Understanding the relationship between computational thinking and computational participation: a case study from Scratch online community

Jiang, Bo; Zhao, Wei; Gu, Xiaoqing; Yin, Chengjiu

doi:10.1007/s11423-021-10021-8

Cited by 16 publications

(16 citation statements)

References 38 publications

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“…For these students it could be important to increase their cooperativity, which is regarded as a core 21 st -century skill (van Laar et al, 2017). Again, Scratch could be a promising approach because it relies on a social learning paradigm and allows members of the community to learn from each other through the opportunity to share and extend projects (Jiang et al, 2021;Repenning et al, 2015;Resnick & Rusk, 2020;Shute et al, 2017). Students in profile 3 could become aware of the benefits of collaboration when working on Scratch projects.…”

Section: Discussionmentioning

confidence: 99%

Computational Thinking Assessment – Towards More Vivid Interpretations

2022

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Computational thinking (CT) is an important 21st-century skill. This paper aims at more useful CT assessment. Available evaluation instruments are reviewed; two generally accepted CT evaluation tools are selected for a comprehensive CT assessment: the CTt, a performance test, and the CTS, a self-assessment instrument. The sample comprises 202 high school students from German-speaking Switzerland. Concerning the CTt, Raschscalability is demonstrated. Utilizing the approach of the PISA studies, proficiency levels are formed that comprise tasks with specific characteristics that students are systematically able to master. This could help teachers to offer individual support to their students. In terms of the CTS, the original version is refined using confirmatory factor and measurement-invariance analysis. A latent profile analysis yielded four profiles, two of which are of particular interest. One profile comprises students with, on the one hand, moderate to high creative thinking ability, cooperativity, and critical thinking skills and, on the other hand, low algorithmic thinking ability. The second remarkable profile consists of students with particularly low cooperativity. Based on these strength and weakness profiles, teachers could offer support tailored to student needs.

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Section: Discussionmentioning

confidence: 99%

Computational Thinking Assessment – Towards More Vivid Interpretations

2022

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“…Compared with other IT content, scratch teaching can stimulate students' curiosity, activate their thirst for knowledge, and help students transfer their IT capabilities. Whether it is animation or game development, it needs to abstract specific problems and stack them with instructions [ 16 , 17 ]. The important performance of CT is reflected in the instruction such as selection, loop, and condition in Scratch teaching from different perspectives [ 18 ].…”

Section: Theoretical Basis and Methods Researchmentioning

confidence: 99%

“…Evaluation is the fnal form of testing a practical achievement. [16,17]. Te important performance of CT is refected in the instruction such as selection, loop, and condition in Scratch teaching from diferent perspectives [18].…”

Section: Te Training Elements Of Ct In Programming Coursesmentioning

confidence: 99%

Computational Thinking Training and Deep Learning Evaluation Model Construction Based on Scratch Modular Programming Course

Chen

Wang

2023

Computational Intelligence and Neuroscience

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To improve the algorithmic dimension, critical thinking, and problem-solving ability of computational thinking (CT) in students' programming courses, first, a programming teaching model is constructed based on the scratch modular programming course. Secondly, the design process of the teaching model and the problem-solving model of visual programming are studied. Finally, a deep learning (DL) evaluation model is constructed, and the effectiveness of the designed teaching model is analyzed and evaluated. The T-test result of paired samples of CT is t = −2.08, P < 0.05. There are significant differences in the results of the two tests, and the designed teaching model can cause changes in students' CT abilities. The results reveal that the effectiveness of the teaching model based on scratch modular programming has been verified on the basis of experiments. The post-test values of the dimensions of algorithmic thinking, critical thinking, collaborative thinking, and problem-solving thinking are all higher than the pretest values, and there are individual differences. The P values are all less than 0.05, which testifies that the CT training of the designed teaching model has the algorithm dimension, critical thinking, collaborative thinking, and problem-solving ability of students' CT. The post-test values of cognitive load are all lower than the pretest values, indicating that the model has a certain positive effect on reducing cognitive load, and there is a significant difference between the pretest and post-test. In the dimension of creative thinking, the P value is 0.218, and there is no obvious difference in the dimensions of creativity and self-efficacy. It can be found from the DL evaluation that the average value of the DL knowledge and skills dimensions is greater than 3.5, and college students can reach a certain standard level in terms of knowledge and skills. The mean value of the process and method dimensions is about 3.1, and the mean value of the emotional attitudes and values is 2.77. The process and method, as well as emotional attitude and values, need to be strengthened. The DL level of college students is relatively low, and it is necessary to improve their DL level from the perspective of knowledge and skills, processes and methods, emotional attitudes and values. This research makes up for the shortcomings of traditional programming and design software to a certain extent. It has a certain reference value for researchers and teachers to carry out programming teaching practice.

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“…Visual programming is widely favored for its less involved programming syntax and low barrier to entry, which allows beginners to focus more on problem-solving strategies [7,8]. On visual programming platforms, students can learn to program, and adopt effective computational practices and design strategies through computational problem-solving [3].…”

Section: Introductionmentioning

confidence: 99%

Exploring the differences and evolution of college students' computational thinking in programming learning through data analysis

Zhang,

Zeng,

et al. 2023

Comp Applic In Engineering

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Programming has received extensive attention for the embodiment of computational thinking (CT) skills. However, the lack of in‐depth research on the characteristics of college students' CT skills in text‐based programming learning has resulted in the unknown distribution characteristics and evolution patterns of their CT skills, which cannot effectively constitute an effective tracking analysis of their CT skills, thus affecting the cultivation of creative thinking and problem‐solving skills for them. Therefore, the purpose of the paper is to explore the characteristics of college students' CT skills through text‐based programming work and discover the overall characteristics, individual differences and evolutionary patterns of their CT skills on the relation between text‐based programming and CT skills through data analysis. It was found that there are roughly five categories of CT difference and four major categories with nine subcategories of CT evolution among college students. The results showed that the Control and Data skills were the students' dominant skills. In contrast, Algorithm skills were relatively weak. Only about 1/3 of the students had the phenomenon of continuous skill improvement, and programming learning did not have a significant impact on the improvement of CT skills for some students. Finally, we propose several suggestions for CT skills development strategies in response to the students' characteristics in CT skills. The study can not only fill the gaps of the current study but also identify the evolutionary characteristics of students' CT skills, which can effectively intervene in college students' programming learning and help improve their CT skills.

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Understanding the relationship between computational thinking and computational participation: a case study from Scratch online community

Cited by 16 publications

References 38 publications

Computational Thinking Assessment – Towards More Vivid Interpretations

Computational Thinking Assessment – Towards More Vivid Interpretations

Computational Thinking Training and Deep Learning Evaluation Model Construction Based on Scratch Modular Programming Course

Exploring the differences and evolution of college students' computational thinking in programming learning through data analysis

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