Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/16527
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars
Authors: Hussain, Amir
Niazi, Muaz A
Contact Email: amir.hussain@stir.ac.uk
Keywords: Agent-based modeling
Cognitive development
Cognitive agent-based computing
Complex adaptive system
Hirsch index
Complex networks
Issue Date: Mar-2014
Publisher: Springer
Citation: Hussain A & Niazi MA (2014) Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars, Cognitive Computation, 6 (1), pp. 113-124.
Abstract: Understanding the cognitive evolution of researchers as they progress in academia is an important but complex problem; one that belongs to a class of problems, which often require the development of models to gain further understanding of the intricacies of the domain. The research question that we address in this paper is: how to effectively model this temporal cognitive mental development of prolific researchers? Our proposed solution is based on noting that the academic progression and notability of a researcher are linked with a progressive increase in the citation count for the scholar's refereed publications, quantified using indices such as the Hirsch index. We propose the use of an yearly increment of a scholar's cognition quantifiable by means of a function of the scholar's citation index, thereby considering the index as an indicator of the discrete approximation of the scholar's cognitive development. Using validated agent-based modeling, a paradigm presented as part of our previous work aimed at the development of a cognitive agent-based computing framework, we present both formal as well as visual agent-based complex network representations for this cognitive evolution in the form of a temporal cognitive level network model. As proof of the effectiveness of this approach, we demonstrate the validation of the model using historic data of citations.
Type: Journal Article
URI: http://hdl.handle.net/1893/16527
DOI Link: http://dx.doi.org/10.1007/s12559-013-9219-y
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Computing Science - CSM Dept
Bahria University

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