東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Integrating and evaluating mathemati...

Hoole, Emily R.

Linked to FindBook

Google Book

Amazon

博客來

Integrating and evaluating mathematical models of assessing structural knowledge: Comparing associative network methodologies.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Integrating and evaluating mathematical models of assessing structural knowledge: Comparing associative network methodologies./
Author:	Hoole, Emily R.
Description:	123 p.
Notes:	Adviser: Christine Demars.
Contained By:	Dissertation Abstracts International67-01B.
Subject:	Education, Educational Psychology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3200713
ISBN:	9780542496202

Integrating and evaluating mathematical models of assessing structural knowledge: Comparing associative network methodologies.
Hoole, Emily R.

Integrating and evaluating mathematical models of assessing structural knowledge: Comparing associative network methodologies. - 123 p.

Adviser: Christine Demars.

Thesis (Ph.D.)--James Madison University, 2005.

Structural knowledge assessment is a promising area of study for curriculum design and teaching, training, and assessment, but many issues in the field remain unresolved. This study integrates an associative network method, the Power Algorithm from the field of text comprehension into the realm to structural knowledge assessment by comparing it to an already established associative network method, Pathfinder Analysis. Faculty members selected the fifteen most important concepts in Classical Test Theory. Students and faculty then completed similarity ratings for each concept pair using an online survey program, SurveyMonkey. A variety of similarity measures for the Power Algorithm networks and Pathfinder networks were used to predict course performance in a graduate level measurement class. For the Power Algorithm networks, the correlation between the student and expert links between the concepts in the associative network were computed, along with the congruence coefficient between the associative network links. Finally, a measure of network coherence, harmony, was calculated for each Power Algorithm network. For the Pathfinder networks, the NETSIM measure of similarity between the student and expert networks was computed. An unusual finding for the Pathfinder measure of similarity, NETSIM, was uncovered, in which NETSIM values negatively predicted course performance. Results indicate that the Power Algorithm similarity measures did not uncover a latent structure in the data, but that network harmony might possibly serve as an indicator of quality for knowledge structures. Further investigation of the use of harmony in structural knowledge assessment is recommended.

ISBN: 9780542496202Subjects--Topical Terms:

1017560
Education, Educational Psychology.

Integrating and evaluating mathematical models of assessing structural knowledge: Comparing associative network methodologies.
LDR:02649nam 2200289 a 45 001 969890
005 20110920
008 110921s2005 eng d
020 $a 9780542496202
035 $a (UnM)AAI3200713
035 $a AAI3200713
040 $a UnM $c UnM
100 1 $a Hoole, Emily R. $3 1293946
245 1 0 $a Integrating and evaluating mathematical models of assessing structural knowledge: Comparing associative network methodologies.
300 $a 123 p.
500 $a Adviser: Christine Demars.
500 $a Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0595.
502 $a Thesis (Ph.D.)--James Madison University, 2005.
520 $a Structural knowledge assessment is a promising area of study for curriculum design and teaching, training, and assessment, but many issues in the field remain unresolved. This study integrates an associative network method, the Power Algorithm from the field of text comprehension into the realm to structural knowledge assessment by comparing it to an already established associative network method, Pathfinder Analysis. Faculty members selected the fifteen most important concepts in Classical Test Theory. Students and faculty then completed similarity ratings for each concept pair using an online survey program, SurveyMonkey. A variety of similarity measures for the Power Algorithm networks and Pathfinder networks were used to predict course performance in a graduate level measurement class. For the Power Algorithm networks, the correlation between the student and expert links between the concepts in the associative network were computed, along with the congruence coefficient between the associative network links. Finally, a measure of network coherence, harmony, was calculated for each Power Algorithm network. For the Pathfinder networks, the NETSIM measure of similarity between the student and expert networks was computed. An unusual finding for the Pathfinder measure of similarity, NETSIM, was uncovered, in which NETSIM values negatively predicted course performance. Results indicate that the Power Algorithm similarity measures did not uncover a latent structure in the data, but that network harmony might possibly serve as an indicator of quality for knowledge structures. Further investigation of the use of harmony in structural knowledge assessment is recommended.
590 $a School code: 1357.
650 4 $a Education, Educational Psychology. $3 1017560
650 4 $a Psychology, Experimental. $3 517106
650 4 $a Psychology, Psychometrics. $3 1017742
690 $a 0525
690 $a 0623
690 $a 0632
710 2 0 $a James Madison University. $3 1023976
773 0 $t Dissertation Abstracts International $g 67-01B.
790 $a 1357
790 1 0 $a Demars, Christine, $e advisor
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3200713