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Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual Differences.
Kamil Akhuseyinoglu1, Peter Brusilovsky1
1School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States.
Frontiers in Artificial Intelligence
|March 4, 2022
Summary
This study explores how student behavior in e-learning relates to individual differences. A new data-driven model significantly improves predictions of learner engagement and performance compared to traditional methods.
Area of Science:
- Educational Data Mining
- Learning Analytics
- Psychology of Education
Background:
- E-learning systems generate vast amounts of data on student behavior.
- Existing research has not sufficiently linked behavioral patterns to individual differences.
- The predictive power of traditional individual difference models for learning outcomes is limited.
Purpose of the Study:
- To investigate the extent to which learner behavior is shaped by known individual differences.
- To identify which individual differences best predict learner engagement and performance.
- To develop and evaluate a data-driven model of individual differences using behavioral patterns for improved prediction of learning outcomes.
Main Methods:
- Utilized a large dataset from an online practice system.
- Applied sequential pattern mining to model individual learner practice behavior.
- Identified latent student subgroups with distinct practice behaviors.
- Quantified learner behavior on a data-driven scale to bridge behavior-based and traditional models.
- Examined relationships between behavioral models and individual differences (self-esteem, gender, knowledge monitoring).
Main Results:
- Revealed latent student subgroups with significantly different practice behaviors.
- Established connections between learner behavior and both incoming (individual differences) and outgoing (performance, engagement) learning parameters.
- The developed data-driven model of individual differences demonstrated superior predictive performance compared to traditional models for engagement and performance.
Conclusions:
- Learner behavior in e-learning is significantly influenced by individual differences.
- A data-driven approach to modeling individual differences based on behavioral patterns offers enhanced predictive capabilities.
- This approach provides a more nuanced and effective way to understand and predict student success in online learning environments.
Keywords:
SQLindividual differenceslearner modelinglearning technologyonline practicesequential pattern miningMore Related Videos
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