=Paper=
{{Paper
|id=None
|storemode=property
|title=Enhanced e-Learning Experience by Pushing the Limits of Semantic Web Technologies
|pdfUrl=https://ceur-ws.org/Vol-1303/paper_5.pdf
|volume=Vol-1303
|dblpUrl=https://dblp.org/rec/conf/semweb/Zielinski14
}}
==Enhanced e-Learning Experience by Pushing the Limits of Semantic Web Technologies==
https://ceur-ws.org/Vol-1303/paper_5.pdf
Enhanced e-Learning Experience by Pushing the
Limits of Semantic Web Technologies
Andrea Zielinski1 , Jürgen Bock2 , Florian Heberle3 , Peter A. Henning3 and
Dan R. Kohen-Vacs4
1
Fraunhofer IOSB, Fraunhoferstr. 1, 76131 Karlsruhe, DE
2
FZI Forschungszentrum Informatik, Haid-und-Neu-Str. 10-14, 76131 Karlsruhe, DE
3
Karlsruhe University of Applied Sciences, Moltkestr. 30, 76133 Karlsruhe, DE
4
HIT Holon Institute of Technology, 52 Golomb Street, POB 305 Holon 5810201, IL
Abstract. We investigate a novel approach to e-Learning using Seman-
tic Web technologies and aiming to optimise the learners’ experience
incorporating pedagogical strategies into the learning process. The in-
creasing availability of Semantic Web based educational resources and
the establishment of open metadata standards like IEEE LOM pave the
way for enhanced e-Learning systems that support personalised learning
based on a reasoning framework and formal ontologies. We use ontologies
to describe learning objects and the learner state, and define pedagogi-
cal recommendation axioms that specify which learning objects are best
suited for a particular learner in a specific situation. Recent pedagogical
findings suggest that the individual learning can be optimised by means
of guidance through learning pathways, i. e., a particular order in which
learning objects have to be studied. To this end, we offer an OWL model
for learning pathways as structured sequences. We show the strengths
and limits of OWL and propose a solution to overcome problems such as
handling of soft constraints and ranking of result sets. The calculation for
ranking is based on individual weights for specific contextual as well as
learner profile features and considers the degree of match between learn-
ing objects and learner’s needs. The validity of our approach is shown
by means of real-life course material, i.e. a complete course on the Phi-
losophy of Didactics, implemented as a prototype system and interfaced
to variouss standard Learning Management Systems.
1 Introduction
Today, sharing and designing course materials is facilitated by the availability of
international standards that allow the integration of freely available resources.
However, to make learning more interesting for an individual student, enhanced
user adaptation is required. This is a key aspect of advanced Technology En-
hanced Learning (TEL) systems as they are expected to be used by several
learners without assistance of a human tutor.
We propose an ontology-based approach for user adaptation based on a set
of learner attributes (e. g., age, gender, learning speed) and the course material
being annotated with essential metadata. Learning Objects (LOs)5 are defined
as small, self-contained, reusable units of learning and generally form part of
a course. More specifically, adaptation takes place according to the learners’
behaviour, performance, profile, learning history, contextual situation (the so-
called Didactic Factors), and the specified learning goal along with the learning
pathway settings, resulting in a personalised recommendation. This kind of adap-
tation was examined before in terms of its potential to provide a personalised
and improved learning pathway offered to students together with a customised
guidance along the learning pathway [26].
Ontologies provide a uniform model for the different aspects of a learning pro-
cess that can be conceptualised as the navigation through a network of available
LOs, thus requiring techniques for dynamic and adaptive sequencing of LOs.
One major contribution of this paper is an enhanced OWL model for e-Learning
that is used in tandem with an OWL reasoning framework. It is also applicable
for dynamic courseware generation based on more abstract concepts, e. g., the
type of the Learning Object (i. e., Knowledge Type). This is particularly useful
in web-scale e-Learning settings, where the set of LOs is generally very large, re-
sulting in a high number of pathway relations between them, and thus extensive
manual annotation effort.
However, two important issues that need to be addressed clearly show the
limits of purely knowledge-based semantic approaches, namely ranking of result
sets and handling of soft constraints. The former problem is particularly im-
portant when dealing with MOOCs (Massive Open Online Courses) that often
deliver a large number of hits. In this case, a prioritisation of LOs w.r.t. an ap-
propriate ranking scheme is required that reflects the relative importance of LOs
for a particular learner. On the other hand, when dealing with closed learning
environments, it can also happen that a complete satisfaction of all constraints
might not be possible. Therefore, we propose an extension of the ontology-based
framework which comprises the calculation for ranking LOs based on individ-
ual weights for specific Didactic Factors and considering the degree of match
between LOs and learner’s needs.
In our work, we focus on the recommendation of LOs considering all required
features are given. In our setting, they will be provided by a standard Learning
Management Systems (LMS) (e. g., Moodle, Clix, ILIAS). In our application
scenario, learners are offered navigational recommendations, yet, they are free
to choose whether or not to follow them.
In the remainder of this paper, Sect. 2 discusses related work in the field
of ontology-based frameworks for personalised e-Learning with a critical review.
Section 3 describes our recommendation approach, including an extended mod-
ule for integrating ranking on top of the reasoning process. Section 4 depicts
implementation and validation aspects, before Sect. 5 concludes the paper.
5
In this paper we also talk about Knowledge Objects.
2 State of the Art: Ontology-based Frameworks for
e-Learning
Various ontological frameworks were proposed for e-Learning [15, 14, 28, 9, 24, 5,
17], focusing on incorporation of open standards and personalised recommenda-
tions [7, 23].
Knowledge integration in most cases considers the metadata of the learning
resources, complemented by a domain model and a learner model [15, 14]. In
our work, we explicitly refrain from integrating domain ontologies for the course
content since these resources are often not readily available, and building up an
ontology from scratch generally requires enormous human effort. We therefore
claim that as few assumptions as necessary should be made about the e-Learning
domain. On the other hand, we stress the importance of domain-agnostic peda-
gogical strategies set up by didactic experts. We build up a knowledge base that
serves as a playground for tutors to evaluate if the didactic recommendations
meet their desires and guide the learner correctly.
Different logical frameworks for ontologies have been used for e-Learning,
with varying expressiveness and complexity, ranging from simple taxonomies [13],
to more complex representations with axioms that constrain the interpretation
of the model. The most common knowledge representation formalisms adopted
for e-Learning are F-logic [15, 14, 28, 9], OWL-DL [12], the Semantic Web Rule
Language (SWRL) [24], and OWL-S [5]. In our approach, we use OWL 2 DL, a
W3C standard that builds on Description Logics and extends the earlier OWL
standard by several language features while still preserving decidability.
A fundamental aspect of e-Learning are so-called “learning pathways” that
aim to optimise the individual learning experience. The most common formal
approach to learning paths is based on the IEEE SCORM and IMS simple se-
quencing specification6 . However, various different implementations are used.
In ontology-based frameworks, often an additional domain ontology layer is
integrated [27, 11] where a “hasResource(C, LO)” relation is used to link a Learn-
ing Object to a domain concept. In order to reach a learning goal, the order of
concepts is constrained by a partial ordering relation “isRequiredBy(C1 , C2 )”. A
similar modelling approach is offered by Yu et al. [30] based on a binary relation
“hasPrerequisite(C1 , C2 )”, which describes content dependency information at
the course level to generate a learning pathway.
We offer an extension of the ontology-based approach that takes into account
the fundamental characteristics of a learning pathway, such as modular compo-
sition, nested composition, optional parts, and sequencing. Thus, our approach
is more expressive, since we seek to model entire structured sequences, following
the learning pathway specification as suggested by Janssen et al. [16]. Finite
state (FS) frameworks also have been used for learning pathway modelling and
almost exclusively rely on Directed Acyclic Graphs (DAGs) [2, 17]. The main
focus of this work, however, has been on efficiency rather than expressiveness
with the aim of shortest path analysis, using Weighted FS networks.
6
http://scorm.com/scorm-explained/technical-scorm/sequencing/
The main approach to ranking considers a hybrid approach (i. e., semantic
and arithmetic). Shen et al. [27, 11] use competency gap analysis to this aim,
favouring learning content that might help the user to progress towards his/her
learning goal. Yu et al. [30] give preference to Learning Objects that are in a
close taxonomic relationship of the respective domain to the learning goal of the
user, specified as dc:subject metadata entry.
Both approaches thus require a separate domain model. Opposed to this, we
rank the Learning Objects according to the degree of relevance to the learner and
select the highest scoring Learning Objects, considering that different Didactic
Factors have a different impact on the overall recommendation and to what
degree learning objects fulfil the recommendation constraints.
2.1 Strengths of Ontology-based Frameworks
Information sharing, integration and reuse. Ontologies support the use
of well-established standards for defining and sharing Learning Objects within
different e-Learning platforms. International metadata standards exist that of-
fer a set of metadata descriptors such as LOM (Learning Object Metadata)
and SCORM (Shareable Content Object Reference Model), cf. Aroyo et al. [4],
Dolog et al. [9]. Specifically, in different educational environments, this increases
interoperability and enables reusability of learning material.
Semantic Search and Reasoning. Ontology-based approaches have be-
come increasingly popular, since they offer additional reasoning capabilities and
thus support semantic search of LOs [8]. Unlike the search paradigm on the
Web, the focus is on searching for structured data, where LOs are semantically
annotated on the metadata level. Consequently, a more precise information need
can be expressed by means of a complex constraint query. Moreover, LOs are
generally related to each other via structural relationships reflecting, i. e., the
learning pathways, and this semantic graph can also be exploited for search.
2.2 Weaknesses of Ontology-based Frameworks
Efficiency. Reasoning in expressive Description Logics has exponential runtime
complexity in the worst case. However, implementations of state-of-the-art OWL
reasoners are typically optimised to show acceptable runtime behaviour in many
real-world scenarios. Modelling in the tractable OWL 2 Profiles, however, comes
with considerable compromises regarding expressiveness.
Support of complex conjunctive queries. Especially in cases where no
resource completely satisfies all conjuncts of a given complex conjunctive class
expression, it would be of interest to determine the winner among the competing
candidates, i. e., the learning resources which fulfil most of the constraints. A
naive approach to instance retrieval inference may often return an empty result
set, since some constraints might not be satisfied. We thus need to find an optimal
solution that satisfies a maximal subset of the constraints.
Sequences. In e-Learning, Learning Objects are organised into sequences
that describe an optimal navigational path towards a learning goal. At present,
there is no support for defining sequences in OWL as would be needed for reason-
ing over learning pathways. However, by use of Ontology Design Patterns (ODP),
best practice solutions can be adopted that allow for regexp-like queries [10].
2.3 Limits of Ontology-based Frameworks
Ranked Retrieval. In order to retrieve a ranked list of suitable Learning Ob-
jects with a recommendation factor, standard Boolean Retrieval, as facilitated in
OWL, is not enough, since it does not support the scoring of objects but deliv-
ers an unordered result set. Instead, the results should be ranked reflecting the
degree of relevance to the learner. A semantic search algorithm that integrates
ranking based on RDF graphs and similar to the PageRank scoring algorithm
has been investigated by Kasneci et al. [18], while an account based on exploiting
semantic relationships between entities has also been proposed [1, 3]. SPARQL
allows to rank results by means of the “ORDER BY” predicate [22] but the data
used for computing the order has to be available in the RDF graph explicitly.
Support of Soft Constraints. Preferences behave like soft selection con-
straints. In this sense, no exact match is required and therefore soft constraints
should be satisfied if possible, but may be violated if necessary. A metric gener-
ally aims to provide an idea for how close a result is to the learners’ needs, i. e.,
to which degree a Learning Object is relevant. The challenge related to the ways
in which such vague knowledge in terms of OWL and its reasoners should be
incorporated is well recognised, and some fuzzy and rough extensions have been
proposed (cf. [19, 25, 21]), however, a standard semantic web compliant solution
regarding vagueness is not available yet.
3 Enhanced Framework for Personalised e-Learning
This section presents an approach for recommending learning material. It is
based on a modular ontology design, and an extension for ranking the results.
3.1 Modular Ontology Design
We propose a modular ontology design in order to cover static pedagogical back-
ground knowledge as well as course and learner specific, dynamic knowledge.
In the pedagogical ontology [29] learning material is organized into Courses
(KDs), Lessons (CC s), and Knowledge Objects (KOs)7 , forming a hierarchical
graph structure. It provides concepts for Knowledge Type (KT ), e. g., orienta-
tion, example, assignment, etc., and Media Type (MT ), e. g., text, video, audio,
etc. and metadata vocabulary for describing KOs, such as, hasDifficultyLevel,
hasEstimatedLearningTime, hasLanguage, hasRecommendedAge, isSuitableFor-
Blind, isSuitebleForDeaf, or isSuitableForMute. Moreover, classes and properties
7
We use the term Knowledge Object instead of the frequently used term Learning
Object in order to differentiate between concrete learning material and the more
abstract course topics.
for specifying learning pathways (LP s) are formalized, distinguishing between
macro- and micro-level learning pathways on the CC and KO level, respectively.
The cognitive map and content map are instantiations of the pedagogical ontol-
ogy.
The learner model ontology with associated instance data, i.e. the learner
state ontology, defines classes and properties for describing the current learner
state, a snapshot characterized by Didactic Factors ( DFs) that currently hold,
including the completion state of KOs and CC s, and current learning pathways.
3.2 Specification of Learning Pathways
We follow a flexible approach to LP modelling that uses auxiliary individuals for
connecting KOs (here: CKO (i,j) ).
MicroLP v LP (1)
MyMicroLP v MicroLP (2)
MyMicroLP (CKO (1,2) ) (3)
hasPredLP (CKO (1,2) , KO 1 ) (4)
hasSuccLP (CKO (1,2) , KO 2 ) (5)
A learning pathway is described as a subclass of MicroLP , which contains
only the connector individuals, in this example MyMicroLP . Using the same
principle, it is possible to specify the currently selected learning pathway:
CurrentLP v LP (6)
CurrentLP v ∃isCurrentLP .Self (7)
MyMicroLP v CurrentLP (8)
3.3 Hard and Soft Criteria Based on Didactic Factors
Hard and soft criteria are defined in the learner model ontology and describe
classes of KOs that fulfill the respective constraints.
Hard criteria define requirements a KO must meet in order to be included
in the recommendation. An example are the disabilities DFs.
Soft criteria define requirements a KO should meet in order to be included in
the recommendation. For instance, the learner might prefer reading text rather
than seeing a video in the user settings. In order to specify the importance of
soft criteria, a weight can be assigned to each soft criterion (see Sect. 3.5).
3.4 Specification of Recommendation Axioms
Knowledge Object Restriction Type 1 (learning pathway successors).
This restriction type describes KOs that are successors of the current or previous
KO w.r.t. the current learning pathways8 . Only KOs are considered, that have
8
Both macro- and micro-level learning pathways are considered.
not yet been completed. There are three sets of KOs determined. For all three
queries, let the current macro- and micro-level learning pathways be defined as
in axioms (2), (3), (4), and (5), and furthermore in axiom (8).
1. Direct successors of the current KO. A property connecting any KO
with its direct successors w.r.t. the current learning pathway can be inferred
via the property chain
hasPredLP − ◦ isCurrentLP ◦ hasSuccLP v hasDirectKOSuccessor (9)
In case the current KO is the last KO within a CC , the current macro-level
LP has to be considered. This requires two additional constructs:
(a) Marking the first and last KO w.r.t. a micro-level LP within a CC by
asserting the connector individuals as described in axiom (3) to an ad-
ditional class FirstLPElement, or LastLPElement, resp.:
FirstLPElement(CKO (i,j) ) (10)
LastLPElement(CKO (k,l) ) (11)
(b) An auxiliary property connecting all KOs of a CC with all KOs of the
successor CC according to the current macro-level LP :
isContainedByCC ◦ hasDirectCCSuccessor ◦ isContainedByCC −
(12)
v nextKOsInNextCCs
.
The direct successors of the current KO can now be determined by retrieving
all individuals for the following class expression:
∃hasDirectKOSuccessor − .CurrentKO
t (∃nextKOsInNextCCs − .(CurrentKO u
(13)
∃hasSuccLP − .(CurrentLP u LastLPElement))
u (∃hasPredLP − .(CurrentLP u FirstLPElement)))
Micro-level learning pathways for KOs do not necessarily have to be explic-
itly defined, but can also be inferred based on general didactical knowledge
in terms of Knowledge Type or Media Type pathways9 represented in the
pedagogical ontology [29]. KO successors based on KT pathways can be
retrieved as in class expression (13), however, two additional subproperty
axioms are necessary. Let KT s be put in a pathway relation using auxiliary
connector individuals as in axioms (3), (4), and (5), with according connector
properties hasPredKT and hasSuccKT . Property assertions for hasPredLP
and hasSuccLP , i. e., on the KO level, can be inferred as follows:
hasPredKT ◦ hasKT − v hasPredLP (14)
−
hasSuccKT ◦ hasKT v hasSuccLP (15)
9
We only discuss KT pathways here. MT pathways are handled analogously.
where hasKT is the property assigning KT s to KOs. In order to infer KO
successors based on a KT pathway now simply requires an axiom making
the class describing the KT pathway subclass of CurrentLP .
2. All (direct and indirect) successors of the current KO. A prop-
erty connecting any KO with all its successors (direct and indirect) w.r.t.
the current learning path can be inferred via a transitive superproperty of
hasDirectKOSuccessor :
hasDirectKOSuccessor v hasKOSuccessor (16)
trans(hasKOSuccessor ) (17)
3. Direct successors of the previous KO. Direct successors of the previ-
ous KO, i. e., the KO visited before skipping, can be retrieved analogously,
replacing CurrentKO with PreviousKO in class expression (13).
Knowledge Object Restriction Type 2 (unfinished predecessors). This
restriction type describes KOs that are predecessors of the current KO the
learner has not yet completed. According to the completion state described by
the classes CompletedKO, PartiallyCompletedKO, and UnseenKO (as disjoint
union of the class KO), the unfinished predecessors can be retrieved as follows:
¬CompletedKO u ∃hasKOSuccessor .CurrentKO (18)
Knowledge Object Restriction Type 3 (soft criteria). This type of KO
restriction describes sets of KOs that fulfill soft criteria. The class expressions
specified in the learner model ontology associated to these soft criteria are eval-
uated independently from each other and deliver different sets of candidate KOs
that are used for the ranking algorithm in the subsequent post-processing.
3.5 Extension: Ranking
We provide an extension of the framework, integrating ranking into the learning
object recommendation in a post-processing step based on the reasoning results.
Our approach is most similar to Kerkiril [20] and Alian et al. [2] based on Simple
Additive Weighting, a widely used multi-attribute decision technique.
In our work, DF features have a weight that indicate their relative impor-
tance. The highest scoring “most recommended” KO is calculated based on this
model, combining all feature weights to an overall score.
We use the RecScore in (19) to calculate the suitability of a learning ob-
ject for a certain learner in a certain context. Linear ranking functions define
the aggregated score of ranking predicates as a weighted sum. In this case, the
weightings need to be defined a-priori by the tutor. The basis for ranking is
provided by
1. Degree of Match. Parameter d is used to define when constraints given as
a key value pairs match. For instance, a Portuguese KO can be recommended
to a native speaker of Spanish because it tends to be comprehensible, even
though it does not match the learner profile perfectly.
2. Weights describing the importance of a feature. Different weights can
be assigned (by the tutor) to individual features, reflecting their importance
with respect to all other feature constraints. For instance, the DF ”gen-
der” in most pedagogical frameworks seems to be of minor importance for
recommendations.
The recommendation score of a learning object and formula used for ranking is
thus:
n
X
RecScore(KOi ) = w(k)d(i, k) (19)
k=1
where
– w(k) is the weight of feature k, and thus its contribution to the final result.
– d(i, k) is the matching degree of the feature k, represented by a floating-point
value ranging from 0 to 1.
– n is the number of DF features.
Accordingly, the results that best match the axiom (figuring the learners’ needs)
are ranked higher in the list. The ranking algorithm takes into account the weight
values for each feature and looks for the highest overall score, so that the number
of features that are satisfied can still be low, if they are assigned a high overall
weight. Since the weight values are subjective, we allow to manually edit them.
In our framework, all DF features are independent of each other.
4 Implementation and Validation
In this section, we present implementation details of our hybrid recommendation
framework. A message-oriented architecture, illustrated in Fig. 1. is used to
implement an asynchronous publication process over a set of loosely coupled
software components which get active in reaction to user actions. The different
ontology modules make up a central part of the application logic.
At first, the learner status is analyzed and the learner state ontology is in-
stantiated accordingly. Information about the course becomes available via the
Cognitive Map and the Content Map10
Then, the reasoning module is invoked to offer enhanced learner adaptation
based on the “recommendation axioms ontology” which comprises all class ex-
pressions and axioms from Sect. 3.4. Since this can partially be done in parallel,
e. g., when evaluating soft criteria, we utilise a reasoning broker framework [6]
10
For the design of the curricula a specific authoring tool has been developed. Thus,
course annotation is supported by a user-friendly visual editor, that strictly adheres
to our OWL modelling approach, used as an intermediate exchange format.
which comprises several standard OWL reasoners (FaCT++, Pellet or HermiT),
and using the latest standards for OWL access protocols (OWLAPI, OWLLink).
Finally, in the ranking stage, the various result sets from the reasoning stage
are taken to compute a recommendation score as presented in Sect. 3.5.
Fig. 1. Implementation architecture of ontologies and software components.
A curriculum of 125 hours from the domain of Philosophy of Didactics, com-
prising 103 CC s, 1133 KOs, and 4 macro- and 2 micro LPs, has been modelled
in adherence to our semantic model to test the functionality of our approach.
It can be adopted to different pedagogical strategies and is highly adjustable,
e.g. allowing to configure individual DF weights and recommendation axioms.
This is important because a didactical theory seeks to investigate how learning
works, and is never fixed from the start.
In the near future we plan to conduct an integrative testing in real settings to
assess a) the contribution to the learning experience, b) the learner’s satisfaction
levels with the system, and c) the usefulness from the learner point of view.
5 Conclusion
We have presented a novel approach to personalized learning based on Semantic
Web technologies, aiming to optimise the individual learning experience.
We show how OWL can be extended to cope with some inherent limits.
In particular, we offer a formalization of a learning pathway as a structured
sequence which can be used for dynamic couseware generation based on more
abstract classes, e. g., KT s and MT s. Moreover, we present a solution to deal
with complex conjunctive queries and show how to incorporate soft constraints:
Different result sets delivered by the OWL reasoner are combined and ranked
according to a specific weighting scheme in a post-processing step.
We plan to extend our framework by a dialogue module that provides meta-
cognitive feedback in terms of motivational messages and explanations why a
specific recommendation was given. Since the feedback messages are generated
together with the reasoning/inferencing, the same justifications for the conclu-
sions can be drawn.
6 Acknowledgements
The research leading to these results has received funding from the EC’s 7th
Framework Programme (FP7/2007-2013) under grant agreement N 318496.
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