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Exploring non-linear relationships between perceived interactivity or interface design and acceptance of

Fareed Al-Sayid1, Gokhan Kirkil2

  • 1Industrial Engineering Department, Faculty of Graduate Students, Kadir Has University, Istanbul, Turkey.

Education and Information Technologies
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a conceptual model to predict non-linear relationships in e-learning. Cubic, quadratic, logarithmic, and s-curve models best described the correlations between human-computer interaction factors and e-learning usability.

Keywords:
Collaborative LearningEase of UseHuman–Computer InteractionInterface and InteractivityNon-LinearityUsefulness

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Area of Science:

  • Human-Computer Interaction
  • Educational Technology
  • Learning Analytics

Background:

  • Traditional linear models often fail to capture complex interactions in e-learning.
  • Understanding non-linear relationships is crucial for optimizing collaborative web-based learning environments.

Purpose of the Study:

  • To develop a conceptual model for predicting non-linear relationships between human-computer interaction (HCI) factors and the ease of use and usefulness of e-learning.
  • To identify the most appropriate non-linear models for describing these relationships.

Main Methods:

  • Examined ten non-linear models (logarithmic, inverse, quadratic, cubic, compound, power, s-curve, growth, exponential, logistic) against linear relationships.
  • Surveyed 103 students on perceived interface and interactivity of e-learning systems.
  • Evaluated model appropriateness using R², adjusted R², and SEE values.

Main Results:

  • Cubic models best described relationships for ease of use and usefulness, visual design, learner-interface interactivity, and course evaluation.
  • Quadratic models were suitable for visual design, system quality, course structure, and environment.
  • Logarithmic and s-curve models also showed strong descriptive power for specific HCI factors and usability outcomes.

Conclusions:

  • Non-linear models, particularly cubic and quadratic, provide a more accurate representation of HCI factor impacts on e-learning usability than linear models.
  • The findings support the development of more sophisticated e-learning system designs that account for complex user interactions.