Building a knowledge-based recommender
for inclusive eLearning scenarios
Olga C. SANTOS1 and Jesus G. BOTICARIO
aDeNu Research Group. Artificial Intelligence Department. UNED
Calle Juan del Rosal, 16. Madrid 28040. Spain
{ocsantos, jgb}@dia.uned.es
Abstract. When building a knowledge-based recommender along the eLearning
life cycle, the following issues have to be considered: a) the user interface design
of the tools required, b) the process to design/generate the recommendations, c) the
process to select the appropriate recommendations, and d) the management of the
users’ interactions. We are defining a user-centered evaluation approach that copes
with those issues and drives the recommender building process in three
consecutive steps: 1) elicitation of pedagogically sound recommendations
validated by users with a collaborative review, 2) acquisition and validation of the
user features to select the appropriate recommendations for the current context,
and 3) analysis of the recommendations provided and evaluation of their impact on
the user.
Keywords. Recommender systems, Life Cycle of eLearning, Accessibility.
Introduction
Building learning systems that care about both learners and tutors and have a good
understanding of the variety of learning contexts is a complex task. However, the goal
should not be to focus on the system itself, but on fitting this system into the full life
cycle of eLearning in a way that provides a personalized support for its users. The real
drivers of the learning process are the users and their evolving needs. These needs can
be of varied nature. Users should not only be supported in their learning needs, but also
on the accessibility preferences in accessing the course contents and carrying out the
course activities.
Our approach is based on the idea of combining design and runtime adaptations.
According to this approach, adaptations should be applied along the full life cycle of
eLearning making a pervasive use of standards to support users in the process. The idea
behind is that adaptation is not an idea that can be plugged in a learning environment,
but a process that influences the full life cycle of learning, which consists on four steps
where the user (and not the system) is the focus. Moreover, this user-centered approach
relies on an appropriate usage of the technology that removes the accessibility barriers
that users with disabilities may face if the appropriate support is not provided. The goal
is to provide an inclusive and personalized support.
1
Corresponding Author.
In this context, our research is focused on a recommendations model [1] that
influences the life cycle of eLearning and can be used to build a knowledge-based
recommender system to provide adaptive capabilities to existing learning management
systems (LMS). To achieve our goal, we have proposed a methodological approach to
drive its development and validation [2]. In this paper, we comment on the four issues
that we have identified as relevant when building a knowledge-based recommender and
how they are mapped against the life cycle of eLearning.
1. Relevant issues and steps of the recommender building process
In the educational domain, recommendations should be pedagogically guided instead of
by learner’s taste [3]. Thus, a knowledge-based recommender, which uses knowledge
about users and the domain, [4] was chosen as the basis for our approach. That
knowledge-based approach aims at generating suitable recommendations and reasoning
about what elements of the domain meet the user’s requirements.
We came up with the following issues when building a knowledge-based
recommender for inclusive eLearning scenarios::
• The user interface design of the tools. There is a need for an authoring tool
to create/view/update/delete recommendations by tutors and a player to
present them to the user when appropriate, from within the LMS user interface.
• The process to design/generate the recommendations. A twofold approach
is considered. On a first stage, pedagogically-oriented recommendations can
be designed by human experts who have experience in online courses
designed for all. In this stage, a bank of recommendations can be constructed
by individual proposals of the experts and a peer-reviewing process of the
proposed recommendations applying user-centered design methods. On a
second stage, the recommendations initially proposed by the experts can be
tuned and complemented from the usage experience in the course, by applying
some artificial intelligent techniques to the interaction data tracked by the
LMS modify the values of the elements of the model initially provided by the
experts. Moreover, these algorithms can also identify troublesome or
promising situations and suggest the tutor to think of appropriate
recommendations.
• The process to select the appropriate recommendations. This process
should take into account the user features, which include individual
preferences (such as learning styles or accessibility preferences) and the
progress in the course (in terms of competences achieved), as well as the
particular context in which the user is (e.g. the learning objectives that are
being worked in the course, users on-line, the capabilities of the device used).
• The management of the users’ interactions. The users’ interactions can be
gathered in different ways. On the one hand, explicit information obtained
from surveys, assessments, questionnaires and tests, or ratings given to objects
available in the LMS. On the other hand, implicit information tracked by the
system on the actions done by the user on the elements of the LMS.
With these issues in mind, we are defining a user-centered evaluation approach
that drives the construction of the recommender system along the different phases of
the eLearning life cycle through the following steps:
•
•
•
At design time, elicitation of pedagogically sound recommendations
following user-centered design methods (questionnaires, interviews,
observations). As a result of this process, a bank of recommendations for
eLearning scenarios validated by the users with a collaborative review can be
obtained. An authoring tool to manage the design of the recommendations is
needed.
At runtime, acquisition and validation of the user features to select the
appropriate recommendations for the current context. The evaluation of the
recommendations followed right on time can be offered to the user optionally
and non-intrusively. A player integrated in the LMS is required to present the
recommendations to the user.
Once the course has ended, validation of the recommendations provided and
the impact on the user. This requires the analysis of the interactions of the user
at runtime, by i) analyzing the outcomes in evaluations within the course, ii)
querying the user satisfaction, iii) analyzing the interaction data. With these
data, compute user indicators regarding the collaboration and knowledge level.
This can also be used to feed back the designed recommendations with the
results from the course experience to modify accordingly the values of the
elements of the model or even generate new rules for recommendations.
2. On going works
We are currently researching 1) what user-centered design methods are the most
appropriate to elicit pedagogically sound recommendations to validate the
recommendations model defined and 2) what user-centered evaluation methods are the
most appropriate to evaluate the impact of a recommender system in inclusive
eLearning scenarios.
Acknowledgements
The work presented here is framed in the context of the projects carried out by the
aDeNu Research group. In particular, the EU4ALL (IST-2006-034478) project funded
by the European Commission and A2UN@ (TIN2008-06862-C04-01/TSI) project
funded by the Spanish Government.
References
[1] Santos, O.C. and Boticario, J.G., 2008. Users' experience with a recommender system in an open source
standard-based learning management system. In proceedings of the 4th Symposium of the WG
HCI&UE of the Austrian Computer Society on Usability & HCI for Education and Work (USAB 2008).
[2] Santos, O.C. 2009. Recommendations support in standard-based learning management systems.
Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED 2009).
[3] Drachsler, H., Hummel, H. G. K., Koper, R. 2007. Personal recommender systems for learners in
lifelong learning: requirements, techniques and model. International Journal of Learning Technology.
[4] Burke, R., 2002. Hybrid recommender systems: survey and experiments. User-Modeling and UserAdapted Interaction, 12, pp. 331-370.