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Muradoglu, Melis; Cimpian, Joseph R.; Cimpian, Andrei – Journal of Cognition and Development, 2023
Mixed-effects models are an analytic technique for modeling repeated measurement or nested data. This paper explains the logic of mixed-effects modeling and describes two examples of mixed-effects analyses using R. The intended audience of the paper is psychologists who specialize in cognitive development research. Therefore, the concepts and…
Descriptors: Cognitive Development, Models, Programming Languages, Psychologists
Fuentes, Pablo; Camarero, Cristobal; Herreros, David; Mateev, Vladimir; Vallejo, Fernando; Martinez, Carmen – IEEE Transactions on Learning Technologies, 2022
Understanding the architecture of a processor can be uninteresting and deterring for computer science students, since low-level details of computer architecture are often perceived to lack real-world impact. These courses typically have a strong practical component where students learn the fundamentals of the computer architecture and the handling…
Descriptors: Computer Science Education, Computer System Design, Programming Languages, Fatigue (Biology)
Hoffman, Heather J.; Elmi, Angelo F. – Journal of Statistics and Data Science Education, 2021
Teaching students statistical programming languages while simultaneously teaching them how to debug erroneous code is challenging. The traditional programming course focuses on error-free learning in class while students' experiences outside of class typically involve error-full learning. While error-free teaching consists of focused lectures…
Descriptors: Statistics Education, Programming Languages, Troubleshooting, Coding
Socratous, Chrysanthos; Ioannou, Andri – Educational Technology Research and Development, 2021
The study aims to compare the effect of a structured versus an unstructured educational robotics (ER) curriculum on (a) the frequency and type of programming errors made by students in block-based programming, (b) their ability to debug a programme, and (c) their engagement in the learning process. The authors' hypothesis is that, in programming…
Descriptors: Robotics, Educational Technology, Programming Languages, Computer Science Education
Robertson, Judy; Gray, Stuart; Martin, Toye; Booth, Josephine – International Journal of Computer Science Education in Schools, 2020
We argue that understanding the cognitive foundations of computational thinking will assist educators to improve children's learning in computing. We explain the conceptual relationship between executive functions and aspects of computational thinking. We present initial empirical data from 23 eleven year old learners which investigates the…
Descriptors: Executive Function, Computation, Thinking Skills, Mathematics Skills
Fagerlund, Janne; Häkkinen, Päivi; Vesisenaho, Mikko; Viiri, Jouni – Informatics in Education, 2020
Computational thinking (CT) has been introduced in primary schools worldwide. However, rich classroom-based evidence and research on how to assess and support students' CT through programming are particularly scarce. This empirical study investigates 4th grade students' (N = 57) CT in a comparatively comprehensive and fine-grained manner by…
Descriptors: Grade 4, Elementary School Students, Preadolescents, Thinking Skills
Moskal, Adon Christian Michael; Wass, Rob – Computer Science Education, 2019
Background and Context: Encouraging undergraduate programming students to think more about their software development processes is challenging. Most programming courses focus on coding skill development and mastering programming language features; subsequently software development processes (e.g. planning, code commenting, and error debugging) are…
Descriptors: Computer Software, Undergraduate Students, Programming, Programming Languages
Lin, Yu-Tzu; Wu, Cheng-Chih; Hou, Ting-Yun; Lin, Yu-Chih; Yang, Fang-Ying; Chang, Chia-Hu – IEEE Transactions on Education, 2016
This study explores students' cognitive processes while debugging programs by using an eye tracker. Students' eye movements during debugging were recorded by an eye tracker to investigate whether and how high- and low-performance students act differently during debugging. Thirty-eight computer science undergraduates were asked to debug two C…
Descriptors: Cognitive Processes, Programming, Computer Software, Computer Science Education