Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 1 |
Since 2006 (last 20 years) | 2 |
Descriptor
Bayesian Statistics | 3 |
Computer Assisted Testing | 3 |
Undergraduate Students | 3 |
Adaptive Testing | 2 |
Comparative Analysis | 2 |
Algorithms | 1 |
Attribution Theory | 1 |
Beliefs | 1 |
College Instruction | 1 |
Computer Assisted Instruction | 1 |
Computer Science | 1 |
More ▼ |
Author
Barberia, Itxaso | 1 |
Kaburlasos, Vassilis G. | 1 |
Marinagi, Catherine C. | 1 |
Rodríguez-Ferreiro, Javier | 1 |
Shermis, Mark D. | 1 |
Tsoukalas, Vassilis Th. | 1 |
Vadillo, Miguel A. | 1 |
Publication Type
Journal Articles | 2 |
Reports - Research | 2 |
Reports - Evaluative | 1 |
Speeches/Meeting Papers | 1 |
Education Level
Higher Education | 2 |
Postsecondary Education | 2 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Rodríguez-Ferreiro, Javier; Vadillo, Miguel A.; Barberia, Itxaso – Teaching of Psychology, 2023
Background: We have previously presented two educational interventions aimed to diminish causal illusions and promote critical thinking. In both cases, these interventions reduced causal illusions developed in response to active contingency learning tasks, in which participants were able to decide whether to introduce the potential cause in each…
Descriptors: Sampling, Inferences, Psychology, Undergraduate Students
Kaburlasos, Vassilis G.; Marinagi, Catherine C.; Tsoukalas, Vassilis Th. – Computers & Education, 2008
This work presents innovative cybernetics (feedback) techniques based on Bayesian statistics for drawing questions from an Item Bank towards personalized multi-student improvement. A novel software tool, namely "Module for Adaptive Assessment of Students" (or, "MAAS" for short), implements the proposed (feedback) techniques. In conclusion, a pilot…
Descriptors: Feedback (Response), Student Improvement, Computer Science, Bayesian Statistics
Shermis, Mark D.; And Others – 1992
The reliability of four branching algorithms commonly used in computer adaptive testing (CAT) was examined. These algorithms were: (1) maximum likelihood (MLE); (2) Bayesian; (3) modal Bayesian; and (4) crossover. Sixty-eight undergraduate college students were randomly assigned to one of the four conditions using the HyperCard-based CAT program,…
Descriptors: Adaptive Testing, Algorithms, Bayesian Statistics, Comparative Analysis