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|title=Experience-Based Recommendation for a Personalised e-Learning System
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==Experience-Based Recommendation for a Personalised e-Learning System==
https://ceur-ws.org/Vol-1520/paper33.pdf
274
Experience-based Recommendation for a
Personalised E-learning System
Blessing Mbipom
School of Computing Science and Digital Media,
Robert Gordon University,
Aberdeen, Scotland,
United Kingdom.
b.e.mbipom@rgu.ac.uk
1 Introduction
A large amount of learning resources is available to learners on the Web. Users of
these resources are often discouraged by the time spent in finding and assembling
relevant resources to support their learning goals, and these users often face the
information overload problem [4]. Personalisation within e-learning would allow
the learning abilities and preferences of individual learners to be taken into
account, thus enabling such systems to offer relevant resources to learners [7].
The interaction of previous learners with resources and the resulting out-
come can be viewed as a learning experience. An experience-based recommen-
dation approach would allow the experiences of similar users to be reused for
making recommendations to new users. Currently, some recommender systems
in e-commerce can capture the experience of users with items and reuse these
to enhance recommendation [2]. However, little work has been done to reuse
experiences in the e-learning domain [1, 3]. There is potential to improve the
recommendations made within e-learning [5], drawing from the impact that the
reuse of user experiences has made within e-commerce. Although recommenda-
tion in the e-learning domain is challenging given that the learning resources
have to be carefully combined unlike individual products in e-commerce.
A key contribution of this research will be the development of innovative
approaches to incorporate the learning experiences of previous learners captured
in outcomes such as reviews and ratings, in the recommendations made to new
learners. This research will harness the wide range of available e-learning re-
sources in order to cater for learners with different preferences. The knowledge
contained in the learning resources will be employed for refining learners’ goals
and indexing new learning resources. This work will improve the current state
of e-learning systems by reusing the experiences of previous learners when rec-
ommending relevant learning resources to new learners.
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes. In Proceedings of the ICCBR 2015 Workshops. Frankfurt, Germany.
275
2 Research Questions
This research aims to capture and reuse the learning experiences of previous
learners to enhance recommendations made to new learners within a personalised
e-learning system? This research seeks to address the following questions:
– How can learners’ goals be refined to improve the recommendation of learning
resources?
– How can learners’ preferences and abilities be captured to enhance person-
alised recommendations?
– How can learning resources be represented to support effective retrieval?
– How can outcomes such as learners’ reviews and ratings, be captured and
reused to enhance e-learning recommendation?
3 Research Plan
This research will involve the development of novel approaches for reusing the
experiences of previous learners to enhance e-learning recommendation. Tech-
niques to capture learners’ preferences and abilities will be developed. Existing
learner models will be adapted for this task with the aim of capturing the pref-
erences and the abilities of learners. This information would be used for making
relevant recommendations to new learners.
Existing knowledge sources will be organised into a coherent background
knowledge structure. Potential knowledge sources such as Microsoft Academic
Search, the ACM Computing Classification System, and Wikipedia have already
been identified. The plan is to employ these in the development of a background
knowledge structure which can be employed for refining learners’ goals and for
indexing learning resources. This structure will be useful for identifying the links
between resources and for recommending relevant resources.
Methods for representing and refining learners’ goals will be developed. This
is necessary in e-learning because learners often have insufficient knowledge of the
domain to formulate suitable goals. The plan is to map the goals to a resource
representation developed using shared background knowledge, this will entail
reasoning with the text in the goals and the learning resources.
Representations that capture learners’ outcomes will be created. Learners’
test scores, reviews and ratings can be viewed as outcomes in an e-learning
domain. Currently, learners’ test scores are the major form of feedback used
in e-learning. However, this does not capture learners’ opinions which can be
effectively employed to inform other learners. The plan is to incorporate user-
opinions with user-performance to enhance the recommendation process.
4 Current Progress
The research methodology has been substantially developed. Various approaches
for representing learning resources have been investigated, these range from
276
knowledge-light to knowledge-rich approaches. Some methods of refining learn-
ers’ goals have also been examined.
Different types of learning resources have been identified to use as data for
this work. These include e-books, online teaching slides and video lectures. They
have been chosen because they contain structure and metadata that will help
with the research, and because of the variety of media types contained.
Preliminary experiments have been carried out to develop a background
knowledge structure to use for the refinement of learners’ goals and the rep-
resentation of new learning resources. A collection of 217 e-book chapters from
the machine learning domain were collected for the experiments. Terms and
phrases were extracted from the Tables-of-contents (TOCs) of the e-books using
some NLP techniques and phrase identification methods.
E-books are used as the primary data source in this work because of the
structure they contain and because they are designed to be effective for teaching
and learning. Furthermore, issues of trust and provenance [6] are catered for
because the nature of books means an author and affiliation exists. Wikipedia is
used as a complementary data source, because it is a knowledge-rich source put
together by many contributors.
Terms and phrases were extracted from the TOCs of e-books and compared
with phrases from the Machine Learning category in Wikipedia to generate a set
of suitable phrases to use for developing the background knowledge structure.
The result was 90 phrases consisting of 17 unigrams, 58 bigrams and 15 trigrams.
Initial output shows the potential to harness the knowledge in e-Books and
Wikipedia for developing a background knowledge structure that will enable
the refinement of learners’ goals and indexing of new learning resources. Further
work will involve evaluation of this method, and the development of a system that
employs the background structure to recommend relevant learning resources.
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