NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing all 7 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Monika Mladenovic; Žana Žanko; Goran Zaharija – Journal of Educational Computing Research, 2024
The use of a pedagogical approach mediated transfer with the bridging method has been successful in facilitating the transitions from block-based to text-based programming languages. Nevertheless, there is a lack of research addressing the impact of this transfer on programming misconceptions during the transition. The way programming concepts are…
Descriptors: Programming, Misconceptions, Teaching Methods, Computer Science Education
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Amedeo Pachera; Stefania Dumbrava; Angela Bonifati; Andrea Mauri – ACM Transactions on Computing Education, 2025
Query languages are the foundations of database teaching and education practices. The broad adoption of graph databases contrasts with the limited research into how they are taught. Contrary to relational databases, graph databases allow navigational queries with higher expressivity and lack an a priori schema. In this article, we design a…
Descriptors: Error Patterns, Graphs, Programming Languages, Databases
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Christina N. Morra; Sarah J. Adkins; M. Elizabeth Barnes; Obadiah J. Pirlo; Ryleigh Fleming; Bianca J. Convers; Sarah P. Glass; Michael L. Howell; Samiksha A. Raut – Journal of Microbiology & Biology Education, 2024
Misinformation regarding vaccine science decreased the receptiveness to COVID-19 vaccines, exacerbating the negative effects of the COVID-19 pandemic on society. To mitigate the negative societal impact of the COVID-19 pandemic, impactful and creative science communication was needed, yet little research has explored how to encourage COVID-19…
Descriptors: Undergraduate Students, COVID-19, Immunization Programs, Pandemics
Peer reviewed Peer reviewed
Direct linkDirect link
Orly Barzilai; Sofia Sherman; Moshe Leiba; Hadar Spiegel – Journal of Information Systems Education, 2024
Data Structures and Algorithms (DS) is a basic computer science course that is a prerequisite for taking advanced information systems (IS) curriculum courses. The course aims to teach students how to analyze a problem, design a solution, and implement it using pseudocode to construct knowledge and develop the necessary skills for algorithmic…
Descriptors: Statistics Education, Problem Solving, Information Systems, Algorithms