and teachers to gather data about students’ computational thinking skills such as abstraction and data representation (Kwon, Lee, and Chung 2018).

Intentional integration

Recognizing that the integration of coding and computational thinking into remote learning can support student motivation and relationship building is simply the first step. Next, we must consider how to intentionally integrate computer science into our science coursework so that we aren’t left trying to teach two discrete subjects. We also want to avoid integrating computer science as an extension or optional work, given the negative racial and gender stereotypes that exist around who should be doing computer science (e.g., Master, Cheryan, and Meltzoff 2016; Robinson, Perez-Quinones, and Scales 2016).

The architects of the K-12 Computer Science Framework (n.d.) have described many areas in which computer science and science/engineering overlap. For remote learning, modeling and simulation are two areas in which the integration of computer science can enhance science instruction.

Enhanced modeling

Science teachers are no strangers to models—sixteen of the Next Generation Science Standards (NGSS Lead States, 2013) middle school performance expectations include the science and engineering practice of modeling. In a typical in-person class, students might be developing a model to illustrate the continuous flow of water on Earth (see NGSS chart in Online Supplemental Materials). Such a model might be developed using paper and pencil or other physical materials over the course of a unit, with students coming back to their models over and over as they learn new concepts and skills. The illustrations or physical models might exist in a classroom with students able to see their peers’ progressions over time.

Asynchronously, the construction, feedback, and iterations could be challenging to accomplish. Students might not all have the same access to materials, and teachers might find it hard to provide clear feedback on uploaded pictures of the models. However, if students are working asynchronously on Scratch projects to create and explain their models, many of these challenges are overcome. Students can integrate written and audio explanations, easily share their projects, and quickly provide feedback on their peers’ work using the comments feature.

Beyond the benefits of increased agency and easy feedback, the integration of computational thinking and coding can support students’ conceptual understanding of modeling. Although students already know the basics of abstraction from their “real lives” (Ridgway 2020), the concept of abstraction in science can be challenging to understand when developing models. When providing feedback, teachers can describe the connections between abstracting when writing code (e.g., the blocks that abstract the underlying computer processes) and abstracting when developing models (e.g., the inclusion of only essential components).

Enhanced simulation

Closely tied to the practice of modeling is simulation. Many teachers use simulations, such as the PhET simulations from the University of Colorado (see Resources), to abstract specific variables or allow students to investigate when lab materials are not available. A similar approach can be taken if teachers want to incorporate Arduino activities during remote learning.

Tinkercad is a free design and simulation platform often used in conjunction with 3D printers (e.g., Angelone 2019). Another feature of Tinkercad is the ability to simulate a working Arduino where students can make the electrical connections and write the necessary code in a block-based environment (see Figure 1). Students might use Tinkercad to both design a 3D model for an insulated cooler (see NGSS chart in Online Supplemental Materials) and demonstrate their understanding of closed circuits using the Arduino simulator to create a temperature sensor that could be used to determine how well the cooler works. Students working with Arduino simulators have been shown to have similar academic performance outcomes to students working with physical Arduinos while also needing less time to set up the kits (Gonçalves et al. 2012). In fact, the use of Arduino simulators can be more inclusive for students who may have challenges with fine motor skills as they might find the use of a mouse more accessible than the connecting of small wires and components on a physical Arduino (Gonçalves et al. 2012). Several resources are available for getting started with Tinkercad and setting up a virtual classroom (see Resources).

arduino board


Remote teaching and learning can be hard. Intentionally integrating computer science within your science content can allow you to leverage the agency developed through learning how to code. The consistent and immediate feedback provided through programming can support students even when you are not able to be there to engage with them in person.

How else are you integrating CS into your middle school science classroom? Do you have a great resource or experience to share? Let me know via email!


Dr. Scratch—

Tinkercad classrooms—


Tinkercad knowledge base—

University of Colorado PhET simulations—

Online Supplemental Materials

NGSS chart—

Raja Ridgway ( is the director of computer science education at Relay Graduate School of Education in Denver, Colorado.


Angelone L. 2019. Blended learning in the science classroom: An introduction to atoms and molecules. Science Scope 43 (4): 58–64.

Bandura A. 2000. Exercise of human agency through collective efficacy. Current Directions in Psychological Science 9 (3): 75–78.

Gadanidis G. 2017. Five affordances of computational thinking to support elementary mathematics education. Journal of Computers in Mathematics and Science Teaching 36 (2): 143–151.

Gonçalves P.F., Sá J., Coelho A., and Durães J.. 2012. An Arduino simulator in classroom—a case study. First International Computer Programming Education Conference (ICPEC 2020) 12:1–12:12.

Hidi S., and Renninger K.A.. 2006. The four-phase model of interest development. Educational Psychologist 41 (2): 111–127.

K-12 Computer Science Framework. (n.d.). Computational thinking.

Kwon K., Lee S.J., and Chung J.. 2018. Computational concepts reflected on scratch programs. International Journal of Computer Science Education in Schools 2 (3): 1–15.

Louwrens N., and Hartnett M.. 2015. Student and teacher perceptions of online student engagement in an online middle school. Journal of Open, Flexible and Distance Learning 19 (1): 27–44.

Malinovski T., Vasileva M., Vasileva-Stojanovska T., and Trajkovik V.. 2014. Considering high school students’ experience in asynchronous and synchronous distance learning environments: QoE prediction model. International Review of Research in Open and Distance Learning 15 (4): 91–112.

Maloney J., Resnick M., Rusk N., Silverman B., and Eastmond E.. 2010, November. The scratch programming language and environment. ACM Transactions on Computing Education 10 (4): Ar. 16.

Master A., Cheryan S., and Meltzoff A.N.. 2016. Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of Educational Psychology 108 (3): 424–437.

NGSS Lead States. 2013. Next Generation Science Standards: For states, by states. Washington, DC: National Academies Press. .

Ridgway R. 2020. Building bridges with computational thinking. Science Scope 43 (8): 12–15.

Robinson A., Perez-Quinones M.A., and Scales G.. 2016. African American middle school girls: Influences on attitudes toward computer science. Computing in Science & Engineering 18 (3): 14–23.

Sentence S., Barendsen E., and Schulte C., eds. 2018. Computer science education: Perspectives on teaching and learning in school. New York, NY: Bloomsbury.

Dr. Scratch

Tinkercad classrooms


Tinkercad knowledge base

University of Colorado PhET simulations

NGSS chart

Computer Science Engineering Inclusion Instructional Materials Interdisciplinary Teaching Strategies

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