Psycho-pedagogical Mash-Up Design for Personalising the Learning Environment

Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings

“…Nussbaumer et al (2012) proposed a mashup recommender for personalized learning environments, which uses a taxonomy of learning activities and recommends widgets in order to support learners' performance of different cognitive and meta-cognitive learning activities. Similarly, Berthold et al (2011) identified principles for a mashup design in personalized learning environments and the importance of aligning widgets to psycho-pedagogical information in order to provide learners with meaningful recommendations and guide them in a self-regulated learning process. Kaochar (2011) performed behavioural studies that aimed to understand human teaching patterns, as a basis to develop future human-robot interaction systems.…”

“…This comes from the focus of a workshop series on Personalization Approaches in Learning Environments (PALE) held annually in conjunction with the conference on User Modeling, Adaptation and Personalization (UMAP) over the last five years, which considered the different and complementary perspectives in which personalization can be addressed in learning environments. The scope includes: i) intelligent tutoring systems (Janning et al 2016;Arevalillo-Herráez et al 2014;Arevalillo-Herráez et al 2013;Costa et al 2012), ii) educational recommender systems (Greer et al 2015;Henning et al 2014;Labaj and Bieliková 2013;Manjarrés-Riesco et al 2013;Nussbaumer et al 2012;Roldan et al 2011;Berthold et al 2011;Thai-Nghe et al 2011;Minguillon et al 2011), iii) learning management systems (Tang and Yacef 2015;Chacón-Rivas et al 2015), iv) personal learning environments Berthold et al 2011), v) educational games (Ghergulescu and Muntean 2016;Pentel 2015;Leite et al 2011;Frias-Martinez and Virseda 2011;Muir et al 2011), vi) agent-based learning environments (Dennis et al 2016;Tamayo and Perez-Marin 2012;Ginon et al 2012;Redondo-Hernandez and Perez-Marin 2011), vii) multi-user virtual environments (Ocumpaugh et al 2014), and viii) other ad-hoc approaches (Sawadogo et al 2014;Koch et al 2013). …”

“…Many sub-fields related to Artificial Intelligence in Education (AIED) have been considered, such as: i) knowledge representation (Khajah et al 2014;ArevalilloHerráez et al 2014), ii) self-regulated learning (Tang and Yacef 2015;Tang and Kay 2014;Nussbaumer et al 2012;Berthold et al 2011), iii) instructional design (Tintarev et al 2015), iv) educational data mining (Ming and Ming 2012;Kaochar 2011;FriasMartinez and Virseda 2011;Thai-Nghe et al 2011), v) collaborative learning (Lobo et al 2014;Roldan et al 2011), and vi) affective computing (Pentel 2015;Bixler et al 2014;Arevalillo-Herráez et al 2014;Ocumpaugh et al 2014;Arevalillo-Herráez et al 2013;Manjarrés-Riesco et al 2013;Dennis et al 2012;Leite et al 2011), which bring together complementary perspectives from computer science, education, psychology or other related fields.…”

Exploring the design of a social learning platform for supporting users' e-teaching skill development

“…One participant could not find his way through the material, making no attempt to search. As the users have different levels of digital literacy skills, the design of the SL platform should be personalized for different user groups, that is, right for the particular learner [7] matching learning techniques to widgets [8].…”

“…Webb and Renshaw in [7] suggest five or six participants for an eye-tracking study and Nielsen [8] says eight is typical for usability testing. Participants attended a face-to-face introduction to teaching in higher education (HE), a demonstration of the SL functions and a hands-on session to produce their own teaching material.…”