Use Hopscotch to Develop Positive Attitudes Toward Programming For Elementary School Students

Wang, Jiahui

doi:10.21585/ijcses.v5i1.122

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…Chittum et al (2017) found that introducing Python programming to high school students improved their attitudes toward programming and careers in computer science. Furthermore, Wang (2021) found that female students’ attitudes toward programming were more positive when they learned through a block-based programming language, as opposed to a text-based programming language. Taken together, an essential aspect of investigating the efficacy of programming modality transition is the examination of learners’ CT skills, programming behaviors, and attitudes toward programming.…”

Section: Literature Reviewmentioning

confidence: 98%

Transitioning From Introductory to Professional Programming in Secondary Education: Comparing Learners’ Computational Thinking Skills, Behaviors, and Attitudes

Sun,

Zhu,

et al. 2023

Journal of Educational Computing Research

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Although previous research has provided some insights into the effects of block-based and text-based programming modalities, there is a dearth of a detailed, multi-dimensional analysis of the transition process from different introductory programming modalities to professional programming learning. This study employed a quasi-experimental design to address this gap, involving 64 secondary school students in two groups. For the beginning five weeks, the first group used an introductory block-based programming environment, while the second group used an introductory text-based programming environment. Then, both groups transitioned to professional text-based programming for the subsequent eight weeks. The results showed that participants who transitioned from introductory text-based programming to professional text-based programming (1) significantly outperformed in computational thinking skills; (2) had more code-writing and debugging behaviors and fewer irrelevant behaviors, and (3) had more interactions with the instructor. No significant differences were observed between the two groups regarding enjoyment, confidence, and interest in programming. Drawing on these findings, this study proposes pedagogical implications that could facilitate the adoption of programming modalities within the broader context of STEM education.

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Section: Literature Reviewmentioning

confidence: 98%

Transitioning From Introductory to Professional Programming in Secondary Education: Comparing Learners’ Computational Thinking Skills, Behaviors, and Attitudes

Sun,

Zhu,

et al. 2023

Journal of Educational Computing Research

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“…There is not much research involving primary school learners, which has been considered a critical stage to develop an interest in this subject. Block-based programming interfaces are useful in reducing the complexity of programming, with respect to text-based programming, and they are also effective at creating interest in programming at primary school [19].…”

Section: Block-based Programming Interfaces In Primary Schoolmentioning

confidence: 99%

Using LSTM to Identify Help Needs in Primary School Scratch Students

Imbernón Cuadrado,

Manjarrés Riesco,

de la Paz López

2023

Applied Sciences

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In the last few years, there has been increasing interest in the use of block-based programming languages as well as in the ethical aspects of Artificial Intelligence (AI) in primary school education. In this article, we present our research on the automatic identification of the need for assistance among primary school children performing Scratch exercises. For data collection, user experiences have been designed to take into account ethical aspects, including gender bias. Finally, a first-in-class distance calculation method for block-based programming languages has been used in a Long Short-Term Memory (LSTM) model, with the aim of identifying when a primary school student needs help while he/she carries out Scratch exercises. This model has been trained twice: the first time taking into account the gender of the students, and the second time excluding it. The accuracy of the model that includes gender is 99.2%, while that of the model that excludes gender is 91.1%. We conclude that taking into account gender in training this model can lead to overfitting, due to the under-representation of girls among the students participating in the experiences, making the model less able to identify when a student needs help. We also conclude that avoiding gender bias is a major challenge in research on educational systems for learning computational thinking skills, and that it necessarily involves effective and motivating gender-sensitive instructional design.

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