Learning number patterns through computational thinking activities: A Rasch model analysis

Abstract

Despite the increasing presence of computational thinking (CT) in the mathematics context, the connection between CT and mathematics in a practical classroom context is an important area for further research. This study intends to investigate the impact of CT activities in the topic of number patterns on the learning performance of secondary students in Singapore. The Rasch model analysis was employed to assess differences of ability between students from the experimental group and control group. 106 Secondary One students (age 13 years old) from a secondary school in Singapore took part in this study. A quasi-experimental non-equivalent groups design was utilized where 70 students were assigned into the experimental group, and 36 students were assigned into the control group. The experimental group was given intervention with CT-infused activities both on- and off-computer, while the control group received no such intervention. Both groups were administered the pretest before the intervention and the posttest after the intervention. The data gathered were analyzed using the partial credit version of the Rasch model. Analysis of pretest and posttest results revealed that the performance of the experimental group was similar to the control group. The findings did not support the hypothesis that integrating CT in lessons can result in improved mathematics learning. However, the drastic improvement was observed in individual students from the experimental group, while there is no obvious or extreme improvement for the students from the control group. This study provides some new empirical evidence and practical contributions to the infusion of CT practices in the mathematics classroom.

Integration of computational thinking in K-12 mathematics education: a systematic review on CT-based mathematics instruction and student learning

“…The immediate feedback and response of the robot when operating with numbers is a powerful and highly motivational tool for students" (p. 1422). Conversely, text-based programming requires higher level of abstraction; these features make it suitable to deal with more advanced number topics, such as statistics (Gilchrist et al, 2021;Psycharis & Kallia, 2017), number sequence (Chan et al, 2021), and algebraic relationships (Kaufmann & Stenseth, 2021). We note that although the spreadsheet (e.g., Excel) is not generally defined as a CT tool, it executes some statistical functions and output upon inputting text-based formulae, and thus, it was considered as a text-based CT tool in both studies of Chan et al (2021) and Valovičová et al (2020).…”

Prospective Science Teachers’ Self-Confidence in Computational Thinking Skills

“…According to Song et al (2021), Chevalier et al (2022), and Ung et al (2022), computational thinking has potential in various fields of science. Barr & Stephenson (2011), Chan et al (2021), and Tikva & Tambouris (2021) use computational thinking skills as the basis for conceptual thinking in social research to generalize concepts in various facts of life. Language arts learners use computational thinking skills to analyze different language features when constructing different sentences.…”

Tools and Approaches for Integrating Computational Thinking and Mathematics: A Scoping Review of Current Empirical Studies

“…Meanwhile, nearly half of the assessments in the reviewed studies were not validated which corroborates the statement of Tang et al (2020) where many assessments lacked evidence of validity and reliability. It is crucial to develop a valid and reliable assessments to ensure the quality of the research findings (Chan et al, 2021) and allow the instructors and researchers to confidently use the assessments during the class and enhance the wide distribution of the assessments (Tang et al, 2020).…”

“…The usage of Scratch enabled the students to engage with tough mathematical concepts in new, meaningful, and generalizable ways (Benton et al, 2018). By using Scratch, students were able to create computer programs without worrying about syntax or spelling in Spreadsheet (Chan, et al, 2021) block-based coding environments (Gadanidis et al, 2018). MATLAB, Mathematica, Thermo-Calc, COMSOL, Python, and statistical programming R were text-based programming.…”