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Hanif Akhtar – International Society for Technology, Education, and Science, 2023
For efficiency, Computerized Adaptive Test (CAT) algorithm selects items with the maximum information, typically with a 50% probability of being answered correctly. However, examinees may not be satisfied if they only correctly answer 50% of the items. Researchers discovered that changing the item selection algorithms to choose easier items (i.e.,…
Descriptors: Success, Probability, Computer Assisted Testing, Adaptive Testing
Gongjun Xu; Zhuoran Shang – Grantee Submission, 2018
This article focuses on a family of restricted latent structure models with wide applications in psychological and educational assessment, where the model parameters are restricted via a latent structure matrix to reflect prespecified assumptions on the latent attributes. Such a latent matrix is often provided by experts and assumed to be correct…
Descriptors: Psychological Evaluation, Educational Assessment, Item Response Theory, Models
Smith, Trevor I. – Physical Review Physics Education Research, 2016
Model analysis provides a mechanism for representing student learning as measured by standard multiple-choice surveys. The model plot contains information regarding both how likely students in a particular class are to choose the correct answer and how likely they are to choose an answer consistent with a well-documented conceptual model.…
Descriptors: Probability, Multiple Choice Tests, Surveys, Teaching Methods
Stambaugh, Laura A.; Dyson, Brian E. – Journal of Research in Music Education, 2016
Two journals reflecting the interests and concerns of music educators are "Music Educators Journal (MEJ)" and "Philosophy of Music Education Review (PMER)". The purpose of this study was to explore the interests of P--12 music teachers and university faculty as represented by the topics of articles in "MEJ" and…
Descriptors: Music, Music Education, Music Teachers, Comparative Analysis
Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers