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1. A folksonomy-based lightweight resource annotation metadata schema
for personalized hypermedia learning resource delivery
Simon Boung-Yew Laua
, Chien-Sing Leeb
* and Yashwant Prasad Singhc
a
Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 53300 Setapak, Kuala
Lumpur, Malaysia; b
Graduate Institute of Network Learning Technology, National Central
University, 300, Jhongda Road, Jhongli City, Taoyuan 32001, Taiwan; c
Faculty of Information
Technology, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia
(Received 1 December 2011; final version received 12 August 2012)
With the proliferation of social Web applications, users can now collaboratively
author, share and access hypermedia learning resources, contributing to richer
learning experiences outside formal education. These resources may or may not
be educational. However, they can be harnessed for educational purposes by
adapting and personalizing them to different learner needs. We propose to
collaboratively annotate learning resources with a lightweight resource annota-
tion metadata schema complemented by a folksonomy-derived semantic model.
The annotation metadata schema follows a novel policy to associate numerical
ratings to learners’ subjective expression of opinions on learning resources. On the
other hand, the semantic model serves to support a collaborative resource
recommendation algorithm based on the k-nearest neighbour approach. Proof-of-
concept is demonstrated via a prototype Web-based recommender. Preliminary
results indicate learners are confident with the recommended learning resources in
terms of accuracy, usefulness, novelty and information adequacy. Formalization
of folksonomy in annotating learning resources as well as in opinion mining from
the crowd to rate resources may not only support personalization but also engage
learners more effectively in technology-enhanced learning.
Keywords: social Web; hypermedia learning resources; personalization;
folksonomy; resource annotation metadata; collaborative filtering; technology-
enhanced learning
1. Introduction and motivation
The nature of conventional education is top-down where learning resources are
mainly prepared and authored by domain experts and instructors. Knowledge is
delivered from the instructors to learners and takes the approach of ‘‘one size fits
all’’. This research is motivated by a hypothetical learning scenario illustrated as
follows:
A young adult, Paul in his early 20s does not like to read academic textbooks or attend
training classes provided by experts. He would like to self-learn a highly technical topic
such as ‘‘Personal Finance’’ (finance) or ‘‘Mobile and Satellite Communication’’
*Corresponding author. Email: cslee@cl.ncu.edu.tw
Ó 2012 Taylor & Francis
Interactive Learning Environments, 2015
Vol. 23, No. 1, 79–105, http://dx.doi.org/10.1080/10494820.2012.745429

2. (engineering) new to him without going to school or without the guidance of any
instructor. As he has mobile broadband access at his fingertips, he intends to source for
useful educational resources suitable for a beginner like him on the Internet. He is on
social network such as Facebook almost daily. He reckons that he can gain access to
relevant learning resources anytime, anywhere and with the help of anyone who shares
and tags various Web articles, blog posts, videos, audio podcasts etc. on a social
network. Hence, such tagging and sharing activities may be of help to Paul provided he
knows where they are (they are easily searchable). Similarly, other users may benefit
from what Paul tags and shares through his daily Web activities.
As reported in Shroff (2011), the ‘‘Y generation’’ or ‘‘Facebook Generation’’
spends an average 5 h a day on social network and Web 2.0 applications. The advent
and prevalence of Web 2.0 or ‘‘Read-Write’’ Web technologies such as social
network, blogs, wikis and social bookmarks among the younger generation has
facilitated social interaction and collaboration among users and, hence, opened the
door for authoring and sharing of educational hypermedia resources such as Web
articles, videos, images and podcasts for personalized self-paced learning on the Web
(Ulbrich, Kandpal, & Tochtermann, 2003).
Although the increase in the number of resources available in the open Web can
be harnessed for educational purposes, they may or may not be fit for educational
purposes for all users. What is evidently necessary for this mode of learning is not
only a facility to understand the educational needs of learners, but also a tool to
enable learning resources to be annotated for personalization of these resources to
different learner needs.
Due to the overwhelmingly huge amount of learning resources generated and
available online, we argue that annotation of such resources by a small group of
instructional designers or domain experts is not only time-consuming, but also
prone to be inaccurate (due to the lack of time interacting with every piece of
information and timely updates). We thus propose that user participation in the
annotation process would not only reduce the cost of updating and maintaining
resource description metadata, but also improve the quality of resource
description such as serendipity (Mathes, 2004; Quintarelli, 2005) and better
generalization of new knowledge (Swarup & Gasser, 2008) from a larger pool of
diverse opinions.
To enable resource discovery and adaptation for personalized learning,
collaborative filtering (CF) is commonly adopted to label, classify, select and filter
hypermedia resources on the Web. The CF based on attribute description or
numerical ratings to enable resource discovery and adaptation for personalized
learning is relatively new (Khribi, Jemni, & Nasraoui, 2007, 2009; Itmazi & Megias,
2008; Tan, Guo, & Li, 2008).
In the latter type of CF, it builds a model from a community of users’ past
behaviour (such as ratings given to items) to predict ratings for items that the user
may have an interest in. However, generally, contextual information such as ‘‘who’’
and ‘‘why’’ a resource is being liked or disliked is not sufficiently captured in CF. In
addition, each user may perceive and rate a resource from different perspectives. For
instance, a video tutorial which is ranked 5 on a scale of 1 (worst) to 5 (best) by one
user may be due to the quality of its content, while the resource may be ranked 5 by
another user due to his preference to the way the content is being presented. Both
users may also differ significantly in the way they learn.
To close the gap, we look towards using collaborative tagging to describe
learning resources.
S.B.-Y. Lau et al.
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3. Collaborative tagging is the collective action of users associating tags to resources
created or experienced by them (Bouillet, Feblowitz, Liu, Ranganathan, & Riabov,
2008; Pfeiffer & Tonkin, 2009). The classification of these resources using tags
produces folksonomy. Folksonomy is spontaneous, non-hierarchical categorization
of Web resources using shared keywords by a community of users (Li & Lu, 2008;
Vanderwal, 2007).
Conceptually, a generic model of collaborative tagging comprises a tripartite
three-uniform structure of entities, i.e. the users, tags and resources. Hence, a
collaborative tagging system can be represented as a tripartite hypergraph with
hyperedges. Each hyperedge will have vertices of three types which represent a user,
a resource being tagged and a tag in a folksonomy where the set of vertices on the
folksonomy can be partitioned into three disjoint sets of users, U ¼ {u1, u2, . . . , uk},
tags, T ¼ {t1, t2, . . . , tl} and resources, R ¼ {r1, r2, . . . , rm}. A folksonomy network
can be visualized as a tripartite hypergraph
H ¼ V; E
ð Þ ð1Þ
where V denotes the vertices in the tripartite graph, V ¼ U [ T [ R and E denotes
the set of hyperedges. Each hyperedge connects exactly three vertices, one from
each set of U, T and R where E U 6 T 6 R ¼ {(u,t,r) j u 2 U, t 2 T, r 2 R)} is a
ternary relation among U, T and R whose elements are instances of annotation or
tag assignment by a particular user from U with a tag in T for a resource in R
(Mika, 2005; Zhang Chuang, 2010). Hence, folksonomy has great potential in
enriching learning resource description metadata as well as automated selection,
filtering and ranking of learning resources on the open Web for personalization of
these resources to learners’ educational needs. To the best of our knowledge, there
is a lack of semantic support for social tags for learning in social contexts. Thus,
the research problem addressed in this article is how to enable the learning system
to make sense of the free, unconstrained and arbitrary tag metadata (Guy
Tonkin, 2006). Our work is centred on the research question: How can we
formalize the definition of user-contributed tag metadata to facilitate
personalization?
We propose a semantic model for folksonomy-derived resource description
metadata and a policy for resource annotation based on the semantic model to
formalize semantic information about learning resources as well as users who tag
them. This enables resources to be selected based on user context profile (e.g.
learning style and competency level) and educational preferences (e.g. theme, content
format and complexity level of resources). The working principle of how the
description metadata schema can be integrated into a learning resource recommen-
dation approach based on k-nearest neighbour will be presented in Section 8.
2. Contribution
The focus of our work is different from the above-mentioned related work on
folksonomy from two perspectives. Firstly, our work focuses on the application of
learning resource personalization in informal, self-directed social learning environ-
ment while most related work focus on conventional e-learning application in formal
education settings.
Interactive Learning Environments 81

4. Secondly, as far as CF of learning resources is concerned, there is currently
limited formal conceptualization that represents folksonomy in a consistent way
(Kim, Breslin, Yang, Kim, 2008) Consistent representation of folksonomy
facilitates systematic annotation process. Furthermore, there is no semantic
annotation schema representing knowledge about learning resources attained from
tags. Hence, we aim to formulate a novel semantic model for tags which serves as a
tool and policy framework to associate and correlate users’ subjective tag keywords
metadata to high-level concepts depicting features of a learning resource such as
theme, content format and complexity level as well as numerical rating of the
resource. The model includes a well-defined resource rating policy based on user
opinion. The semantic model and annotation technique address the issue of
formalizing collaborative social tags in informal learning. Besides, a novel domain
ontology for ‘‘personal finance’’ was devised for experimentation because as far as
we have surveyed, there is currently no readily defined ontology for this domain.
Thirdly, another important aspect of learning resource description profiling is
mining the opinions of the user community about learning resources. At present, two
popular opinion mining techniques on Web 2.0 systems are the ‘‘Like’’ voting on the
Facebook social network platform and numerical ratings. These techniques generally
suffer from a weakness: They do not naturally take into consideration ‘‘context’’ in
which an item is being rated (Nakamoto, Nakajima, Miyazaki, Uemura, 2008).
Hence, we also contribute to fill this gap by devising a user ‘‘opinion-rating’’ policy.
This policy is devised to extract the feelings of users about how ‘‘good’’ a learning
resource is for the purpose of rating and ranking the resources.
Lastly, our semantic model provides the framework for
(1) item-based filtering of learning resources prior to a user-based classification
of resources and
(2) ranking of learning resources based on numerical ratings inferred from tags
within a collaborative resource recommendation algorithm based on the k-
nearest neighbour.
3. Related work
Learning resource recommendation is at a relatively early stage of research and
development (Itmazi Megias, 2008). A major concern among the research
fraternity is identifying the requirements of learning recommender systems
(Drachsler, Hummel, Koper, 2007; Santos, Baldiris, Boticario1, Restrepo1,
Fabregat, 2011; Santos Boticario, 2011) with issues and challenges specific to the
learning domain (Tang McCalla, 2005). Most current efforts on recommender
systems are related to the formation of conceptual models, frameworks or
architecture (Itmazi Gea; 2006; Itmazi Megias, 2008; Kerkiri, Manitsaris,
Mavridou, 2007; Otair Al Hamad, 2005; Tan et al., 2008) focusing on formal
learning (Karampiperis Sampson, 2005). The designs are, in essence, top-down
and highly structured where learning material and learning path are designed and
administered by domain experts to guarantee quality. However, implementation
details of these efforts are not in abundance for conclusive remarks to be made about
the level of success of such applications.
Meanwhile, learning recommender systems for informal learning are slowly
developing (Drachsler Manouselis, 2009; Soonthornphisaj, Rojsattarat,
S.B.-Y. Lau et al.
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5. Yim-ngam, 2006). However, there is limited documented information about the
implementation detail and the level of success of these systems. Overview of the
requirements for learning personalization and the types of recommendation
approaches are summarized in Figure 1.
As far as learning resource description profiling is concerned, Fok and Ip (2007)
designed and constructed personalized education ontology (PEOnto) through an
iterative ontology construction process. The PEOnto is made up of five ontologies of
vocabularies. These are: the users, the learning content, the curriculum, the pedagogy
and the software agents for describing, organizing, retrieving and recommending
hypermedia educational resources. Fok and Ip (2007) have since adopted PEOnto in
a prototype called personalized instruction planner (PIP). The PIP is implemented to
facilitate instructors in annotating learning objects with semantic description.
Meanwhile, Jovanovic, Gašević, and Devedžić (2009) devised an ontological
framework for learning personalization, which includes a learning content structural
ontology, a context ontology to specify the pedagogical role of the content, a domain
ontology of concepts, a learning path ontology and a user ontology. These
ontologies are used to personalize learning content to the users’ domain knowledge,
preferences and learning styles in the domain of intelligent information systems.
Both research efforts have demonstrated the use of ontologies in authoring learning
resources in formal education.
Other related work on ontology-based recommendation systems driven by
similar motivation (Buriano et al., 2006; Cantador, Bellogı́n, Castells, 2008; Costa,
Guizzardi, Guizzardi, Filho, 2007; Naudet, Mignon, Lecaque, Hazotte, Groues,
2008) have endeavoured to enhance the semantic description of user preferences and
items with concepts and instances defined in ontologies. Efforts to construct
ontologies to represent knowledge and concepts in e-learning include those by
Heiyanthuduwage and Karunaratne (2006), Šimún, Andrejko, and Bieliková (2007)
and Tsai, Chiu, Lee, and Wang (2006). Some of these efforts are summarized in
Table 1. These approaches exploit learning personalization based on domain
knowledge and user context.
Figure 1. Framework for learning resource recommender systems.
Interactive Learning Environments 83

6. There are various approaches employed in constructing domain ontologies.
Apart from the knowledge engineering approach adopted in this work (Yun, Xu,
Wei, Xiong, 2009), there are the iterative convergence model (Pinto Peralta,
2003), composition/integration model (Lin, Tseng, Weng, Lin, Su, 2007), skeletal
(Uschold Grüningerm, 1996), seven-step method (Yun et al., 2009) and five-step
recipe (Yun et al., 2009), whose phases of development are conceptually similar. In
this article, we adopt the widely applied knowledge engineering approach which is
generically modelled after the IEEE 1074-2006 Software Development Life Cycle
processes and proven effective in the e-learning domain (Yun et al., 2009) to develop
the ontology for the personal finance domain.
More recently, there is growing popularity on leveraging collaborative tags for
context clues to supersede CF based on user ratings (Gadepalli, Rundensteiner,
Brown, Claypool, 2010; Milicevic, Nanopoulos, Ivanovic, 2010; Nakamoto
et al., 2008; Tso-Sutter, Marinho, Schmidt-Thieme, 2008). This initiative is based
on the assumption that similar users will use similar or related keywords to express
their opinions on similar items. This is also the opinion harvesting approach that our
article is presenting. Details of the approach will be presented in Section 6.4.
Meanwhile, resource recommendation approaches can be classified based on the
way filtering and selection of resources are done, i.e. content-based (item similarity)
and CF-based (user similarity) or based on the ways in which ratings of the resources
are being estimated, i.e. memory-based and model-based approaches. Hybrid
approaches combine more than one approach for specific purposes. Taxonomy of
the resource recommendation approaches is shown in Figure 2.
In general, most recommendation approaches in personalized learning systems
employ the CF approach compared to the content-based approach due to the
presence of the concept of ‘‘community’’ in CF, which makes it a natural choice for
learning in a social-networked environment (Khribi et al., 2007, 2009). User-based
CF such as k-nearest neighbour (k-NN) is the most prevalent approach employed for
learning resource recommendation (Itmazi Megias, 2008; Khribi et al., 2009; Tan
et al., 2008). For instance, Khribi et al. (2009) classify learners using k-NN to
recommend Web links to learners based on cosine similarity. On the other hand,
Shih and Lee (2001) employ the k-NN to provide adaptive learning materials for a
Table 1. Summary of work which exploits ontologies to represent knowledge and concepts.
Semantic technology
exploited Personalization Application
Heiyanthuduwage
and
Karunaratne
(2006)
Ontology that defines
relationship between
metadata for learning
objects
Maps user preferences
with learning content
Conventional
open sourced
learning
management
system (LMS)
Šimún et al.
(2007)
Ontology of knowledge
item space and
concept space
Adapt learning
application to user
model
Web-based
e-learning
application
Tsai et al.
(2006)
Ontology of course
content
Rank the degree of
relevance of learning
objects to a user’s
preference and nearest
neighbours’
preferences
Not stated
S.B.-Y. Lau et al.
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7. computer networking course. It is interesting to note that to date, as far as we have
surveyed, item-based CF is yet to be employed prevalently in the e-learning domain
in any significant way.
4. Outline of the article
The rest of this article is organized as follows. Firstly, we conceptualize the
formalization of the meanings of tags in keyword metadata in folksonomy. In Section
6, we present the ontology engineering process of a semantic model for tags. In
Section 7, we demonstrate how the semantic ontology for tags can be applied in the
resource annotation process for a collaborative resource recommendation algorithm
based on k-nearest neighbour approach. In Section 8, we present the user evaluation
results of a prototype Web-based learning recommender system implementing the
said model and algorithm.
5. Formalizing collaborative tags
Conventionally, there is no formal conceptualization for representing folksonomy in a
consistent way (Kim et al., 2008). There is no systematic process to guide the tagging
process and a semantic annotation schema representing the knowledge attained from
tags. As far as we know, there is no commonly known popular Web 2.0 tagging system
representative of the learning scenario we are targeting, which focuses on educational
resources with large enough data size that warrants a conclusive analysis of tags.
As far as we know, currently there is no commonly known popular Web 2.0
tagging system on educational resources that has large enough data size which
warrants a conclusive analysis of tags. Hence, we select Delicious.com as the sample
tag database for our observation. We assume every piece of Web resource can be
educational to specific segments of audiences in specific contextual situations.
Our study indicates that folksonomy can be principally classified into categories as
follows (Table 2; Marchetti, Tesconi, Ronzano, 2007; Xu, Fu, Mao, Su, 2006):
(1) Factual: up to 80% of tags describe the theme of the content being tagged and
approximately 17% describe the content format of the resource or the
context in which the resource is used;
Figure 2. Taxonomy of resource recommendation approaches.
Interactive Learning Environments 85

8. (2) Descriptive: about 3% of tags describe users’ subjective emotions or opinions.
Though the study stated above clearly indicates the presence of distinct types of
semantic information in tag data, it is inherently difficult to automatically extract the
implicit meanings of tag data since there is lack of organization or structure of
higher-level semantic relations between tags. A single user ‘‘votes’’ for a resource
with its tag and the votes are counted only as one in standard tagging applications.
That is, if tag1 ¼ tag2, then there is no difference semantically between the first and
subsequent assertions, which means the additional assertions are logically
redundant. For the purpose of harnessing tags to describe learning resources, we
need to devise a policy to guide the tagging process and organize of the meanings of
tags. Simply put, it is to make folksonomy able to ‘‘talk’’ to a learning system (Li
Lu, 2008). We propose to undertake this by constructing a semantic model for tags
to address the following semantic information about learning resources:
(1) the theme of the subject matter the learning resource is related to: what the
resource is about (tags such as ‘‘personal finance’’ and ‘‘mobile
communication’’),
(2) the content format of the learning resource (tags such as ‘‘video’’ and ‘‘text’’),
(3) the quality attributes of the resource perceived by users (tags such as
‘‘boring’’ and ‘‘cool’’) and
(4) profile of the tagger who creates and owns the tag.
We thus propose formalizing folksonomy with a semantic model for tags as
shown in Figure 3 with the following features:
(1) a domain ontology that describes the relations of concepts,
(2) a content format ontology and
(3) an opinion-rating policy to associate numerical rating to subjective opinions
of users to grade the learning resources.
For proof-of-concept, formulation of the semantic model is presented in detail in
the following sections.
6. Semantic model for tags
6.1. Domain ontology
In this section, we present the knowledge engineering process to constructing a novel
domain ontology on ‘‘personal finance’’ for illustration purpose. The goal of the
Table 2. Breakdown of tags by type of semantic information.
Category Type of semantic information Percentage (%)
Factual Theme of a resource (content-/context-based tags) 78.06
Content format (the kind of informative content) 17.35
Descriptive Subjective opinion or emotion about the level of
‘‘goodness’’
3.06
Others Example: preposition, conjunction and
organizational
1.53
S.B.-Y. Lau et al.
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9. domain ontology is to provide the basis for organizing the domain themes. The
purpose of the domain ontology is to guide the learning resource annotation process
to define concepts in the knowledge domain. The domain ontology is extensible with
concepts defined by users in the form of tags. It will be implemented in the future
versions of the prototype system described in Section 9.
The knowledge engineering approach to construct the domain ontology is
adopted from the IEEE 1074-2006 Software Development Life Cycle standard
reported in Yun (2009) and Yun et al. (2009). Phases of the ontology construction
process include ontology competence specification, ontology acquisition, ontology
implementation and ontology evaluation as shown in Figure 4.
Firstly, the goal and scope of the ontology, its intended uses and target end users
are defined and specified. Competency questions for the ontology are designed
through a few iterations of deliberations and reviews to test whether the goals and
scope of the ontology have been expressively covered. Whether a concept or axiom is
to be included in the domain ontology are verified by the questions. For instance,
competency questions for ‘‘fundamental concepts in personal finance’’ are such as:
(1) What are the general principles of personal finance?
(2) What are the fundamental concepts of personal finance for a learner
(beginner) to know?
(3) What are the key knowledge areas related to personal finance?
According to the above-mentioned ontology competency questions, relevant
concepts, relations, properties and roles are identified and organized into a semantic
structure by following these steps (Lau Lee, 2012):
Figure 4. Ontology engineering process of the domain ontology.
Figure 3. Semantic model for tags.
Interactive Learning Environments 87

10. (1) Enumerate important concepts and terms: important concepts and terms for
the subject domain are manually extracted from related closed-corpus and
open-corpus materials via a careful process of deliberation. Closed-corpus
materials include textbooks and reference books (Altfest, 2007; Bajtelsmit,
2006; Downey, 2005; Frasca, 2009; Kapoor, 2009; Michael, 1997) and online
educational materials (Blecksmith, 2010; Keown, 2010) authored by domain
experts. Open-corpus materials include open Web resources such as wiki or
blog posts such as GetRichSlowly.com, MyMoney.com and Wisebread.com,
news portals (e.g. CNN Money, Forbes Finance and Yahoo Finance) and
DMOZ Open Directory Project (2012) about personal finance. The titles,
subtitles, outlines, table of content and so on were observed to extract the
intrinsic semantic structure of the subject domain. Terms or keywords
extracted from these materials form the conceptual model.
Apart from the information extraction approach described above, in recent
years, it may be also possible to learn the ontologies from the Web using
automated tools. However, as far as we know, most of these semantic Web
tools are still at the early stage of implementation and there is rarely
universally constructed and accepted ontologies on domains such as
‘‘personal finance’’. An experiment with Swoogle, a semantic Web search
engine shows that there is currently no any readily defined ontology which is
near to the targeted domain in the area of ‘‘personal finance’’.
(2) Define concepts, attributes and relations of concepts and organize hierarchy
structure: Concepts in the domain are categorized and organized top-down
into hierarchical structures as shown in Figure 5.
For this research, the approach proposed by Leo, Ceusters, Mani, Ray, and
Smith (2007) is adopted to verify the ontology where the ontology is assessed by
domain experts against a set of criteria. The purpose of the evaluation is not only to
assess the quality of the ontology, but also to enhance the ontology based on the
suggestions of the experts.
The quality of the domain ontology was evaluated against a set of 12
competency statements by a total of five subject experts in the Finance
Department, Faculty of Management of Multimedia University on a Likert scale
from ‘‘strongly disagree’’ (1) to ‘‘strongly agree’’ (5). The results of the assessment
are as shown in Table 3.
For the purpose of comparison, the ratings are aggregated into a quality score by
summing the product of rating and the number of evaluators. In conclusion, the
domain ontology passes all the 12 quality criteria by attaining average scores
between 17 and 20 out of 25. Subsequently, the ontology was also validated against
the competency questions by the same group of experts. The domain ontology under
experiment was unanimously rated as fulfilling all competency questions.
6.2. Hypermedia content format ontology
Content format indicates whether the learning resource is a text, image, audio, video,
vendor specific file format, Web service, etc. The hypermedia content format
ontology is devised with reference to the definition of Internet media type (Grafman
Productions, 1997) to guide users to tag learning resources with the right content
format. The tags specifying the content format are used to filter learning resources.
S.B.-Y. Lau et al.
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11. Figure 5. Sample ontology of fundamental concepts in personal finance.
Interactive
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12. As shown in Figure 6, common content formats for hypermedia resources are text,
image, audio, video and Web applications.
6.3. Complexity level
Apart from the above-mentioned keywords, complexity level of the learning
resources can also be tagged with a set of tag keywords, T ¼ {‘‘Not Complex’’,
Table 3. Ontology quality evaluation results.
Number of evaluators who rated Score
1 2 3 4 5
No. Criteria
Strongly
disagree Neutral
Strongly
agree Score Average
Std
dev.
1. The ontology sufficiently and completely
defines all the principal high-level
concepts/terms in personal finance.
– 1 – 4 – 18 3.6 2.12
2. The ontology has completely defined all
the principal relations in personal
finance and used them in consistent
ways.
– 1 – 4 – 18 3.6 2.12
3. The use of vocabularies in the ontology is
consistent and unambiguous.
– – – 5 – 20 4 –
4. The ontology is correct and does not
require much corrections and/or
additions of terms.
– 1 – 4 – 18 3.6 2.12
5. The ontology is accurate for its intended
use, i.e. for learning and mastering a
wide spectrum of concepts in personal
finance.
– – 2 3 – 18 3.6 0.71
6. The ontology is compact and does not
have any redundant definition of
vocabularies or ‘‘circular definitions’’.
– – 2 3 – 18 3.6 0.71
7. The ontology is precise and does not give
a new meaning to a term in place of its
original vocabulary already established
in use in personal finance.
– – 3 2 – 17 3.4 0.71
8. The ontology is comprehensive and
contains sufficient high-level concepts
to understand the overall big picture of
‘‘personal finance’’.
– – 2 3 – 18 3.6 0.71
9. The ontology is detailed enough to
contain sufficient low-level concepts to
have in-depth and comprehensive study
of personal finance.
– – 1 4 – 19 3.8 2.12
10. The ontology contains vocabularies which
are formal and standard in the area of
personal finance. There is small or no
informal or ad hoc non-standard terms
used.
– – 1 4 – 19 3.8 2.12
11. The ontology is easily extensible. It is
possible to easily add lower-level
concepts into this ontology and extend
the knowledge domain with its current
structure.
– – 2 3 – 18 3.6 0.71
12. The ontology is up to date. It reflects the
most current and up-to-date view of
the fundamental concepts in personal
finance.
– – 2 3 – 18 3.6 0.71
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13. ‘‘Complex’’, ‘‘Quite Complex’’, ‘‘Very Complex’’, ‘‘Extremely Complex’’} which can
be translated into numerical values g(t) as shown in Table 4. For instance, if a user
thinks that a resource is ‘‘Not Complex’’ at all, he is indirectly assigning a value of
‘‘0’’ to the ‘‘Complexity’’ field of the resource query.
6.4. Opinion-rating policy
The domain ontology and hypermedia content format ontology are used to
determine the degree of relevance of learning resources to a user’s query. They are
not able to capture quality of the resources as perceived by users. To fill this gap, a
user ‘‘opinion-rating’’ policy is devised to extract the opinions of users about how
‘‘good’’ a learning resource is for the purpose of rating and ranking the resources.
The policy functions to associate commonly expressed subjective opinion keywords
of users appearing in tag data to objective numerical rating scores as a product
function:
Composite rating score; g ¼ g d
ð Þ g l
ð Þ g o
ð Þ ð2Þ
Table 4. Quantifying complexity level of resources.
Tag, t Complexity level, g(t)
‘‘Not’’ ‘‘Complex’’ 0
‘‘’’ 1
‘‘Quite’’ 2
‘‘Very’’ 3
‘‘Extremely’’ 4
Figure 6. Content format ontology.
Interactive Learning Environments 91

14. where d is the member of a set of keywords expressing intensity of feeling ¼
{‘‘Slightly’’, ‘‘Moderately’’, ‘‘Very’’, ‘‘Extremely’’}, l ¼ {‘‘–’’, ‘‘not’’} and o is the
member of a set of opinion keywords such as ‘‘cool’’, ‘‘boring’’ and ‘‘bad’’. Each of
the terms stated above are translated into corresponding numerical rating score,
g ¼ g(d) 6 g(l) 6 g(o) as shown in Table 5.
For instance, a learning resource tagged as ‘‘very boring’’ will be rated with
rating score, g ¼ 4 6 1 6 (71) ¼ 7 4 on a scale of 75 to 5 where ‘‘very’’ ¼ 4,
‘‘7’’ ¼ 1 and ‘‘boring’’ ¼ 71.
The strength of this model is
(1) Users are able to rate the learning resources subjectively with phrases
expressing their feelings without having to think about the corresponding
rating value;
(2) Tags are standardized to a standardized system interpretable format. Rating
values can converge to values that rank resources without regard to the
diverse ways of expressions of feelings by users.
7. Collaborative resource recommendation
In a personalized learning process, context metadata (e.g. learning style and
competency level) of a learner, and the need and intention of a learner (e.g. the
desired theme, content format and complexity level) are elicited indirectly or
specified directly by a learner. The query metadata schema is composed of two
components, namely the learner context profile and information need profile. A
query is defined as an ordered vector, Q ¼ ((Learning Style, Competency Level),
(Theme, Content Format, Complexity)). The resource query is the result of a learner
context profiling and a resource annotation process. Schema and policy described of
the semantic model for tags described above formalize these processes. As a result, a
resource query initializes a learning resource recommendation process as shown in
Figure 7.
We propose a collaborative resource recommendation algorithm based on
k-nearest neighbour with the following stages as shown in Figure 8:
Table 5. ‘‘Opinion-rating’’ policy for grading the resources.
Intensity, d g(d) l g(l) Opinion keywords, o g(o)
‘‘Slightly’’ 2 ‘‘–’’ 1 Negative ‘‘boring’’ 71
‘‘Moderately’’ 3 ‘‘tough’’
‘‘Very’’ 4 ‘‘not’’ 71 ‘‘bad’’
‘‘Extremely’’ 5 ‘‘silly’’
‘‘lousy’’
Ambivalent ‘‘no comment’’ 0
Positive ‘‘cool’’
‘‘interesting’’
‘‘good’’ 1
‘‘useful’’
‘‘fun’’
S.B.-Y. Lau et al.
92

15. (1) Stage 1 – resource matching: the purpose of resource matching is to compute
semantic relevance of learning resources based on a learner’s context profile
and pedagogical need specified in a resource query. Resources whose tags
fulfil the query of the current active learner are shortlisted. The degree of
matching of resources is carried out by computing semantic relevance
measures between the learner’s need (preferred theme, content format and
complexity level) and features of learning resources as tagged by users based
on framework of the semantic model for tags presented in Section 7. The level
of matching of a resource follows the function:
Smatching ¼ a sTheme þ b sFormat þ ð1 a bÞ sComplexity ð3Þ
where Smatching 2 [ 0,1], coefficient for Theme, b is the coefficient for Content
Format, sTheme is the similarity between themes, sFormat is the similarity
between content formats and sComplexity is the similarity between the
complexity levels. Resources with Smatching 4 mth, a matching cut-off thresh-
old value will be considered to be relevant to a learner’s query shortlisted for
next stage of processing.
(2) Stage 2 – resource classification: resources are classified into binary classes:
{‘‘Good’’, ‘‘Poor’’} based on the aggregated similarity measures of context
profiles of the current active learner and the learners who tag the resources as
well as the average rating inferred from tags. Resources are classified as ‘‘Good’’
if the predicted composite rating scores are above a ‘‘Good’’ness threshold.
Definition 1 (‘‘Good’’ or ‘‘Poor’’ Resources): Resources fulfil the basic criteria
of being relevant to the theme, content format and interest of the learner, as
well as with high quality content. Technically, ‘‘Good’’ness, grtij means the
computed rating score is greater than a ‘‘Good’’ness threshold value, i.e.
grtij 4 gth. The ‘‘Good’’ness value is an aggregation of relevance and quality
rating of content. Hence, if grtij gth, a resource will be classified as ‘‘Poor’’.
Figure 7. Overview of the personalized learning process.
Interactive Learning Environments 93

16. Definition 2 (Resource Classification): A resource from a sample set of
resources, R ¼ {r1, r2, . . . , rk}, is mapped to two classes C ¼ {c1, c2} ¼
{‘‘Good’’, ‘‘Poor’’} by a classification function f: ri ! cj 2 C. The classifica-
tion function is weighted on the similarities between learners’ context profiles:
(learning style, competency level). Resources aggregately rated higher than
the threshold value, gth by similar learners and other dissimilar learners will
be classified as ‘‘Good’’, while others are classified as ‘‘Poor’’.
Classification based on a rating function takes into account the similarity of
the tagger’s context profiles: (learning style, competency level) to the current
active learner and the aggregate ratings of the resources weighted by learners’
similarities:
Aggregated rating of a resource,
grtj ¼
1
N
X
N
i¼1
wi gi ð4Þ
Figure 8. Overview of the resource recommendation algorithm.
S.B.-Y. Lau et al.
94

17. where N is the total number of rating instances, gi is the rating by ith learner
on the resource j, and wi is the weight factor for ri based on learner
similarities. Similarity or distance measure between ith learner and the current
active learner can be the value for wi to distinguish between the relative
significance of ratings imposed by learners. We may also use two distinct
values for w where a much larger w, e.g. 100, is used for similar learners
(nearest neighbours) compared to smaller w, e.g. 10, for dissimilar learners.
(3) Stage 3 – resource filtering: if the rating score computed for a learning
resource as grti 4 gth, a resource is considered to be a ‘‘Good’’ resource. Else,
it is excluded from further consideration as shown in Figure 9. A set of
‘‘Good’’ resources, R0
are considered for ranking.
(4) Stage 4 – resource ranking: resources, which are classified as ‘‘Good’’ are
ranked in the descending order of predicted rating or degree of ‘‘goodness’’.
(5) Stage 5 – resource presentation: ranked list of resources are presented to the
current active learner for interaction.
8. User study
To verify the effectiveness of the semantic model for tags, a prototype Web-based
learning resource recommender system has been implemented with PHP-MySQL
which is accessible via URLs such as http://3ants.vacau.com. Architecture of the
prototype learning resource recommender system is shown in Figure 10. The
Figure 9. Resource classification and filtering.
Interactive Learning Environments 95

18. prototype allows users to share and tag hypermedia resources obtained from the
Internet as shown in Figure 11.
Users are able to annotate resources with concept(s) or theme(s) for a knowledge
domain such as ‘‘personal finance’’, the content type of the resource and the
complexity of the resources. Besides, the key novelty of the prototype system is the
subjective opinions expressed by users that can be automatically converted to
numerical ratings via the use of commonly found vocabularies of emotion as shown
in Figure 12 following an opinion-rating policy described in Section 6.4.
The purpose of this pilot user study is aimed at quantifying the various aspects of
quality of the recommendation results and the recommender system from the
perspectives of the users. As far as we know, currently there is yet any comprehensive
framework to evaluate a recommender system for the education domain. Hence, we
formulate a novel seven-pillar evaluation framework for learning recommender
system as shown in Figure 13. The evaluation framework is formulated via a
rigorous process as shown in Figure 14.
The system is assessed via questionnaires that encompass evaluation metrics such
as user-perceived accuracy, novelty and serendipity, usefulness, domain coverage and
diversity of information, confidence and trust of user, and information adequacy and
usability of a learning recommender system. Each of the metrics is explained briefly
as follows:
(1) User-perceived accuracy is a measure of the level of relevance of the
recommender results to what is being expected by users. Though accuracy
metrics of recommender systems such as precision, recall and F-measure are
more readily measurable via simulated data, it is helpful to confirm the
relevance of the recommended results judged by users.
(2) Novelty and serendipity of a recommender system ensure that users have
access to non-obvious, unexpected and ‘‘surprisingly interesting’’ items.
Figure 10. Resource sharing and tagging.
S.B.-Y. Lau et al.
96

19. (3) Domain coverage or information diversity is a measure of the size of the
domain of resources over which the system can make recommendations
which may include the content format, themes and topics towards fulfilling
the learning needs.
(4) It is assumed that higher confidence and trust in the recommendation would
lead to more recommendations being used.
(5) Information adequacy is concerned with why an item is recommended as
adequately explained and conveyed to the users.
(6) Usefulness or effectiveness of the recommendation is concerned with whether
the recommendation is suitable to the information needs, learning goal or
task performed by the users.
(7) Usability is concerned with whether users enjoy the features of the user
interface of the recommender system and whether users perceive the tasks as
easy to complete. It is more a measure for the recommender system rather
than the recommendation results.
The pilot user study was carried out as a semi-controlled field study with users in
a social Web environment. The user study was piloted for the topic: ‘‘Introduction to
personal finance’’. Invitation to pilot test the prototype system was broadcasted to
over 100 direct or indirect friends on the social network. Friends who accepted the
Figure 11. Annotating with tags expressing subjective feelings about resource.
Interactive Learning Environments 97

20. Figure 12. Architecture of the learning resource recommender system.
Figure 13. The seven-pillar evaluation framework.
S.B.-Y. Lau et al.
98

21. invitation would log in to the prototype system for pilot runs at a time and place
convenient to them over a period of 2 weeks. At the end of the period of experiment,
there are a total of 186 resources shared and tagged by a total of 92 friends who
accessed the system. However, only about less than half (or 42%) of the respondents
completed the learning style and competency-level assessments. The age group of all
the respondents is between 20 and 40 with more than half completed or currently
pursuing tertiary education.
At the end of the process as shown in Figure 15, users went through a 30-
question evaluation based on the seven-pillar evaluation framework for the
recommended results and recommender system. For reasons unknown, only 11 of
the respondents completed the evaluation cycle. The evaluation results are
summarized in Table 6.
Although the sample size may not be statistically significant at this stage (where
only 11 respondents completed the evaluation cycle), the user evaluation results
provide useful hints into the factors that affect users’ perception on the quality of
recommendation. Preliminary results show that the recommender system and the
recommendation results are rated in the positive territory (more than 3.5 from a scale
of 1–7) overall, including the relevance of the recommended results (accuracy). The
recommendation generally gains confidence and trust from the users by scoring more
than 5 out of 7. Based on Table 6, the recommender is rated between 4.6 and 5 for
other quality criteria such as novelty, serendipity, domain coverage and information
diversity. As far as we know, as of today, there is no any traditional tagging system
specifically designed for the educational domain. Effort is underway to build a
learning resource recommender system with the traditional tagging approach, and
more interesting results will be presented in upcoming publications.
9. Conclusion
To sum up, this article has successfully demonstrated the formulation of a
lightweight folksonomy-based resource annotation metadata schema based on a
semantic model for tags. The framework includes not only a domain that is formally
consistent, but is also accurate, extensible and current (based on experts’
assessment). Most importantly, the testing of this framework provides the
underlying guiding principles for collaborative annotation of learning resources
using semantic tags. The semantic model and metadata schema form essential parts
of a personalized learning resource recommendation. The semantic model and
Figure 14. Formulation steps of the seven-pillar evaluation framework.
Interactive Learning Environments 99

22. Figure 15. Overview of the procedure for the execution of user study.
S.B.-Y. Lau et al.
100

23. Table 6. Evaluation results for user study.
No. Statement
Average
rating
A Accuracy
1 The items recommended to me matched my needs/preferences. 4.7692
2 The recommended items I received better-fits my needs/preferences
than what I search using a search engine.
4.8462
3 The recommended results are ranked in the correct order of
relatedness to my needs.
4.6923
4 The recommended results are ranked in the correct order of rated
quality (in terms of usefulness of the item).
4.6154
Average 4.7308
B Novelty and serendipity
5 The system suggested unexpected new items that are relevant to my
needs and preferences.
4.6923
6 The recommender gave me good suggestions. 4.6923
Average 4.6923
C Domain coverage and information diversity
7 The items recommended to me cover diverse content types. 4.2308
8 The items recommended to me cover diverse themes/topics. 4.7692
9 There is enough diversity of items in the results to fulfil my learning
needs.
4.6154
Average 4.5385
D Confidence and trust
10 I trust the recommended items that are ranked correctly. 5.0769
11 I am confident in the quality of the items recommended to me. 5.0000
Average 5.0385
E Information adequacy
12 The information provided for the recommended items is sufficient for
me.
4.8461
13 I understood the reason why the items were recommended to me. 4.6923
Average 4.7692
F Usefulness and effectiveness
14 The recommender helped me be more effective in learning. 5.0000
15 The recommender helped me be more productive in learning. 5.0000
16 The recommended items influenced my way of learning. 4.7692
17 I gained new knowledge/skills interacting with the recommended
items.
5.0769
18 I am positively stimulated/encouraged by the system to pursue the
learning of the subject matter further.
5.0000
19 The system was fun. 4.9231
20 The recommender helped me to decide better. 4.7692
Average 4.9341
G Usability
21 It was easy to look for and access a recommended item(s) I need/like. 4.9231
22 The system was flexible. The recommender provides an adequate way
for me to express and revise my needs/preferences.
4.9231
23 I felt comfortable using this system. 4.8461
24 It was easy for me to inform the system if I disliked/liked the
recommended item (provide feedback).
4.7692
Average 4.8654
(continued)
Interactive Learning Environments 101

24. metadata schema provide the framework for item-based filtering of learning
resources prior to a user-based classification of resources as well as ranking of
learning resources based on numerical ratings inferred from tags within a
collaborative resource recommendation algorithm based on the k-nearest neighbour.
At present, as a proof-of-concept, the semantic model and metadata schema are
implemented in a prototype Web-based learning resource recommender system.
Preliminary results of an ongoing user study show that associating collaborative
tagging with controlled vocabularies in learning resource annotation is effective in
personalizing learning resources. Under the current framework, a learner is able to
leverage the opinions of others in order to locate the right, relevant resource of good
quality while avoiding less useful ones.
Notes on contributors
Simon Boung-Yew Lau is a lecturer and researcher at the Faculty of Engineering and Science,
Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia. He earned his master of science
degree from the Malaysia University of Science and Technology. He teaches undergraduate
and postgraduate courses, specializing in software engineering. His research areas of interest
include context-aware computing, e-learning, social Web and parallel computing.
Chien-Sing Lee’s research spans across instructional design, computer-supported collaborative
learning, game-based learning, mobile learning, data mining and intelligent agents. She has
experimented with the use of service-oriented architectures to deliver personalized instruction
and the use of knowledge management not only to organize knowledge but also to derive best
practices from prior knowledge so that these best practices can be easily adapted to similar
scenarios. Another area of interest is interoperability through the semantic Web due to her
concern that best practices should be shared, adopted and adapted locally and globally to
improve teaching and learning practices in half the time half the cost but with the highest
effectiveness and efficiency. Her latest experiments deal with scaffolding creativity in learning
environments.
Yashwant Prasad Singh received the B.Sc.Engg. (Electrical) degree (first-class honours) from
Bihar College of Engineering, Bhagalpur, India, the M.E. degree in electronics and
communications (first-class honours) from the University of Roorkee, Roorkee, India, and
the Ph.D. degree in electrical engineering from the Indian Institute of Technology, Kanpur,
India, in 1966, 1975 and 1980, respectively. He is currently a professor of computer
engineering, Faculty of Information Technology, Multimedia University, Cyberjaya,
Table 6. (Continued)
No. Statement
Average
rating
H Overall
25 Overall, I am satisfied with the recommender. 5.0000
26 I am always confident I will like the items recommended to me. 4.7692
27 The system was able to convince me about the goodness of the
recommendations.
5.0000
Average 4.9231
I Continuance and frequency (future use)
28 I will use this recommender again. 4.7692
29 I will use this recommender frequently. 4.7692
Average 4.7692
J Recommendation to friends
30 I will recommend this recommender to my friends. 5.0000
Overall average 4.8261
S.B.-Y. Lau et al.
102

25. Malaysia. His research interests include complexity theory, cognitive science, computational
neuroscience, Artificial Neural Network (ANN), fuzzy systems, artificial intelligence algorithms
and intelligent systems, machine learning and data mining, natural computation (evolutionary
computing, neural computation and artificial immune systems) and their applications to
classification, clustering, anomaly detection, association rule mining, ranking and routing, as well
as computer and microprocessor architectures, including transputers. He has published more
than 100 papers in international and national journals and conference proceedings.
Acknowledgement
This work is part of the first author’s PhD research conducted partly in Multimedia University
and partly while the second author was a faculty at Multimedia University.
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