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Effect of active learning using program visualization in technology-constrained college classrooms.

Gargi Banerjee1, Sahana Murthy1, Sridhar Iyer2

  • 11Interdisciplinary Program in Educational Technology, Indian Institute of Technology Bombay, Mumbai, 400076 India.

Research and Practice in Technology Enhanced Learning
|January 8, 2019
PubMed
Summary
This summary is machine-generated.

Integrating visualizations in Computer Science (CS) education, especially in technology-constrained settings, can be challenging. The "Responding" strategy, where students predict with visualizations, significantly improves engagement and learning outcomes compared to "Viewing" alone.

Keywords:
Active learningEngagement levelsPredictionProgram visualizationViewing

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Area of Science:

  • Computer Science Education
  • Educational Technology
  • Human-Computer Interaction

Background:

  • Computer Science (CS) instructors struggle with integrating visualizations into teaching.
  • Technology-constrained classrooms, common in developing countries, limit direct student interaction with visualizations.
  • Instructor-mediated settings require specific strategies for effective visualization integration.

Purpose of the Study:

  • To compare learning outcomes of two visualization engagement levels in instructor-mediated settings.
  • To evaluate the impact of "Responding" (prediction activity) versus "Viewing" (watching with commentary) on student learning.
  • To provide guidance for designing effective visualization integration in classrooms with limited technology.

Main Methods:

  • A study was conducted in a programming topic of medium complexity.
  • Two engagement levels were contrasted: "Responding" (prediction with visualization) and "Viewing" (watching with commentary).
  • Learning outcomes measured included active behavioral engagement, perception of learning, and cognitive achievement (problem-solving rate).

Main Results:

  • The "Responding" strategy led to statistically significant higher active behavioral engagement and perception of learning.
  • Students in the "Responding" group showed significantly higher cognitive achievement in problem-solving rate, particularly with prior active learning training.
  • The "Viewing" strategy resulted in lower engagement and learning outcomes compared to "Responding".

Conclusions:

  • Integrating visualizations through active prediction ("Responding") is more effective than passive viewing ("Viewing") in instructor-mediated CS classrooms.
  • The "Responding" strategy enhances student engagement, learning perception, and cognitive achievement.
  • Findings offer a practical guide for optimizing visualization use in technology-constrained educational environments.