“These kids can’t think,” grumbled a frustrated middle school teacher when asked during a faculty meeting why so many students struggled to complete rigorous, open-ended tasks that invite creativity and problem solving. “That’s right!” a colleague added.
One by one, exasperated teachers voiced their concerns about their students’ passive approach to learning and seeming incapacity to transfer their conceptual understanding to authentic contexts. The teachers attributed these issues either to students’ inability to process information at a deep level of cognition or to their unwillingness to struggle through academic challenges, despite having numerous tech tools at their disposal. New technology acquired during the pandemic to support student learning had come hand-in-hand with various scripted curricula; although these materials had helped both students and teachers make sense of these new tools, the technology would often “think” for the students. Passive learning had eroded students’ sense of self-efficacy. Something, the teachers agreed, needed to change.
As the newly hired directors of innovation and instruction at Trinity Area School District in Pennsylvania, we knew we stood at the precipice of timely reform. To capitalize on the opportunity to reimagine teaching and learning for the 21st century, we decided to provide professional development on how teachers might use educational technology tools for designing innovative, open-ended learning experiences that require students to solve problems and generate new knowledge. But first, we needed to investigate the issue of passive learning.
We began by talking with teachers and students. Science teachers reported that students now needed explicit instructions that left no room for original thought, when, prior to the pandemic, they had often enjoyed solving real-world problems creatively. English teachers observed that students muddled through open-ended writing tasks, often leaving them incomplete. Math teachers noted that students routinely failed tests unless the problems presented looked identical to those they had solved for practice. And high school students admitted that they avoided assignments that confused them because they felt overwhelmed by having to think beyond the context of a lesson.
We also looked at student achievement data. We compared student performance on high-stakes standardized tests with their scores on locally designed summative assessments, such as teacher-created tests and quizzes. We discovered that students who excelled on teacher-made tests often scored lower on nationally normed assessments. We scrutinized report card grades and looked for patterns in assignment completion. We found that, when given the choice, students at the secondary level more readily attempted assignments requiring low-level cognition.
When instructed to recall, define, explain, or sequence events, most students met expectations. However, when students were asked to apply their understanding to new contexts or work creatively within a loose set of parameters, their performance dropped. For example, students were quick to build a block-based program for a robot on a screen; however, when tasked to actually program a robot using their knowledge of block-based programming, students struggled. In some cases, they refused to attempt the work. The more open-ended and challenging the assignment, the less willing students were to engage.
Our research led to two epiphanies: First, providing students with opportunities to exercise critical thinking skills doesn’t equate to teaching critical thinking skills. Second, just because students have access to educational technologies to solve real-world problems doesn’t mean they know how to use those tools to generate new knowledge.
We realized that students didn’t lack the ability to think, but rather struggled to develop and execute plans for completing open-ended tasks; some students didn’t know how to start thinking critically at all. Many students, accustomed to simply clicking through pre-programmed digital content, had lost the will to fully engage with challenging material.
We needed a new approach—one that would alter the way that Trinity Area School District’s teachers and students thought about thinking and about the role of technology in education. So, we reached out to ISTE’s director of online learning, who told us about a 15-hour graduate-level course called An Introduction to Computational Thinking for Every Educator. The director explained that the course would require teachers to learn a framework for thinking that aligns with the way technology works, as well as a formula for teaching students to think and actively reflect on their own thinking processes.
This was exactly what we needed. As our Superintendent of Schools Michael Lucas noted, “Let’s get back to basics by teaching critical thinking skills, but with a digital twist!”
Providing students with opportunities to exercise critical thinking skills doesn't equate to teaching critical thinking skills.
The Importance of Computational Thinking
Computational thinking is “an essential literacy for all students” that “combines four pillars: decomposition, pattern recognition, abstraction, and algorithms.” Just as technology expresses solutions to problems as a series of steps to automate a process, computational thinking can serve as a formula for teaching students the steps to take when thinking critically.
For example, a teacher might pose the following computational question to students: How might we allocate scarce resources among a large population to equitably meet individuals’ most pressing needs? The teacher would then direct students to decompose, or break down, the complex question into a series of simpler questions such as: Where is this population located? What resources are available in the region? What are the most plentiful resources? What are the population’s most pressing needs? Do all members of the population have the same needs?
The teacher would then provide students with data sets to help them answer the decomposed questions. Perhaps students might notice that specific subgroups within the population have similar needs, or they might identify surrounding geographic areas where resources are more plentiful. Once students have recognized these patterns inherent in the data, the teacher might guide them to map a plan for reallocating resources equitably. Through an abstraction exercise, students would delineate between relevant and irrelevant details to identify a potential response to the complex question. Finally, students would be called on to develop an algorithm or formula that would help reallocate resources consistently in an equitable manner. This is similar to the way computers use algorithms to execute tasks, which leads to consistent outcomes.
When students are faced with solving a complicated problem that requires intense scrutiny and a thorough plan of action, thinking computationally can help them dissect the problem to envision a solution. Moreover, working through the computational thinking process invites metacognitive awareness; the very nature of thinking computationally invites humans to troubleshoot and reflect. Thinking computationally enables students and technology to work as equals in education so no one individual or machine does all the work.
Credit: Photo Courtesy of Constance DeMore Savine and Samantha Shinsky
Trinity’s intermediate music students began the 2024-25 school year by learning how neural pathways work in the brain and identifying strengths in their thinking.
Taking the Plunge
At the start of the 2022–23 school year, we enrolled approximately 285 teachers and administrators in ISTE’s computational thinking course with funds from the PAsmart Grant (a grant awarded to districts in Pennsylvania that aim to advance STEM education and the teaching of computer science). Initially, some participants expressed feelings of discomfort and apprehension, but we reminded everyone that learning is worth the struggle and ensured them our continued support. Showing students how to think critically and use technology to solve real-world problems was imperative, and we wanted all Trinity educators to learn a framework for teaching the thinking process.
Teachers were prompted to think computationally as learners as they solved a problem they identified; at the same time, they were called on to envision how they might teach students to think computationally. Teachers created annotated lesson plans detailing their role as facilitators of instruction and predicted how their students would respond to instruction. As teachers began to trust the learning process, they identified gaps in their own metacognition. For example, they began to notice when they had neglected to follow the computational thinking process with fidelity. Some envisioned how students’ use of the computational thinking framework might lead them to reflect on their thinking as well, a big plus according to Moss and Brookhart.
Computational Thinking in Music
Trinity’s music teachers were among the first to embrace the new initiative because music naturally invites computational thinking. These teachers have always focused on two pillars of computational thinking: teaching students to decompose complex compositions and teaching them to recognize patterns within the pieces themselves. Since engaging in the course, however, the teachers have realized they can exercise computational thinking to design cohesive music instruction across K–12.
First, each music teacher decomposed their own yearlong course into 36 weeks so they could visualize the course in its entirety while simultaneously mapping each week in isolation. The goal was to ensure that learning experiences designed for students at one grade level prepared them to achieve success in subsequent grade levels. Once they sequenced standards in an order that made sense, teachers recognized patterns, such as commonalities among standards that called for composing and performing. Students would need to exercise skills in those areas at increasingly more sophisticated levels as they continued through the K–12 music program. Collectively, the teachers determined an order for teaching concepts and essential skills in each music course that also systematized all courses into a cohesive whole. Finally, they identified core concepts and skills they would all need to teach in sequence, as well as areas where the curriculum left space for them to teach more autonomously.
Computational Thinking in Math
A high school math teacher, who formerly began each class with direct instruction, now teaches using an inquiry-based approach. “I used to tell students what I wanted them to do and how to do it, and then I’d tell them to practice doing exactly what I showed them to do,” she admitted. “Since I learned how to teach students to think, I make them do the hard work.”
The teacher now introduces every unit by presenting a complex word problem, which students decompose into a series of less complex problems to solve. As she and students collaborate to solve the less complicated problems, patterns in their thinking emerge. Students might recognize patterns through graphing and statistical analysis, or they might note recurring patterns in arithmetic sequences. By breaking down a complex word problem into smaller, more manageable chunks of information, students can more easily abstract information, delineating between relevant and irrelevant information. As they come to better understand the actions needed to complete the task and envision a solution, they can develop an algorithm for solving the problem, mirroring how a computer would approach completing the same task.
Credit: Photo Courtesy of Constance DeMore Savine and Samantha Shinsky
Teachers in a PD workshop use sensors, motors, and other materials to create robots as a springboard for teaching students computational thinking skills.
Computational Thinking in Science
Likewise, middle school students have responded positively to problem-based academic challenges after learning how to break down problems into manageable chunks, identify trends and patterns, and abstract key information to develop algorithms—or step-by-step procedures—for completing similar tasks.
When 6th graders were challenged to map and execute a plan for living on Mars, they collaborated to decompose the challenge by identifying a series of smaller problems to solve, thus making their work far more manageable. They drew on their understanding of concepts like sustainability, and they looked for patterns in the data they studied, asking questions to determine how a given concept might apply to life on Mars. Students used abstraction to distinguish among information that was relevant only to Earth or to Mars or that applied to both planets. With this information in hand, students developed their plan for living on Mars.
As teachers paused work sessions to guide students to reflect on their development, students began to notice if their thinking was counterproductive to the cause. Teachers reported hearing students say things like, “Wait, this makes no sense. We need to go back and redo . . .” while collaborating with peers. Students who self-assessed their progress and recognized gaps in their thinking reworked their plans. Consequently, students and teachers alike began to value the thinking process as proof of academic achievement.
Computational Thinking in Writing
High school students have proven their willingness to work hard when teachers scaffold open-ended challenges using the computational thinking framework. For example, students are no longer expected to write a scholarly research paper about a teacher-assigned topic in English language arts class. Instead, they’re challenged to identify a complex task they want to accomplish—such as creating an app to chart weight loss, authoring and illustrating a children’s book, or building a computer from scratch—using technology to meet that goal. Instead of spending hours researching online and synthesizing findings into a manuscript on a teacher-assigned topic, students describe in writing the process they used to complete their chosen task.
Students now confidently articulate in writing how their use of computational thinking and educational technology tools led them to attain their goals. Their writing becomes an algorithm, the final step in their process of computational thinking. They’re now thinking deeply, independently, and in tandem with technology. The change from assigning traditional writing tasks that force an outcome to equipping students with critical thinking skills—and allowing them the space to think creatively—has reinvigorated classrooms.
Thinking computationally enables students and technology to work as equals in education so no one individual or machine does all the work.
Empowering Active, Engaged Learners
This transformation is not only about improving students’ scores on summative assessments; it’s also about eliminating barriers to teaching and learning. When Trinity teachers learned how to think computationally, they inspired the changes they wanted to see in their own classrooms. When students began to think computationally, they proved willing and able to use technology to explore and create.
This shift toward active, engaged learning, supported by technology and computational thinking, is laying the foundation for the future of education at Trinity. Students are no longer just consumers of information. Instead, they’re participants in their learning journey, equipped with the skills they need to tackle complex problems and create meaningful solutions. Here, at Trinity, we’re reimagining teaching and learning for the 21st century.
Reflect & Discuss
➛ Are you finding that it’s difficult for today’s students to apply their understanding to new contexts or work creatively within a loose set of parameters? If so, how are you responding?
➛ What is the most effective step you have taken to promote critical thinking in your classroom using technology?
➛ How might an emphasis on the four pillars of computational thinking—decomposition, pattern recognition, abstraction, and algorithms—revitalize a current unit or course that you teach?
