Abstract
Literature indicated that attitude toward programming, programming self-efficacy, gender, and students’ department has been related to achievement in computer programming. However, there is a need for further studies investigating to what extent these factors explain programming achievement in a model. This study aimed to investigate the effects of programming self-efficacy, attitude towards programming, gender, and students’ department on their perceived learning. A correlational study design was adopted for this study. The sample of the study was 742 students of an engineering faculty at a state university inTurkey. To collect data, Programming Self-Efficacy Scale, Computer Programming Attitude Scale, and Perceived Learning Scale were used. To analyze data, descriptive statistics e.g. mean and standard deviation, and Pearson Correlation tests were administered. In addition, to determine the factors affecting perceived learning, multiple regression analysis was employed. The results indicated that the engineering faculty students’ attitudes towards programming, programming self-efficacy and perceived learning were at high level. In addition, significant correlations between perceived learning and predictive variables were found. Finally, it was concluded that gender, attitude towards programming and programming self-efficacy significantly predicted perceived learning. The results of the study provide a deeper understanding of how students’ learning was affected in programming courses.
Highlights
Computational thinking has been regarded as one of the crucial skills of next-generation students (International Society for Technology in Education [ISTE], 2016)
This study aims to investigate the factors related to students’ perceived learning on computer programming (PLCP), and to what extent gender, department, computer programming self-efficacy (CPSE), and attitude toward computer programming (ATCP) predict students’ PLCP
As this study aims to examine to what extent the selected variables accounts for engineering students’ PLCP, a correlational study was considered to be appropriate for this study
Summary
Computational thinking has been regarded as one of the crucial skills of next-generation students (International Society for Technology in Education [ISTE], 2016). Core components of computational thinking curated by ISTE (2016) are decomposition, gathering and analyzing data, abstraction, and algorithm design. Algorithm design is the process of designing a step-by-step process to achieve a task (ISTE, 2016) Such skill is crucial for students' professional careers, and for the industry’s economic competitiveness and the ability to find qualified employees (Gardiner, 2017). For this reason, as a fundamental tool of computational thinking, many studies have been carried out to introduce students to computer programming in all levels of education, from elementary school to graduate level. Programming education is essential for shaping the perceptions and thinking strategies of engineering students
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