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Showing 1 to 15 of 25 results Save | Export
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Christina Glasauer; Martin K. Yeh; Lois Anne DeLong; Yu Yan; Yanyan Zhuang – Computer Science Education, 2025
Background and Context: Feedback on one's progress is essential to new programming language learners, particularly in out-of-classroom settings. Though many study materials offer assessment mechanisms, most do not examine the accuracy of the feedback they deliver, nor give evidence on its validity. Objective: We investigate the potential use of a…
Descriptors: Novices, Computer Science Education, Programming, Accuracy
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Ünal Çakiroglu; Seval Bilgi – Interactive Learning Environments, 2024
The aim of this explanatory study is to identify the causes of intrinsic cognitive load in programming process. For this purpose, a method based on two dimensions; programming knowledge types (syntactic, semantic, and strategic) and programming constructs was proposed. The proposed method was tested with high school students enrolled in Computer…
Descriptors: Cognitive Processes, Difficulty Level, Programming, Interaction
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Paola Iannone; Athina Thoma – International Journal of Mathematical Education in Science and Technology, 2024
Programming is becoming increasingly common in mathematics degrees as it is a desirable skill for new graduates. However, research shows that its use is mostly restricted to computational or modelling tasks. This paper reports a study on students' perceptions of and difficulties with Lean, an interactive theorem prover introduced as part of a…
Descriptors: Programming, Mathematics Instruction, Computer Science Education, Student Attitudes
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Anna Y. Q. Huang; Cheng-Yan Lin; Sheng-Yi Su; Stephen J. H. Yang – British Journal of Educational Technology, 2025
Programming education often imposes a high cognitive burden on novice programmers, requiring them to master syntax, logic, and problem-solving while simultaneously managing debugging tasks. Prior knowledge is a critical factor influencing programming learning performance. A lack of foundational knowledge limits students' self-regulated learning…
Descriptors: Artificial Intelligence, Technology Uses in Education, Coding, Programming
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Icy Zhang; Yunqi Jia; Xiaoxuan Cheng; Ji Y. Son; James W. Stigler – Journal of Educational Computing Research, 2025
Although programming is often learned through formal instruction or self-paced tutorials, informal learning, for example, through publicly available online documentation, is also a significant resource for skill development among novices. However, many novices struggle to extract useful information from documentation. This work aims to answer two…
Descriptors: Programming, Novices, Informal Education, Documentation
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Daniele Traversaro; Giorgio Delzanno; Giovanna Guerrini – Informatics in Education, 2024
Concurrency is a complex to learn topic that is becoming more and more relevant, such that many undergraduate Computer Science curricula are introducing it in introductory programming courses. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi, a…
Descriptors: Misconceptions, Programming Languages, Computer Science Education, Undergraduate Students
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Camilo Vieira; Andrea Vásquez; Federico Meza; Roxana Quintero-Manes; Pedro Godoy – ACM Transactions on Computing Education, 2024
Currently, there is little evidence about how non-English-speaking students learn computer programming. For example, there are few validated assessment instruments to measure the development of programming skills, especially for the Spanish-speaking population. Having valid assessment instruments is essential to identify the difficulties of the…
Descriptors: Programming, Spanish Speaking, Translation, Test Validity
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Jaewon Jung; Yoonhee Shin; HaeJin Chung; Mik Fanguy – Journal of Computing in Higher Education, 2025
This study investigated the effects of pre-training types on cognitive load, self-efficacy, and problem-solving in computer programming. Pre-training was provided to help learners acquire schemas related to problem-solving strategies. 84 undergraduate students were randomly assigned to one of three groups and each group received three different…
Descriptors: Training, Cognitive Processes, Difficulty Level, Self Efficacy
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Yun Huang; Christian Dieter Schunn; Julio Guerra; Peter L. Brusilovsky – ACM Transactions on Computing Education, 2024
Programming skills are increasingly important to the current digital economy, yet these skills have long been regarded as challenging to acquire. A central challenge in learning programming skills involves the simultaneous use of multiple component skills. This article investigates why students struggle with integrating component skills--a…
Descriptors: Programming, Computer Science Education, Error Patterns, Classification
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Yoonhee Shin; Jaewon Jung; Seohyun Choi; Bokmoon Jung – Education and Information Technologies, 2025
This study investigates the effects of metacognitive and cognitive strategies for computational thinking (CT) on managing cognitive load and enhancing problem-solving skills in collaborative programming. Four different scaffolding conditions were provided to help learners optimize cognitive load and improve their problem-solving abilities. A total…
Descriptors: Scaffolding (Teaching Technique), Mental Computation, Cognitive Processes, Difficulty Level
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Sigal Levy; Yelena Stukalin; Nili Guttmann-Beck – Teaching Statistics: An International Journal for Teachers, 2024
Probability theory has extensive applications across various domains, such as statistics, computer science, and finance. In probability education, students are introduced to fundamental principles which may include mathematical topics such as combinatorics and symmetric sample spaces. Students pursuing degrees in computer science possess a robust…
Descriptors: Programming, Probability, Mathematics Skills, Computer Science Education
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Renske Weeda; Sjaak Smetsers; Erik Barendsen – Computer Science Education, 2024
Background and Context: Multiple studies report that experienced instructors lack consensus on the difficulty of programming tasks for novices. However, adequately gauging task difficulty is needed for alignment: to select and structure tasks in order to assess what students can and cannot do. Objective: The aim of this study was to examine…
Descriptors: Novices, Coding, Programming, Computer Science Education
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Arthur William Fodouop Kouam – Discover Education, 2024
This study investigates the effectiveness of Intelligent Tutoring Systems (ITS) in supporting students with varying levels of programming experience. Through a mixed-methods research design, the study explores the impact of ITS on student performance, adaptability to different skill levels, and best practices for utilizing ITS in heterogeneous…
Descriptors: Intelligent Tutoring Systems, Instructional Effectiveness, Programming, Skill Development
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Saso Koceski; Natasa Koceska; Limonka Koceva Lazarova; Marija Miteva; Biljana Zlatanovska – Journal of Technology and Science Education, 2025
This study aims to evaluate ChatGPT's capabilities in certain numerical analysis problem: solving ordinary differential equations. The methodology which is developed in order to conduct this research takes into account the following mathematical abilities (defined according to National Centre for Education Statistics): Conceptual Understanding,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Number Concepts, Problem Solving
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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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