Communication of information in the digital age among social sciences students: Uncovering a synthetic indicator of performance

Purpose The purpose of this paper is to analyze the informational behavior of a group of future professionals in the field of social sciences (SS), in terms of their competence in the communication–dissemination of information. Design/methodology/approach The IL-HUMASS, EVALCI/KN and EVALCI/SK tests regarding the affective (attitudes, motivations) and cognitive (knowledge, skills) dimensions are distributed to a stratified sample of five universities and eight degree courses in Spain. Infographics and non-parametric methods allow to compare the disciplinary profiles with regard to gender, academic course and academic degrees. An Information Literacy Communication synthetic indicator based on structural equation modeling includes the subjective and effective dimensions to measure the holistic learning outcomes in communication–dissemination of information. Findings Significant differences regarding the informational behavior of future professionals in SS are discovered. The synthetic indicator allows academic degrees to be ranked in order to identify those in need of initiatives aimed at improving communication–dissemination competence. Practical implications Findings must be taken into account to design effective learning programs. This methodological approach can be expanded to scientific and academic environments. Originality/value The paper puts forward the first evidence-based study on communication–dissemination competence among future SS professionals, as no similar research has been found in the scientific literature. It is also the first time that the definition of a predictive performance indicator, based on a powerful statistical methodology, has been proposed.

[1]  María Pinto,et al.  An Approach to the Internal Facet of Information Literacy Using the IL-HUMASS Survey. , 2011 .

[2]  R. D. Bock,et al.  Analysis of covariance structures , 1966, Psychometrika.

[3]  María Rosaura José A. Aurora Ximo Susana David Carmen Ro Pinto,et al.  Attitudes toward Information Competency of University Students in Social Sciences , 2016 .

[4]  J. Drisko Holistic Competence and Its Assessment , 2015 .

[5]  E. Meschi,et al.  A new dataset on educational inequality , 2010 .

[6]  Jaap Scheerens,et al.  Measuring Educational Quality by Means of Indicators , 2011 .

[7]  J. Hair Multivariate data analysis : a global perspective , 2010 .

[8]  Rebecca S. Albitz The What and Who of Information Literacy and Critical Thinking in Higher Education , 2007 .

[9]  Emma Coonan A New Curriculum for Information Literacy (ANCIL)- Teaching learning: perceptions of information literacy , 2011 .

[10]  Jens-Christian Smeby,et al.  Measuring learning outcomes , 2017 .

[11]  R. Kline Principles and practice of structural equation modeling, 4th ed. , 2016 .

[12]  L. Wang Sociocultural Learning Theories and Information Literacy Teaching Activities in Higher Education , 2007 .

[13]  B. Cope,et al.  The Things You Do to Know: An Introduction to the Pedagogy of Multiliteracies , 2015 .

[14]  Karen Bordonaro,et al.  Exploring the connections between information literacy and writing for international students , 2008 .

[15]  David F. Midgley,et al.  Formative versus reflective measurement models: two applications of formative measurement | NOVA. The University of Newcastle's Digital Repository , 2008 .

[16]  Maria Pinto,et al.  Design of the IL-HUMASS survey on information literacy in higher education: A self-assessment approach , 2010, J. Inf. Sci..

[17]  Daniele Archibugi,et al.  The technological capabilities of nations: the state of the art of synthetic indicators , 2009 .

[18]  Thomas P. Mackey,et al.  Metaliteracy: Reinventing Information Literacy to Empower Learners , 2014 .

[19]  Joyce L. Ogburn Lifelong learning requires lifelong access Reflections on the ACRL Plan for Excellence , 2011 .

[20]  Dora Sales,et al.  Interactive Self-Assessment Test for Improving and Evaluating Information Competence , 2010 .

[21]  Yutao Sun,et al.  Measuring international trade-related technology spillover: a composite approach of network analysis and information theory , 2012, Scientometrics.

[22]  David Pace,et al.  Decoding the Disciplines: A Model for Helping Students Learn Disciplinary Ways of Thinking. , 2004 .

[23]  María Pinto,et al.  A Diagnosis of the Levels of Information Literacy Competency among Social Sciences Undergraduates , 2017 .

[24]  M. Browne,et al.  Alternative Ways of Assessing Model Fit , 1992 .

[25]  D. Sales Towards a student-centred approach to information literacy learning: A focus group study on the information behaviour of translation and interpreting students , 2008 .

[26]  Edward E. Rigdon,et al.  Proportional structural effects of formative indicators , 2008 .

[27]  Maria Pinto,et al.  Uncovering information literacy’s disciplinary differences through students’ attitudes: An empirical study , 2015, J. Libr. Inf. Sci..

[28]  Stephanie Davis-Kahl,et al.  Engaging undergraduates in scholarly communication: Outreach, education, and advocacy , 2012 .

[29]  Thomas P. Mackey,et al.  Proposing a Metaliteracy Model to Redefine Information Literacy , 2013 .

[30]  Mark Wilson,et al.  A Competence Model for Environmental Education , 2014 .

[31]  F.-J. García-Marco The Relevance of Communicative Competence in the Context of Information Literacy Programs , 2017 .

[32]  Teodoro Luque-Martínez,et al.  Constructing a synthetic indicator of research activity , 2016, Scientometrics.

[33]  Thomas P. Mackey,et al.  Reframing Information Literacy as a Metaliteracy , 2011, Coll. Res. Libr..