Table 1.
Automatic modeling of student characteristics in learning studies using interaction-based data.
| Studies | Number of students | Learning environments | Features | Student characteristic modeled | Machine learning models | Models accuracy |
|---|---|---|---|---|---|---|
| Botelho et al. (2017) | 646 students | ASSISTments learning platform | Interaction features | Affective states | Recurrent neural network | AUC is 78% |
| DeFalco et al. (2018) | 119 university students | TC3Sim game learning environment | Interaction features | Affective states | Logistic regression | A' is 85% |
| Ghaleb et al. (2019) | 23 university students | Technology-enhanced learning system | Interaction features | Affective states | Support vector machine | Precision is 67% |
| Hutt et al. (2019) | 69,174 high school students | Online math learning platform | Interaction features | Affective states | Genetic algorithm | None |
| Khan et al. (2019) | 81 students | Learning management system (Moodle) | Interaction features | Affective states | Bayesian network | Precision is 67.9% |
| Salmeron-Majadas et al. (2018) | 41 students | Essay writing tool named MOKEETO | Mouse and keyboard interactions | Affective states | Random forest | 70% |
| Wang et al. (2019) | 269 undergraduates | Cloud Classroom | Interaction features | Affective states | K* | F score is 70% |
| Edmond Meku Fotso et al. (2020) | 3,617 university students | MOOC | Interaction features | Engagement | Recurrent neural network | 89.2% |
| Erkan et al. (2020) | 12,447 university students | MOOC (Peer-review participation) | Interaction features, Student performance | Engagement | Random forest | 80% |
| Raj and Renumol (2022) | 7,775 university students | Virtual learning environment course | Interaction features, student performance | Engagement | Random forest | 95% |
| Aissaoui et al. (2019) | 1,235 students | MOOC | Interaction features | Learning style | Naïve bayes | 89% |
| Amir et al. (2017) | 200 university students | Learning management system | Interaction features | Learning style | Support vector machine | 83.6% |
| Hmedna et al. (2020) | 52,735 university students | Edx course | Interaction features | Learning style | Decision Tree | 98% |
| Lwande et al. (2019) | 199 students | eLearning course | Interaction features | Learning style | K-nearest neighbors | None |
| Al-Shabandar et al. (2018) | 7,000 university students | MOOC (Edx) | Interaction features | Motivation | Decision tree | 75.5% |
| Babić (2017) | 129 university students | Learning management system | Interaction features | Motivation | Neural network | 76.9% |
| Abyaa et al. (2018) | 48 university students | Online learning platform (piazza) | Interaction features | Personality | Random forest | 83.3% |