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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
Rimma Nyman; Kajsa Bråting; Cecilia Kilhamn – International Journal of Mathematical Education in Science and Technology, 2025
In the wake of the present inclusion of programming in mathematics education, which is a feature of curricular revisions in many countries, we have analysed newly inserted programming activities in mathematics textbooks. The aim was to investigate how such activities relate to and potentially affect students' opportunities to learn mathematics.…
Descriptors: Secondary School Students, Mathematics Instruction, Programming, Computer Science Education
Competitive Programming Participation Rates: An Examination of Trends in U.S. ICPC Regional Contests
Jeremy J. Blum – Discover Education, 2023
A wide range of benefits have been posited from participation in competitive programming contests. However, an analysis of participation in north American regional contests in the International Collegiate Programming Contest (ICPC) shows that participation in these contests is sharply declining, coinciding with the COVID-19 pandemic. Moreover,…
Descriptors: Programming, Higher Education, Competition, Trend Analysis
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
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
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
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
Tianxiao Yang; Jongpil Cheon – Computer Science Education, 2025
Background and context: There were few studies indicating if students' computational thinking (CT) self-efficacy and their CT performance were aligned with each other. Objectives: The study was to investigate if there was a discrepancy between students' CT self-efficacy and their CT performance. Method: Involving 104 non-CS undergraduate students…
Descriptors: Self Efficacy, Computer Science Education, Prediction, Teacher Expectations of Students
Sirazum Munira Tisha – ProQuest LLC, 2023
Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the…
Descriptors: Computer Software, Grading, Programming, Assignments
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
Using Analytics to Predict Students' Interactions with Learning Management Systems in Online Courses
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
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
Roxana Quintero-Manes; Camilo Vieira – Journal of Computing in Higher Education, 2025
This study had two objectives: (1) to evaluate the validity of an instrument for measuring differentiated cognitive loads in its Spanish version; and (2) to evaluate the three types of cognitive loads and their relationship with self-efficacy, self-concept, and interest in programming of students in an introductory course. Understanding and…
Descriptors: Cognitive Ability, Programming, Computer Science Education, Self Efficacy
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
Christopher Hundhausen; Phill Conrad; Olusola Adesope; Ahsun Tariq – ACM Transactions on Computing Education, 2023
Assessing team software development projects is notoriously difficult and typically based on subjective metrics. To help make assessments more rigorous, we conducted an empirical study to explore relationships between subjective metrics based on peer and instructor assessments, and objective metrics based on GitHub and chat data. We studied 23…
Descriptors: Teamwork, Computer Software, Undergraduate Students, Computer Science Education

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