TRAILS Deliverable 1.1 -
An e-Learning Perspective of Cognitive and Pedagogical Aspects of Trails
(14/7/2004)
Learners' observable behaviour in following trails of learning objects
can be interpreted at a cognitive level, and, vice versa, cognitive assumptions
will have impact on the observable trails. This implies a mutual dependence
between observable and non-observable, or latent trails. The observable
trails consist of sequences of learning objects, and describe the way
in which users navigate through a learning environment. The (latent) cognitive
trails consist of sequences of cognitive states that a learner can be
in, and which may represent the learner's preferences, expectations, skills,
or some prior knowledge.
This document provides a theoretical framework that models the relationship
between the observable and cognitive trails in terms of a belief network
that can be viewed as a variant of a hidden Markov model. A worked example
shows that this theory may be employed to infer the state of knowledge
of a learner navigating in an e-learning environment, and we consider
still more potential applications of the framework in various learning
scenarios, and web-usage in general.
TRAILS Deliverable 1.2 - Cognitive and Pedagogical Aspects of Trails:
A Case Study (31/12/2004)
In this deliverable we elaborate the Markov chain model (from Deliverable
1.1) interlinking the observable trails of learning objects with the associated
latent trails in cognitive space in order to make it ready for application.
Within a general scenario we develop methods that allow for identifying
the user’s cognitive trail, for estimating the model parameters,
for validating the model, and for evaluating the effectiveness of trails.
Moreover, we devise a method for semi-automatic construction of a cognitive
space for a given learning environment. Application of these methods is
exemplified in two case studies, which are an English grammar course and
a course teaching basics in the SQL query language. We demonstrate how
the methods are put to work by means of web data mining techniques in
connection with pre- and post-assessments of the users’ cognitive
states before and after interacting with the learning environment. The
deliverable concludes with a discussion of other potential applications,
for instance in general web-usage and e-commerce, and possible extensions
of the model to further improve its predictive power.
TRAILS Deliverable 2.1 - Trails of Digital
and Non-Digital Learning Objects (14/7/2004)
This document investigates scenarios where learners navigate (follow trails)
through environments containing digital and non-digital learning objects,
considers some user requirements of systems designed to support the use
of trails and presents a possible classification of different types of
trail to form an ontology of trails. Separate sections include investigations
of trails in different pedagogical approaches, trails in mobile learning
scenarios, trails in computer conferencing and the provision of adaptive
navigation support. A case study of using the IMS-LD to work with trails
is also included.
TRAILS Deliverable 2.2 - Visualising Trails as a Means for Fostering Reflection
(31/12/2004)
The focus in this deliverable is on visualisations of trails as a means
for fostering reflection. The trails of learners in two different user
studies are visualised in several ways, each visualisation providing its
own specific benefits. We investigate what these benefits are, and what
role the visualisation of trails can play in fostering reflection in learners
on what they have done and learned. We propose a metadata profile for
trails that contains all the descriptive fields necessary to build a visualisation
of a trail that can easily be used and interpreted by a learner or teacher.
TRAILS Deliverable 3.1 - Learning Objects
in the Form of Code (30/9/2004)
Learning objects in the form of computer code raise unique questions,
particularly issues relating to their re-use by second parties who did
not originally author the code. A three-layered, learning objective-based
design approach is described, with the aim to produce learning objects
with rich pedagogical content that can be easily re-used. The potential
of increasing the re-usability of learning objects through the deployment
of large scale LO repositories is discussed, taking advances in modern
distributed computing platforms (web services, peer-to-peer and Grid technologies)
into consideration as possible platforms to support such LO repositories.
Re-use in the three different arenas of formal education, business and
a research-based virtual organisation is discussed, including some possible
solutions to the difficulties of re-use, both cultural and technical,
that arise in these fields.
TRAILS Deliverable 3.2 - Case Studies of Learning Objects in the Form
of Code (31/12/2004)
In this document presents a case study of trails in the field of image
and signal processing, with the learning at issue being that of PhD students,
post-doctoral research workers, and similar researchers. Image and signal
processing programs have been packaged into coherent groups. Learning
trails give structure to an otherwise daunting array of program packages.
The information provided is to help users navigate and make sense of the
packages, and the many program options available. In the vocabulary of
our taxonomy of Trails the work of this deliverable is an authored LO
trail.
TRAILS Deliverable 4.1 - Personalised Trails and Learner Profiling within
e-Learning Environments (31/12/2004)
This deliverable focuses on personalisation and personalised trails. We
begin by introducing and defining the concepts of personalisation and
personalised trails. Personalisation requires that a user profile be stored,
and so we assess currently available standard profile schemas and discuss
the requirements for a profile to support personalised learning. We then
review techniques for providing personalisation and some systems that
implement these techniques, and discuss some of the issues around evaluating
personalisation systems. We look especially at the use of learning and
cognitive styles to support personalised learning, and also consider personalisation
in the field of mobile learning, which has a slightly different take on
the subject, and in commercially available systems, where personalisation
support is found to currently be only at quite a low level. We conclude
with a summary of the lessons to be learned from our review of personalisation
and personalised trails.
TRAILS Deliverable 4.2 - Collaborative trails in e-Learning environments
(31/12/2004)
This deliverable focuses on collaboration within groups of learners, and
hence collaborative trails. We begin by reviewing the theoretical background
to collaborative learning and looking at the kinds of support that computers
can give to groups of learners working collaboratively, and then look
more deeply at some of the issues in designing environments to support
collaborative learning trails and at tools and techniques, including collaborative
filtering, that can be used for analysing collaborative trails. We then
review the state-of-the-art in supporting collaborative learning in three
different areas – experimental academic systems, systems using mobile
technology (which are also generally academic), and commercially available
systems. The final part of the deliverable presents three scenarios that
show where technology that supports groups working collaboratively and
producing collaborative trails may be heading in the near future.