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Showing 1 to 15 of 23 results Save | Export
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Yi Liu; Leen-Kiat Soh; Guy Trainin; Gwen Nugent; Wendy M. Smith – Computer Science Education, 2025
Background and Context: Professional development (PD) programs for K-12 computer science teachers use surveys to measure teachers' knowledge and attitudes while recognizing daily sentiment and emotion changes can be crucial for providing timely teacher support. Objective: We investigate approaches to compute sentiment and emotion scores…
Descriptors: Computer Science Education, Faculty Development, Elementary School Teachers, Secondary School Teachers
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Xin Gong; Weiqi Xu; Ailing Qiao; Zhixia Li – Journal of Computer Assisted Learning, 2025
Background: Robot programming can simultaneously cultivate learners' computational thinking (CT) and spatial thinking (ST). However, there is a noticeable gap in research focusing on the micro-level development patterns of learners' CT and ST and their interconnections. Objectives: This study aims to uncover the intricate development patterns and…
Descriptors: Mental Computation, Thinking Skills, Skill Development, Robotics
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Niloofar Mansoor; Cole S. Peterson; Michael D. Dodd; Bonita Sharif – ACM Transactions on Computing Education, 2024
Background and Context: Understanding how a student programmer solves different task types in different programming languages is essential to understanding how we can further improve teaching tools to support students to be industry-ready when they graduate. It also provides insight into students' thought processes in different task types and…
Descriptors: Biofeedback, Eye Movements, Computer Science Education, Programming Languages
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Henrique Mohallem Paiva; Flávia Maria Santoro; Victor Takashi Hayashi; Bianca Cassemiro Lima – IEEE Transactions on Education, 2025
Contribution: This article analyzes student assessment within a computing faculty employing a full project-based learning (PBL) approach. Examining 2078 final grades across 60 classes and periods, the study reveals a significant correlation between graded self-studies, exams, and projects. This result contributes to understanding the reliability…
Descriptors: Student Evaluation, Computer Science Education, College Faculty, Correlation
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Yong-Woon Choi; In-gyu Go; Yeong-Jae Gil – International Journal of Technology and Design Education, 2024
The purpose of this study is to derive a correlation between the technological thinking disposition and the computational thinking ability of gifted students in Korea. The correlation between each element was analyzed by looking at the sub-elements of computational thinking according to the components of technological thinking disposition. The…
Descriptors: Thinking Skills, Mental Computation, Gifted, Correlation
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Muhammed Murat Gümüs; Volkan Kukul; Özgen Korkmaz – Informatics in Education, 2024
This study aims to explain the relationships between secondary school students' digital literacy, computer programming self-efficacy and computational thinking self-efficacy. The study group consists of 204 secondary school students. A relational survey model was used in the research method and three different data collection tools were used to…
Descriptors: Correlation, Middle School Students, Thinking Skills, Digital Literacy
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Václav Dobiáš; Václav Šimandl; Jirí Vanícek – Informatics in Education, 2024
The paper discusses an alternative method of assessing the difficulty of pupils' programming tasks to determine their age appropriateness. Building a program takes the form of its successive iterations. Thus, it is possible to monitor the number of times such a program was built by the solver. The variance of the number of program builds can be…
Descriptors: Difficulty Level, Computer Science Education, Programming, Task Analysis
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Ali Alshammari – Education and Information Technologies, 2024
In online education, it is widely recognized that interaction and engagement have an impact on students' academic performance. While previous research has extensively explored interactions between students, instructors, and content, there has been limited exploration of course design elements that promote the fourth type of interaction:…
Descriptors: Learning Analytics, Learning Management Systems, Academic Achievement, Correlation
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Ramon Mayor Martins; Christiane G. Von Wangenheim; Marcelo F. Rauber; Adriano F. Borgatto; Jean C. R. Hauck – ACM Transactions on Computing Education, 2024
As Machine Learning (ML) becomes increasingly integrated into our daily lives, it is essential to teach ML to young people from an early age including also students from a low socioeconomic status (SES) background. Yet, despite emerging initiatives for ML instruction in K-12, there is limited information available on the learning of students from…
Descriptors: Artificial Intelligence, Computer Science Education, Socioeconomic Status, Correlation
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Jinbo Tan; Lei Wu; Shanshan Ma – British Journal of Educational Technology, 2024
The purpose of this study was to investigate the collaborative dialogue patterns of pair programming and their impact on programming self-efficacy and coding performance for both slow- and fast-paced students. Forty-six postgraduate students participated in the study. The students were asked to solve programming problems in pairs; those pairs'…
Descriptors: Coding, Programming, Computer Science Education, Self Efficacy
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Xinlei Li; Guoyuan Sang; Martin Valcke; Johan van Braak – Education and Information Technologies, 2024
Computational thinking (CT) is valued as a thinking process that is required to adapt to the development of curriculum in primary education. In the context of modern information technology, English as a language subject emphasizes the necessity for changes in both learning and teaching modes. However, there is a lack of up-to-date synthesis…
Descriptors: Computer Science Education, Technological Literacy, Pedagogical Content Knowledge, Elementary School Students
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Miitta Järvinen; Katriina Sipiläinen; Janne Roslöf; Sami Lehesvuori; Lauri Kettunen; Raija Hämäläinen – European Journal of Engineering Education, 2025
This study explored the learning experiences of first-year information technology students at the beginning of their studies. Identifying the early experiences is important, as we know they can predict later challenges and persistence in studies. We focus on a novel understanding of relations between learning approaches, self-efficacy and burnout…
Descriptors: Information Technology, College Freshmen, Computer Science Education, Self Efficacy
Amy Esther Kuiken – ProQuest LLC, 2024
The cybersecurity landscape in the United States has rapidly evolved, marked by risk and novel threats (Georgescu, 2021; Siraj et al., 2021). Today, security mindsets are "widely accepted in the computer security community as … essential … for successful cybersecurity practice" (Peterson, 2021, para. 1), valuable to aspiring…
Descriptors: Second Language Learning, Second Language Instruction, Information Security, Computer Security
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Karolína Dockalová Burská; Jakub Rudolf Mlynárik; Radek Ošlejšek – Education and Information Technologies, 2024
In cyber security education, hands-on training is a common type of exercise to help raise awareness and competence, and improve students' cybersecurity skills. To be able to measure the impact of the design of the particular courses, the designers need methods that can reveal hidden patterns in trainee behavior. However, the support of the…
Descriptors: Computer Science Education, Information Security, Computer Security, Training Methods
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Chih-Chang Yu; Leon Yufeng Wu – Educational Technology & Society, 2024
This study presents a new blended learning model that combines a computer-assisted learning system called Cognitive Apprenticeship Programming Learning System (CAPLS) with instructor co-teaching in an introductory programming course. CAPLS, as its unique aspect, functions as a master in cognitive apprenticeship, guiding learners throughout their…
Descriptors: Programming, Computer Science Education, College Entrance Examinations, Mathematics Tests
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