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May I suggest? Three PLE recommender strategies in comparison

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Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their activities in different learning contexts and even for transitions between these contexts. Hereby, recommender systems which are highly successful in other application areas comprise one relevant technology for supporting learners in PLE-based activities. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts and due to different research approaches dealing with recommenders, we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general.
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May I suggest? Three PLE recommender strategies in comparison
Felix Mödritscher and Barbara Krumay, Vienna University of Economics and
Business, Austria – felix.moedritscher@wu.ac.at and barbara.krumay@wu.ac.at
Sandy El Helou and Denis Gillet, Ecole Polytechnique Fédérale de Lausanne
(EPFL), Switzerland – sandy.elhelou@epfl.ch and denis.gillet@epfl.ch
Sten Govaerts and Erik Duval, Katholieke Universiteit Leuven, Belgium –
sten.govaerts@cs.kuleuven.be and erik.duval@cs.kuleuven.be
Alexander Nussbaumer and Dietrich Albert, Graz University of Technology, Austria –
alexander.nussbaumer@tugraz.at and dietrich.albert@tugraz.at
Ingo Dahn, University of Koblenz-Landau, Germany – dahn@uni-koblenz.de
Carsten Ullrich, Shanghai Jiao Tong University, China – ullrich_c@sjtu.edu.cn
Abstract
Personal learning environment (PLE) solutions aim at empowering learners to design
(ICT and web-based) environments for their activities in different learning contexts
and even for transitions between these contexts. Hereby, recommender systems
which are highly successful in other application areas comprise one relevant
technology for supporting learners in PLE-based activities. In this paper we examine
the utilization of recommender technology for PLEs. However, being confronted by a
variety of educational contexts and due to different research approaches dealing with
recommenders, we present three strategies for providing PLE recommendations to
learners. Consequently, we compare these recommender strategies by discussing
their strengths and weaknesses in general.
1. Introduction
Over the last decades, recommender systems have been successfully applied in
various areas, like online retailing (cf. Amazon) or social networking (cf. Facebook).
Due to the success of this kind of technology, research on technology-enhanced
learning (TEL) has started to deal with recommender strategies for learning, as
documented by workshop proceedings (Manouselis et al., 2010) and special issues
in journals (Santos and Boticario, 2011). Addressing more learner-centric TEL
streams, recommendations seem to be a powerful tool for personal learning
environment (PLE) solutions (Mödritscher, 2010). In PLEs, personalized
recommendations help filtering information based on “soft” but significant context
boundaries (Wilson et al. 2007), giving learners the opportunity to take the best of an
environment where shared content differed in quality, target audience, subject
matter, and is constantly expanded, annotated, and repurposed (Downes, 2010).
This paper addresses the generation and provision of PLE recommendations within
the EU project ‘ROLE’ (abbreviation for ‘Responsive Open Learning Environments’,
cf. http://www.role-project.eu). As ROLE deals with a wide range of educational
scenarios and even with transitions between learning contexts, we present three
different strategies, each one aiming at supporting certain needs of learners.
The paper is structured as follows. The upcoming section summarizes our
understanding of personal learning environments and gives a brief overview of
recommenders for TEL and PLEs. Then, we describe the three recommender
approaches being developed in the ROLE project. Furthermore we discuss benefits
and disadvantages for their application in PLEs before the paper is concluded, and
future work is indicated.
2. PLEs, PLE recommendations, and related work
According to Henri et al. (2008), personal learning environments (PLEs) refer to a set
of learning tools, services, and artifacts gathered from various contexts to be used by
the learners. A typical situation for PLE-based collaboration is depicted in Figure 1. A
learner is involved in two activities, an individual tutoring session in which she
consults the facilitator via Facebook and a task in which she collaboratively works on
an outcome together with a peer actor using four different tools (RSS Feed, Google
Mail, YouTube, and Twitter). This example illustrates how learners interact with their
PLEs consisting of different entities, i.e. tools, content artifacts (like emails or
Tweets), peer actors, etc.
Fig. 1: Example scenario for PLE-based collaboration (see also Wild et al., 2008).
According to Van Harmelen (2008), web-based PLEs aim at empowering learners to
design (ICT-based) environments for their activities by allowing them to build,
connect, and expand learner networks in order to collaborate on shared outcomes
and acquire necessary (professional and rich professional) competences. However
user studies in the fields of higher education (Ullrich et al., 2010) and workplace
learning (Kooken et al., 2007) evidence that learners – and even teachers!
(Windschitl and Sahl, 2002) – have varying attitudes towards hand-on skills in using
ICT for learning.
Against this background, PLE solutions should provide facilities for empowering
learners in using this kind of technology. One possible solution is the application of
recommender technology, because recommendations are necessary if users have to
make choices without sufficient personal experiences of alternatives (Resnick and
Varian, 1997). This aspect is considerably the case for informal learning activities of
(lifelong) learners who try to utilize PLE technology in highly different contexts in
order to achieve their goals. Thus recommendations could be valuable for various
aspects of PLE-based learning activities, e.g. for formulating concrete learning goals
or needs, retrieving relevant artifacts, finding relevant peers or tools, getting
suggestions for learner interactions in a specific situation, etc.
Coming to fame particularly by their application in eCommerce (like Amazon.com) or
social networking platforms (like Facebook.com), recommender systems describe
systems that produce individualized recommendations as output or have the effect
of guiding the user in a personalized way to interesting or useful objects in a large
space of possible options” (Burke, 2002). Recommenders can follow various
different strategies, such as item-based ones (e.g. content artifacts or links to users),
model-based ones (e.g. by applying probabilistic models or networked structures),
collaborative filtering (based on user-given data-sets), or hybrid strategies (cf.
Mödritscher, 2010). Moreover, Verbert and Duval (2011) outline the importance of
building upon real-world data-sets, e.g. in the form of user interaction data or (implicit
and explicit) user feedback, to develop and improve TEL recommender systems.
A lot of research on recommendation services has been done in the last few years.
Amongst others, theoretical work on this issue proposes models and ontologies for
recommendations in the educational domain (Santos and Boticario, 2010) or
recommendation frameworks based on content and context (Broisin et al., 2010). On
a more practical level, other approaches deal with concrete facilities like social
navigation elements for educational libraries (Brusilovsky et al., 2010), ranking
algorithms for lecture slides (Wang and Sumiya, 2010), people finder for workplace
learning (Beham et al., 2010) or even algorithms for predicting student performance
(Thai-Nghe et al., 2010).
However, in the ROLE project we are facing new challenges which have led to the
development of different recommender strategies for PLE settings.
3. Three different PLE recommender approaches
A grand challenge of the EU project ROLE concerns the wide range of learning
contexts to be supported through responsive open learning environments. As being
targeted by the vision of the project (cf. http://www.role-project.eu/?page_id=406),
ROLE claims to support learners in different educational contexts, starting with
formal and informal learning scenarios at universities and at workplaces and
reaching to the many contexts of lifelong learning. Moreover, it is even a goal to
support transitions between these contexts, as indicated by the five test-beds
(‘university to company’ transition, ‘individual to shared competences’ transition,
‘formal to informal learning’ transition etc). Consequently, the project focuses on
integrating flexible infrastructures, i.e. widget technology, into existing learning
platforms and on different approaches to personalize learning, amongst others by
providing context-sensitive PLE recommendations to the learners.
In the upcoming subsections we briefly describe three of these recommender
strategies being developed in the project and following different paradigms.
3.1 Federated Search and Collaborative Recommendation Widget
The first approach developed within the ROLE project is implemented as a federated
search and recommendation widget exploiting the usage of resources by people
sharing the same learning and/or social context. The ‘Binocs’ widget (see Figure 2)
employs a federated search engine that aggregates heterogeneous resources and
forwards them to a recommender system. Recommended resources ranging from
wiki pages, videos, to presentations can be saved, shared, assessed, and re-
purposed according to each user’s interest.
Fig. 2: Federated search and collaborative recommendation widget ‘Binocs’
displaying the results for the query ‘learn french introduction’ and the opened
settings menu.
To rank resources, the recommender system takes the following user actions into
account: (1) selecting a resource from a search result, (2) liking or disliking a search
result (using a thumbs up and down feature) and (3) previewing a search result. The
learning and social context can be derived from the course (e.g. all students from a
course share similar interests), the business setting (e.g. all employees of the sales
department) or from the user’s friends and contacts in the widget container (via the
OpenSocial API (Mitchell-Wong et al., 2007)). The recommender system relies on an
algorithm influenced by Google's original PageRank algorithm (Page et al., 1999)
and based on the 3A interaction model (El Helou et al, 2009). In the absence of
previous user interaction with a resource, ranking is still possible based on the
resource relevance to the search query.
A preliminary evaluation of the widget’s usability and recommendation usefulness is
summarized in Govaerts et al. (2011a). The evaluation helped to improve the user
interface, and revealed that users prefer Google results due to their diversity. The
widget’s results were biased to media, while Google provides a wider range of Web
pages. This can be remedied by adding more repositories to the federated search
engine to drive the recommendations. On the other hand, pilot users agreed on the
usefulness of the collaborative recommendations on top of the search results. We
plan to evaluate the use of the recommender system further through the analysis of
user online feedback (by clicking on top N recommended items) and through user
surveys in real-life scenarios.
Two more usability and usefulness evaluation studies of the Binocs widget being
used in a PLE were conducted (Govaerts et al., 2011b). One was done in the context
of Business English courses at the Shanghai Jiao-Tong University (SJTU,
http://www.sjtu.edu.cn) where the widget is used to provide access to social media
resources (e.g. YouTube and SlideShare). The second evaluation was conducted in
a business setting, more specifically within an international corporation, FESTO
(http://www.festo.de) where the widget is used to assist sales people by offering
more efficient search over multiple product databases. The results for the widget in
the business setting are more positive than in the university. Potential explanations
are the higher stability of the learning environment at FESTO and the slow internet
connection perceived at the SJTU, which could have biased the evaluation of our
federated search and recommendation services. Moreover it was noted that
extending the available repositories would be helpful to get richer search results.
3.2 Community-based PLE recommender
A second recommender going beyond collaborative recommendations within a single
widget is implemented as part of a practice sharing approach for learning
communities (see Mödritscher et al., 2010). Basically, the idea is to integrate a
pattern repository into existing PLE solutions so that users can voluntarily share their
PLE usage experiences as ‘good practices’ with peers. Thereby, a pattern repository
is a web-based service (with a RESTful API) which allows storing and retrieving
patterns of PLE-based activities, i.e. recordings of learner interactions with a tool
mash-up used for a specific situation (see also right-hand side of Figure 3). Overall,
this practice sharing approach is intended to be for informal learning settings, thus
supporting life-long learners in achieving their personal needs but also in succeeding
at the workplace or in further education.
Fig. 3: Client-sided PLE solution PAcMan (left) and proposed architecture of a PLE
practice sharing infrastructure (right, taken from Mödritscher et al., 2010).
The data for this recommender approach is captured through facilities of the PLE
which enables users to share such an activity pattern in a simply way. A prototypic
version of the pattern repository has been integrated in two different PLE like
solutions, a client-sided one (PAcMan add-on, cf. https://addons.mozilla.org/en-
US/firefox/addon/176479) and in OpenSocial-based widget containers (like iGoogle
or Liferay). The format of the activity patterns to be shared has to be specified by the
PLE developers who aim at integrating the pattern repository. For the PAcMan add-
on, the shared data is given as JSON which consists of web resources being
structured according to a simple activity model (an activity is a list of user-tagged
URLs; see also left-hand side of Figure 3). Data capturing in OpenSocial containers
is realized through a widget which records all events triggered by the widget on a
mash-up page if it has been added to this page. After pressing the ‘Share’ button,
the recording of learner-triggered events (user interactions) is stored to the
repository on the basis of the Contextualized Attention Metadata (CAM) schema.
As the format of the shared activity patterns depends on the PLE solution submitting
the data, a recommender strategy has to be implemented for each data format.
Currently, the standard algorithm available can be characterized as a collaborative
filtering (CF) technique, as it measures the occurrences of each item (pattern titles,
users having shared patterns, user-generated tags, and URLs). The
recommendations can be retrieved by the PLE solutions through the RESTful API
and according to different entities (patterns, peers, user tags, tools, and artifacts)
and different strategies. Next to the default strategy (‘global top-n’) it is planned to
provide local top-n recommendations. Hereby, locality could refer to the patterns
used for generating the recommendations, e.g. by using the patterns of a clique or
for a specific search term only. For the first case, Mödritscher (2010) describes a
study in which a few patterns of a research group was captured for a (work-related)
scenario. Results showed that the distribution of item occurrences follows a power
law, and the network of activities, resources (URLs) and user-generated tags tend to
have characteristics of a scale-free network, which is an indicator that this
collaboratively created data-set is suitable for generating useful recommendations
for users (cf. experiences on music recommendations by Cano et al., 2006).
Overall, this strategy for generating and providing PLE recommendations seems to
be reasonable, as it already works with smaller sets of data and allows personalizing
recommendations e.g. according to learner’s clique, a search term, or other
contextual information. So far, recommendations are only provided on the level of
activity patterns – if a user opens the ‘Pattern Store’ of the PAcMan add-on (see
Figure 3) she can either query the patterns or receives recommendations in terms of
the most frequent downloaded patterns. A more sophisticated strategy would be to
suggest items (peers, artifacts, tools, or resource tags) according to specific
situations, e.g. for a certain clique or a given goal of a learner. As retrieved sub-sets
of activity patterns lead to scale-free networks, it is planned to provide two kinds of
recommendations: (a) the must-sees which comprise the hubs in the PLE network
structure and are always displayed to the user; (b) the might-be-of-interest
suggestions, i.e. items of the long tail which are recommended from time to time or
also triggered by a certain context or user interaction.
3.3 Psycho-pedagogical recommender
In contrast to collaborative filtering strategies, the psycho-pedagogical recommender
is not based on large, community-generated data-sets. However, it is developed
according to a theoretical model and relevant taxonomies (Fruhmann et al., 2010) on
the one hand and user data on the other hand. In order to empower learners to build
their learning environments and to use those for learning, this recommender strategy
deals with providing guidance in self-regulated learning situations. While
experienced learners are capable in using PLE technology without getting external
support, many learners need some kind of guidance and support to go through the
learning process (cf. Dabbagh and Kitsantas, 2004; Efklides, 2009). The main aim of
the psycho-pedagogical recommender is to provide guidance especially with respect
to self-regulated learning and to find appropriate resources (artifacts, tools, peers)
fitting to the competence of the learner.
There are two kinds of data which is used for generating psycho-pedagogical
recommendations. First user model data is taken into account, comprising learning
goals and competences required at the moment. Also preferences, such as the
degree of guidance needed, are considered. A second kind of data is given in the
form of learning models which serve as basis for the recommender algorithm. The
SRL process model describes how learning should ideally happen in a self-regulated
way. It is a formalization of self-regulated learning in the context of ROLE. The SRL
process model is related to general and concrete learning activities on the cognitive
and meta-cognitive level. Learning tools are also related to learning activities, which
describes the way of learning possible with certain tools. These relations are
specified in advance and form an important basis of the recommendation strategy.
The recommendation strategy is closely related to these learning models and to
each of its elements. The recommender tries to guide the learner though the learning
process according the SRL process model. Therefore (cognitive and meta-cognitive)
learning activities are recommended depending on what the learner has already
done. The learner has to give feedback what has been done (which recommended
learning activity has been performed). In order to recommend learning resources (at
the moment only tools), the learning goals and competences are taken into account.
Tools are recommended if they fit to the goals of the learner and if learners can
actually use them for successful learning. Preferences such as the degree of
guidance are also taken into account, which has effect how detailed
recommendations are.
Fig. 4. Psycho-pedagogical recommender realized and provided in the form of
widgets (left: guidance widget, right: learning planning widget).
According to the recommendation strategy the learner is provided with two kinds of
recommendations, learning activities and learning resources. Both are presented on
a list of possible choices, where the user can also report back which one she has
chosen. In addition to these recommendations the learner also gets explanations,
which is important because self-regulated learning is difficult to adopt. Furthermore,
the learner gets an automatically generated learning plan which is updated each time
an interaction takes place. So the learner gets visual feedback and orientation what
has been planned or completed and a general overview on this state in the SRL
process. The user interface has been implemented as a widget (see Figure 4). It
uses a service in the background where the models and user data are stored and
where the recommendation strategy is implemented.
Further work will concentrate on the integration of artifacts and peers to be
recommended, usage of log data as input data, and on an improved user interface.
4. Discussion of the PLE recommender strategies and future work
Considering the different goals and techniques of the PLE recommenders being
developed in the ROLE project, it is obvious that each one has specific benefits and
shortcomings. Basically a user scenario for our recommenders could look like this. In
the beginning a learner has a specific need and decides to start a new activity to
address this need and achieve some goal, e.g. creating an outcome like a document
together with some colleagues. In a first step, a PLE recommender has to support
the learner by formulating her learning need and suggesting PLE designs so that she
gets an idea what an environment for fulfilling the need could look like. Then, after
reusing and adjusting such a PLE design or creating a new one from scratch, a PLE
recommender should provide links to artifacts, peer users, or tools which are
appropriate for the current activity.
Collaborative recommendations are realizable with a certain degree of accuracy
without threatening the users’ privacy (see also Machanavajjhala et al., 2011).
However, this recommender is highly tailored to a specific context, namely
information retrieval, as the Binocs widget enables federated search in different
media and content repositories. In the scope of PLEs, this recommender supports
learners in finding appropriate artifacts for their different activities. Additionally it is
also possible that the widget points to peers that are relevant to query terms, if the
privacy policy allows this. However, the widget does not recommend learning
activities and does not take learner network structures in to account. So, the
usefulness of the federated search and collaborative recommendation widget
supports learners in the second phase of PLE-based collaboration rather than in
designing their environment.
The community-based PLE recommender, on the other hand, has been developed
on top of a simple semantic model, namely the notion of activities which are used to
structure one’s learning context and to capture information on user interactions and
the context. Following a collaborative filtering (CF) approach, the pattern repository
provides both recommendations of pre-given (shared) PLE designs in the form of
tagged bookmarking collections as well as recommendations on artifacts, tools, and
peers generated according to contextual information. Both kinds of
recommendations can be requested by a PLE solution through the Web-API,
whereby items can be differentiated between ‘must-haves’ (most frequent items) and
‘might-be-of-interest’ (items from the long tail; see also Mödritscher , 2010). Although
perfectly supporting the two phases of the before-mentioned PLE scenario, this
recommender suffers from typical weaknesses of CF techniques, namely the cold-
start problem (no data on new user and items) and sparsity (no or less user ratings;
cf. Adomavicius and Tuzhilin, 2005). The application of clustering techniques and
usage data is currently evaluated in order to refine the recommender algorithm.
Finally, the psycho-pedagogical recommender also supports the two phases of PLE-
based learning. On the one hand, a learner can use the planning widget to start an
activity and determine her goal. On the other hand, she can use the guidance widget
to design and adjust the environment for her current activity. As this recommender is
based on a more complex and pre-defined semantic model and structured, pre-
processed usage data, it has clear advantages if less or no data is given. In this
case, the psycho-pedagogical recommender claims to use expert-given rules to
suggest goals and/or widgets. On the negative side, it can identify and recommend
new items much slower, as the generation of recommendations is at least a semi-
controlled process which involves pedagogical experts.
With these recommender approaches we believe that we cover the most critical
issues for supporting learners in designing and using their PLEs. The most positive
aspect of developing these three strategies next to each other concerns the
weaknesses of single recommenders we have highlighted before. In case of lacking
good recommendations for a specific case - e.g. if the community-based
recommender does not have enough data on items or users – the learner can try to
make use of suggestions of another recommender. This multi-approach also gives
us flexibility to support different scenarios in the very heterogeneous test-beds of the
ROLE project. While some test-beds are based on instructions and organizational
driven learning (SJTU, FESTO) others have a strong focus on informal settings and
collaboration. Here we can vary the strategies for learner support.
To conclude, at this point the three recommenders are on rather different maturity
levels. While Binocs is ready to be used by end-users the pattern repository
approach relies on the integration within existing PLE systems, i.e. also facilities to
provide recommendations to the end-users, and the psycho-pedagogical
recommender lacks the full implementation of all features. So, next to finishing
development work on the latter two recommenders future work also comprises a
user study for evaluating the recommenders ‘in action’.
5. Acknowledgements
The research leading to these results has received funding from the European
Community's Seventh Framework Programme (FP7/2007-2013) under grant
agreement no 231396 (ROLE project).
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... Therefore, these systems could offer guidance to learners without limiting their freedom, by mediating the relationship between users' existing knowledge and potential knowledge acquisition (Lichtnow et al. 2006). According to Drachsler, Hummel and Koper (2008), with recommendations, users are able to be self-regulated and responsible for their own learning, while they also have the opportunity to find peers and/or tools and get suggestions and motivational support from interaction with peers (Mö dritscher et al. 2011). ...
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