Midterm Paper
Impacts of Analysts’ Cognitive Styles on the Analytic Process
Eugene Santos, Jr.*, Hien Nguyen+, Fei Yu*, Deqing Li*, John T. Wilkinson*,
*Dartmouth College
Thayer School of Engineering
8000 Maclean
Hanover, NH 03755
Eugene.Santos.Jr@Dartmouth.EDU
+University of Wisconsin-Whitewater
Dept. of Math and Computer Science
800 W. Main Street
Whitewater, WI 53190 nguyenh@uww.edu Introduction
With the increasing availability of online resources, collecting information on the Web and analyzing data play important roles in today’s problem solving task. 1. Baum L. E., Petrie T. , Soules G. , and Weiss N. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics, 41 (1), pp. 164-171. 1970. It has be found that a user’s cognitive styles affect their searching/browsing behaviors, their assessment of the relevancy of a web page, and their decision making processes. However, little research has been conducted that explore the impacts of a user’s cognitive styles on an analytical process. The challenges here are three fold: First, popular information retrieval and filtering systems on the Web do not take into account a user’s cognitive styles even though this factor is known to affect a user’s information seeking behaviors, and a user’s assessment of text summarization. Secondly, the unavailability of well-defined and relevant testbeds poses critical challenges to the process of capturing the goal-oriented and compound nature of an analytic process. Third, the open and diverse nature of the Web creates uncontrollable noise/influences such as environmental factors (variations in interfaces, dynamics of web information, etc.) or even credibility of participants that can affect the results of such a study. Therefore, in this paper, we explore the problem of the impacts of a user’s cognitive styles on analytical processes in the domain of intelligence analysis. Our results
Eugene Santos, Jr.*, Hien Nguyen+, Fei Yu*, Deqing Li*, John T. Wilkinson*,
*Dartmouth College
Thayer School of Engineering
8000 Maclean
Hanover, NH 03755
Eugene.Santos.Jr@Dartmouth.EDU
+University of Wisconsin-Whitewater
Dept. of Math and Computer Science
800 W. Main Street
Whitewater, WI 53190 nguyenh@uww.edu Introduction
With the increasing availability of online resources, collecting information on the Web and analyzing data play important roles in today’s problem solving task. 1. Baum L. E., Petrie T. , Soules G. , and Weiss N. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics, 41 (1), pp. 164-171. 1970. It has be found that a user’s cognitive styles affect their searching/browsing behaviors, their assessment of the relevancy of a web page, and their decision making processes. However, little research has been conducted that explore the impacts of a user’s cognitive styles on an analytical process. The challenges here are three fold: First, popular information retrieval and filtering systems on the Web do not take into account a user’s cognitive styles even though this factor is known to affect a user’s information seeking behaviors, and a user’s assessment of text summarization. Secondly, the unavailability of well-defined and relevant testbeds poses critical challenges to the process of capturing the goal-oriented and compound nature of an analytic process. Third, the open and diverse nature of the Web creates uncontrollable noise/influences such as environmental factors (variations in interfaces, dynamics of web information, etc.) or even credibility of participants that can affect the results of such a study. Therefore, in this paper, we explore the problem of the impacts of a user’s cognitive styles on analytical processes in the domain of intelligence analysis. Our results
References: 1. Baum L. E., Petrie T. , Soules G. , and Weiss N. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics, 41 (1), pp. 164-171. 1970. 2. Blei, D. M.; Ng, Andrew Y.; Jordan, Michael I; Lafferty, J. Latent Dirichlet allocation. Journal of Machine Learning Research, 3,pp. 993–1022. 2003. 3. Chen, Y. S. and Macredie, D. R. Cognitive styles and hypermedia navigation: Development of a learning model. Journal of the American Society for Information Science and Technology, 53(1), pp. 3-15. 2002.