Behaviour & Information Technology Special Issue Call for Papers

Advanced Decision Making in Higher Education: Learning Analytics Research and Key Performance Indicators

Behaviour & Information Technology

Behaviour & Information Technology seeks original manuscripts for a Special Issue on “ADVANCED DECISION MAKING IN HIGHER EDUCATION: LEARNING ANALYTICS RESEARCH AND KEY PERFORMANCE INDICATORS” scheduled to appear in an early 2018 issue.

From the well-defined scientific and application domain described as Information and Communication Technologies we focus on some of the most evolutionary technologies of the last years namely Learning Analytics and Cognitive Computing. These technologies and methodologies are in the focus of this special issue aiming to foster a scientific debate for the new era of Higher Education, where academic institutions would have to develop strategies for the adoption of technologies in the daily educational process beyond limitations and barriers promoting advanced decision making capabilities.  

Learning Analytics and applications have received growing attention in recent years from various perspectives. The thriving numbers of Big Data creation in Higher Education have captured the attention of Higher Education, computer engineering and business researchers that, in the past years, have been trying to decipher the phenomenon of Higher Education Performance and Innovation, its relation to already-conducted research, and its implications for new research opportunities that effect innovations in teaching and higher education dynamics.

The current applications of Learning Analytics in Higher Education worldwide present a very interesting picture. Several medium/big scale information systems provide a variety of services to all the stakeholders in Higher Education institutions including students, professors, managers and professionals.  A key strategic shift in the focus of education is evident, from a core-knowledge oriented education to a collaborative-dynamic evolving paradigm. It seems that we are in a crossroad where the traditional classroom based model of Higher Education must be critically enriched with technology enabled added value components. Toward this direction, it is critical to reveal hidden pattern in educational data, to develop, understand and measure key performance indicators and to promote sophisticated decision making

These widely-accepted Learning Analytics systems endeavors demonstrate that a wide range of decision making capabilities are available and present a viable and robust alternative to traditional strategies to Higher Education.  In parallel several surveys in Higher Education directly link the response to students’ inefficiencies to the use of advanced analytics in Higher Education.  

The objective of the special issue is to communicate and disseminate recent higher education, computer engineering, social, behavioral and business research and success stories that demonstrate the power of Learning Analytics to improve the Quality of Higher Education and the Decision making capabilities. The purpose of the special issue is to demonstrate state-of-the art approaches of Learning Analytics and to show how new, advanced, educational models and adoption strategies can expand the sustainability frontiers in advanced applied computer engineering and knowledge management towards Smart Education and knowledge society vision.

Consequently, manuscripts are sought that touch on these aspects and extend technical and domain knowledge in the global economy and Higher Education. This special issue is intended to initiate a dialog between the educational, social, computing, business, human, and technical views of the field that effect the Higher Education environment through the adoption of novel Learning Analytics solutions. Novel Higher Education approaches and sound technological learning analytics solutions will be expected.

Submission Details

Important Dates:

  • Manuscript submission deadline: November, 20, 2017
  • Notification of Decision: December, 15, 2017
  • Submission of final revised paper: Feb, 15, 2018
  • Publication of special issue (tentative): Mid 2018 

Submission Procedure:

Authors should follow the Behaviour & Information Technology manuscript format described in the Instructions for Authors.

Manuscripts should be submitted on-line through https://mc.manuscriptcentral.com/tbit

A copy of the manuscript should also be emailed to the following email: patriop@uniovi.es

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Topics

Topics of interest include, but are not limited to, the following scope:

  • Analytics Studies and Key Performance Indicators in Higher Education
  • Advanced Machine Learning approaches for personalized Learning systems
  • Learning Cloud Analytics Research
  • Analysis of Big Data and Educational Knowledge Management
  • The Internet of Everything (IoE) for Campus-wide analytics Infrastructure 
  • Scientometrics and Library Analytics 
  • Innovations for Improving Student Retention through Predictive Modeling
  • Innovative Solutions for Student-retention Models, KPIs, Data dashboards, and Mobile Alerts to Identify At-risk Students
  • Case Studies of Ethical Use of Student Data for Learning Analytics
  • Novel approaches to Analyze Individual Student Interactions in Online Learning Activities
  • Integrating Analytics with Online Texts, Courseware, and Learning 
  • Case Studies of Personalized Learning Using Analytics
  • Visual Analytics to Identify Patterns and Processes for Mining Large Educational Datasets
  • Intelligent Student Progress Dashboard
  • Predictive Analytics Reporting (PAR) Framework, Learning, and Transfer
  • Analytics, Business Intelligence, and Data Management for Higher Education
  • Data-driven Evidence of Effective Blended Learning
  • Stimulated Social Activities and Critical Thinking within an Online Environment
  • Emerging Challenges of Higher Education - The Sustainable Education
  • Advanced Analytics for Higher Education
  • Strategies for Innovative TEL
  • TEL and Innovations in Higher Education Institutions
  • Learning Analytics for advanced learning experience
  • Data Mining in MOOCs
  • Best Practices for TEL Research
  • Large Scale MOOCs Implementations

Editorial information

  • Guest Editor: Miltiadis D. Lytras, Deree College - The American College of Greece, Greece (mlytras@acg.edu)
  • Guest Editor: Patricia Ordonez De Pablos, University of Oviedo, Spain (patriop@uniovi.es )
  • Guest Editor: Dragan Gasevic, The University of Edinburgh, United Kingdom (dragan.gasevic@ed.ac.uk )
  • Guest Editor: Naif Radi Aljohani, King Abdulaziz University, Saudi Arabia (nraljohani@kau.edu.sa)