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
| Goodness of Fit | 4 |
| Learning Processes | 4 |
| Mathematical Models | 4 |
| Adult Learning | 1 |
| Adults | 1 |
| Age Differences | 1 |
| Algorithms | 1 |
| Attention Span | 1 |
| Computer Programs | 1 |
| Computer Software Reviews | 1 |
| Construct Validity | 1 |
| More ▼ | |
Author
| Aleven, Vincent | 1 |
| Donders, Jacobus | 1 |
| Huizinga, David | 1 |
| Kingma, Johannes | 1 |
| Olsen, Jennifer | 1 |
| Polson, Peter G. | 1 |
| Reuvekamp, Johan | 1 |
| Rummel, Nikol | 1 |
Publication Type
| Journal Articles | 3 |
| Reports - Descriptive | 1 |
| Reports - Evaluative | 1 |
| Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Olsen, Jennifer; Aleven, Vincent; Rummel, Nikol – Journal of Educational Measurement, 2017
Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors…
Descriptors: Mathematical Models, Information Retrieval, Data Analysis, Educational Research
Donders, Jacobus – Assessment, 2008
The purpose of this study is to determine the latent structure of the California Verbal Learning Test-Second Edition (CVLT-II; Delis, Kramer, Kaplan, & Ober, 2000) at three different age levels, using the standardization sample. Maximum likelihood confirmatory factor analyses are performed to test four competing hypothetical models for fit and…
Descriptors: Attention Span, Verbal Learning, Factor Structure, Factor Analysis
Peer reviewedPolson, Peter G.; Huizinga, David – Psychometrika, 1974
Descriptors: Algorithms, Computer Programs, Goodness of Fit, Learning Processes
Peer reviewedKingma, Johannes; Reuvekamp, Johan – Educational and Psychological Measurement, 1987
This paper describes a PASCAL program that computes both different types of transitions and learning statistics suitable for learning experiments in which a two-stage Markov model is used. The frequency counts of the different transitions are used for estimating the parameters of the two-stage Markov model. (Author/LMO)
Descriptors: Computer Software Reviews, Error of Measurement, Goodness of Fit, Input Output

Direct link
