Comparing the Effectiveness of Scratch and App Inventor with Regard to Learning Computational Thinking Concepts

Park, Youngki; Shin, Youhyun

doi:10.3390/electronics8111269

Cited by 28 publications

(19 citation statements)

References 16 publications

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“…The increase in the number of participants over the years from 105 in 2016 to 124 in 2017 and 135 in 2018 confirmed that the contest is gaining in popularity in the education sector. Our findings were congruent with the study of Nouri et al, [40] which highlighted that teaching programming to k-9 students not only develops CT skills but alsopromotes skills and attitudes of a more general character that are strongly associated with 21 st -century skills and digital competence/literacy. Moreover, Turchi et al, [5] argued that embracing a playful experience in programming could support pupils' progress in STEM subjects by learning CT skills while enjoying the experience.…”

Section: Comments From Studentssupporting

confidence: 92%

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Codecraft Competition: Learning to Code Through Contests Using Scratch

2020

JST

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In this article, the design principles that guided the development of the Codecraft competition are discussed together with strategies for making the contest engaging for everyone. A random selection of 30 educators and 30 students who participated in the contests were also interviewed to find out their views about the competition and its impact on their computational thinking and creativity skills. Furthermore, 85 projects made up of codes from the Scratch program and submitted during the final round of the contest during the years 2016 to 2018 were processed using an online analytical tool known as Dr Scratch. To identify the progress made by the students, the different computational thinking concepts were analysed more deeply. The study revealed that over three years of the Codecraft competition, there was a significant improvement regarding the use of programming concepts, logic, and computational practices. Moreover, the average computational thinking (CT) score of the projects increased from 11.8 in 2016 to 14.2 in 2017 and 16.0 in 2018. Learning of computational concepts through an active contestbased approach also highlighted several advantages, such as motivation, fun, commitment, and enthusiasm, showing improvements related to creativity, computational thinking, and computational practices.

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Section: Comments From Studentssupporting

confidence: 92%

“…It also improves the level of concentration and encourages collaborative learning. Furthermore, Nouri et al, [40] stated that computational thinking, digital competence, and 21 st -century skills of students were enhanced when they learnt programming.…”

Section: Results Of Semi-structured İnterviewsmentioning

confidence: 99%

Codecraft Competition: Learning to Code Through Contests Using Scratch

2020

JST

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“…The recognition of sadness and fear is more dependent on the information provided by the eyes, the recognition of surprise and calm emotions is more dependent on the information provided by the mouth, and the recognition of happiness, anger, and disgust is more dependent on the eyes and mouth [ 39 – 41 ]. The degree of dependence on the provided information is indistinguishable, among which the recognition of happy and angry emotions relies more on eye information, and the recognition of disgust emotions relies more on mouth information [ 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data

Zhang

2022

Occupational Therapy International

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At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. There are not many researches on the application of artificial intelligence-based neural network facial expression recognition technology in practical teaching. With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. Method. A Hadoop cluster consisting of 3 nodes is built on the Linux system, and the environment required for Opencv execution is compiled for each node, which provides support for subsequent parallel optimization, feature extraction, feature fusion, and recognition of student facial images. The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. The color features, texture features, and Sift features of students’ facial images and common distractors were analyzed. A parallel extraction framework of student facial image features is designed, and based on this, the student facial image feature extraction under Hadoop platform is implemented. This paper proposes a dynamic sequential facial expression recognition method that combines shallow and deep features with an attention mechanism. The relative position of facial landmarks and local area texture features based on FACS represent shallow-level features. At the same time, the structure of ALexNet is improved to extract the deep features of sequence images to express high-level semantic features. The effectiveness of the facial expression recognition system is improved by introducing three attention mechanisms: self-attention, weight-attention, and convolutional attention. Results/Discussion. Through the analysis of the teaching effect, we found that when teachers can obtain the correct student’s academic mood, they can intervene on the students’ positive academic mood. The purpose of the intervention is to improve the positive academic emotions of students. After the students receive the intervention, their academic emotions are also improved and are positively correlated with their academic performance. Through the analysis of teaching effect, the research can achieve the predetermined goal. From the specific teaching effect of this study, it is concluded that in classroom teaching, teachers should devote energy to intervene in students’ positive academic emotions, in order to improve students’ positive academic emotions, which will improve students’ academic performance and teaching.

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“…Therefore, it is important to investigate the factors that lead to students learning difficulty in programming. Several studies already examine the learning of specific concepts when developing apps with App Inventor, including procedural abstraction concepts [31], events [9], programmatic sophistication [32], effectiveness [33], or appropriateness [10] of App Inventor as an educational environment. Others study the learning progression of students in computing courses in K-12, e.g., Xie and Abelson [34], who analyze the relationship between the progression of skill in using App Inventor functionality and in using computational thinking concepts as learners create more apps.…”

Section: Introductionmentioning

confidence: 99%

An Item Response Theory Analysis of Algorithms and Programming Concepts in App Inventor Projects

Alves

Wangenheim

Hauck

et al. 2021

Anais Do I Simpósio Brasileiro De Educação Em Computação (EDUCOMP 2021)

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Computing education is often introduced in K-12 focusing on algorithms and programming concepts using block-based programming environments, such as App Inventor. Yet, learning programming is a complex process and novices struggle with several difficulties. Thus, to be effective, instructional units need to be designed regarding not only the content but also its sequencing taking into consideration difficulties related to the concepts and the idiosyncrasies of programming environments. Such systematic sequencing can be based on large-scale project analyses by regarding the volition, incentive, and opportunity of students to apply the relevant program constructs as latent psychometric constructs using Item Response Theory to obtain quantitative ?difficulty? estimates for each concept. Therefore, this article presents the results of a large-scale data-driven analysis of the demonstrated use in practice of algorithms and programming concepts in App Inventor. Based on a dataset of more than 88,000 App Inventor projects assessed automatically with the ANON rubric, we perform an analysis using Item Response Theory. The results demonstrate that the easiness of some concepts can be explained by their inherent characteristics, but also due to the characteristics of App Inventor as a programming environment. These results can help teachers, instructional and curriculum designers in the sequencing, scaffolding and assessment design of programming education in K-12.

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Comparing the Effectiveness of Scratch and App Inventor with Regard to Learning Computational Thinking Concepts

Cited by 28 publications

References 16 publications

Codecraft Competition: Learning to Code Through Contests Using Scratch

Codecraft Competition: Learning to Code Through Contests Using Scratch

Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data

An Item Response Theory Analysis of Algorithms and Programming Concepts in App Inventor Projects

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