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A Computer-Supported Collaborative Learning Environment for Computer Science Education

Michael Holly, Jannik Hildebrandt, Johanna Pirker

TL;DR

The paper tackles the challenge of teaching complex CS concepts by integrating collaborative and competitive elements into a computer-supported virtual laboratory, addressing the social shortfalls of digital learning. It reports an AB study with 35 CS students comparing a multi-user sorting-algorithms lab against a single-user version, using immersive 3D visualization, a detail-view and a competitive battle-view, and measures usability, emotions, engagement, and learning outcomes. The findings indicate that collaborative pairs can achieve higher learning gains and engagement, while usability remains a concern and suggests usability-focused improvements and easier access for multi-user sessions. The work offers actionable insights for designing social, interactive CS education tools that can be deployed in remote or home-learning contexts and informs future classroom-oriented evaluations.

Abstract

Skills in the field of computer science (CS) are increasingly in demand. Often traditional teaching approaches are not sufficient to teach complex computational concepts. Interactive and digital learning experiences have been shown as valuable tools to support learners in understanding. However, the missing social interaction affects the quality of the learning experience. Adding collaborative and competitive elements can make the virtual learning environment even more social, engaging, and motivating for learners. In this paper, we explore the potential of collaborative and competitive elements in an interactive virtual laboratory environment with a focus on computer science education. In an AB study with 35 CS students, we investigated the effectiveness of collaborative and competitive elements in a virtual laboratory using interactive visualizations of sorting algorithms.

A Computer-Supported Collaborative Learning Environment for Computer Science Education

TL;DR

The paper tackles the challenge of teaching complex CS concepts by integrating collaborative and competitive elements into a computer-supported virtual laboratory, addressing the social shortfalls of digital learning. It reports an AB study with 35 CS students comparing a multi-user sorting-algorithms lab against a single-user version, using immersive 3D visualization, a detail-view and a competitive battle-view, and measures usability, emotions, engagement, and learning outcomes. The findings indicate that collaborative pairs can achieve higher learning gains and engagement, while usability remains a concern and suggests usability-focused improvements and easier access for multi-user sessions. The work offers actionable insights for designing social, interactive CS education tools that can be deployed in remote or home-learning contexts and informs future classroom-oriented evaluations.

Abstract

Skills in the field of computer science (CS) are increasingly in demand. Often traditional teaching approaches are not sufficient to teach complex computational concepts. Interactive and digital learning experiences have been shown as valuable tools to support learners in understanding. However, the missing social interaction affects the quality of the learning experience. Adding collaborative and competitive elements can make the virtual learning environment even more social, engaging, and motivating for learners. In this paper, we explore the potential of collaborative and competitive elements in an interactive virtual laboratory environment with a focus on computer science education. In an AB study with 35 CS students, we investigated the effectiveness of collaborative and competitive elements in a virtual laboratory using interactive visualizations of sorting algorithms.
Paper Structure (16 sections, 3 figures, 5 tables)

This paper contains 16 sections, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Learning Application
  4. Collaborative Experiment Setup
  5. Experiments and Simulations
  6. Evaluation
  7. Material and Setup
  8. Method and Procedure
  9. Participants
  10. Results
  11. Usability and User Experience
  12. Learning Experience and Outcome
  13. Collaboration and Online Engagement
  14. Discussion
  15. Limitations
  16. ...and 1 more sections

Figures (3)

  • Figure 1: The detail-view of the experiment shows the sorting visualization including the current execution line and a description of the loaded algorithm.
  • Figure 2: The battle-view of the sorting experiment allows the comparison of two different algorithms on a given input array to evaluate their efficiency.
  • Figure 3: Results of the ten theory questions before and after the learning experience. Each question was graded with 0, 0.5, or 1 point, depending on how accurate the answer was.