Journal Article10.15388/infedu.2024.11
Active Learning Methodologies for Teaching Programming in Undergraduate Courses: A Systematic Mapping Study
Maria Ivanilse Calderón Ribeiro,Williamson Silva,Eduardo Feitosa +2 more
TL;DR: A Systematic Mapping Study (SMS) identifies and categorizes active learning methodologies (ALMs) employed in teaching programming. The study evaluated 3,850 papers and identified 37 different ALMs, including combined approaches. The results provide a comprehensive overview of ALMs used in teaching programming, including the most commonly reported methodologies of Flipped Classroom and Gamification-Based Learning.
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Abstract: Teaching programming is a complex process requiring learning to develop different skills. To minimize the challenges faced in the classroom, instructors have been adopting active methodologies in teaching computer programming. This article presents a Systematic Mapping Study (SMS) to identify and categorize the types of methodologies that instructors have adopted for teaching programming. We evaluated 3,850 papers published from 2000 to 2022. The results provide an overview and comprehensive view of active learning methodologies employed in teaching programming, technologies, programming languages, and the metrics used to observe student learning in this context. In the results, we identified thirty-seven different ALMs adopted by instructors. We realized that seventeen publications describe teaching approaches that combine more than one ALM, and the most reported methodologies in the studies are Flipped Classroom and Gamification-Based Learning. In addition, we are proposing an educational and collaborative tool called CollabProg, which summarizes the primary active learning methodologies identified in this SMS. CollabProg will assist instructors in selecting appropriate ALMs that align with their pedagogical requirements and teaching programming context.
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Figures
Table 1 Sub-questions. Source: The authors. 
Fig. 1. Results of systematic mapping filters. Source: The authors. 
Fig. 2. Publication trend by year. Source: The authors. 
Table 12 – continued from previous page 
Fig. 4. First version of CollabProg. Source: The authors. 
Table 10 Metrics X Methodology. Source: The authors
Citations
User Experience Measurement in Design-Based Research on Online Educational Platforms: Contextualization of Real-World Environments Within Sustainable Development Goals for Computational Thinking
Rasikh Tariq,María Soledad,Tabbi Wilberforce Awotwe,V. Fernández-Castro,Magally Martínez Reyes +4 more
TL;DR: This study measures user experience on online educational platforms powered by AI and data mining, analyzing 1,573 users' responses to identify improvements for redesign, aligning with Sustainable Development Goals and computational thinking.
Learning programming: exploring the relationships of self-efficacy, computational thinking, and learning performance among minority students
Yu Tung Kuo,Yu Chun Kuo +1 more
TL;DR: This study explores the relationships between self-efficacy, computational thinking, and learning performance among minority undergraduate students, finding positive correlations and significant predictors of learning performance, particularly for learning self-efficacy and computational thinking.
Board 59: Work in Progress: Streamer and Viewer Interactions in Software and Game-Development Live Streams
Ella Kokinda,D. Matthew Boyer +1 more
- 03 Aug 2024
TL;DR: This study investigates interactions between streamers and viewers in software and game development live streams on Twitch and YouTube, analyzing types of interactions, knowledge transfer, and informal learning opportunities, and their impact on real-time problem-solving for streamers.
The role of AI in shaping educational experiences in computer science: A systematic review
Anahita Golrang,Kshitij Sharma +1 more
Abstract: The integration of artificial intelligence (AI) in computer science education (CSE) has earned significant attention due to its potential to enhance learning experiences and outcomes. This systematic literature review provides one of the first domain-specific and methodologically robust syntheses of AI applications in undergraduate CSE. Through a comprehensive analysis of 40 peer-reviewed studies, we offer a fine-grained categorization of course contexts, AI methods, and data types. Our findings reveal a predominant use of supervised learning, ensemble methods, and deep learning, with notable gaps in generative and explainable AI. The review highlights the post-pandemic increase in AI-driven programming education and the growing recognition of AI’s role in addressing educational challenges. This study offers technical and pedagogical insights that inform future research and practice at the intersection of AI and computer science education.
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TL;DR: In this article, the authors argue that the success of an active learning methodology depends not on methodology alone but, ultimately, on the constantly evolving, dialectical relationship between methodology and learners, mediated by the educator.
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TL;DR: In this article, the authors present an experience-based guideline to aid researchers in designing systematic literature studies with special emphasis on the data collection and selection procedures, and provide a blueprint for a practical and pragmatic path through the plethora of currently available practices and deliverables.
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Getting Started With Team-Based Learning
Larry K. Michaelsen,Billie Bennett Franchini,Jim Sibley,Peter M. Ostafichuk,Bill Roberson +4 more
- 27 Jun 2023
TL;DR: Michaelsen as discussed by the authors identifies the four key principles that govern the effective use of learning teams and then describes what happens from start to finish in a team-based learning course (Part II) and also has a few comments about why he believes teambased learning is such an attractive option for teachers in a variety of teaching situations.
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