












































Ontology for the User-Learner Profile Personalizes the Search Analysis of Online Learning Resources: The Case of Thematic Digital Universities  


ARTICLE 

Ontology for the User-Learner Profile Personalizes the 
Search Analysis of Online Learning Resources 
The Case of Thematic Digital Universities 
Marilou Kordahi 

 

INFORMATION TECHNOLOGY AND LIBRARIES | JUNE 2022  
https://doi.org/10.6017/ital.v41i2.13601 

Marilou Kordahi (marilou_kordahi@yahoo.fr) is Assistant Professor, Faculty of Business 
Administration and Management, Saint-Joseph University of Beirut, and Associate Researcher, 
Paragraph Research Laboratory, Paris 8 University. 

 © 2022.  

ABSTRACT 

We hope to contribute to the field of research in information technology and digital libraries by 
analyzing the connections between Thematic Digital Universities and digital user-learner profiles. 
Thematic Digital Universities are similar to digital libraries, and focus on creating and indexing open 
educational resources, as well as improving learning in the information age. The digital user profile 
relates to the digital representation of a person’s identity and characteristics. In this paper we 
present the design of an ontology for the digital User-Learner Profile (OntoULP) and its application 
program. OntoULP is used to structure a user-learner’s digital profile. The application provides each 
user-learner with tailor-made analyses based on informational behaviors, needs, and preferences. We 
rely on an exploratory research approach and on methods of ontologies, user modeling, and semantic 
matching to design the OntoULP and its application program. Any user-learner could use the 
OntoULP and its application program. 

INTRODUCTION 

More online learning environments are supporting the creation and dissemination of quality Open 
Educational Resources (OER) to facilitate change in the education sector, improve education, 
ensure longlife learning, reduce cost, and other motives.1 In 2002, the United Nations Educational, 
Scientific and Cultural Organization (UNESCO) recommended the definition of OER as follows: “the 
open provision of educational resources, enabled by information and communication 
technologies, for consultation, use and adaptation by a community of users for non-commercial 
purposes.”2 The William and Flora Hewlett Foundation defined OER as “freely licensed, remixable 
learning resources—[They] offer a promising solution to the perennial challenge of delivering 
high levels of student learning at lower cost.”3 In 2012, UNESCO noted that OER offer education 
stakeholders an opportunity to access textbooks and other learning contents to enhance their 
knowledge and professional experiences.4 Education stakeholders may choose OER based on their 
informational needs, behaviors, and preferences.5 

We hope to contribute to the field of research in information technology and digital libraries by 
analyzing the connections between Thematic Digital Universities and digital user-learner profiles. 
We are conducting a case study using the Digital University Engineering and Technology.6 In the 
following we will explain these topics and the interest in the Digital University Engineering and 
Technology. 

In 2003, the French Ministry of Higher Education, Research, and Innovation initiated the creation 
of Thematic Digital Universities to facilitate the integration and use of information and 

mailto:marilou_kordahi@yahoo.fr


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communication technologies for education in university teaching practices.7 In total, there are six 
Thematic Digital Universities which are organized by broad disciplines: health sciences and sports, 
engineering sciences, environment and sustainable development, humanities, economics and 
management, as well as technical studies. Thematic Digital Universities are similar to digital 
libraries, and focus on creating and indexing OER, as well as improving learning in the information 
age.8 Although Thematic Digital libraries are mostly comprised of OER, they also develop complete 
training programs with some of these resources (e.g., Massive Open Online Courses, or MOOCs). 
They are partners with Canal-U, the video library for higher education, as well as the French 
national platform for Massive Open Online Courses (FUN-MOOC). Thematic Digital Universities 
are mostly created for learners and teachers, as they offer complementary educational resources 
to bachelor, masters, and doctoral programs.9  

To date, learners and teachers have free access to most Thematic Digital Universities and 
corresponding educational resources. Registration is not required; however, without registration 
neither the learner nor the teacher can analyze her/his search for OER based on informational 
behaviors, needs, and preferences.10 

We will focus on the analysis of OER metadata records in the context of Thematic Digital 
Universities. Each OER in the repository holds a metadata record to precisely describe its 
specifications to the learner or teacher (e.g., the learning level, language, and topics). 
Specifications are written according to the Institute of Electrical and Electronics Engineers (IEEE) 
standards for Learning Object Metadata (LOM),11 LOMFR, and SupLOMFR. LOM provides an 
accurate descriptive schema of a learning object suitable for educational resources12 (e.g., the 
classification and identification of an educational resource). LOMFR and SupLOMFR are currently 
applications of LOM in the French educational community.13 

The Digital University Engineering and Technology attracted our attention because of the 
following characteristics: clear presentation of its objectives, regular information updates, priority 
for free access to OER and open data, 3,000 published educational resources, extensive 
documentation of OER indexing, interoperability of OER and metadata records, and an advanced 
search engine for OER. Each metadata record describes precise information on the OER, including 
the main title, keywords, descriptive text, educational types (or resources), learning level, 
copyrights, knowledge domains, topics, authors, and publishers. It is processed and structured 
with XML language which is human-readable and machine-readable.  

Digital user profiles relate to the digital representation of a person’s identity and characteristics.14 
Digital identity is the sum of digital traces (or “footprints”) relating to an individual or a 
community found on the Web or in digital systems. Digital traces correspond to the user’s profile, 
browsing history, and contribution actions.15  

Our focus is the learner who wishes to use the Thematic Digital Universities for tailor-made 
analysis of retrieved information based on her/his needs and preferences. We offer the learner an 
option to register on these platforms to track behavior over time while searching for OER. 
Analyses are based on criteria the learner has previously chosen to personalize this search. 
Subsequently, we suggest using the term “digital user-learner profile.” We will do our best to 
respect the General Data Protection Regulations when collecting information on the digital user-
learner profile.16 The General Data Protection Regulations are privacy laws drafted and passed by 



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the European Union that prohibit the processing, storage, or sharing of certain types of 
information about individuals without their knowledge and consent. 

The research questions are as follows:  

1. In the context of Thematic Digital Universities, how can a user-learner personalize the 
search for open educational resources according to her/his digital profile?  

2. In this same context, what kinds of information can a user-learner analyze in a search for 
open educational resources according to her/his digital profile? 

The objectives of this article are to present the preliminary results of work in progress on the 
design of the ontology for the digital User-Learner Profile (OntoULP) and its application program, 
the Personalized modeling System for the User-Learner profile (PSUL). We rely on the methods of 
ontology,17 user modeling,18 and semantic matching.19 The method of ontology is used to describe 
in a formal manner a set of concepts and objects which represent the meaning of an information 
system in a specific area and the relationships between these concepts and objects.20 The method 
of user modeling describes the process of designing and changing a user’s conceptual 
understanding. It is applied to customize and adjust systems to meet the user’s needs and 
preferences. The method of semantic matching is used to identify and relate a meaning concept 
(or class) to its homologous concept in tree-like schemas and to consider the concept’s position in 
these schemas (e.g., mapping a class in an ontology to homologous concepts in metadata records). 
This relationship can be a one-to-one concept or one-to-many concepts. 

The OntoULP is a first approach, and it will be used to structure a user-learner’s digital profile in 
the context of Thematic Digital Universities. We design this ontology for three main reasons: to 
structure collected and generated information21 (e.g., structuring a user-learner’s learning 
preferences will enable the identification of learning behaviors and activities), to analyze collected 
and generated information22 (e.g., analyzing generated information by a user-learner may predict 
a search for OER), as well as to facilitate relationships between a user-learner and Thematic 
Digital Universities23 (e.g., analyzing user-learner informational behaviors may improve OER 
creation and dissemination).  

The PSUL will be designed as an application program for the OntoULP. It will be used to provide 
each user-learner with tailor-made analyses based on informational behaviors, needs, and 
preferences. PSUL will include a secure database and web pages, namely those for registering and 
editing the user-learner profile and its dashboard.24 OntoULP and its application program will 
offer each registered user-learner an opportunity to analyze the search for OER according to 
informational behaviors and needs.  

OntoULP and PSUL could be implemented in the structure of information systems for educational 
and research institutions, documentation and information centers, and many others. We will fine-
tune our analysis by relying on a case example—the Thematic Digital Universities. 

This article comprises six sections. First, we will explain the exploratory research carried out in 
the context of Thematic Digital Universities. Second, we will present the main published works 
related to the subject of the article. Third, we will explain the approach followed to design and 
write the OntoULP. Fourth, we will discuss the creation of the PSUL application program. Fifth, we 
will demonstrate the integration of the designed ontology and its application program into a 



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mirror site to perform a technical test. Finally, we will discuss the completed work before 
concluding the article.  

EXPLORATORY RESEARCH APPROACH 

This exploratory research is based on an analysis of the literature, a semistructured questionnaire, 
and an in-depth documentary research. We check the consistency of collected information and 
identify the need to personalize the search for OER as well as make tailor-made analysis of 
information. 

Methods used 
During the first 18 months of the COVID-19 pandemic (November 2020–May 2021), we conducted 
qualitative research to deepen our comprehension of the practices of Thematic Digital 
Universities. We collected and interpreted primary and secondary information.  

Primary information: We contacted the Digital University Association and their six Thematic 
Digital Universities.25 Because of their extensive expertise and robust knowledge in leading or 
managing Thematic Digital Universities, Directors and General Secretaries were chosen to self-
administer an electronic semistructured questionnaire. We contacted seven individuals and 
received six responses. In this questionnaire, we asked about the following topics: the recent 
knowledge of Thematic Digital Universities, conditions of access to OER, metadata records 
indexing as well as user-learner’s expectations. An example of the questionnaire is included in the 
appendix. 

Secondary information: We analyzed a report by the French General Inspectorate of the National 
Education and Research Administration. We have also studied recently-published scientific 
articles by Anne Boyer (2011), Deborah Arnold (2018),26 and Sihem Zghidi and Mokhtar Ben 
Henda (2020). 

The results and findings will be explained in the following paragraphs. 

Results of information collection 
We have compared responses to the questionnaire and contents of published documents and 
articles.  

For the Digital University in Health Sciences and Sports, “resources are mostly accessible to 
learners from member universities, through an identification system based on the university email 
address.”27 Only a few resources are open to the public. Otherwise, according to comments 
gathered from the other four digital universities and Digital University Association, “Thematic 
Digital Universities are part of global movements providing access to OER by promoting open 
access to knowledge.”28 They are an opportunity for learners to discover new disciplines and 
explore new areas.29 In fact, “the process for indexing metadata records meets standards for 
education, such as LOM, LOMFR and SupLOMFR.”30 At present, there is no feedback on the use of 
Thematic Digital Universities platforms. In other words, “Thematic Digital Universities have no 
information about learners who view OER, because there is no login and password. This is done on 
purpose to make them as open as possible.”31 These platforms are considered as a means of self-
training with quality assurance, as the documents have been produced and validated by higher 
education teachers. “Thematic Digital Universities provide a certain flexibility allowing learners to 
work when and where they want.”32 



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Findings 
Five Thematic Digital Universities and the Digital University Association responded to the 
semistructured questionnaire. Two Thematic Digital Universities can track user-learners’ 
behaviors. These digital universities are related to the disciplines of health and sport in addition to 
technical studies. To date, four Thematic Digital Universities cannot track user-learners’ 
interactions based on informational behaviors and preferences. OntoULP and its application 
program could be implemented in four Thematic Digital Universities, which are related to the 
disciplines of engineering sciences, environment and sustainable development, humanities, 
economics, and management. 

LITERATURE REVIEW 

To our best knowledge, published research works addressing this research subject are limited in 
the context of Thematic Digital Universities.33 We analyze the most recent ontologies and user 
modeling systems that are close to our research objectives. The main works we use are those of 
Bloom et al. (1984),34 Smythe et al. (2001),35 Green and Panzer (2009),36 and Kordahi (2020),37 in 
addition to Kelly and Belkin (2002). The work methods and field studies these researchers have 
developed are useful to design the structure of the OntoULP and the model of its application 
program. In the following paragraphs, we will explain these works and the relationships with this 
research article. 

Selection of recently published works 
In 2020 and 2021, Kordahi designed an ontology and a personalized dashboard for user -
learners.38 The objectives of these works were to track individual searches for OER and compare 
them with a user-learner’s field of work. To design her ontology, Kordahi relied on standardized 
ontologies and validated taxonomies which are used in online learning environments, namely the 
IMS Learner Information Profile (IMS LIP)39 and Bloom’s Taxonomy. The personalized dashboard 
was linked to the user-learner ontology. The designed dashboard was tested technically with its 
ontology in a digital library environment to examine its performance. Kordahi used the methods of 
ontologies and semantic matching. 

Learner model 

We are mostly interested in the learner model40 as it “is a model of the knowledge, difficulties and 
misconceptions of the individual [learner].” 41 As students learn the educational resources they 
find, the learner model is updated to display their current progress. The model can continue to 
tailor students’ interactions as they learn. There are several learner models, such as the IMS LIP.42 
We examine the IMS LIP, which is based on a standardized data model describing a learner’s 
characteristics. It is mainly used to manage a student’s learning history to discover her/his 
learning opportunities. IMS LIP is made from 11 categories that gather learning information: “the 
identification, goals, qualifications and licenses, activity, interest, competency, accessibility, 
transcript, affiliation, security, and relationships.”43 This model has been successfully used by 
many renowned researchers (e.g., Paquette 201044) to design a learner model and then adapt it to 
appropriate contexts. 

IMS LIP’s reliability, accuracy, and flexibility match well with the OntoULP motives. We will use it 
to begin designing the structure of the OntoULP and adapt it to the Thematic Digital Universities 
context. We will also consider the IEEE LOM, LOMFR, and SupLOMFR classification fields. This 
measure will be used to improve semantic matching between the OntoULP and OER metadata 
records.  



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Taxonomy of educational objectives 
We examine the user-learner’s educational objectives to meet informational needs and 
expectations.45 In each OER metadata record, educational objectives are defined based on Bloom’s 
Taxonomy (e.g., “understand the context and rules of scientific publication” 46). Bloom et al. have 
developed a taxonomy for educational objectives to classify statements teachers expected 
students to learn as a result of lessons and instructions. The researchers described a method for 
allowing students to achieve educational goals while carrying out exercises utilizing the resources 
of the environment. Bloom et al. relied on in-depth qualitative studies to design and validate this 
taxonomy. Bloom’s Taxonomy contains the following six major categories related to the cognitive 
domain: knowledge, comprehension, application, analysis, synthesis, and evaluation. This 
taxonomy was revised in 2001 by Lorin Anderson et al.47 Bloom’s Taxonomy is still in use 
internationally as in the works of Kordahi. 

Integrating Bloom’s Taxonomy into the OntoULP will enhance the structure of a user-learner’s 
educational objectives. These educational objectives will be organized in six categories allowing 
the user-learner to refine her/his informational goals. Therefore, we will create a mutual link 
between the user-learner’s educational objectives and OER educational objectives. 

Knowledge domains 

Knowledge organization systems48 are seen as a valuable component for searching for OER.49 Our 
research includes analyses of OER metadata records to establish relationships between their 
knowledge topics and the user-learner’s topics of interest. In the Thematic Digital Universities’ 
metadata records, a precise classification is reported respecting both knowledge topics and Dewey 
Decimal Classification (e.g., geographic information systems (526.028 5)). 50 The Dewey Decimal 
Classification and Relative Index 22nd edition,51 published in 2003 by the Online Computer Library 
Center,52 is being used worldwide in digital libraries and by the Thematic Digital Universities.53 

In their works published in 2009, Green and Panzer have developed an ontology to structure 
knowledge domains.54 This ontology recognizes two classes, which are Dewey classes and 
knowledge topics. We selected the Dewey Decimal Classification for the OntoULP because the 
Thematic Digital Universities are already using it. We will rely on Green and Panzer’s ontology to 
structure the knowledge domains in the OntoULP (e.g., the use of Dewey classes and knowledge 
topics). We will establish relationships between the knowledge domains and user-learner model, 
allowing the user-learner to choose the most appropriate learning topics. 

User modeling system 
The “user modeling system for personalized interaction and tailored retrieval” is useful for 
analyzing each user-learner’s informational needs and preferences.55 Kelly and Belkin’s system 
helps the user to track informational needs over time. It contains three classes of models and a set 
of interactions. The “general behavioral model” tracks information seeking and user behavior to 
determine informational needs. The “personal behavioral model” characterizes each user’s 
information search according to specific preferences and behaviors. The “topical models” are 
associated with concepts related to each user’s informational behaviors. 

This model is developed by renowned researchers specialized in information retrieval and 
corresponds to the objectives of the research article. We will use the structure of Kelly and 
Belkin’s model (2002) to design the PSUL application program, in the context of Thematic Digital 



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Universities. Relationships between both the PSUL and OntoULP ontology will be established to 
carry out personalized analyses of OER search. 

ONTOULP ONTOLOGY 

OntoULP’s design is based on the works discussed in the previous section. It consists of two 
stages. We start by writing it. We then describe the ontology and emphasize the relationships 
between different entities.  

Writing the ontology 

We write OntoULP with Protégé Editor and use the HermiT inference engine to check the 
consistency of classes and their relationships with objects. The ontology’s first approach is saved 
in OWL format, which is compliant with the Semantic Web technologies. 

OntoULP description 
The ontology is comprised of five subsystems. These are: user-learner, user-learner model, 
educational objectives, learning design, and knowledge domains. Each subsystem is composed of 
classes that inherit the attributes of the subsystem on which they depend. For brevity, the figures 
show the hierarchical representation of these subsystems.  

The user-learner subsystem contains all recorded private information on the digital user-learner 
profile. The classes personal information, identification sessions, and traces provide information 
about the user-learner’s behavior and search history for OER, e.g., the search duration for OER 
(see fig. 1). 

The user-learner model subsystem is responsible for structuring collected information related to 
learning behaviors and needs, namely the classes identification, interest, learning level (or 
qualifications and licenses), personal preferences (or accessibility), activities, learning objectives 
(or goals), affiliation, and network of contacts (or relationships). In the context of Thematic Digital 
Universities, the resulting subsystem is composed of eight classes instead of eleven. The user-
learner model subsystem conveys the structured information to the user-learner subsystem. 
Figure 1 shows the structure of both subsystems, the user-learner and user-learner model. 



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Figure 1. Hierarchical representation of both subsystems, the user-learner and user-learner model. 

The educational objectives subsystem includes cognitive objectives involved in the process of 
acquiring knowledge. We design their structure by adapting Bloom’s Taxonomy. The cognitive 
objectives class includes six interrelated subclasses: remember (or knowledge), understand (or 
comprehension), apply (or application), analyze (or analysis), synthetize (or synthesis), and 
evaluate (or evaluation). The cognitive objectives class is enhanced with the IEEE LOM, LOMFR, 
and SupLOMFR classification fields enabling the user-learner to choose objectives which best 
describe their needs and preferences, e.g., the class apply has subclasses design, choose (see fig. 2). 



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Figure 2. Hierarchical representation of educational objectives and learning design subsystems. 

The learning design subsystem is an adaptation of the IMS Learning Design model, in the context 
of Thematic Digital Universities.56 The learning design subsystem has two main classes: the user-
learner’s environment and learning activities. The environment class has six Thematic Digital 
Universities as subclasses. In a general manner, information about the environment class comes 
from Thematic Digital Universities platforms (e.g., the viewed metadata records). The learning 
activities class has resources as a subclass. The resources subclass is also enriched with the IEEE 
LOM, LOMFR, and SupLOMFR classification fields to complete its structure and meet the user-
learner’s needs and expectations. Further, we have connected the learning activities with cognitive 
objectives classes to ensure continuity between them (e.g., the subclass experimentation is 
associated with subclass analyze). Figure 2 illustrates the main structure of both subsystems, the 
learning objectives and learning design. 

The knowledge domains subsystem contains the main class Dewey Decimal Classification and class 
contacts. This main class has two subclasses: Dewey classes, with the corresponding divisions as 
subclasses, and knowledge topics, with the corresponding subtopics as subclasses (e.g., Science 
topic corresponds to Dewey class 500, Manufacturing subtopic corresponds to division 670).  



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Figure 3. Hierarchical representation of the subsystem knowledge domains. 

The subclass knowledge topics is related to the subclass user-learner’s learning topics to improve 
informational behavior analyses. The class contacts is linked to the subclass user-learner’s network 
of contacts to analyze the strength or weakness of networks between the user-learner and OER 
publishers/authors (see fig. 1). 

The subsystem knowledge domains can deal with questions which belong to different levels in the 
OntoULP. For example, which learning topics is the user-learner looking for? Which network of 
contacts is the user-learner interested in? What are the activities related to the user-learner 
learning topics? What keywords searched relate to the user-learner’s learning topics?57 In Figure 
3, we show some of the subsystem’s elements. 

PERSONALIZED MODELING SYSTEM FOR THE USER-LEARNER PROFILE 

The PSUL is based on the works discussed in the previous sections. It is written with PHP, 
JavaScript, and XML, computing languages for the Web. This new modeling system comprises 
three classes of models: the general behavioral, personal behavioral, and topical (see fig. 4). 

The general behavioral model has two roles. It registers a user-learner’s digital profile in order to 
determine informational needs and preferences for OER. It also collects informational behaviors of 
a user-learner while viewing OER metadata records for tailor-made analyses. The general 



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behavioral model includes the ontology OntoULP as well as user-learner registration and editing 
pages. The registration page contains relevant information about a user-learner, an option to 
accept or reject data collection, and a list of choices for behavioral analyses. Once registered, the 
user-learner can modify her/his profile from the editing page. Both pages are mapped to the 
OntoULP to populate criteria fields. The user-learner profile information is stored in a secure 
database (as described in the introduction). 

The personal behavioral model is used to analyze information according to the registered digital 
user-learner profile and informational behaviors. It contains a set of queries to collect and tailor 
information for each user-learner. The sources of information are the general behavioral model 
and OER metadata records. This model is designed based on analyses of the general behavioral 
model. When a user-learner begins searching for OER, the general behavioral model provides the 
personal behavioral model with all profile information as well as the history of OER search. This 
information is transmitted to make an adjustment to the personalized user-learner profile. The 
user-learner profile changes as the personal behavioral model receives more information from the 
general behavioral model. Informational interactions connect the personal behavioral model to 
topical models. 

The topical models bring together all analyses of OER search for each user-learner.58 They are 
inferred from the personal behavioral model. Informational interactions connect the topical 
models to the general behavioral model. For now, we have designed four topical models and 
present their outcome in the user-learner dashboard page. This page may be used as a practical 
dashboard providing feedback to each user-learner, who can use these analyses to adjust or make 
changes in the profile or the OER search.  

Topical model 1 is used to synthesize each user-learner’s search history and to suggest a profile 
adjustment. The suggested adjustment is based on analyses of user-learner behavioral trends.59 

Topical model 2 allows each user-learner to examine the list of knowledge topics which have 
caught her/his attention. It contains two separate lists describing viewed OER metadata records 
and matching them to the chosen topics of interest. 

Topical model 3 shows comparative analyses between the user-learner’s preference criteria and 
viewed metadata records. The user-learner can interact with this model by comparing the chosen 
topics of interest to the viewed knowledge topics. The user-learner can also compare the chosen 
learning activities to the viewed teaching pedagogies. The teaching pedagogies as well as 
knowledge topics are extracted from OER metadata records (see fig. 5a). 

Topical model 4 highlights each user-learner’s interest based on the keyword search volume. The 
user-learner can interact with this model by studying the relationships between searched 
keywords and chosen topics of interest (see fig. 5a and fig. 5b). Figure 4 shows the diagram of 
PSUL as explained in the paragraph. 



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Figure 4. The PSUL diagram based on the Kelly and Belkin’s system (2002).60 

ONTOULP AND ITS MODELING SYSTEM IN THE CONTEXT OF A THEMATIC DIGITAL UNIVERSITY  

For now, OntoULP and its application program are implemented in the Digital University 
Engineering and Technology private platform which is hosted on a private server. We conducted a 
technical test to mainly assess OntoULP’s precision and performance.  

The digital university’s team has sent us a complete archive of their OER metadata records. These 
OER metadata records are saved on the private server with the Digital University Engineering and 
Technology platform. Once a user-learner is registered to this platform, she/he can carry out 
actions through the PSUL. For example, these actions are a search by keyword, personalization of 
profile, tailored-made analysis of OER search, and visualization of analyses in the dashboard. 



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Figure 5a. Screenshot of a section of the dashboard. The bar chart shows comparative analyses 
between a user-learner’s topic of interest and knowledge topics. The knowledge topics are 
extracted from the viewed OER metadata records. 



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Figure 5b. Screenshot of a section of the dashboard. The pie chart highlights a user-learner’s 
interest based on a keyword search volume. The bar chart shows comparative analyses between a 
user-learner learning activities and viewed teaching pedagogies. The keywords are extracted from 
the search. The teaching pedagogies are extracted from OER metadata records.  

To avoid making the article longer, in figures 5a and 5b, we show brief results of a technical test. In 
this example, the user-learner’s identity is fictitious, or the user-learner’s persona is a construct.61 



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In other words, the user-learner’s identity is not real, it is fabricated to conduct and complete the 
technical test. When registering, this user-learner has selected the technology topic (Dewey class 
600) in addition to the management and public relations subtopic (Dewey division 650). This user-
learner has also selected all topical models. During a viewing session, this user-learner chose to 
search for OER while using a few keywords. The keywords were chosen according to the user-
learner’s profile and in order to continue the technical test.  

DISCUSSION AND CONCLUSION 

The ontology for the digital User-Learner Profile is a first approach based on the Semantic Web. It 
is designed for the personalization of interactions and retrieval of tailored information. We have 
combined standardized and validated resources, such as the IMS LIP, Bloom’s Taxonomy, and 
knowledge domains ontology, to allow the user-learner’s search analyses. 

We have discussed the design of a new application program prototype allowing a user-learner to 
analyze the search for OER according to her/his digital profile. PSUL provides automated real-time 
feedback based on the user-learner’s search history and information she/he has inserted about 
herself/himself. We have then demonstrated the integration of the OntoULP and PSUL into a 
mirror site to perform a technical test. 

The ontology’s main characteristics are flexibility and adaptability. While designing OntoULP, we 
have reused or restructured resources to allow its use in other Thematic Digital Universities and 
online learning environments, including digital libraries. Another advantage of OntoULP is the 
application of several information processing techniques. For example, a registered user-learner 
can self-assess her/his search for OER by keywords. She/he can also analyze the relevance of the 
search for OER through the PSUL. 

We have successfully overcome three essential limitations. The first limitation concerns the 
literature on the subject (see Literature review section). While contributing to the field of research 
in information technology and digital libraries, this work has also drawn on disciplines as diverse 
as those of education as well as cognitive, social, and human sciences. The terminological 
definitions of disciplines, concepts, and even methods vary over decades or centuries, and among 
groups of researchers. We have made every effort to define the different terms correctly and to 
cite the corresponding researchers. The second difficulty relates to the design of OntoULP. 
Published works dealing with this topic are rare. We used an exploratory research approach and 
the published works of renowned international researchers to fine-tune our study (see the 
Exploratory research approach and Literature review sections). We then determined the classes 
and objects as well as relationships between them. The third constraint concerns the design of the 
PSUL by following the Thematic Digital Universities policies and respecting the General Data 
Protection Regulations. According to the regulations, we have opted for an optional registration to 
Thematic Digital Universities and to collecting information on the digital user-learner profile. 
Thus, the user-learner will always have the possibility of registering to these platforms to make a 
tailor-made information analysis according to the digital profile. 

As we conclude our work, we have a plan to focus our research and initiatives in the following 
areas. Firstly, we will further deepen our study of OntoULP classes to further increase their 
precision. We will also examine the search personalization of OER based on uses and practices of 
algorithms in the OntoULP.62 For example, by relying on newer version of the ontology we will 
identify the topics of interest, which may interest a specific user-learner. We will implement this 



INFORMATION TECHNOLOGY AND LIBRARIES JUNE 2022 

ONTOLOGY FOR THE USER-LEARNER PROFILE | KORDAHI 16 

newer version in some Thematic Digital Universities to perform technical tests. Secondly, we will 
conduct qualitative and quantitative studies to analyze participants’ behavior while using 
OntoULP and its application program, in the context of Thematic Digital Universities. For example, 
we will examine how many participants would choose to use the OntoULP and PSUL as well as 
how many wouldn’t (e.g., the usefulness of ontologies to participants). We will analyze the 
behavior of individuals with digital personae and make connections between their searches for 
OER.63 We will study their profiles, behaviors, and interests to ultimately suggest OER (e.g., the use 
of recommendation systems). We will also analyze how participants’ behavior and feedback may 
affect future findings. Participants would be previously selected to contribute to these studies. 
Thirdly, we will study the effects of OntoULP and PSUL practices on the Thematic Digital 
Universities. This study will concern an analysis of the Thematic Digital Universities’ search 
engines and users-learners’ needs. For example, exploratory research will allow us to better 
understand user-learners’ informational needs and expectations when using the OER search 
engines. We will analyze the design of OER search engines considering these informational needs 
and expectations. We will then utilize and integrate these findings to suggest alternatives to the 
Thematic Digital Universities to further improve these search engines. 

ACKNOWLEDGMENTS 

We thank the Digital University Association and Thematic Digital Universities for their elaborate 
and enlightening explanations concerning the platforms. We thank the reviewers and Claude Baltz, 
emeritus professor in information and communication Sciences at the Paris 8 University, for 
carefully reviewing this article and for enriching it with their expert observations. Thanks to 
Mohammad Hajj Hussein, communication and IT engineer, for his help programming the 
dashboard. 



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ONTOLOGY FOR THE USER-LEARNER PROFILE | KORDAHI 17 

APPENDIX: SEMISTRUCTURED QUESTIONNAIRE EXAMPLE 

Email subject: Digital University Engineering and Technology 

 

Dear Sir, Madam, 

I am affiliated to the Paragraph research laboratory at the Paris 8 University (Laboratoire de 
recherche Paragraphe, Université Paris 8). 

I am writing to you to gather further information concerning the Digital University Engineering 
and Technology. The objective of the semistructured questionnaire is to deepen my 
comprehension of the practices of Digital University Engineering and Technology in order to write 
a research article and contribute to its improvement. 

I would be grateful if you could answer the following questions: 

• What are your responsibilities at the Digital University Engineering and Technology? 
• Do the Thematic Digital Universities as well as Digital University Engineering and 

Technology provide “open” educational resources? 
• Are the educational resources accessible only to students enrolled in the training programs 

of partner universities? 
• How is the access to educational resources made?  
• Do the educational resources follow document processing for their indexing? 
• Is the document processing specific to the Thematic Digital Universities? 
• What are the expectations of “users” searching for educational resources? 

 

Thank you in anticipation 

 

Sincerely yours, 

 

Marilou Kordahi 

  



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ONTOLOGY FOR THE USER-LEARNER PROFILE | KORDAHI 18 

ENDNOTES 
 

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http://www.capetowndeclaration.org/read-the-declaration
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https://doi.org/10.4000/dms.5347
https://doi.org/10.1002/meet.1450390135
http://ltsc.ieee.org/wg12


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ONTOLOGY FOR THE USER-LEARNER PROFILE | KORDAHI 19 

 

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	Abstract
	Introduction
	Exploratory Research Approach
	Methods used
	Results of information collection
	Findings

	Literature Review
	Selection of recently published works
	Learner model
	Taxonomy of educational objectives
	Knowledge domains
	User modeling system

	OntoULP Ontology
	Writing the ontology
	OntoULP description

	Personalized Modeling System for the User-Learner Profile
	OntoULP and Its Modeling System in the Context of a Thematic Digital University
	Discussion and Conclusion
	Acknowledgments
	Appendix: SemiStructured Questionnaire Example
	Endnotes

