Publication Date
| In 2025 | 0 |
| Since 2024 | 1 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Artificial Intelligence | 1 |
| Causal Models | 1 |
| Computer Software | 1 |
| Error of Measurement | 1 |
| Intelligent Tutoring Systems | 1 |
| Learning Analytics | 1 |
| Learning Management Systems | 1 |
| Outcomes of Education | 1 |
| Prediction | 1 |
| Randomized Controlled Trials | 1 |
| Sample Size | 1 |
| More ▼ | |
Source
| Grantee Submission | 1 |
Publication Type
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkYanping Pei; Adam Sales; Johann Gagnon-Bartsch – Grantee Submission, 2024
Randomized A/B tests within online learning platforms enable us to draw unbiased causal estimators. However, precise estimates of treatment effects can be challenging due to minimal participation, resulting in underpowered A/B tests. Recent advancements indicate that leveraging auxiliary information from detailed logs and employing design-based…
Descriptors: Randomized Controlled Trials, Learning Management Systems, Causal Models, Learning Analytics
