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Predicting the Persuasiveness of Influence Strategies From Student Online Learning Behaviour Using Machine Learning

Fidelia A Orji1, Julita Vassileva1

  • 1Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.

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|May 4, 2026
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Summary

Machine learning models can predict the persuasiveness of influence strategies in online education systems. This allows systems to adapt automatically, enhancing student engagement and learning outcomes.

Keywords:
K-means clusteringlearning behaviourmachine learningonline educational systemspersuasive strategiespersuasive technologypersuasiveness

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Area of Science:

  • Educational Technology
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Limited understanding exists regarding the impact of persuasive influence strategies on student behavior within online educational systems.
  • Current methods for assessing system persuasiveness rely on static, subjective measures like questionnaires, hindering real-time adaptation.
  • Automated, real-time prediction of system persuasiveness is crucial for dynamic personalization of online learning environments.

Purpose of the Study:

  • To investigate the relationship between the persuasiveness of influence strategies and student behavior in an online educational setting.
  • To determine if machine learning models can effectively predict the impact of persuasive strategies on student engagement.
  • To explore the feasibility of using student learning data for real-time persuasiveness assessment.

Main Methods:

  • Implementation and testing of Machine Learning (ML) classification models.
  • Utilizing student learning session data as input for the ML models.
  • Analyzing the predictive power of ML models on the persuasiveness of different influence strategies.

Main Results:

  • Student learning data can be successfully used to predict the persuasiveness of various influence strategies.
  • Machine learning classification models demonstrated a significant impact of persuasiveness on student usage patterns.
  • The study confirmed the potential for automated prediction of system persuasiveness.

Conclusions:

  • Machine learning classifiers, when trained on learning session data, can automatically predict the persuasiveness of influence strategies.
  • Online educational systems can leverage these ML models to dynamically adapt persuasive tactics.
  • This adaptive capability holds significant potential for improving student engagement and overall learning in digital environments.