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Bérénice Lemoine; Pierre Laforcade; Sébastien George – Journal of Computer Assisted Learning, 2025
Background: Training the memorization of declarative knowledge requires the repetitive presentation of various forms of factual questions to learners. Educational games designed for this purpose should offer activities that are both tailored to individual learners and varied to prevent boredom. Whilst the Technology-Enhanced Learning (TEL)…
Descriptors: Educational Games, Design, Computer Science Education, Training
Mohammed Nazel Alshammari; Wendy Yánez-Pazmino; Carlos Mera-Gómez; Rami Bahsoon – ACM Transactions on Computing Education, 2025
Background: In agile software engineering team projects, effective collaboration and communication between members are essential for promoting agility and lean software development. Agile methodologies, which emphasize iterative development, collaboration and adaptability, are well suited to dynamic educational environments. However, agile teams…
Descriptors: Computer Software, Productivity, Computer Science Education, Cooperative Learning
Shin-Shing Shin; Yu-Shan Lin; Yi-Cheng Chen; Wei-Ru Chiou – Journal of Engineering Education, 2025
Background: Learners of database courses usually encounter difficulties in building entity-relationship (ER) models and relational models for database problems. These difficulties may arise because of semantic gaps between the stages of database design. To investigate this issue, we employed semantic network theory--particularly the concept of…
Descriptors: Database Design, Semantics, Computer Science Education, Networks
Jinshui Wang; Shuguang Chen; Zhengyi Tang; Pengchen Lin; Yupeng Wang – Education and Information Technologies, 2025
Mastering SQL programming skills is fundamental in computer science education, and Online Judging Systems (OJS) play a critical role in automatically assessing SQL codes, improving the accuracy and efficiency of evaluations. However, these systems are vulnerable to manipulation by students who can submit "cheating codes" that pass the…
Descriptors: Programming, Computer Science Education, Cheating, Computer Assisted Testing
Meija Lohiniva; Ville Isomöttönen – ACM Transactions on Computing Education, 2025
Context: Introductory programming courses often face high dropout and failure rates, a challenge widely addressed in computing education research. Collaborative methods, such as group work and pair programming, have been proposed as potential solutions, as they are believed to enhance students' study motivation. Objective: This article provides a…
Descriptors: Cooperative Learning, Student Motivation, Introductory Courses, Computer Science Education
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
Haoze Du; Richard Li; Edward Gehringer – International Educational Data Mining Society, 2025
Evaluating the performance of Large Language Models (LLMs) is a critical yet challenging task, particularly when aiming to avoid subjective assessments. This paper proposes a framework for leveraging subjective metrics derived from the class textual materials across different semesters to assess LLM outputs across various tasks. By utilizing…
Descriptors: Artificial Intelligence, Performance, Evaluation, Automation
Jesper Dannath; Alina Deriyeva; Benjamin Paaßen – International Educational Data Mining Society, 2025
Research on the effectiveness of Intelligent Tutoring Systems (ITSs) suggests that automatic hint generation has the best effect on learning outcomes when hints are provided on the level of intermediate steps. However, ITSs for programming tasks face the challenge to decide on the granularity of steps for feedback, since it is not a priori clear…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Science Education, Undergraduate Students
Radek Pelánek – ACM Transactions on Computing Education, 2025
Learning environments for programming education need a comprehensive task set that guides students from basic programming concepts to complex challenges. For creating such a task set, it is beneficial to utilize the concept of a design space--a systematic mapping of design dimensions and choices along these dimensions. We propose an iterative…
Descriptors: Computer Science Education, Programming, Design, Task Analysis
Jérôme Brender; Laila El-Hamamsy; Christian Giang; Laura Mathex; Tanja Käser; Francesco Mondada – Educational Technology Research and Development, 2025
Generalist primary school computer science (CS) teachers are often reluctant to introduce CS activities that go beyond CS unplugged tasks. To address this challenge, we drew from constructive alignment principles to implement a new programming modality for primary school: the handwriting programming language (HPL). HPL brings programming…
Descriptors: Handwriting, Programming Languages, Computer Science Education, Teaching Methods
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
Ruohan Liu; Jennifer L. Maeng; Shanan C. Moots; Joanna K. Garner – Contemporary Issues in Technology and Teacher Education (CITE Journal), 2025
This randomized control trial evaluated the impact of a year-long professional development (PD) program on elementary teachers' CS content knowledge, self-efficacy, and CS implementation. It also investigated teachers' PD experiences and expectations. The findings suggest that treatment teachers' self-efficacy for teaching CS significantly…
Descriptors: Elementary School Teachers, Capacity Building, Faculty Development, Computer Science Education
Atharva Naik; Jessica Ruhan Yin; Anusha Kamath; Qianou Ma; Sherry Tongshuang Wu; R. Charles Murray; Christopher Bogart; Majd Sakr; Carolyn P. Rose – British Journal of Educational Technology, 2025
The relative effectiveness of reflection either through student generation of contrasting cases or through provided contrasting cases is not well-established for adult learners. This paper presents a classroom study to investigate this comparison in a college level Computer Science (CS) course where groups of students worked collaboratively to…
Descriptors: Cooperative Learning, Reflection, College Students, Computer Science Education
Aoife Hennessy; Kieran Murphy – Irish Educational Studies, 2025
The importance of student engagement is long recognised. Students who are more engaged will be more motivated and inclined to complete their studies. The aim of this study is to understand barriers to engagement for first-year computing students, a cohort that traditionally have high non-progression rates. A qualitative descriptive design was…
Descriptors: Barriers, Learner Engagement, Computer Science Education, College Freshmen

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