Abstract
The rapid development of computers and technology affects modern daily life. Individuals in the digital age need to develop computational thinking (CT) skills. Existing studies have shown that programming teaching is conducive to cultivating students’ CT, and various learning models have different effects on the cultivation of CT. This study proposed a problem-oriented learning (POL) model that is closely related to programming and computational thinking. In all, 60 eighth-grade students from a middle school in China were divided into an experimental group (EG) which adopted the POL model, and a control group (CG) which adopted the lecture-and-practice (LAP) learning model. The results showed that the students who were instructed using the POL model performed better than those who were instructed using the LAP model on CT concepts, CT practices, and CT perspectives. Significant differences were found for CT concepts and CT perspectives, but not for CT practices. Findings have implications for teachers who wish to apply new learning models to facilitate students’ CT skills, and the study provides a reference case for CT training and Python programming teaching.
Highlights
The younger generation interacts frequently with technologies that permeate all aspects of their lives on a daily basis (Baruch and Erstad, 2018), and they are increasingly expected to be consumers and producers of technology (Kong et al, 2020)
This study constructed a problem-oriented learning (POL) model oriented to programming problems, and used a quasi-experiment to verify its effect on the cultivation of computational thinking (CT)
60 eighth-grade students from a middle school in China were divided into an experimental group (EG) which adopted the POL model, and a control group (CG) which adopted the LAP learning model
Summary
The younger generation interacts frequently with technologies that permeate all aspects of their lives on a daily basis (Baruch and Erstad, 2018), and they are increasingly expected to be consumers and producers of technology (Kong et al, 2020). Computational thinking (CT) can develop students’ abilities of critical thinking, creative thinking, and problem solving (Ananiadou and Claro, 2009; Mishra and Yadav, 2013; Repenning et al, 2015). According to Bundy (2007), CT influences the research of almost all disciplines in natural science and human science. Many researchers consider that CT should be integrated into the formal education system as a learning objective to cultivate students’ ability to guide their future lives (Grover and Pea, 2013). There is a consensus that students’ CT can be nurtured via programming education (Rich et al, 2017; Nouri et al, 2020). Most studies use visual programming tools, such as App Inventor and Scratch, which are closer to the representation of human language, helping students concentrate on the logic and structure, and become involved
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