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Oudeng Jia; Qingsong Tan; Sihan Zhang; Ke Jia; Mengyuan Gong – npj Science of Learning, 2025
Reward-predictive items capture attention even when task-irrelevant. While value-driven attention typically generalizes to stimuli sharing critical reward-associated features (e.g., red), recent evidence suggests an alternative generalization mechanism based on feature relationships (e.g., redder). Here, we investigated whether relational coding…
Descriptors: Attention, Rewards, Interference (Learning), Coding
Xieling Chen; Di Zou; Gary Cheng; Haoran Xie – Education and Information Technologies, 2024
The rise of massive open online courses (MOOCs) brings rich opportunities for understanding learners' experiences based on analyzing learner-generated content such as course reviews. Traditionally, the unstructured textual data is analyzed qualitatively via manual coding, thus failing to offer a timely understanding of the learner's experiences.…
Descriptors: Artificial Intelligence, Semantics, Course Evaluation, MOOCs
Zack J. Damon; Michael E. Ellis – Sport Management Education Journal, 2025
Sport analytics remains a growing area in the sport industry. As such, the demand for skills and knowledge in this area has grown. This demand includes off-field data, such as marketing trends, as well as financial data related to sport organizations. There has been a trickle-down effect in sport management (and other) education programs to teach…
Descriptors: Athletics, Data Collection, Data Analysis, Coding
Leah Bidlake; Eric Aubanel; Daniel Voyer – ACM Transactions on Computing Education, 2025
Research on mental model representations developed by programmers during parallel program comprehension is important for informing and advancing teaching methods including model-based learning and visualizations. The goals of the research presented here were to determine: how the mental models of programmers change and develop as they learn…
Descriptors: Schemata (Cognition), Programming, Computer Science Education, Coding
Muntasir Hoq; Ananya Rao; Reisha Jaishankar; Krish Piryani; Nithya Janapati; Jessica Vandenberg; Bradford Mott; Narges Norouzi; James Lester; Bita Akram – International Educational Data Mining Society, 2025
In Computer Science (CS) education, understanding factors contributing to students' programming difficulties is crucial for effective learning support. By identifying specific issues students face, educators can provide targeted assistance to help them overcome obstacles and improve learning outcomes. While identifying sources of struggle, such as…
Descriptors: Computer Science Education, Programming, Misconceptions, Error Patterns
Nicole S. Fenty; Leyli Nouraei Yeganeh; Vanessa D. Uhteg – Reading Teacher, 2025
Employment in science, technology, engineering, and mathematics (STEM) fields will continue to grow in the coming years. Those entering the STEM workforce will be expected to demonstrate proficiency in areas such as literacy and problem solving, among other domains. Early access to STEM concepts such as coding may support skill development and…
Descriptors: STEM Education, Coding, Literacy Education, Preschool Children
Ainhoa Berciano; Astrid Cuida; María-Luisa Novo – Education and Information Technologies, 2025
In the last two decades, computational thinking has gained wide relevance in international educational systems. The inclusion of this new type of thinking poses educational challenges with some underlying research questions that need to be answered to meet these challenges with quality. Thus, this study focuses on analyzing the difficulties that…
Descriptors: Coding, Translation, Programming Languages, Sequential Approach
Mehmet Basaran; Ömer Faruk Vural; Sermin Metin; Sabiha Tamur – International Journal of Early Childhood, 2025
This study investigates ChatGPT's perspectives on coding education for preschool children to provide a comprehensive understanding that is valuable for educators in early childhood education. An instrumental case study approach was employed, utilizing qualitative research design and case study methods. Data were gathered using a structured…
Descriptors: Preschool Education, Computer Science Education, Coding, Artificial Intelligence
Per Anderhag; Niklas Salomonsson; Andre Bürgers; Cesar Estay Espinola; Birgit Fahrman; Dana Seifeddine Ehdwall; Maria Sundler – International Journal of Technology and Design Education, 2024
During a relatively short period of time, programming has been implemented in the national curriculum of the compulsory school in Sweden. Since 2018, programming is a new content in the technology subject and the research field has discussed some of the challenges teachers and students, who generally have little experiences of programming, face…
Descriptors: Learning Strategies, Programming, Robotics, Technology Education
Gus Greivel; Alexandra Newman; Maxwell Brown; Kelly Eurek – INFORMS Transactions on Education, 2024
Industrial-scale models require considerable setup time; hence, once built, they are used in myriad ways to consider closely related cases. In practice, the code for these models frequently evolves without appropriate notational choices, largely as a result of the lengthy development time of, and the number of individuals contributing to, their…
Descriptors: Models, Best Practices, Mathematical Concepts, Energy
Karen M. Lionello-DeNolf; David Eckerman; Rebecca Hise; Elizabeth Pinzino; Roger Ray – Journal of Applied Behavior Analysis, 2025
Procedural fidelity is an important component of behavioral intervention programs. The "Train-to-Code" software was used to teach skilled observation of implementation of three types of discrete-trial programs, and improvement to procedural fidelity was assessed. Participants completed a training package that involved coding video…
Descriptors: Fidelity, Computer Assisted Instruction, Applied Behavior Analysis, Behavior Modification
David Shilane; Nicole Di Crecchio; Nicole L. Lorenzetti – Teaching Statistics: An International Journal for Teachers, 2024
Educational curricula in data analysis are increasingly fundamental to statistics, data science, and a wide range of disciplines. The educational literature comparing coding syntaxes for instruction in data analysis recommends utilizing a simple syntax for introductory coursework. However, there is limited prior work to assess the pedagogical…
Descriptors: Programming, Data Science, Programming Languages, Coding
Alain Kuzniak; Blandine Masselin – Educational Studies in Mathematics, 2024
This paper describes how the notion of the strongly didactic contract can serve to characterize the teaching adopted to implement a task in probability. It is particularly focused on the reality of mathematical work performed by students and teachers. For this research, classroom sessions were developed in an in-service teacher training course…
Descriptors: Mathematics, Learning Theories, Teaching Methods, Teacher Education
Pauline Megan Fox – Teacher Education Advancement Network Journal, 2024
Despite growing interest in spaced retrieval methods, a research gap has been discovered, with insufficient data to support best practises in year-one science. After analysing five systematic action cycles, four interrelated themes emerged: dual coding, cognitive load, peer communication, and feedback-driven metacognition. While literature…
Descriptors: Cognitive Science, Information Retrieval, Learning Processes, Short Term Memory
Abdullahi Yusuf; Norah Md Noor – Smart Learning Environments, 2024
In recent years, programming education has gained recognition at various educational levels due to its increasing importance. As the need for problem-solving skills becomes more vital, researchers have emphasized the significance of developing algorithmic thinking (AT) skills to help students in program development and error debugging. Despite the…
Descriptors: Students, Programming, Algorithms, Problem Solving

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