Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Data Analysis | 2 |
| Data Processing | 2 |
| Models | 2 |
| Outcome Measures | 2 |
| Academic Achievement | 1 |
| Blended Learning | 1 |
| Case Studies | 1 |
| College Students | 1 |
| Computer Mediated… | 1 |
| Courseware | 1 |
| Data Collection | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 2 |
| Reports - Evaluative | 1 |
| Reports - Research | 1 |
Education Level
| Early Childhood Education | 1 |
| Higher Education | 1 |
| Postsecondary Education | 1 |
| Preschool Education | 1 |
Audience
Location
| Netherlands | 1 |
| North Carolina | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkConijn, Rianne; Snijders, Chris; Kleingeld, Ad; Matzat, Uwe – IEEE Transactions on Learning Technologies, 2017
With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…
Descriptors: Blended Learning, Predictor Variables, Predictive Validity, Predictive Measurement
Gee, Kevin A. – American Journal of Evaluation, 2014
The growth in the availability of longitudinal data--data collected over time on the same individuals--as part of program evaluations has opened up exciting possibilities for evaluators to ask more nuanced questions about how individuals' outcomes change over time. However, in order to leverage longitudinal data to glean these important insights,…
Descriptors: Longitudinal Studies, Data Analysis, Statistical Studies, Program Evaluation
