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…

Real-time collaborative programming (RCP), which allows multiple programmers to work concurrently on the same codebase with changes instantly visible to all participants, has garnered considerable popularity in higher education. Despite this trend, little work has rigorously examined how undergraduates engage in collaborative programming when…

Probability theory has extensive applications across various domains, such as statistics, computer science, and finance. In probability education, students are introduced to fundamental principles which may include mathematical topics such as combinatorics and symmetric sample spaces. Students pursuing degrees in computer science possess a robust…

Generative AI has recently gained the ability to generate computer code. This development is bound to affect how computer programming is taught in higher education. We used past programming assignments and solutions for textbook exercises in our introductory programming class to analyze how accurately one of the leading models, ChatGPT, generates…

Background and Context: Productive failure (PF) is a learning paradigm that flips the order of instruction: students work on a problem, then receive a lesson. PF increases learning, but less is known about student emotions and collaboration during PF, particularly in a computer science context. Objective: To provide insight on students' emotions…

Purpose: This study aims to develops an interdisciplinary business and computer science pedagogy for teaching and learning computer programming in business schools at higher education institutions and explores its associated benefits, challenges and improvement. Design/methodology/approach: Based on a body of theories, an interdisciplinary…

In this article, we leverage ideas from the theory of coevolutionary computation to analyze interactions of students with problems. We introduce the idea of "informatively" easy or hard concepts. Our approach is different from more traditional analyses of problem difficulty such as item analysis in the sense that we consider Pareto…

Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students' discourses, behaviors, and socio-emotions, it is of critical importance to examine…

Aim/Purpose: This paper explores the potential value of critical thinking in computer science education and discusses strategies for its integration across the curriculum. Background: As technology rapidly evolves and becomes increasingly integrated into society, there is a growing need for computer science graduates who can think critically about…

The study focuses on the identification of relationships and/or rules between computational thinking (CT) concepts among the undergraduate students of Applied Informatics due to their attitudes towards mathematics. We analyze three CT concepts -- decomposition, pattern recognition, and algorithmic thinking. We assume that students who have a…

Deep learning is a very popular topic in computer sciences courses despite the fact that it is often challenging for beginners to take their first step due to the complexity of understanding and applying Artificial Neural Networks (ANN). Thus, the need to both understand and use neural networks is appearing at an ever-increasing rate across all…

Resilience is a critical personality trait that allows one to deal with difficulties, learn from failures, and maintain a positive attitude during task performance. However, it has not been understudied in a complex and challenging educational domain. The current research intends to address this gap by analyzing the specific characteristics of…

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…

This study explores the effects of worked-out examples and metacognitive scaffolding on novice learners' knowledge performance, cognitive loads, and self-regulation skills in problem-solving programming. 126 undergraduate students in a computer programming fundamentals course were randomly assigned to one of four groups: (1) task performance with…

This study investigated the computational thinking (CT) practices of eight pre-service teachers through their Scratch and Python programs. Conducted within an undergraduate-level computer science education course, students learned CT concepts via parallel instruction in block-based programming (Scratch) and text-based programming (Python). The…