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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Improving Learner-Computer Interaction through Intelligent Learning Material Delivery Using Instructional Design

Christos Troussas1, Akrivi Krouska1, Cleo Sgouropoulou1

  • 1Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece.

Entropy (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for teaching Java programming by personalizing learning materials using instructional theory and intelligent techniques. This approach enhances student interaction and knowledge acquisition compared to traditional methods.

Keywords:
Component Display TheoryIntelligent Tutoring SystemsMultiple-Criteria Decision AnalysisWeighted Sum Modeladaptive learning material deliverycontent-based filteringonline learning

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Area of Science:

  • Computer Science Education
  • Artificial Intelligence in Education
  • Human-Computer Interaction

Background:

  • Traditional approaches often adapt content presentation based on student characteristics.
  • There's a need to focus on delivering the appropriate *type* of learning material for better pedagogical affordance.
  • Personalized learning material delivery is crucial for effective computer programming education.

Purpose of the Study:

  • To improve learner-computer interaction in Java programming tutoring.
  • To provide personalized learning material by combining instructional theory and intelligent techniques.
  • To enhance student engagement and knowledge acquisition in programming education.

Main Methods:

  • Integration of Component Display Theory with content-based filtering and multiple-criteria decision analysis.
  • Development of an intelligent educational software merging pedagogical and technological approaches.
  • Evaluation of the software with undergraduate students learning Java programming.

Main Results:

  • The developed intelligent system effectively delivered personalized learning material.
  • The approach significantly surpassed traditional methods in student satisfaction.
  • The personalized delivery method led to superior knowledge acquisition in Java programming.

Conclusions:

  • Blending instructional design theories with intelligent techniques offers a superior personalized and adaptive learning experience.
  • The innovative approach enhances both student satisfaction and learning outcomes in programming education.
  • This study validates the effectiveness of sophisticated learning material delivery in intelligent tutoring systems.