Pedagogical based Learner Model Characteristics

The personalisation and adaptation of content creation, distribution and presentation aim to increase learner quality of experience, improve the learning process and increase the learning outcomes. This paper introduces a novel Learner Model that is integrated by the NEWTON project into the NEWTELP learning platform in order to support personalisation and adaptation. The NEWTON’s Learner Model includes a multitude of learner characteristics, including pedagogical, disability, affective and multi-sensorial.

[1]  Cristina Hava Muntean,et al.  MoGAME : Motivation based Game Level Adaptation Mechanism , 2010 .

[2]  Nian-Shing Chen,et al.  Learning styles and cognitive traits - Their relationship and its benefits in web-based educational systems , 2009, Comput. Hum. Behav..

[3]  Scotty D. Craig,et al.  Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program , 2016 .

[4]  Huong May Truong Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities , 2016, Comput. Hum. Behav..

[5]  Cristina Hava Muntean,et al.  ToTCompute: A Novel EEG-Based TimeOnTask Threshold Computation Mechanism for Engagement Modelling and Monitoring , 2016, International Journal of Artificial Intelligence in Education.

[6]  Gordon I. McCalla,et al.  Artificial Intelligence in Education - Supporting Learning through Intelligent and Socially Informed Technology, Proceedings of the 12th International Conference on Artificial Intelligence in Education, AIED 2005, July 18-22, 2005, Amsterdam, The Netherlands , 2005, AIED.

[7]  Cristina Hava Muntean,et al.  Motivation Monitoring and Assessment Extension for Input-Process-Outcome Game Model , 2014, Int. J. Game Based Learn..

[8]  Quanyu Wang,et al.  Ontology-Based Ecological System Model of e-Learning , 2012 .

[9]  Cristina Hava Muntean,et al.  Measurement and Analysis of Learner’s Motivation in Game-Based E-Learning , 2012 .

[10]  Gabriel-Miro Muntean,et al.  Quality of Experience-LAOS: create once, use many, use anywhere , 2007, Int. J. Learn. Technol..

[11]  A. P. Madurapperuma,et al.  Affective e-learning model for recognising learner emotions in online learning environment , 2013, 2013 International Conference on Advances in ICT for Emerging Regions (ICTer).

[12]  Jorge Bacca,et al.  Augmented Reality Trends in Education: A Systematic Review of Research and Applications , 2014, J. Educ. Technol. Soc..

[13]  Mihaela Cocea,et al.  Disengagement Detection in Online Learning: Validation Studies and Perspectives , 2011, IEEE Transactions on Learning Technologies.

[14]  Bart Rienties,et al.  A Multi-level Longitudinal Analysis of 80,000 Online Learners: Affective-Behaviour-Cognition Models of Learning Gains , 2016 .