{"id":12858,"date":"2023-05-04T15:55:46","date_gmt":"2023-05-04T15:55:46","guid":{"rendered":"https:\/\/citejournal.org\/\/\/"},"modified":"2023-08-15T14:39:01","modified_gmt":"2023-08-15T14:39:01","slug":"examining-spaces-for-integrating-physics-and-computing-through-classroom-inquiry","status":"publish","type":"post","link":"https:\/\/citejournal.org\/volume-23\/issue-2-23\/science\/examining-spaces-for-integrating-physics-and-computing-through-classroom-inquiry","title":{"rendered":"Examining Spaces for Integrating Physics and Computing Through Classroom Inquiry"},"content":{"rendered":"\n
Computing tasks increasingly make up the majority of STEM (science, technology, engineering, mathematics) jobs, and noncomputing STEM jobs require more knowledge of computing than ever before (Adams, 2020). This is evident in fields such as computational biology, bioinformatics, chemometrics, and computational physics. Recognizing this demand, the Next Generation Science Standards<\/em> (NGSS; National Research Council, 2013) in the United States included the use of mathematics and computational thinking as two of eight disciplinary practices that are essential for science students to learn.<\/p>\n\n\n\n While computer science (CS) and computational thinking (CT) hold natural places in science classrooms, there is little guidance for teachers about how to integrate CS and CT (Grover & Pea, 2013; Weintrop et al., 2015). While some efforts have been made to prepare secondary science teachers to integrate CT (e.g., Vieyra & Himmelsbach, 2022), most teachers lack the knowledge to meaningfully integrate CT with disciplinary content (Sands et al., 2018). Thus, the integrated scientific and CS skills necessary to get students on a pathway toward computational STEM careers remain elusive .<\/p>\n\n\n\n One promising approach to meaningfully integrating CT in science classrooms is through maker education. Maker education grows out of the broader Maker Movement (Peppler & Bender, 2013), engaging students and teachers in hands-on, project-based learning that combines physical making with computational thinking. Prior research has shown that maker education is successful in engaging students in science, especially students who do not normally feel a sense of belonging in the science classroom (Calabrese Barton & Tan 2018; Tofel-Grehl, 2023). This finding is significant, because early academic preparation predicts students\u2019 career choices (Tai et al., 2006) and the formulation of personal identities compatible with STEM pursuits (Calabrese Barton et. al, 2013; deWitt & Archer, 2015).<\/p>\n\n\n\n This article shares findings from one high school teacher\u2019s efforts to engage physics students in CT within the context of their physics classroom. We report on how physics students engaged CT as an intellectual tool to design, construct, and code escape rooms that leveraged physical science content. Escape rooms, puzzles that participants solve in order to win the game and escape the room, are currently popular with adults and young people alike (Fotaris & Mastoras, 2019). Leveraging this interest, this study explored how to integrate computing into high school physics classes by integrating physics knowledge and computational thinking.<\/p>\n\n\n\n Engaging students directly with STEM content and skills through the design and prototyping inherent to making can provide a meaningful context to further develop STEM identities through interest (Vossoughi & Bevan, 2014). In the study reported here, students utilized low-cost microcontrollers (Micro:bits<\/em>) and recyclable craft materials to design and build puzzles for their peers to solve (see Figure 1). These puzzles collectively formed a class-wide escape room that students needed to solve to receive a prize.<\/p>\n\n\n\n Figure 1 The making of these puzzles incorporated elements of embedded computing for controlling the behavior of puzzle artifacts, which showcased various aspects of NGSS-based physics content. In contrast to conventional physical science projects, these artifacts were created using novel materials such as conductive tape or conductive Velcro, sensors for light, sound, and pressure, and actuators such as LEDs and speakers. With these materials, students engaged in designing intellectually rigorous, content-driven, and personally meaningful solutions.<\/p>\n\n\n\n Computing is an essential skill set for many STEM professions. By 2028, the U.S. Bureau of Labor Statistics predicts that three out of four new STEM job openings and three out of five STEM job openings overall will be in computing (Adams, 2020). Increasingly, efforts are underway to engage secondary science teachers in meaningful disciplinary integration of computer science and computational thinking (e.g., Hutchins et al., 2020; Vieyra & Himmelsbach, 2022), but disciplinary integration remains challenging for learners and teachers alike (Basu et al., 2016; Hurley, 2001; Pang & Good, 2000).<\/p>\n\n\n\n Furthermore, despite recent efforts, most teachers are not adequately prepared to effectively guide students in authentic scientific inquiry using traditional classroom tools and practices (Johnson, 2006, 2007). They are even less likely to engage in inquiry using programming, computing, or simulation tools, as they feel less skilled in teaching with these technologies (Belland, 2009; Hargrave & Hsus, 2000). Additional research is needed into how CS and CT concepts should be integrated into science classrooms to establish the necessary foundations for postsecondary and professional pathways toward computational STEM careers (Lee et al., 2020).<\/p>\n\n\n\n Low consensus in the field on the definition of CT further complicates efforts to enhance integration into classrooms. Wing (2006) suggested that everyone should learn to think like a computer by learning how to \u201c[solve] problems, [design] systems, and [understand] human behavior, by drawing on the concepts fundamental to computer science\u201d (p.33). In recent years, several attempts have been made to more precisely define computational thinking (e.g., Barr & Stephenson, 2011; Grover & Pea, 2013; Weintrop et al., 2015). Brennan and Resnick (2012) operationalized CT as sets of key concepts, skills, and practices.<\/p>\n\n\n\n While there are more targeted frameworks for understanding computational thinking in STEM contexts (e.g., Barr & Stephenson, 2011; Weintrop et al., 2015), most of the participants in our study had no prior programming experience. As such, Brennan and Resnick\u2019s (2012) framework for assessing the development of computational thinking in novice learners made the most sense. <\/p>\n\n\n\n The Brennan and Resnick (2012) framework focuses on three dimensions of CT. Computational concepts refer to key programming concepts that appear in many programming languages. These include sequences, loops, events, parallelism, conditionals, operators, and data. Computational practices refer to specific concepts that programmers engage in when building their code, including being incremental and iterative, testing and debugging, reusing and remixing, and abstracting and modularizing. By being incremental and iterative, Brennan and Resnick referred to the ways in which a program is developed, not in one cohesive chunk, but rather through building and testing smaller code segments in increments. As new ideas emerge, the goal for the code might change, requiring the programmer to build and test something new.<\/p>\n\n\n\n Testing and debugging encompasses the practices used to test a piece of code and problem solve when the code does not work as expected. Reusing and remixing describes a common practice in programming of taking and building upon code created by someone else. For novice programmers, this is a way to extend their programming skills beyond where they could go on their own. Finally, abstracting and modularizing is a practice in programming whereby smaller parts are put together to form something much larger.<\/p>\n\n\n\n In addition to computational concepts and practices, Brennan and Resnick (2012) included computational perspectives in their framework. These include expressing, connecting, and questioning. Novice programmers learn that they can use computing to express themselves and to connect with others through what they create. The practices laid out by Brennan and Resnick also map onto several NGSS practices (see Table 1). Through these overlapping practices, connections and integrations can be made to deepen student understanding within and across disciplines.<\/p>\n\n\n\n Table 1<\/strong>
<\/strong>Example of Student Fabricated Codable ESCAPE Puzzle<\/em><\/p>\n\n\nBackground<\/h2>\n\n\n\n
Aligned Standards-Based Practices<\/p>\n\n\n\n