| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0468589 |
| utime |
20240103213321.8 |
| mtime |
20170109235959.9 |
| title
(primary) (eng) |
Student Skill Models in Adaptive Testing |
| specification |
| page_count |
12 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0462433 |
| ISSN |
Proceedings of the Eighth International Conference on Probabilistic Graphical Models |
| title
|
Proceedings of the Eighth International Conference on Probabilistic Graphical Models |
| page_num |
403-414 |
| publisher |
| place |
Brookline |
| name |
Microtome Publishing |
| year |
2016 |
|
| editor |
|
| editor |
|
| editor |
| name1 |
Polpo de Campos |
| name2 |
C. |
|
|
| keyword |
Bayesian networks |
| keyword |
computerized adaptive testing |
| keyword |
item response theory |
| keyword |
generalised linear models |
| author
(primary) |
| ARLID |
cav_un_auth*0329423 |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| full_dept |
Department of Decision Making Theory |
| share |
50 |
| name1 |
Plajner |
| name2 |
Martin |
| institution |
UTIA-B |
| country |
CZ |
| garant |
K |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101228 |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| full_dept |
Department of Decision Making Theory |
| share |
50 |
| name1 |
Vomlel |
| name2 |
Jiří |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0332303 |
| project_id |
GA16-12010S |
| agency |
GA ČR |
| country |
CZ |
|
| project |
| ARLID |
cav_un_auth*0340587 |
| project_id |
SGS16/175/OHK3/2T/14 |
| agency |
Studentské grantová soutěž ČVUT |
| country |
CZ |
|
| abstract
(eng) |
This paper provides a common framework, a generic model, for Computerized Adaptive Testing (CAT) for different model types. We present question selection methods for CAT for this generic model. We use three different types of models, Item Response Theory, Bayesian Networks, and Neural Networks, that instantiate the generic model. We illustrate the usefulness of a special model condition – the monotonicity – and discuss its inclusion in these model types. With Bayesian networks we use specific type of learning using generalized linear models to ensure the monotonicity. We conducted simulated CAT tests on empirical data. Behavior of individual models was assessed based on these tests. The best performing model was the BN model constructed by a domain expert; its parameters were learned from data under the monotonicity condition. |
| action |
| ARLID |
cav_un_auth*0343879 |
| name |
International Conference on Probabilistic Graphical Models 2016 /8./ |
| dates |
20160906 |
| mrcbC20-s |
20160909 |
| place |
Lugano |
| country |
CH |
|
| RIV |
JD |
| reportyear |
2017 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0269442 |
| confidential |
S |
| arlyear |
2016 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0462433 Proceedings of the Eighth International Conference on Probabilistic Graphical Models Microtome Publishing 2016 Brookline 403 414 JMLR: Workshop and Conference Proceedings vol. 52 1938-7228 |
| mrcbU67 |
340 Antonucci A. |
| mrcbU67 |
340 Corani G. |
| mrcbU67 |
340 Polpo de Campos C. |
|