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Integrating and evaluating mathemati...

Hoole, Emily R.

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Integrating and evaluating mathematical models of assessing structural knowledge: Comparing associative network methodologies./
作者:	Hoole, Emily R.
面頁冊數:	123 p.
附註:	Adviser: Christine Demars.
Contained By:	Dissertation Abstracts International67-01B.
標題:	Education, Educational Psychology. -
電子資源:	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.
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