The study aims to investigate the effects of educational robotics (ER) in programming education on students' programming success, computational thinking (CT), and transfer of learning and to reveal the students...
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The study aims to investigate the effects of educational robotics (ER) in programming education on students' programming success, computational thinking (CT), and transfer of learning and to reveal the students' views about robotics programming. For this purpose, an explanatory sequential mixed-method research design was used. In this research design, a qualitative data collection process follows a quantitative one to be able to further explain the novel results obtained through analysis of the quantitative data. In the quantitative part, a quasiexperimental study was designed, and one experimental group (EG) and one control group (CG) were formed randomly. The study was conducted with sixth-grade middle school students for 14 weeks. In the process, students were given project tasks based on some real-life problems, and they were asked to transfer their programming skills. It was preferred to use mBot as an ER. While the EG students performed programming activities with the mBot, the CG students performed only block-based programming activities. Both groups were given pre- and posttests to collect data. Finally, semistructured interviews were conducted with the EG students to enrich quantitative findings. The results showed that robotics programming activities increased the students' programming success and transfer of learning, and scores of the EG were significantly higher than the CG. In addition, it was determined that both the EG and CG students' CT skills increased, but there was no significant difference between the groups. Finally, most of the students reported positive views on the use of ER in programming education.
In the current study we aimed to determine which cognitive skills play a role when learning to program. We examined five cognitive skills (pattern recognition, algebra, logical reasoning, grammar learning and vocabula...
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In the current study we aimed to determine which cognitive skills play a role when learning to program. We examined five cognitive skills (pattern recognition, algebra, logical reasoning, grammar learning and vocabulary learning) as predictors of course-related programming performance and their generalised programming performance in 282 students in an undergraduate introductory programming course. Initial skills in algebra, logical reasoning, and vocabulary learning predicted performance for generalised programming skill, while only logical reasoning skills predicted course-related programming performance. Structural equation modelling showed support for a model where the cognitive skills were grouped into a language factor and an algorithmic/mathematics factor. Of these two factors, only the algorithmic/mathematics factor was found to predict generalised and course-related programming skills. Our results suggested that algorithmic/mathematical skills are most relevant when predicting generalised programming success, but also showed a role for memory-related language skills.
The abstract structure, logic, negative perceptions, and anxiety of programming are seen as obstacles to novice programmers. The importance of educational programming languages is increasing day by day in overcoming t...
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The abstract structure, logic, negative perceptions, and anxiety of programming are seen as obstacles to novice programmers. The importance of educational programming languages is increasing day by day in overcoming these obstacles. In this study, it was aimed to investigate the effect of educational programming language integration on academic achievement and programming anxiety level. The pretest-posttest test design without control group, which is one of the experimental methods, was used in the study, which was carried out on three groups consisting of the theory, practice and integration of the course into both theory and practice part. The groups determined by random sampling method consist of 87 people, 61 boys and 26 girls. Pretest-posttest method was used to determine academic success. During the application process, five performance tests were used to determined the change in success. The scale developed by Cheung (1990) in determining computer programming anxiety was adapted to Turkish by the validity and reliability study by the researcher and was used as pre-test and post-test. Variance and covariance analyzes were used to determine anxiety about academic success and programming, and the results of Kruskal-Wallis test analyzes were used for analysis of performance tests. It is concluded that educational programming languages can be used by integrating both the theory and practice of the course in order to increase academic success and in-class performance and reduce anxiety about computer programming.
This paper reports the results of mental effort measures and comments collected as part of a study of 44 introductory programming courses in 28 Australian universities, conducted in the latter months of 2010. Academic...
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ISBN:
(纸本)9781921770043
This paper reports the results of mental effort measures and comments collected as part of a study of 44 introductory programming courses in 28 Australian universities, conducted in the latter months of 2010. Academic staff were interviewed regarding their perceptions of the mental effort that is required by themselves, an average student, and a low-performance student while attempting to solve and learn from a novice programming *** responses were also gathered from academics to gain insight into the various student profiles and impediments to learning for low-performing students. Mental effort results indicated that many low-performance students typically experience high to extreme levels of mental effort. Verbal responses obtained from academics also indicate an awareness that for many low-performance students learning fails due to excessive demands being placed upon their cognitive *** is suggested that for many low-performance students learning fails due to cognitive overload. The implications for the selection of languages and environments and for the design of introductory programming courses (units) are discussed.
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