Learning in dynamic decision making: The usability process

Usability of websites is an important issue for any entity operating in the virtual environment. Dynamic decision refers to the ability to choose (evaluate) between different actions at different points in time, in order to control and optimize performance. Currently, increasing attention is paid to the role of informal learning in the adaptation of learning to individual needs and circumstances in order to maximize knowledge. This paper approaches usability in the context of the theory of dynamic decisions. In the authors' view, the usability evaluation of a website becomes efficient on condition that it is repeated over time with the same group of individuals, resulting in a learning situation. The experiment consisted of measuring usability on a sample of individuals (experts) at consecutive time points to determine the degree of similarity of their behavior during the evaluation process. Starting from these assumptions, we demonstrated that usability may be considered a dynamic process, which could be very useful in reorganizing websites by identifying areas of intervention for the purpose of allowing users to learn and thus getting maximum effect from dynamic decisions.

[1]  Ronen Feldman,et al.  The Data Mining and Knowledge Discovery Handbook , 2005 .

[2]  Jakob Nielsen,et al.  Eyetracking Web Usability , 2009 .

[3]  Robert C. Williges,et al.  Criteria For Evaluating Usability Evaluation Methods , 2001, Int. J. Hum. Comput. Interact..

[4]  C. Spencer Visual decision making , 1987 .

[5]  Mieke van der Bijl-Brouwer,et al.  Strategies to design for dynamic usability , 2009 .

[6]  Patrick W. Jordan,et al.  An Introduction to Usability , 1998 .

[7]  J. Dorado,et al.  Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions. , 2010, Journal of proteome research.

[8]  A. Tversky Features of Similarity , 1977 .

[9]  Alfred Ultsch,et al.  Self Organizing Neural Networks perform different from statistical k-means clustering , 2003 .

[10]  Thomas Spyrou,et al.  Evaluating Usability Evaluation Methods: Criteria, Method and a Case Study , 2007, HCI.

[11]  Donald A. Norman,et al.  Emotional design , 2004, UBIQ.

[12]  H. Simon,et al.  The Central Role of Learning in Cognition , 2012 .

[13]  Morten Hertzum,et al.  The evaluator effect in usability tests , 1998, CHI Conference Summary.

[14]  Ramona Lacurezeanu,et al.  Continuous Training Possibilities in a Company Through Blended Learning , 2012 .

[15]  Harris Papadopoulos,et al.  A Soft Computing Approach for Osteoporosis Risk Factor Estimation , 2010, AIAI.

[16]  Humberto González-Díaz,et al.  Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. , 2010, Bioorganic & medicinal chemistry.

[17]  Inderjit S. Dhillon,et al.  Concept Decompositions for Large Sparse Text Data Using Clustering , 2004, Machine Learning.

[18]  Shuliang Wang,et al.  Data Mining and Knowledge Discovery , 2005, Mathematical Principles of the Internet.

[19]  Morten Hertzum,et al.  The Evaluator Effect: A Chilling Fact About Usability Evaluation Methods , 2001, Int. J. Hum. Comput. Interact..

[20]  Noam Tractinsky,et al.  Evaluating the consistency of immediate aesthetic perceptions of web pages , 2006, Int. J. Hum. Comput. Stud..

[21]  Cleotilde Gonzalez,et al.  Learning in a Dynamic Decision Making Task : The Recognition Process , 2002 .

[22]  Gitte Lindgaard,et al.  Attention web designers: You have 50 milliseconds to make a good first impression! , 2006, Behav. Inf. Technol..

[23]  Steven M. Belz,et al.  The user action framework: a reliable foundation for usability engineering support tools , 2001, Int. J. Hum. Comput. Stud..

[24]  Mounir Boukadoum,et al.  Hidden Neural Network for Complex Pattern Recognition: A Comparison Study with Multi- Neural Network Based Approach , 2013 .

[25]  Francisco J. Prado-Prado,et al.  Multi-Target Spectral Moment QSAR vs. ANN for antiparasitic drugs against different parasite species , 2010 .

[26]  J. Sethuraman Soft Computing Approach for Bond Rating Prediction , 2006 .

[27]  Vasile Paul Bresfelean,et al.  Student profile ergonomically adapted to e-learning. a data clustering and statistical analysis based survey , 2011 .

[28]  Lorenzo Cantoni,et al.  Connecting Usages with Usability Analysis through the User Experience Risk Assessment Model: A Case Study in the Tourism Domain , 2011, HCI.