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May I suggest? Comparing Three PLE Recommender Strategies


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Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts 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? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
May I Suggest? Comparing Three PLE Recommender Strategies
Felix Mödritscher
Barbara Krumay
Vienna University of Economics and Business, Austria
Sandy El Helou
Denis Gillet
Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Alexander Nussbaumer
Dietrich Albert
Graz University of Technology, Asutria
Ingo Dahn
University of Koblenz-Landau, Germany
Carsten Ullrich
Shanghai Jiao Tong University, China
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
Personal learning environment (PLE) solutions aim at empowering learners to
design (ICT and web-based) environments for their learning activities, mashing-
up content and people and apps for different learning contexts. Widely used in
other application areas, recommender systems can be very useful for supporting
learners in their PLE-based activities, to help discover relevant content, peers
sharing similar learning interests or experts on a specific topic. In this paper we
examine the utilization of recommender technology for PLEs. However, being
confronted by a variety of educational contexts we present three strategies for
providing PLE recommendations to learners. Consequently, we compare these
recommender strategies by discussing their strengths and weaknesses in general.
Personal learning environments; Recommender technology; Federated search;
collaborative recommendations; Community-based recommendations; Psycho-
pedagogical recommender; Technology review.
I. 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
developments, recommendations seem to be a powerful tool for personal learning environment
(PLE) solutions (Mödritscher, 2010).
Unlike traditional LMS (Learning Management Systems) where content is predefined, PLEs are
based on “soft” context boundaries (Wilson et al., 2007), with resources and apps being added at
run time. In such “open corpus” environments (Brusilovsky and Henze, 2007), personalized
recommendations give learners the opportunity to take the best of an environment where shared
content differs in quality, target audience, subject matter, and is constantly expanded, annotated,
and repurposed (Downes, 2010).
As being addressed within the EU project ‘ROLE’ (abbreviation for ‘Responsive Open Learning
Environments’, cf. http://www.role‐, this paper deals with the generation and provision of
recommendations which should support learners in using PLE technology. Such recommendations
could comprise artifacts, peers or experts, pre-defined PLE designs as well as shared experiences
which are helpful in designing or making use of PLEs for learning (Mödritscher, 2010). As the ROLE
project deals with a wide range of educational scenarios, we present three different strategies,
each one aiming at supporting certain needs of learners.
The paper is structured as follows. The next section summarizes our understanding of personal
learning environments and gives a brief overview of recommenders for TEL and PLEs. Then, we
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
describe the three recommender approaches developed in the ROLE project. A discussion of the
benefits and limitations of applying each approach in PLEs follows, before the paper is concluded
and future work is summarized.
II. 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. Figure 1
depicts a scenario where a PLE is used for student collaboration. 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, 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.
Figure 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 enabling them to build their own learning environments,
where they can discover, create, reuse, and share content and applications as well as easily
expand their learner networks in order to collaborate on shared outcomes and acquire necessary
(professional and rich professional) competences based on their self-defined learning goals.
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, like PLE technology, 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, as
Resnick and Varian (1997) state that recommendations are useful if users have to make choices
without sufficient personal experiences of alternatives. In that, recommendation services could be
valuable for various aspects of PLE-based learning activities especially informal ones, where they
help formulate concrete learning goals or needs, discover relevant artifacts and tools, and find new
interactions opportunities with peers and experts sharing similar interests.
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
Coming to fame particularly by their application in eCommerce (like or social
networking platforms (like, recommender systems are “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 adopt 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),
user-based collaborative filtering (based on user-related data-sets), or hybrid strategies
(Mödritscher, 2010). Verbert et al. (2011) emphasize 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 recommender systems for TEL 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.,
However, in the ROLE project we are facing new challenges that have led to the development of
different recommender strategies for PLE settings.
III. 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., 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.
a. 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.
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
Figure 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., 2010). In the absence of previous user
interaction with a resource, ranking is still possible based on the resource relevance to the search
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, 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
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
( 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.
b. 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 (Mödritscher et al.,
2011). 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.
Figure 3. Client-sided PLE solution PAcMan (left) and proposed architecture of a PLE practice sharing
infrastructure (right, taken from (Mödritscher et al., 2011))
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. 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
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
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 were
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.
c. 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 to use PLE technology without getting external
support, many learners need some kind of guidance and support to go through the learning
process (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 competences of the learner.
Psycho-pedagogical recommendations are generated by taking into account two different
information sources. First user model data is used, comprising learning goals and competences
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
required at the moment. Also preferences, such as the degree of guidance needed, are considered.
A second information source 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 in the context of ROLE. This model includes taxonomies of general and concrete
learning activities on the cognitive and meta-cognitive level. Learning tools are related to these
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 their
elements. The recommender tries to guide the learner though the learning process according to 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 on 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.
This is realized by recommending at first activities to achieve the envisaged goals. Learning
techniques and recommended activity pattern can provide a basis for this step. Each of these
activities requires certain tool functionalities. Then tool descriptions, listing the functionalities of the
respective tools, allow recommendation of specific tools. In this way learning spaces can be
dynamically adapted to the current activities, thus avoiding a cognitive overload of the learner by
tools that are actually not needed. Preferences such as the degree of guidance are also taken into
account, which has an effect on the level of detail of the recommendations.
Figure 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 as a list of
possible choices. The choices are recorded and used for further recommendations, because this
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
information is needed to guide the learner through the learning process. In addition to these
recommendations the learner also gets explanations, which should help the learner to adopt the
concept of self-regulated learning. 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 on 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.
IV. Comparison and discussion of the PLE recommender strategies
Considering the different goals and techniques of the three PLE recommenders adopted in ROLE, it
becomes clear that each one has its own benefits and shortcomings. Basically, 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 of
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 deemed relevant to her current activity.
Binocs widget
Collaborative Filtering
(CF), PageRank-like,
CF and information
(cliques, topics)
Rule and profile-based
Data & data
On entering search
terms, automated
Tagged bookmarks,
voluntarily shared
automated (profile)
High (works well in
specialized scopes;
fallback through IR)
Average (requires
‘initialization’, cf. cold
start & sparsity)
Average (rules and
profile must be given)
PLE scenario
support &
Average (not
considering PLE design
phase); good usability
Good (currently only
focusing on PLE
designs); usable
Good (no cold-start
problem but restricted
to pre-def. domains);
average usability
Privacy statement,
anonymized activity
recordings (=patterns)
Raw usage data not
used; user profiles not
addressed yet
Preferences for Google
results; uptake in
business setting better
3 studies; works but
requires pilot users
sharing patterns (e.g.
Internal evaluations;
efforts to integrate new
data; requires
modeling expertise
Table 1. Comparison of the three PLE recommender strategies developed in ROLE
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
Table 1 gives an overview of the strengths and weaknesses of the three recommender solutions
which are being developed within the ROLE project. The comparison is conducted along six
dimensions, namely (a) the recommender strategy, (b) data and data gathering, (c) the estimated
accuracy, (d) the usefulness for the PLE scenario and the usability, (e) privacy issues, and (f)
experiences. The comparison evidences that the three recommender approaches in ROLE are based
on very different techniques and data-sets, which consequently leads to certain advantages and
disadvantages of each PLE recommender. In the following paragraphs we briefly summarize and
discuss the characteristics of the three developments.
Collaborative recommendations are realizable with a certain degree of accuracy without
threatening the users’ privacy (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 possible that the widget points to peers that are relevant to query terms, if privacy
policy allows it. However, the widget does not recommend learning activities and does not
take learner network structures into 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 (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 complex and structured semantic model and
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 involving 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
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May I Suggest? Comparing Three PLE Recommender Strategies
F.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich
Digital Education Review -
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
V. Conclusions and future work
To conclude, at this point the three recommenders are at rather different maturity levels. While
Binocs is being used by end-users, the pattern repository approach relies on the integration within
existing PLE systems to give recommendations to end users, and the psycho-pedagogical
recommender lacks the full implementation of all its 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’.
The authors would like to thank Erik Duval and Sten Govaerts for their valuable comments and
suggestions to improve the paper. 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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Digital Education Review -
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... PLEs have been defined using two approaches. The first approach views PLEs as a set of tools (Haworth 2016;Mödritscher et al. 2011;Wilson et al. 2007); the other approach considers PLEs as innovative learning platforms or environments that organize a variety of Web 2.0 tools according to the individual learner's needs and characteristics (Kompen et al. 2009;Tu et al. 2015). More recently, Patterson et al. (2017) described PLEs as "spaces in which individuals interact and communicate to develop collective know-how through digital technologies" (p. ...
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To explore students’ use of Web 2.0 tools and their perceptions of using Web 2.0 as a personal learning environment (PLE), quantitative surveys (n = 113) and interviews (n = 12) were conducted. In the survey, we identified that students already have familiarity with using Web 2.0 tools, as well as a positive attitude toward using Web 2.0 for learning. However, in the interview, students referenced challenges with using Web 2.0 in their PLE. The results imply that there are gaps between students’ perceived comfort and familiarity with using Web 2.0 tools and their readiness to be an active and successful designer for Web 2.0-based PLEs. This research also identified that there may be other factors influencing students’ building PLEs with Web 2.0 tools including the knowledge of different tools, the abilities of identifying learning objectives and learning styles, access to the tools, motivation, and knowing how to locate the correct information through students’ interviews.
... It is within this specific context that the concept of Personal Learning Environments (PLEs) appeared some years ago as a new way to understand how students learn as well as how educators teach (Attwell, 2007;Schaffert & Hilzwnsauer, 2008;Adell & Castañeda, 2010;Santamaría, 2010;Modritscher et al., 2011;Barroso, Cabero, & Vázquez, 2012;Cabero, 2012). ...
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On the basis of the Research Project funded by the Spanish Ministry of Education under the title “Design, production and evaluation of a 2.0 learning environment for faculty training in the use of Information and Communication Technologies (ICTs)” (EDU2009-08 893), experts have used the external competence coefficient to evaluate the different dimensions of Personal Learning Environments (PLE), namely: technical and aesthetic aspects, ease of navigation, or quality of the didactic elements that make up the environment. A quantitative methodology along with a questionnaire prepared by the author served this purpose. The results obtained highlight technical environment operation, the tools forming the PLE, or the learning object repository as being “very positive.” In conclusion, experts emphasise the user-friendliness of environment and tools alike, as well as the educational aspects of the contents available in materials guides.
... Other authors show that current technological resources based on diverse digital tools and social networks allow teachers to personalize learning by planning experiences centered on the students and by considering their level of knowledge, competencies and potential cognitive development (Yang, 2013). The virtual environment where all the required resources for the students converge is called a Personal Learning Environment (PLE) and is defined as a set of learning tools, services and artifacts collected from various contexts to be used by the student to work, learn, reflect and collaborate with others (Attwell, 2010; Mödritscher et al., 2011). Some researchers consider that the PLE is not a particular place or tool that contains all the applications and provides the users with access, but instead a working space to include the chosen web tools and services by the learner to obtain and process information, connect with others and create knowledge. ...
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Introduction: This paper presents a pilot experience on the use of an Institutional Personal Learning Environment (iPLE) which aimed to describe the conception, design and development of the iPLE, as well as to determine how users approached the iPLE, and to identify the structure of the Personal Learning Environments (PLEs) designed by students. Method: The iPLE supported graduated students - specialized on research for social sciences and education –of the University Casa Grande (Guayaquil, Ecuador) - in the development of their final master projects and to support other people interested in building and using a PLE. The experiment data sources included academic records, virtual classrooms design, the very PLEs built by the students, statistics of use and access to the iPLE; and a questionnaire held to the participants. Results: The initial results allow the research team to report a favorable acceptance of the iPLE by the students not only as a support for research work, but also to provide a model for the construction of PLEs. In addition, the questionnaire shows that the users of the iPLE rated the environment as having high usability and felt a high grade of satisfaction. Conclusions: The conclusions point out different lines of research related to iPLEs, such as use an iPLE as portfolio of evidence and interaction among students, peers and teachers or the customization of an iPLE by using technological and teaching learning resources.
... Unlike traditional Learning Management Systems (LMS) where content and tools are predefined for the learner, PLEs are based on soft context boundaries with resources and tools being added at run time [4]. In order to provide some help with self-created PLEs, different recommendation approaches have been proposed in [5]. A tool (Binocs) is described that recommends learning content using federated search in the background. ...
Conference Paper
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This paper presents an approach and an integrated tool that supports the creation of personal learning environments suitable for self-regulated learning. The rationale behind this approach is an ontology of cognitive and meta-cognitive learning activities that are related to widgets from a Widget Store. Patterns of such learning activities allow for providing the user with appropriate recommendations of widgets for each learning activity. The system architecture follows a Web-based approach and includes the Mashup Recommender widget and its backend service, the ontology available through a Web service, the Widget Store with its interface to retrieve widgets, and the integration into the learning environment framework. The pedagogical approach regarding the usage of this technology is based on self-regulated learning taking into account different levels between guidance and freedom. A quantitative and qualitative evaluation with teachers describes advantages and ideas for improvement.
... Si bien es cierto que con un poco de esfuerzo (por ejemplo llamando herramientas a todos los componentes del PLE) se puede clasificar esta propuesta dentro de la línea amplia de significación, también es cierto que la clasificación que proponen puede resultar útil para determinar los objetos de estudio de las investigaciones empíricas sobre PLE. Por ejemplo, partiendo de esta significación, resulta que el sujeto y las herramientas son los grandes protagonistas en las investigaciones sobre el tema PLE, como se observa en las investigaciones de , Kop & Fournier (2013), Cataldi & Lage (2013), Sousa et al. (2011), Mödritscher et al. (2011), Aresta et al. (2012, Rahimi et al. (2012), Paz (2012), Ivanova et al. (2012), Harris et al. (2012, García et al. (2012), Gallego & Gámiz (2012), Dahrendorf et al. (2012), Chatterjee & Mirza (2012), Panckhurst & Marsh (2011), White et al. (2010), Honegger & Neff (2010), Torres et al. (2010), Fournier & Kop (2010), Llorente (2013), Gil (2012), Castañeda & Soto (Estos últimos investigadores intentan construir, modelar y conceptualizar un PLE en una plataforma educativa de microblogging llamada El trabajo aprovechó la convocatoria a la Conferencia PLE de 2010 (Barcelona , España) y mediante un mensaje inicial se logró la participación de educadores, formadores, estudiantes y otras personas interesadas en PLE y PLN. ...
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Se presenta una revisión de investigaciones publicadas en los últimos años sobre Entornos Personales de Aprendizaje (Personal Learning Environments -PLE-); entre los documentos analizados se incluyen artículos, capítulos de libros y actas de congresos. Como resultado de la revisión se proponen tres tópicos relacionados con la investigación sobre PLE. En primer lugar, se muestra cómo algunos estudios empíricos se agrupan en torno al discurso teórico y pedagógico de PLE, que suele justificar estas investigaciones. En segundo lugar, alrededor del concepto de PLE se ofrecen tres líneas de significación que son útiles para categorizar investigaciones empíricas. Finalmente, se analiza la forma en que se han orientado las investigaciones empíricas hacia algunos objetos de investigación y se utilizan algunos elementos del PLE para clasificar publicaciones. Abstract. This article presents a review of documents published in recent years. All publications are empirical researches about Personal Learning Environments (PLE). The analysis includes journal articles, chapters of books, doctoral theses and conference papers. The authors have established three topics about trends of research from the exam of theoretical and empirical documents. First, the article show how some empirical studies are grouped around the discourse of PLE, which is used for justify these researches. Second, the article shows three lines of PLE's significations; they are useful for categorising empirical researches. Finally, the authors analyze how the empirical researches have been oriented towards some objects of investigation, and then, the authors use some elements of PLE to categorizing the publications.
... As PLEs take an adaptable rather than adaptive approach to the construction of the learning environment the focus has been on supporting the learning in the discovery of appropriate services from the large repositories of available services. This has been achieved through a combination of search and recommendation [23] allowing the learner to search for appropriate services while using recommendation to support the learner by tailoring the recommendations to the needs of the individual. This personalisation of the suggested services has been based both on the competencies of the learner [24] and also on pedagogical considerations [24]. ...
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Personalised web information systems have in recent years been evolving to provide richer and more tailored experiences for users than ever before. In order to provide even more interactive experiences as well as to address new opportunities, the next generation of Personalised web information systems needs to be capable of dynamically personalising not just web media but web services as well. In particular, eLearning provides an example of an application domain where learning activities and personalisation are of significant importance in order to provide learners with more engaging and effective learning experiences. This paper presents a novel approach and technical framework called AMASE to support the dynamic generation and enactment of Personalised Learning Activities, which uniquely entails the personalisation of media content and the personalisation of services in a unified manner. In doing so, AMASE follows a narrative approach to personalisation that combines state of the art techniques from both adaptive web and adaptive workflow systems.
Conference Paper
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Resumen Este artículo presenta una experiencia piloto sobre el uso de un entorno personal de aprendizaje institucional (iPLE), especializado en el área de investigación para ciencias sociales y educación, para apoyar a los estudiantes que se encuentran desarrollando su TFM o tomando cursos de investigación. El objetivo principal de este estudio fue determinar el nivel de aceptación de los usuarios del iPLE y su frecuencia de uso. La experiencia se realizó con estudiantes de los programas de postgrado de la Universidad Casa Grande, en Guayaquil, Ecuador. Para la recolección de datos se aplicó una encuesta, y se analizaron las estadísticas de uso y frecuencias de acceso al iPLE generadas por WebStat. Los resultados iniciales permiten destacar una aceptación muy favorable del iPLE como espacio de apoyo al trabajo de investigación de los estudiantes, que se reflejan en la evaluación de usabilidad que señalan un alto nivel de satisfacción tanto con la interface como con los servicios que este ofrece, y en la cantidad de usuarios tanto nuevos como recurrentes. Palabras clave Entorno personal de aprendizaje, entorno personal de aprendizaje institucional, aprendizaje personalizado, redes sociales, educación superior, usabilidad.
Conference Paper
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Este artículo presenta una experiencia piloto sobre el uso de un entorno personal de aprendizaje institucional (iPLE), especializado en el área de investigación para ciencias sociales y educación, para apoyar a los estudiantes que se encuentran desarrollando su TFM o tomando cursos de investigación. El objetivo principal de este estudio fue determinar el nivel de aceptación de los usuarios del iPLE y su frecuencia de uso. La experiencia se realizó con estudiantes de los programas de postgrado de la Universidad Casa Grande, en Guayaquil, Ecuador. Para la recolección de datos se aplicó una encuesta, y se analizaron las estadísticas de uso y frecuencias de acceso al iPLE generadas por WebStat. Los resultados iniciales permiten destacar una aceptación muy favorable del iPLE como espacio de apoyo al trabajo de investigación de los estudiantes, que se reflejan en la evaluación de usabilidad que señalan un alto nivel de satisfacción tanto con la interface como con los servicios que este ofrece, y en la cantidad de usuarios tanto nuevos como recurrentes.
Is Personal Learning Environment (PLE) a new concept for effective teaching and learning? Shouldn’t learning always be personalized and individualized? How may digital technology enhance PLE? Web 2.0 tools integrated with the concept of PLE will enable authentic learner-centered and learner-driven applications for more individualized learning instructions. Self-regulated learning is critical to both face-to-face learning and online learning, and is indispensable in PLEs. Learning is always personal, constructive, ubiquitous, collaborative, and connective. While PLE is powered by technology, its design and applications should be firmly rooted in the theoretical framework of pedagogy.
E-learning has brought an enormous change to instruction, in terms of both rules and tools. Contemporary education requires diverse and creative uses of media technology to keep students engaged and to keep up with rapid developments in the ways they learn and teachers teach."Media RichInstruction" addresses these requirements with up-to-date learning theory and practices that incorporate innovative platforms for information delivery into traditional areas such as learning skills and learner characteristics. Experts in media rich classroom experiences and online instruction delve into the latest findings on student cognitive processes and motivation to learn while offering multimedia classroom strategies geared to specific curriculum areas. Advances such as personal learning environments, gamification, and the Massive Open Online Course are analyzed in the context of their potential for collaborative and transformative learning. And each chapter features key questions and application activities to make coverage especially practical across grade levels and learner populations. Among the topics included: Building successful learning experiences online.Language and literacy, reading and writing.Mathematics teaching and learning with and through education technology.Learning science through experiment and practice.Social studies teaching for learner engagement.The arts and Technology.Connecting school to community.At a time when many are pondering the future of academic standards and student capacity to learn, "Media Rich Instruction" is a unique source of concrete knowledge and useful ideas for current and future researchers and practitioners in media rich instructional strategies and practices.
Conference Paper
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To empower the learner for true lifelong and personalised learning with a responsive open learning environment (ROLE) is one aim of the ROLE project. A psycho-pedagogical integration model (PPIM) towards supporting learning has been developed by facilitating the concept of personalised self-regulated learning. The first version of the ROLE PPIM is presented in this paper and gives a general view of the components of this model. The central part of the PPIM is the description of the self-regulated learning process and how it can be personalised by learners using adaptive guidance of ROLE.
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Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
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This exploratory paper discusses the learning potential of PLE, not simply as a technological artefact but as an instrument of the learning process. It tries to identify the role of PLE in learning process and to point out the conditions to become more efficient learning instrument. Firstly, PLE should foster self-direction and reflexivity, and learning resources should be made available to the learner to support its metacognitive activity. Secondly, since PLE's are more and more inter-connected thanks to online tools, they raise the same issues of knowledge exchange as for online communities: difficulties to connect resources and to exploit the data available. Sharing should be improved by developing solutions bridging personal annotations (personomies) with their collections (folksonomies) and more structured knowledge representations (ontologies). Thirdly, research results should be used by institutions to improve the process of learning and teaching, and the design of VLEs.
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
Recommender systems have shown to be successful in many domains where information overload exists. This success has motivated research on how to deploy recommender systems in educational scenarios to facilitate access to a wide spectrum of information. Tackling open issues in their deployment is gaining importance as lifelong learning becomes a necessity of the current knowledge-based society. Although Educational Recommender Systems (ERS) share the same key objectives as recommenders for e-commerce applications, there are some particularities that should be considered before directly applying existing solutions from those applications. Educational Recommender Systems and Technologies: Practices and Challenges aims to provide a comprehensive review of state-of-the-art practices for ERS, as well as the challenges to achieve their actual deployment. Discussing such topics as the state-of-the-art of ERS, methodologies to develop ERS, and architectures to support the recommendation process, this book covers researchers interested in recommendation strategies for educational scenarios and in evaluating the impact of recommendations in learning, as well as academics and practitioners in the area of technology enhanced learning.
Numerous benefits of student-centered web-based learning environments have been documented in the literature; however the effects on student learning are questionable, particularly for low self-regulated learners primarily because these environments require students to exercise a high degree of self-regulation to succeed. Currently few guidelines exist on how college instructors should incorporate self-regulated strategies using web-based pedagogical tools. The scope of this paper is to (a) discuss the significance of self-regulation in student-centered web-based learning environments; (b) demonstrate how instructional designers and educators can provide opportunities for student self-regulation using web-based pedagogical tools; and (c) redefine the role of the instructor to support the development of independent, self-regulated learners through the use of web-based pedagogical tools.
Recommender systems assist and augment a natural social process. In a typical recommender system people, provide recommendations as inputs, which tile system then aggregates and directs to appropriate recipients. In some cases, the primary transformation is in the aggregation; in others, the system's value lies in its ability to make good matches between recommenders and those seeking recommendations. This special section includes descriptions of five recommender systems. A sixth article analyzes incentives for provision of recommendations. Recommender systems introduce two interesting incentive problems. First, once one has established a profile of interests, it is easy to free ride by consuming evaluations provided by others. Second, if anyone can provide recommendations, content owners may generate mountains of positive recommendations for their own materials and negative recommendations for their competitors. Recommender systems also raise concerns about personal privacy.
Research on ubiquitous computing in schools has documented that teachers often change instructional practices over time when using technology with students and has further suggested that teachers’ use of technology may play a role in their shifting toward more constructivist pedagogy. Our two-year study takes an ethnographic perspective in examining how three middle school teachers learned to use technology in the context of a laptop computer program. The ways in which those teachers eventually integrated computers into classroom instruction were powerfully mediated by their interrelated belief systems about learners in their school, about what constituted “good teaching” in the context of the institutional culture, and about the role of technology in students’ lives. The condition of ubiquitous technology did not initiate teachers’ movement toward constructivist instruction. Rather, the laptops were a catalyst that enabled one participant, who had a pre-existing dissatisfaction with teacher-centered practices, to transform her classroom through collaborative student work and project-based learning.
Increasingly, there is a shared understanding that the educational approach driving the development of Personal Learning Environments (PLEs) is one of learner empowerment and facilitation of the efforts of self-directed learners. This approach fits well with concepts of social constructivism, constructionism, and the development and execution of learning plans. This paper describes how these have been increasingly supported in four successive PLE prototypes. These prototypes have successively moved towards support for communities of learners by basing PLE design on social networking services, and support for the formulation of learning plans and transformation of those plans into public exhibits of growing knowledge.