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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
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
Manuel B. Garcia – Education and Information Technologies, 2025
The global shortage of skilled programmers remains a persistent challenge. High dropout rates in introductory programming courses pose a significant obstacle to graduation. Previous studies highlighted learning difficulties in programming students, but their specific weaknesses remained unclear. This gap exists due to the predominant focus on the…
Descriptors: Programming, Introductory Courses, Computer Science Education, Mastery Learning
Diana Franklin; Paul Denny; David A. Gonzalez-Maldonado; Minh Tran – Cambridge University Press & Assessment, 2025
Generative AI is a disruptive technology that has the potential to transform many aspects of how computer science is taught. Like previous innovations such as high-level programming languages and block-based programming languages, generative AI lowers the technical expertise necessary to create working programs, bringing the power of computation…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Science Education, Expertise
Anna Rechtácková; Radek Pelánek; Tomáš Effenberger – ACM Transactions on Computing Education, 2025
Code quality is a critical aspect of programming, as high-quality code is easier to maintain, and code maintenance constitutes the majority of software costs. Consequently, code quality should be emphasized in programming education. While previous research has identified numerous code quality defects commonly made by students, the current state…
Descriptors: Programming, Computer Science Education, Error Patterns, Automation
Rajagopal Sankaranarayanan; Mohan Yang; Kyungbin Kwon – Journal of Computing in Higher Education, 2025
The purpose of this study is to explore the influence of the microlearning instructional approach in an online introductory database programming classroom. The ultimate goal of this study is to inform educators and instructional designers on the design and development of microlearning content that maximizes student learning. Grounded within the…
Descriptors: Teaching Methods, Introductory Courses, Databases, Programming
Smitha S. Kumar; Michael A. Lones; Manuel Maarek; Hind Zantout – ACM Transactions on Computing Education, 2025
Programming demands a variety of cognitive skills, and mastering these competencies is essential for success in computer science education. The importance of formative feedback is well acknowledged in programming education, and thus, a diverse range of techniques has been proposed to generate and enhance formative feedback for programming…
Descriptors: Automation, Computer Science Education, Programming, Feedback (Response)
Mark Frydenberg; Anqi Xu; Jennifer Xu – Information Systems Education Journal, 2025
This study explores student perceptions of learning to code by evaluating AI-generated Python code. In an experimental exercise given to students in an introductory Python course at a business university, students wrote their own solutions to a Python program and then compared their solutions with AI-generated code. They evaluated both solutions…
Descriptors: Student Attitudes, Programming, Computer Software, Quality Assurance
Ting-Ting Wu; Hsin-Yu Lee; Pei-Hua Chen; Wei-Sheng Wang; Yueh-Min Huang – Journal of Computer Assisted Learning, 2025
Background: Conventional reflective learning methodologies in programming education often lack structured guidance and individualised feedback, limiting their pedagogical effectiveness. Whilst computational thinking (CT) offers a systematic problem-solving framework with decomposition, pattern recognition, abstraction, and algorithm design, its…
Descriptors: Computation, Thinking Skills, Educational Diagnosis, Diagnostic Tests
Leonard J. Mselle – Discover Education, 2025
In this paper the "Memory Transfer Language" program visualization (MTL PV) technique is combined with "constructivism" ("conceptual contraposition and colloquy") and "reversibility" to evolve a new approach for instructional design for teaching and learning introductory programming. A sample of 1,364…
Descriptors: Introductory Courses, Computer Science Education, Constructivism (Learning), Comparative Analysis
Gayithri Jayathirtha; Gail Chapman; Joanna Goode – Journal of Research on Technology in Education, 2025
We report findings from qualitatively analyzing audio recordings and teacher-generated products from a two-day in-person collaborative design (co-design) workshop where nine experienced high school CS teachers revised parts of an existing introductory high school program toward justice. In response to our research question of how teachers…
Descriptors: Experienced Teachers, Team Teaching, Design, High School Teachers
Ethan C. Campbell; Katy M. Christensen; Mikelle Nuwer; Amrita Ahuja; Owen Boram; Junzhe Liu; Reese Miller; Isabelle Osuna; Stephen C. Riser – Journal of Geoscience Education, 2025
Scientific programming has become increasingly essential for manipulating, visualizing, and interpreting the large volumes of data acquired in earth science research. Yet few discipline-specific instructional approaches have been documented and assessed for their effectiveness in equipping geoscience undergraduate students with coding skills. Here…
Descriptors: Earth Science, Undergraduate Students, Programming Languages, Computer Software
Nicholas A. Bowman; Federick J. Ngo; Jeongmin Ji – Review of Higher Education, 2025
Research has frequently demonstrated negative effects of placing students into developmental education, but very little inquiry has considered the impact of placing students into different levels of non-developmental coursework. The present study explored this issue within sequenced pairs of STEM gateway courses using doubly-robust propensity…
Descriptors: Student Placement, STEM Education, Outcomes of Education, Undergraduate Students
Rita Garcia; Michelle Craig – ACM Transactions on Computing Education, 2025
Introduction: Computer Science Education does not have a universally defined set of concepts consistently covered in all introductory courses (CS1). One approach to understanding the concepts covered in CS1 is to ask educators. In 2004, Nell Dale did just this. She also collected their perceptions on challenging topics to teach. Dale mused how the…
Descriptors: Replication (Evaluation), Teaching Methods, Computer Science Education, Introductory Courses
Ayaan M. Kazerouni; Melissa Lee; Aleata Hubbard Cheuoua; Aakash Gautam; Sahar Hooshmand; Paul Salvador Inventado; Eun-Young Kang; Jane Lehr; Yu Sun; Kevin A. Wortman; Ilmi Yoon; Zoë Wood – ACM Transactions on Computing Education, 2025
Background and Context. Computing is considered a fundamental skill for civic engagement, self-expression, and employment opportunity. Despite this, there exist significant equity gaps in post-secondary computing enrollment and retention. Specifically, in the California State University (CSU) system, which serves close to half a million…
Descriptors: Computer Science Education, Social Responsibility, Introductory Courses, Undergraduate Students
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