Computers in Human Behavior


 Cognitive Computing in Learning Systems:

Learning innovation beyond learning analytics

 Guest Editors:

  • Ernesto Damiani, Professor, EBTIC/Khalifa University, Abu Dhabi, UAE
  • Miltiadis D. Lytras, Research Professor, DEREE – The American College of Greece, Greece

Personalized Learning and Advanced Data Mining Research in Computers in Human Behavior Research are recognized in our days as two of the most promising areas of research with an estimated critical impact on the way that learners will interact with content management systems or tutoring applications. Huge investments on the domain of the Personalized Learning from well-established vendors and startup companies provide a large scale of constitutional tools and applications for an enriched user experience. The Learning Technologies domain is one of the first that will be challenged by the evolution of Cognitive Computing, Deep Learning and OpenAI. The provision of content, the design of learning contexts, the justification of effective learning scenarios and strategies are key requirements for the next generation of Personalized Learning Systems powered by Analytics. A number of Organizations and Businesses foresee the use of Data Mining for the enhancement of their innovation capabilities aiming to release the creativity and the brain capacity of their personnel.

Cognitive Computing in Learning Systems: Learning innovation beyond learning analytics are the focus of this special issue aiming to foster a scientific debate for the new era of Learning Technologies and Systems. The move toward Personalized Learning Environments will require sophisticated approaches for enhanced learning contexts with enriched digital media elements. New learning strategies will also be required for the adoption of technologies in the daily educational process beyond limitations and barriers of the traditional classroom based paradigm.

Cognitive Computing in Learning Systems: Learning innovation beyond learning analytics will provide an insight of the technologies and applications that have received growing attention in recent years from various perspectives. The thriving numbers behind their adoption and exploitation in different application contexts have captured the attention of Learning Technologies specialists, computer engineering and business researchers that, in the past years, have been trying to decipher the phenomenon of Personalized e-learning, its relation to already-conducted research, and its implications for new research opportunities that effect innovations in teaching.

The focus of the special issue is on the integration of the following research areas:

  • Cognitive Computing and Artificial Intelligence
  • Learning Analytics as it is applied to Big or Smart Data
  • Personalization in Computers in Human Behavior.

 The objective of the special issue is to communicate and disseminate recent higher education, computer engineering and business research and success stories that demonstrate the power of Cognitive Computing in Learning Systems: Learning innovation beyond learning analytics to improve the user experience with the provision of advanced Learning Analytics Services. The purpose of the special issue is to demonstrate state-of-the art approaches of Advanced Data Mining Systems in e-leaning, such as MOOCs and other innovative technologies that have had successful application worldwide and to show how new, advanced, user interaction designs, educational models and adoptive strategies can expand the sustainability frontiers in advanced applied Learning Technologies towards Smart Learning and knowledge society vision. Consequently, manuscripts are sought that touch on these aspects and extend technical and domain knowledge in the global economy. This special issue is intended to initiate a dialog between the educational, computing, business, human, behavioral, cognitive and technical views of the field that effect the Learning environment through the adoption of novel Cognitive Computing in Learning Systems: Learning innovation beyond learning analytics solutions. Novel approaches and sound technological solutions with emphasis on their impact in human behavior will be expected.


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

  • Cognitive Computing to Learning Systems
  • Machine Learning approaches to personalized Learning systems
  • Deep Learning and Reinforcement Algorithms for Learning Innovation
  • Scholarly and Scientific research customized for academic researchers to leverage Big Data Computations, Collaboration, and Data-intensive Processing in the Cloud
  • The Internet of Everything (IoE) Campus Infrastructure through Analytics Deployment
  • Architecting University, Library, and Research Digital Repositories
  • e-research, Data Curation, Management of Scholarly Identity
  • 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
  • Data-driven Learning and Assessment
  • Data Science with the aim of Learner Profiling
  • Novel approaches to Analyze Individual Student Interactions in Online Learning Activities
  • Integrating Analytics with Online Texts, Courseware, and Learning Environments to Measure Student Progress and Interaction
  • Data Analytics Platform for Detailed Reporting, Assessment, and Collaboration
  • Analytical Models on Students Data on Library use, Attendance, and Grades
  • Intelligent Student Progress Dashboard
  • Cloud-based audio and video creation tools to capture important human gestures, including voice, eye contact, and body language, which all foster an unspoken connection with learners
  • Predictive Analytics Reporting (PAR) Framework, Learning, and Transfer
  • Analytics, Business Intelligence, and Data Management for Higher Education
  • Cutting-edge Asynchronous and Synchronous Tools to Advance the State of Online Learning
  • Data-driven Evidence of Effective Blended Learning
  • Stimulated Social Activities and Critical Thinking within an Online Environment
  • The Effect of Analytics on Learning Outcomes and Advanced Learning Tasks
  • Advanced Learning Labs Powered by Analytics
  • Project-based Interactions with Attention to Mobility, Flexibility, and Multiple Device Usage
  • Integration of Semantic Web Approaches in Educational Learning Systems
  • Case Studies of Personalized Learning Using Analytics: range of educational programs, learning experiences, instructional approaches, and academic-support strategies intended to address the specific learning needs, interests, aspirations, or cultural backgrounds of individual students.
  • Advances in Online Learning Environments and Adaptive Learning Technologies
  • Visual Analytics to Identify Patterns and Processes for Mining Large Educational Datasets
  • Social Media Technologies in Education: Collaborative Environments, Collective Intelligence, Crowdfunding, Crowdsourcing, Digital Identity, Social Networks, and Tacit  intelligence
  • Visualization Technologies in Education: Case Studies on Utilizing 3D Printing/Rapid Prototyping, Augmented Reality, Information Visualization, Visual Data Analysis, Volumetric and Holographic Displays
  • Modeling Students in Massive Open Online Course