Evaluating Alternative Training Interventions Using Personalized Computational Models of Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909294977351680 |
|---|---|
| author | MacLellan, Christopher James Stowers, Kimberly Brady, Lisa |
| author_facet | MacLellan, Christopher James Stowers, Kimberly Brady, Lisa |
| contents | Evaluating different training interventions to determine which produce the best learning outcomes is one of the main challenges faced by instructional designers. Typically, these designers use A/B experiments to evaluate each intervention; however, it is costly and time consuming to run such studies. To address this issue, we explore how computational models of learning might support designers in reasoning causally about alternative interventions within a fractions tutor. We present an approach for automatically tuning models to specific individuals and show that personalized models make better predictions of students' behavior than generic ones. Next, we conduct simulations to generate counterfactual predictions of performance and learning for two students (high and low performing) in different versions of the fractions tutor. Our approach makes predictions that align with previous human findings, as well as testable predictions that might be evaluated with future human experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13684 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Alternative Training Interventions Using Personalized Computational Models of Learning MacLellan, Christopher James Stowers, Kimberly Brady, Lisa Artificial Intelligence Computers and Society Human-Computer Interaction Evaluating different training interventions to determine which produce the best learning outcomes is one of the main challenges faced by instructional designers. Typically, these designers use A/B experiments to evaluate each intervention; however, it is costly and time consuming to run such studies. To address this issue, we explore how computational models of learning might support designers in reasoning causally about alternative interventions within a fractions tutor. We present an approach for automatically tuning models to specific individuals and show that personalized models make better predictions of students' behavior than generic ones. Next, we conduct simulations to generate counterfactual predictions of performance and learning for two students (high and low performing) in different versions of the fractions tutor. Our approach makes predictions that align with previous human findings, as well as testable predictions that might be evaluated with future human experiments. |
| title | Evaluating Alternative Training Interventions Using Personalized Computational Models of Learning |
| topic | Artificial Intelligence Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.13684 |