Measuring coding ability in young children: relations to computational thinking, creative thinking, and working memory

Wang, Lin; Geng, Fengji; Hao, Xiaoxin; Shi, Donglin; Wang, Tengfei; Li, Yan

doi:10.1007/s12144-021-02085-9

Cited by 19 publications

(11 citation statements)

References 27 publications

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“…Learning support provided by peers and teachers allowed students to seek help in problem-solving processes and strengthened their willingness to debug and simulate solutions (Chou, 2020). Some studies used problemsolving strategies based on visual programming game activities to develop problem-solving skills, critical thinking, and creativity in high school students (Pellas & Vosinakis, 2018;Wang et al, 2021;Durak, 2020). It has been suggested that using various game tools can help students express their ideas creatively and communicate and cooperate to complete game work (Fields et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Game-Based Learning for Students’ Computational Thinking: A Meta-Analysis

Zhang

Zhu

et al. 2023

Journal of Educational Computing Research

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Computational thinking (CT) education has drawn increasing attention from educators and researchers. This study conducted a meta-analysis of 27 empirical studies to examine the effectiveness of game-based learning (GBL) for fostering students’ CT. The effects of various factors on the learning process for acquiring CT were also examined. The results showed that (a) conducting GBL can foster students’ CT, and the overall effect was at the upper-middle level (Hedges’ g = .600, 95% CI [.465, .735], p < .001). (b) Furthermore, conducting GBL can improve students’ CT concepts (Hedges’ g = .916, 95% CI [.410, 1.423], p < .001), CT skills (Hedges’ g = .494, 95% CI [.389, .600], p < .001), and CT perspectives (Hedges’ g = .927, 95% CI [.039, 1.816], p < .05). (c) Additionally, game mode, teaching context, and participant characteristics have positive effects on CT. Based on the findings, it is suggested that using more unplugged games and video games, designing collaborative game activities, and tailoring approaches according to gender difference and programming experience can effectively promote CT. The results have significance for fostering students’ CT in GBL; it is further suggested that instruction processes be rationally designed.

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Section: Discussionmentioning

confidence: 99%

Game-Based Learning for Students’ Computational Thinking: A Meta-Analysis

Zhang

Zhu

et al. 2023

Journal of Educational Computing Research

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“…Some research scientists proved the connection of students’ coding and programming ability with their computational thinking, creative thinking and working memory (Wang et al, 2021 ); the influence of application of different tools to teach students to program on their computational thinking when they solve problems (Yildiz Durak, 2020 ); the influence of robotic programming on students’ computational thinking and creative abilities (Noh & Lee, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Educational Robotics: Development of computational thinking in collaborative online learning

et al. 2023

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In the previous study the work experience on organization of teaching Robotics to secondary school students at school lessons and in study groups was introduced. This study which was conducted within 2019 and 2021 covered the period of distant learning caused by COVID-19 pandemic and even post-pandemic period, when a part of school students continued learning online. The study deals with the problem of developing school students’ computational thinking in online learning. We consider computational thinking as a set of cognitive skills of solving educational and cognitive problems. The research questions raised were aimed at solving the problem of the influence of Educational Robotics on developing computational thinking. During the research we have found out that due to the adaptability of robots, Educational Robotics, the development of individual learning programs, and the arrangement of collaborative online learning are instruments and a solution to the problem of developing computational thinking. The main components of computational thinking, which were studied within those 3 years, are the following: algorithmic thinking, ability to program, and efficiency in team work. The influence of the learning strategy we chose enabled us to determine the level of computational thinking and its dependence on learning Robotics. We used statistical criteria in order to summarize the results of our research. The statistics provided suggests progress in the indicator tracked. Based on the experimental data received we approximated reliability (R 2 ) and relevant exponential equation (trend lines). The research we carried out also has led to the general conclusion that Educational Robotics helps to create synergistic learning environment for stimulating students’ motivation, collaboration, self-efficacy and creativity.

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“…Second, summative assessment was conducted to measure the final products of PP. Drawing from the previous relevant literature (Wang et al, 2021;Xu et al, 2022;Zheng et al, 2022), we proposed a three-level summative assessment framework (1 = low, 3 = medium, 5 = high), including two dimensions of problem solving and coding skill Table 1 The process assessment framework of collaborative quality (Meier et al, 2007)…”

Section: Assessment and Clusteringmentioning

confidence: 99%

“…Two requirements of the final task were rated on the completeness dimension, respectively. On the dimension of coding skill, two sub-dimensions (i.e., coding structure, coding complexity) were used to assess whether the student pair could apply the coding skills that they have learned appropriately to solve the task (Wang et al, 2021;Xu et al, 2022). Summative assessment of final programming products was completed by two raters.…”

Section: Rating Rulesmentioning

confidence: 99%

Multimodal learning analytics of collaborative patterns during pair programming in higher education

Ouyang

2023

Int J Educ Technol High Educ

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Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students’ discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs’ multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.

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Measuring coding ability in young children: relations to computational thinking, creative thinking, and working memory

Cited by 19 publications

References 27 publications

Game-Based Learning for Students’ Computational Thinking: A Meta-Analysis

Game-Based Learning for Students’ Computational Thinking: A Meta-Analysis

Educational Robotics: Development of computational thinking in collaborative online learning

Multimodal learning analytics of collaborative patterns during pair programming in higher education

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