Cognitive Big Data Analytics and Persuasive Social Influence Diffusion

CUITcnt demand ' i n I cal and global economie and the pur uit of competi t ivenes are cal l ­ ing for data-driven, trategi , . Data-dri en olut ions analyze trend . make prediction about future events. and pre. cribe what Lo do ne t in an act ionable manner. However. cogni ­ t ive and behavioral data are d ist i ngui hed by the ir mu lt ip l ic i ty and rapid change to meet e I ing and dynamic goal of indiv idual . ThL re. earch work i concerned with the ut i l ity of analyt ical o lu t ion. to s nthe ize and i n fl uence cogn i t ive and behav ioral adopt ion. We propose a mul t id imen, ional data model Lo ident ify and extract cogni t ive indicator for analy�i� and pel'. ua ive i ntervent ion . The proce tart by di covering behav ioral feature to create a cogni t i e profi le and d iagno e i ndiv idual defic iencie . Then. we develop a fuzzy c lu. ter ing algori thm that predict im i lar pattern with control led con tra int-violation to construct a ocia l _ tru ture for col laborat ive cogni t ive attainment . Thi oc ial framework fac i l i tates the deployment of novel i nfluence diffu ion approache . whereby we propose a reciprocal-weighted im i l arity funct ion and a tr iadic c lo ure approach . I n doing o. we i nve t igate contemporary ocia l network analytics to max imize influence diffu ion acro our nthe ized oc ial network. The outcome of thi oc ial comput ing approach lead to a per ua ive model to support beha ioral change and developments . The performance re­ su l t obtained from both analyt ical and experimental evaluations val idate the propo ed our data-driven trategy for per ua ive behav ioral change. In order to i l l u trate our olut ion . we elec ted h igher education appl ication domain, where our data analyt ic techn iques have hown a potent ial for maximizing cogni t ive reten­ t ion in treaml i n ing career development path . Thi i in re pon e to 2 1 st Century profesional competencies expected from h igher education to de l iver career-ready graduates who are immediately ready to meet current industry need . We deploy our data-driven olut ions across a pectrum of career development tage to bridge formal h igher education program and indu t ry. Toward c lo ing th i d igital gap, we propo e career development framework that ut i l i ze our cogni t ive analyt ic models acro three equent iaJ pha e : (I) career readi ­ nes to mea ure the general cogni t ive di po i t ion requ ired for a ucce sful career in the

[1]  John F. Welsh,et al.  Institutional strategy and information support: the role of data warehousing in higher education , 2002 .

[2]  Z. Kovacic,et al.  Predicting student success by mining enrolment data. , 2012 .

[3]  N. Ellison,et al.  Social capital, self-esteem, and use of online social network sites: A longitudinal analysis , 2008 .

[4]  Douglas T. Hall,et al.  The interplay of boundaryless and protean careers: Combinations and implications , 2006 .

[5]  Barbara Dinter,et al.  Extending the E/R Model for the Multidimensional Paradigm , 1998, ER Workshops.

[6]  Marek Hatala,et al.  A qualitative evaluation of evolution of a learning analytics tool , 2012, Comput. Educ..

[7]  Ryan D. Zimmerman.  UNDERSTANDING THE IMPACT OF PERSONALITY TRAITS ON INDIVIDUALS' TURNOVER DECISIONS: A META‐ANALYTIC PATH MODEL , 2008 .

[8]  D. Watts,et al.  Influentials, Networks, and Public Opinion Formation , 2007 .

[9]  David Guest,et al.  Who's in charge? Graduates' attitudes to and experiences of career management and their relationship with organizational commitment , 2000 .

[10]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[11]  Bora Uçar,et al.  On the minimum edge cover and vertex partition by quasi-cliques problems , 2013 .

[12]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[13]  Rebecca Ferguson,et al.  Social Learning Analytics , 2012, J. Educ. Technol. Soc..

[14]  S. Beechler,et al.  The global "war for talent" , 2009 .

[15]  Birgit Wirtz.  Persuasive Technology Using Computers To Change What We Think And Do , 2016 .

[16]  Nectaria Tryfona,et al.  starER: a conceptual model for data warehouse design , 1999, DOLAP '99.

[17]  Jie Tang,et al.  Uncovering the Formation of Triadic Closure in Social Networks , 2015, IJCAI.

[18]  Dan Pelleg,et al.  K -Means with Large and Noisy Constraint Sets , 2007, ECML.

[19]  Padhraic Smyth,et al.  Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.

[20]  Matt Glowatz,et al.  Utilising A Social Networking Site As A Learning Tool In An Academic Environment: Advancing Practice From Information-Sharing To Collaboration And Innovation (ICI) , 2013 .

[21]  Limin Fu,et al.  FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data , 2007, BMC Bioinformatics.

[22]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[23]  T. Vicsek,et al.  Clique percolation in random networks. , 2005, Physical review letters.

[24]  Ruth Deakin Crick,et al.  Learning how to learn: the dynamic assessment of learning power , 2007 .

[25]  Martin Gogolla,et al.  An Extended Entity-Relationship Model , 1994, Lecture Notes in Computer Science.

[26]  Xiaoming Fu,et al.  Mining triadic closure patterns in social networks , 2014, WWW.

[27]  P. Tynjälä,et al.  Pedagogical perspectives on the relationships between higher education and working life , 2003 .

[28]  M. Blankenship.  How Social Media Can and Should Impact Higher Education. , 2011 .

[29]  Douglas R. Vogel,et al.  Can learning be virtually boosted? An investigation of online social networking impacts , 2010, Comput. Educ..

[30]  J. Brown,et al.  Minds on Fire: Open Education, the Long Tail, and Learning 2.0 , 2008 .

[31]  Geraldine Fitzpatrick,et al.  Situated and mobile displays for reflection on shopping and nutritional choices , 2014, Personal and Ubiquitous Computing.

[32]  James Dean Brown,et al.  Choosing the Right Type of Rotation in PCA and EFA , 2009 .

[33]  Hujun Yin,et al.  Intelligent Data Engineering and Automated Learning — IDEAL 2002 , 2002, Lecture Notes in Computer Science.

[34]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[35]  Reynol Junco,et al.  The effect of Twitter on college student engagement and grades , 2011, J. Comput. Assist. Learn..

[36]  Laurian M. Chirica,et al.  The entity-relationship model: toward a unified view of data , 1975, SIGF.

[37]  K. N. Dollman,et al.  - 1 , 1743 .

[38]  Margie H. Jantti,et al.  Unlocking the Potential of Library Generated Data to Assess Value, Impact, and Influence , 2014 .

[39]  C. Gunawardena,et al.  A theoretical framework for building online communities of practice with social networking tools , 2009 .

[40]  Xin Wang,et al.  Semi-supervised K-Means Clustering by Optimizing Initial Cluster Centers , 2011, WISM.

[41]  J. Helliwell,et al.  The social context of well-being. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[42]  L. S. Vygotskiĭ,et al.  Mind in society : the development of higher psychological processes , 1978 .

[43]  Ulrik Schroeder,et al.  A reference model for learning analytics , 2012 .

[44]  Ramanathan V. Guha,et al.  Information diffusion through blogspace , 2004, SKDD.

[45]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[46]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[47]  Thomas D. Sandry,et al.  Applied Data Mining , 2005, Technometrics.

[48]  S. Gill,et al.  BUILDING TRUST , 2020, Tao te Ching.

[49]  Hassan Raza,et al.  Facebook as e-learning tool for higher education institutes , 2014 .

[50]  Gina Saliba,et al.  Blended learning , 2010, BDJ.

[51]  E. Bernays.  Manipulating Public Opinion: The Why and The How , 1928, American Journal of Sociology.

[52]  Cameron Marlow,et al.  Social network activity and social well-being , 2010, CHI.

[53]  Sijia Liu,et al.  DifFUZZY: a fuzzy clustering algorithm for complex datasets , 2010, CI 2010.

[54]  Neil Conway,et al.  Managing the career deal: the psychological contract as a framework for understanding career management, organizational commitment and work behavior , 2005 .

[55]  J. Coleman,et al.  Social Capital in the Creation of Human Capital , 1988, American Journal of Sociology.

[56]  Enrico Franconi,et al.  The GMD Data Model for Multidimensional Information: a brief introduction , 2003, SEBD.

[57]  Matthew O. Ward,et al.  XmdvTool: integrating multiple methods for visualizing multivariate data , 1994, Proceedings Visualization '94.

[58]  Noah E. Friedkin,et al.  A Structural Theory of Social Influence: List of Tables and Figures , 1998 .

[59]  Winnie Wing-Yi Chan.  A Survey on Multivariate Data Visualization , 2006 .

[60]  R. Burt.  The Social Capital of Opinion Leaders , 1999 .

[61]  Mauro Coccoli,et al.  Smarter Universities: a Vision for the Fast Changing Digital Era , 2014, DMS.

[62]  Hung-Ming Chen,et al.  Persuasive feedback model for inducing energy conservation behaviors of building users based on interaction with a virtual object , 2012 .

[63]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[64]  Rebecca Ferguson,et al.  Visualizing social learning ties by type and topic: rationale and concept demonstrator , 2013, LAK '13.

[65]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[66]  José Samos,et al.  YAM2: a multidimensional conceptual model extending UML , 2006, Inf. Syst..

[67]  Francesco De Pellegrini,et al.  Distributed k-Core Decomposition , 2013 .

[68]  K. Dewettinck,et al.  The professional career on the right track: A study on the interaction between career self-management and organizational career management in explaining employee outcomes , 2009 .

[69]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[70]  Tomer Hertz,et al.  Boosting margin based distance functions for clustering , 2004, ICML.

[71]  Franziska Frankfurter,et al.  Sociocultural Studies Of Mind , 2016 .

[72]  J. Tan.  Digital kids, analogue students : a mixed methods study of students' engagement with a school-based Web 2.0 learning innovation , 2009 .

[73]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .