Building a computational model of food webs: Impacts on middle school students' computational and systems thinking skills

Abstract

Integral to fostering computational thinking (CT) skills, which are increasingly essential in today's digital era, has been the shift of paper‐based pictorial modeling activities to computational modeling. Research has indicated that modeling activities can advance students' understanding of a system's mechanism (i.e., systems thinking), such as an ecosystem. The current study examines the impacts of paper‐based pictorial and computational modeling activities on students' systems thinking and CT skills. A total of 751 seventh‐grade students were involved in online modeling activities, spanning over 4 days, and were assigned purposefully to a paper‐based modeling condition (n = 374) or a computational modeling (n = 377) condition. They took systems‐thinking‐embedded food web and CT assessments before and after the four activities, in addition to a formative assessment after each activity. Multilevel modeling and repeated‐measures correlation tests were used to analyze the students' quantitative data. Epistemic network analysis (ENA) was utilized to map out students' perceptions of what they believed they learned from the activities. The results revealed significant increases in the systems thinking‐embedded food web constructs in both conditions. However, the increase of CT skills in the paper‐based pictorial condition was not as significant as the increase in the computational modeling condition. ENA results showed that students in the computational modeling condition had more co‐occurrences between science and computer science or CT concepts than those in the paper‐based pictorial condition. These findings illuminate the benefit of engaging students in computationally rich science activities to advance both systems thinking and CT skills.

The transfer effects of computational thinking: A systematic review with meta‐analysis and qualitative synthesis

“…Most of the studies focusing on science subjects examined the CT transfer effects in biology ( n = 6) (Arık & Topçu, 2022; Irgens et al, 2020; Nguyen & Santagata, 2021; Peel et al, 2019; Peel & Friedrichsen, 2018; Rachmatullah & Wiebe, 2022), physics ( n = 5) (Aksit & Wiebe, 2020; Hutchins et al, 2020; Orban et al, 2018; Svensson et al, 2020; Vieira et al, 2018) and integrated science contexts ( n = 5) (Basu et al, 2017; Guven et al, 2020; Lin et al, 2021; Sáez‐López et al, 2019; Sengupta et al, 2013). In addition, two studies assessed the effects in a chemistry context ( n = 2) (Bortz et al, 2020; Chongo et al, 2021).…”

“…Being a competent learner is a prerequisite for the effective transfer of knowledge and skills, as students with a broader understanding of a situation will not be constrained by rules or plans (Botma et al, 2015). An effective approach for competence demonstration is through group or individual presentations or artefact displays, as students achieve a sense of authority on the knowledge that they learned during the presentational activities (e.g., Manfra et al, 2021; Rachmatullah & Wiebe, 2022). Students may also develop their competence by learning from mistakes as they progress from novice to competent learners (e.g., Miller, 2019; Svensson et al, 2020; Vieira et al, 2018; Zha et al, 2020).…”

How do thinking styles and STEM attitudes have effects on computational thinking? A structural equation modeling analysis

“…The interaction between CT and STEM also provides new insight into the construct, nature, and definition of CT. Wing (2006) stated that CT “complements and combines mathematical and engineering thinking” (p. 35). It is also pointed out that CT skills are required by and can be learned in various disciplines (Sung & Black, 2020), especially the STEM disciplines (e.g., Chen & Terada, 2021; Gunckel et al, 2022; Kite & Park, 2023; Lilly et al, 2022; NGSS Lead States, 2013; Peters‐Burton et al, 2023; Rachmatullah & Wiebe, 2022). For instance, The Next Generation Science Standards (NGSS) advocate using CT to develop scientific understanding (NGSS Lead States, 2013).…”

“…Recently, much research has highlighted the critical importance of CT (e.g., Dagli & Tokmak, 2021; Grizioti & Kynigos, 2021; Guggemos, 2021; International Society for Technology in Education, 2016; Kynigos & Grizioti, 2020; Tikva & Tambouris, 2021; Wing, 2006). In response to the constantly evolving needs for computationally literate workforces (Asbell‐Clarke et al, 2021; Rowe et al, 2021; Song et al, 2021), CT has been integrated into the curricula (e.g., Lei et al, 2020; Sun, Hu, & Zhou, 2021), especially the science, technology, engineering and mathematics (STEM) curricula (e.g., Chen & Terada, 2021; Gunckel et al, 2022; Kite & Park, 2023; Lilly et al, 2022; NGSS Lead States, 2013; Peters‐Burton et al, 2023; Rachmatullah & Wiebe, 2022) of many countries.…”

Exploring middle school students' interests in computationally intensive science careers: The CISCI instrument validation and intervention

“…In science learning activities, students engage in CT mostly through computationally rich science activities where students work to build scientific computational models using computer programming (Weintrop et al, 2016). Research has suggested that computationally rich activities have been found to improve students' understanding of scientific concepts (e.g., Aksit & Wiebe, 2020; Peel et al, 2019; Rachmatullah & Wiebe, 2022) and science interest and motivation (e.g., Wagh et al, 2017). Of particular interest in this study is how students' learning experiences with computationally rich science activities may support the development of their interests in computationally intensive science careers, such as computational biology, computational physics, and other computational sciences (Navlakha & Bar‐Joseph, 2011).…”