Language:

Multidimensional Item Response Theory in the Style of Collaborative Filtering

Psychometrika, 2022-03, Vol.87 (1), p.266-288 [Peer Reviewed Journal]

The Psychometric Society 2021 ;2021. The Psychometric Society. ;The Psychometric Society 2021. ;Attribution ;ISSN: 0033-3123 ;EISSN: 1860-0980 ;DOI: 10.1007/s11336-021-09788-9 ;PMID: 34698979

Full text available

Citations Cited by

Actions
1. Add to My Research
2. Remove from My Research
3. E-mail
4. Print
5. Permalink
6. Citation
7. EasyBib
8. EndNote
9. RefWorks
10. Delicious
11. Export RIS
12. Export BibTeX

Title:
Multidimensional Item Response Theory in the Style of Collaborative Filtering
Author: Bergner, Yoav ; Halpin, Peter ; Vie, Jill-Jênn
Subjects: Application Reviews and Case Studies ; Application Reviews and Case Studies (ARCS) ; Assessment ; Behavioral Science and Psychology ; Cognitive science ; Collaboration ; Computer Science ; Education ; Humanities ; Humanities and Social Sciences ; Humans ; Item response theory ; Law ; Machine Learning ; Psychology ; Psychometrics ; Statistical Theory and Methods ; Statistics ; Statistics for Social Sciences ; Students ; Testing and Evaluation
Is Part Of: Psychometrika, 2022-03, Vol.87 (1), p.266-288
Description: This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course. The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative “validation” of the factor model, using auxiliary information about the popularity of items consulted during an open-book examination in the course.
Publisher: New York: Springer US
Language: English
Identifier: ISSN: 0033-3123
EISSN: 1860-0980
DOI: 10.1007/s11336-021-09788-9
PMID: 34698979
Source: ProQuest One Psychology
HAL SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access)
Hyper Article en Ligne (HAL) (Open Access)
MEDLINE
ProQuest Central

Back to results list


INSPIRE LIBRARY - TON DUC THANG UNIVERSITY	(84-028) 37 755 057	Feedback
19 Nguyen Huu Tho St. Dist.7, HCM	thuvien@tdtu.edu.vn	Feedback

Multidimensional Item Response Theory in the Style of Collaborative Filtering

The Psychometric Society 2021 ;2021. The Psychometric Society. ;The Psychometric Society 2021. ;Attribution ;ISSN: 0033-3123 ;EISSN: 1860-0980 ;DOI: 10.1007/s11336-021-09788-9 ;PMID: 34698979

Searching Remote Databases, Please Wait