{"id":12976,"date":"2023-07-31T19:05:15","date_gmt":"2023-07-31T19:05:15","guid":{"rendered":"https:\/\/citejournal.org\/\/\/"},"modified":"2023-11-15T16:45:25","modified_gmt":"2023-11-15T16:45:25","slug":"teaching-science-via-computational-thinking-enabling-future-science-teachers-access-to-computational-thinking","status":"publish","type":"post","link":"https:\/\/citejournal.org\/volume-23\/issue-3-23\/science\/teaching-science-via-computational-thinking-enabling-future-science-teachers-access-to-computational-thinking","title":{"rendered":"Teaching Science via Computational Thinking? Enabling Future Science Teachers\u2019 Access to Computational Thinking"},"content":{"rendered":"\n

As an approach to solving problems by using computer science techniques and methods, computational thinking (CT) has been emphasized as a critical skill necessary for a society that relies on complex computer technologies (Grover & Pea, 2013; Wing, 2008). The growing importance of CT in education has also become clear in the national standards. For instance, computational thinking is included as a one of the core scientific and engineering practices in the Next Generation Science Standards<\/em> (NGSS; NGSS Lead States, 2013), while it can also be a thinking process that can support other practices (e.g., developing and using models) and help address the key cross-cutting concepts (e.g., patterns). Such an interest in integrating CT in teaching is due to the increasing computational nature of the science and mathematics fields, which are demanded for current and future workforce (e.g., bioinformatics, computational statistics, or computational neuroscience; Weintrop et al., 2016).<\/p>\n\n\n\n

The growing number of educational initiatives (e.g., CS4All) that focus on coding and programming reflects the assertion that such tasks promote CT, as they may provide students with opportunities to explicitly practice CT skills (Voskoglou & Buckey, 2012). However, such efforts tend to target middle and high school in-service teachers in big cities and large school districts (Google Inc. & Gallup Inc., 2016; Guzdial, 2016), while the focus on preservice teachers (PSTs) is limited (Yadav et al., 2017). Consequently, PSTs placed in poorer and rural schools with limited technology access will likely have fewer opportunities to learn how to develop their future students\u2019 CT skills.<\/p>\n\n\n\n

Thus, equitable CT opportunities in teacher education programs that work with rural placement-schools are essential, and the efforts need to take into account potential digital divide barriers to make CT more accessible to PSTs. As an initial step, the current examined how a specific workshop, designed to infuse CT in a science methods course, may influence PSTs\u2019 access to CT.<\/p>\n\n\n\n

Further, the skepticism over the limited evidence that CT can transfer to other areas beyond computer science (Denning, 2017; Guzdial, 2015) highlights the potential difficulty that educators may have in relating it to pedagogy. Given the importance of understanding both content and pedagogy in successful use of any technological interventions, the ways in which PSTs\u2019 understanding of CT may be connected to their pedagogical approach are also essential to this study. As such, the second aim of the study was to examine the relationship between PSTs\u2019 exposure to the CT-infused workshop and their lesson plans designed based on the 5E Model (Bybee, 2015a), a widely accepted inquiry-based instructional model for teaching science conceptual understanding (Contant et al., 2018).<\/p>\n\n\n\n

Literature<\/h2>\n\n\n\n

CT to Problem Solving<\/h3>\n\n\n\n

CT has been considered as an effective approach that uses computer science concepts and techniques to solve complex problems in a society that relies on complex computer technologies (Wing, 2008). The view that CT can be used to engage students consciously in making artifacts (e.g., design systems) in situated environments reflects the constructionist perspective in education (Papert & Harel, 1991). In particular, CT may start with encountering a problem, breaking it down into smaller components, identifying the patterns among them, abstracting a generic solution, generating algorithms to automate the solution, and analyzing the solution effectiveness (Barr et al., 2011; Grover & Pea, 2013; Wing, 2008), which mirror the problem-solving process, namely understanding and representing, planning, and executing and self-regulation (Mayer & Wittrock, 2006; OECD, 2003, 2015).<\/p>\n\n\n\n

Although CT may be broader than just a method to solve problems, such as developing computational models for inquiring and understanding phenomena (Denning, 2017), the problem solving nature of CT has been well recognized and emphasized in the literature (e.g., Voogt et al., 2015). As such, the opportunities that engage students in the CT processes are naturally of interest to educational research and practice focusing on promoting problem solving (Kale et al., 2018). Programming, in the form of developing and executing codes to instruct computers to complete specific tasks, has specifically been viewed as an effective means enabling students to explicitly practice CT skills (Voskoglou & Buckey, 2012). Due to the recursive process of coding, students can have multiple attempts and use multiple problem elements to break down challenging tasks and to develop and test their solutions. As such, coding and programming tasks to promote students\u2019 CT may potentially engage students in the problem-solving process.<\/p>\n\n\n\n

Coding to Think Computationally<\/h3>\n\n\n\n

The assertion that programming tasks promote CT is also reflected in the national standards (e.g., NGSS & Common Core), the increasing number of programming classes (Google Inc. & Gallup Inc., 2016), and coding initiatives such as Digital Promise, MakerEd, and CS4All (Herold, 2017; Madda, 2016; Smith, 2016; The White House, 2017). The noticeable surge in research studies on CT also echoes the same trend. According to a recent meta-analysis of empirical papers on CT published between 2006 and 2017 (Hsu et al., 2018), coding and programming was the most focused area. Another meta-analysis focusing on CT assessment also revealed that computing and programming was the most frequently assessed subject (Tang et al., 2020). Further, visual coding programs to promote CT (e.g., block-based programs) have been shown to increase students\u2019 self-efficacy toward coding (Arslan & Isbulan, 2021), programming performance (Namli & Aybek, 2022), thinking skills (Gunbatar & Turan, 2019), and academic achievements (Hu et al., 2021).<\/p>\n\n\n\n

CT in Teacher Education<\/h3>\n\n\n\n

Despite the increasing number of coding initiatives to promote CT and the efforts to emphasize critical aspects of CT, their main focus on middle and high school students (Google Inc. & Gallup Inc., 2016) is far from exemplifying opportunities for PSTs to learn how to promote CT in elementary school settings, which may be problematic for the future efforts to support students\u2019 CT. Also, while the growing number of new initiatives signals the potential spread of CT to teacher education programs, the majority of the existing efforts still focus on in-service teachers (Yadav et al., 2017).<\/p>\n\n\n\n

The growing efforts in teacher education programs, on the other hand, has mostly focused on computer science concepts as a stand-alone subject while a few initiatives emphasize subject-specific CT. For instance, as part of undergraduate educational technology courses, PSTs have been provided with opportunities to practice coding to improve algorithm and debugging skills (Angeli, 2022), develop knowledge of and positive attitudes toward CT (Chan, 2021), or enhance the knowledge and disposition of CT process (Butler & Leahy, 2021; Mouza et al., 2017).<\/p>\n\n\n\n

Only a few studies have aimed to increase PST\u2019s CT skills and conceptual understanding of science concepts. For example, Bati (2022) asked preservice teachers to design algorithms to solve science problems (e.g., designing a thermometer conversion that converts a temperature value from Celsius to Fahrenheit, or vice versa). In another study, preservice teachers worked on using Micro:bit (a tiny circuit board designed to introduce computing to young children) to create a simulation of the moon\u2019s phases and using Microcontroller to create a pH meter (Pewkam & Chamrat, 2022).<\/p>\n\n\n\n

Nevertheless, these existing studies solely examined the impact of coding activities on PSTs\u2019 CT knowledge and understanding, while access to technology, including motivation and actual usage (which can be limited in remote schools), were not investigated. Given that existing initiatives have predominantly focused on large school districts in big cities and that remote regions have not been the center of previous efforts (Guzdial, 2016), PSTs placed in poorer and rural school districts may have limited opportunities to learn how to develop their students\u2019 CT skills. This becomes even more problematic given that rural schools tend to have limited access to advanced technology infrastructure as well as opportunities for professional development (Aduwa-Pgiegbaen & Iyamu, 2009;  Palamakumbura, 2009; Trinidad, 2007; Wood & Howley, 2012).<\/p>\n\n\n\n

Although unplugged activities (e.g., noncoding activities without computers) can provide effective means to support students\u2019 CT skills in rural areas with limited technology access (Yuliana et al., 2021), the plugged activities (e.g., coding) can still provide opportunities for students to make multiple attempts and use various problem elements to decode challenging tasks and to develop, test, and refine solutions. Thus, while it is essential to provide equitable CT opportunities via plugged activities in teacher education programs whose PSTs are placed in rural school settings, the efforts need to consider potential digital divide barriers.<\/p>\n\n\n\n

Information Communication Technology Access<\/h3>\n\n\n\n

Digital divide is not just about physical access to technologies but also related to an individual\u2019s motivation, skills, culture, and attitudes toward using information communication technology (ICT: DiMaggio & Hargittai, 2001). To better conceptualize and study the digital divide, van Dijk (2005) conceptualized an ICT access model with four distinct levels, which includes motivation, physical, skills, and usage. The first level refers to users\u2019 needs and motivation to use technologies. Next comes physical access in the form of having technologies. Skills access as the third level is about possessing skills to use technologies. Usage access, the last level, refers to the applications and usage frequency.<\/p>\n\n\n\n

Van Dijk (2005) argued that unequal access at these levels would limit the degree of participation in society. Extending this theory to computational thinking, PSTs with unequal access levels would likely have limited participation in efforts to learn and teach CT and, consequently, be less likely to help develop the CT skills of the future workforce. While teacher education programs should be integral to helping PSTs\u2019 preparation in this aspect, the ways specific course activities can, in fact, impact PSTs\u2019 CT access have yet to be examined. As a preliminary step, the current study examined how a specific workshop, designed to infuse CT in a science methods course, influenced PSTs\u2019 motivation, skill, and usage access to CT and CT tools. The specific research questions within this research focus included the following:<\/p>\n\n\n\n