Fig 1.
A model that shows the recursive relationship between a student’s resources, engagement, and chemistry outcomes.
Fig 2.
Mapping the research questions to the REACT framework.
Fig 3.
A representation of the learning path in Cerego.
Pathways for two different concepts for a single student, showing how the software can adaptively modulate the time of review based on a student’s level of understanding.
Fig 4.
Histogram showing the relationship between hours spent on the Cerego app and number of students.
Fig 5.
Histogram showing the relationship between level reached on the Cerego app and number of students.
Table 1.
Correlations between main variables used in this study.
Table 2.
GKτ across all categorical variables.
Fig 6.
Estimated marginal means (predicted CHEM102 grade) predicted by Disciplinary Engagement, while controlling for CHEM101 grade.
(0 = opted out of using adaptive learning, 1 = opted in to using adaptive learning).
Table 3.
Effect of opting-in adaptive learning intervention on CHEM102 scores.
Table 4.
Multivariate regression estimates comparing the effect of App Usage with Disciplinary Engagement.
Table 5.
Multivariate regression estimates for CHEM102 grade for the interaction of Disciplinary Engagement and CHEM101 grade.
Fig 7.
Estimated marginal means for the final grade in CHEM102.
Predicted by their incoming CHEM101 grade and their level of engagement with adaptive learning.
Fig 8.
Hypothesized mediation model, testing the impact of adaptative learning intervention on follow up course.
Table 6.
Mediation test for the long-term effect of adaptative intervention usage on the follow-up course.