Personalised Learning - Special Issue of IJPOP

Personalised Learning - Special Issue of IJPOP

Personalised Learning Special Issue of Journal of People-Oriented Programming (IJPOP)

Ville: 
Sussex
Pays: 
Royaume_Uni
Date limite de soumission: 
31 octobre, 2014
Dates de début et fin de la manifestation: 
31 octobre, 2014

Personalised learning focuses on the individual to a greater degree than any earlier approach to learning. In doing so it has been concerned with adapting learning experiences and resources based on a wide variety of factors including personal learning history, current location and available interactive devices, cultural preference, personality, learning preferences and gender among others. A dominant source of personalisation derives from cognitive differences between individuals, whether in terms of approaches to learning or, more commonly, what knowledge is already understood and what skills have so far been mastered.

Recently however, there has been an increasing focus on the importance of affect in learning. The cognitive and the affective are deeply intertwined, both in general and in learning in particular. Affective state is not only limited to the transient emotions that might be experienced during the learning process itself (such as frustration, or pride), but also those which learners bring to a learning situation, based on familial, cultural or other influences. Despite the acknowledged importance of emotions for learning, relatively little is known about how technology enhanced learning environments can, and importantly should, respond to these emotions in ways that enhance motivation and promote learning.

Additionally, new generation learners often like to customise their learning content and learning spaces. They will personalise their interactions in the learning process, expressing themselves with their own user generated content, as much as the technology allows. And given fewer constraints than traditional learning has afforded, they will learn whatever, wherever and whenever they desire, usually intermingled with other non-learning activities.

This special issue focuses on the interplay between cognitive and affective factors in personalised learning, and will examine work that operates over two timescales – months and years vs minutes and hours. For the longer term timescale, we hope to elicit papers on the ways in which we can construct/facilitate personalised learning trajectories that take into consideration both factors. For the shorter term timescale, we hope to elicit papers on detailed pedagogical tactics that take into account the interplay between the two factors, and also the learning behaviour of new generation students. In each case, review papers and research papers (empirical studies, system evaluations, new learning technologies, technology enabled tutoring, etc.) are equally welcome.

Journal of People-Oriented Programming (IJPOP)

The international journal was founded in 2011 and is cross-discipline in range. It focuses on the user's needs and aspirations of applications in different domains. Articles published in IJPOP deal with the composition, development and customization of products for oneself, upon theory, concepts, techniques, methodologies and ultimately tools that service a market of one. (http://www.igi-global.com/journal/international-journal-people-oriented-...)

Submission Procedure

Researchers and practitioners are invited to submit abstracts before October 31, 2014 to the Guest Editors. All submissions must be original and may not be under review by another publication. Interested Authors can consult the Journal’s Guidelines for manuscript submissions at http://www.igi-global.com/Files/AuthorEditor/guidelinessubmission.pdf.
All submitted papers will be reviewed on a double-blind, peer review basis.

Please direct all submissions and enquiries to:

Judith Good & Ben du Boulay
Guest Editors
[J.Good |B.du-Boulay]@sussex.ac.uk

Benedict du Boulay & Judith Good
Department of Informatics
University of Sussex


Dernière mise à jour : 27 octobre, 2014 - 12:35