Identifying Knowledge Structures in Construction Innovation Research Using the Mapping Knowledge Domain Method

ICCREM 2014 ◽  
2014 ◽  
Author(s):  
Rui Liu ◽  
Xiaolong Xue ◽  
Hengqin Wu
2001 ◽  
Vol 209 (3) ◽  
pp. 277-315 ◽  
Author(s):  
Klaus Korossy ◽  
Theo Held

Summary. The knowledge structures theory has been developing highly efficient computer-based procedures for the assessment of knowledge. However, a crucial precondition of a practical application of these assessment procedures in some knowledge domain is the establishment of a psychologically valid knowledge structure capturing the solvability dependencies among items of the domain. Several approaches for establishing a knowledge structure have been developed - each with its benefits and drawbacks. In an earlier paper ( Held and Korossy, 1998 ) we contrasted an a-priori structure generated by an expert with an a-posteriori structure generated by a data-analytic method (the Item Tree Analysis) and discussed the possible contribution of data-analytic methods (such as the Item Tree Analysis) to an integrated methodology of establishing well-founded solvability structures on item sets. The current investigation takes up this line of research. Based on a large set of 21 carefully constructed problems, a theory-based knowledge modeling of a subdomain of elementary algebra has been developed and empirically examined. Moreover, the sample of observed response patterns has been taken as input for a data-analytic procedure (the Item Tree Analysis). The comparison of the theory-based knowledge structure with the data-analytically generated structure provides convincing arguments for the theory-based construction of knowledge structures. The present paper describes in detail the theoretical modeling of surmised solution dependencies and the results of the empirical examination of the model. A subsequent paper will present the aspects of applying the Item Tree Analysis to the empirical data.


Author(s):  
Morteza Akbari ◽  
Hamid Padash ◽  
Zahra Shahabaldini Parizi ◽  
Haniye Rezaei ◽  
Elmira Shahriari ◽  
...  

2021 ◽  
pp. 1-17
Author(s):  
Yinfeng Zhou ◽  
Jinjin Li ◽  
Hongkun Wang ◽  
Wen Sun

In knowledge space theory (KST), knowledge structure is an effective feature to evaluate individuals’ knowledge and guide future learning. How to construct knowledge structures is one of the key research problems in KST. At present, the knowledge structure has been generalized to the polytomous knowledge structure. This article mainly focuses on the special polytomous knowledge structures delineated by Skills, which are called fuzzy knowledge structures. We consider how to construct fuzzy knowledge structures based on the relationship between items and skills, and how to find the learning paths for specific knowledge domains. First, we construct knowledge structures in four models, which are the conjunctive model of skill maps, the disjunctive and conjunctive models of fuzzy skill maps, and the competency model of fuzzy skill multimaps. Second, we assess individuals’ skills and find the learning paths for the specific knowledge domains in the first three models. Finding the learning paths for a specific knowledge domain can guide learning and improve the learning efficiency of individuals. Finally, we analyze some data sets to show that the algorithms proposed are effective and applicable. These works can be applied to adaptive learning systems, which bring great convenience for assessing individuals’ knowledge and guiding future learning.


2013 ◽  
Vol 80 (6) ◽  
pp. 1085-1093 ◽  
Author(s):  
Ichiro Sakata ◽  
Hajime Sasaki ◽  
Masanori Akiyama ◽  
Yuriko Sawatani ◽  
Naoki Shibata ◽  
...  

2007 ◽  
Author(s):  
Eric A. Day ◽  
Matthew J. Schuelke ◽  
Jazmine Espejo ◽  
Paul R. Boatman ◽  
Vanessa Kowollik ◽  
...  

2011 ◽  
Author(s):  
Nathan M. Greenauer ◽  
Catherine Mello ◽  
Marios N. Avraamides ◽  
Jonathan W. Kelly

1997 ◽  
Author(s):  
Elke E. van der Meer ◽  
Dirk Strauch ◽  
Reinhard Beyer ◽  
Herbert Hagendorf

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