=Paper=
{{Paper
|id=Vol-3179/Short_7.pdf
|storemode=property
|title=Use of Semantic Technologies for Validation of e-Learning Outcomes
|pdfUrl=https://ceur-ws.org/Vol-3179/Short_7.pdf
|volume=Vol-3179
|authors=Julia Rogushina,Sergiy Priyma,Oksana Strokan,Anatoly Gladun
|dblpUrl=https://dblp.org/rec/conf/iti2/RogushinaPSG21
}}
==Use of Semantic Technologies for Validation of e-Learning Outcomes==
https://ceur-ws.org/Vol-3179/Short_7.pdf
Use of Semantic Technologies for Validation of e-Learning
Outcomes
Julia Rogushinaa, Serhii Pryimab, Оksana Strokanb and Anatoly Gladunc
a
Institute of Software Systems, National Academy of Sciences of Ukraine, 44 Glushkov Pr., Kyiv, 03680,
Ukraine
b
Dmytro Motornyi Tavria State Agrotechnological University, 18 B.Khmelnytsky Ave, Melitopol, Zaporizhzhia
region, 72312, Ukraine
c
International Research and Training Centre of Information Technologies and Systems, National Academy of
Sciences and Ministry of Education of Ukraine, 44 Glushkov Pr., Kyiv, 03680, Ukraine
Abstract
The paper offers means, sources and methods for semantic validation of learning out-
comes (including non-formal and informal distant learning) based on the ESCO onto-
logical model and matching of its main concepts. Atomic competencies are used for
representation of different components of ontological model that formalizes relations
between main subjects and objects of learning process.
We analyze specifics of the Web-oriented semantic technologies used for support of e-
learning. Ontological analysis provides representation of distributed background
knowledge about users, their professions, competencies, lifelong learning outcomes,
etc., and standards of the Semantic Web project provide the technological foundation
for creation of intelligent advisory applications. Validation of learning outcomes of in-
formal and non-formal learning needs in semantic matching of user profiles with clas-
sifications of professions and qualifications.
Advantages of proposed approach are demonstrated on example of applied information
system AdvisOnt developed to combine the market of educational services with the la-
bor market that can use validation of the informal and non-formal learning outcomes.
Keywords
Learning outcomes, validation, ontology, competencies, ESCO, Semantic Web.
1. Introduction
Now the Semantic Web and associated semantic technologies become standards of the mainstream
industry dealt with a huge number of the Web-oriented intelligent information systems (IISs). Such
IISs migrate from the processing and storage of large amounts of data to processing and storage of
more compact knowledge with a much more complex structure. They apply semantization for differ-
ent steps of information processing and try to take into account meaning of data and relations with
domain knowledge. One of the prospective spheres of IISs is e-learning and such it`s subdomain as
validation and formalization of learning outcomes. Now this research direction is very actual because
of COVID pandemia and transition to distant forms of lifelong learning and online estimations of re-
ceived skills and competencies. Lifelong learning [1] is a key factor in personal and professional de-
velopment that needs in cooperation of industry leaders with educational organizations and non-
formal learning courses and services. The accession of Ukraine to the European educational space [2]
causes important changes in the structure and semantics of the educational process.
Information Technology and Implementation (IT&I-2021), December 01–03, 2021, Kyiv, Ukraine
EMAIL: ladamandraka2010@gmail.com(Rogushina J.); pryima.serhii@tsatu.edu.ua (Pryima S.); oksana.strokan@tsatu.edu.ua(Strokan O.);
glanat@yahoo.com(Gladun A.).
ORCID: 0000-0001-7958-2557 (Rogushina J.); 0000-0002-2654-5610 (Pryima S.); 0000-0002-6937-3548 (Strokan O.); 0000-0002-4133-
8169 (Gladun A.).
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
338
The concept of lifelong learning, which was not only invoked with reference to education and
schooling, but also as a way to solve the problems of unemployment and preparation for the job mar-
ket. Labor market change, reflecting evolutions in technologies, markets and organizations, requires
that skills and competences can be transferred within a new working environment.
Recognition of learning outcomes (knowledge, skills, abilities and competencies) achieved through
non-formal and informal (spontaneous) learning, including through open educational resources, is
necessary for access to the labor market and lifelong learning. Employer organizations, trade unions,
chambers of commerce and industry, national bodies involved in the recognition of professional quali-
fications, employment services, youth organizations, education providers, and civil society organiza-
tions are the most interested parties in providing opportunities for the recognition of non-formal and
informal learning. All stakeholders contribute to more systematic approach to the recognition of non-
formal and informal learning outcomes [3]. Now lifetime employment becomes an exception because
the majority of employees change job several times in their work lifespan.
Successful implementation of this task facilitates the development of vertical mobility of citizens in
vocational education and labor market, and horizontal mobility from one professional trajectory to
another through the mechanism of official recognition of partial qualifications acquired in formal,
non-formal and informal learning. Subjects of the labor market have possibilities to describe their
proposals and requirements through various informal characteristics which are often intangible (e.g.
team spirit, social skills, leadership skills). Use of different terms to describe such characteristics
actualizes the problem of comparing the semantics of such descriptions.
Semantic technologies (STs) that are aimed at knowledge-level processing of information can solve
this problem because they are able to formalize, analyze and process the content of information re-
sources (IRs) and personal data dealt with learning process and learning outcomes. STs are based on
the application of domain knowledge and knowledge about the users of these information technolo-
gies, providing automated analysis of information on the Web. One of the results of such processing is
an achievement of semantic compatibility of open educational resources that allows to use and inte-
grate information about results of non-formal and informal learning from different sources and data-
bases in various information systems (IS). Thus, the implementation of ISs of semantic identification
and documentation of the results of non-formal and informal learning is an urgent and timely scien-
tific task. The materials presented in our article are structured as follows. The first section points out
the relevance of the development and implementation of IIS to create an advisory system based on the
ontological presentation of knowledge about the subject area.
The second section presents the structure and functionality of the European classifier ESCO. The
ESCO contains a description of the professions, skills, and competencies required to obtain a particu-
lar job or position. The third section explains the essence of semantic technologies and the effective-
ness of ontological models for creating IIS. It was considered the questions of knowledge manage-
ment based on Semantic Web technology. As the basic tool it was used ontological knowledge model-
ing. The fourth section explains that tools for semantic identification and documentation of learning
outcomes (including non-formal and informal ones) should be quite dynamic. We actively use soft-
ware agents and semantic Web-services to improve the functions of our system. There are analyzing
of the methods of obtaining knowledge from Web-resources, Wiki-resources and natural language
documents. The means of intellectualization of software agents, search engines and Web-services was
demonstrated. The fifth section presents the ontological model of competencies that represents the
structure of relations between learning outcomes and professions, jobs, knowledge, skills, competen-
cies and qualifications. The sixth section presents the architecture and description of the functionality
of IIS AdvisOnt Advisory System.
2. Multilingual classifier of European skills, abilities, qualifications and pro-
fessions
In order to support the process of recognition of learning outcomes in formal and non-formal and
informal learning, the European Commission has developed free Internet portal ESCO [4]. ESCO
(European Skills, Competencies, Qualifications and Occupations) is a multilingual classifier of Euro-
pean skills, abilities, qualifications and professions.
339
ESCO classifier is published in the Linked Open Data format. This conception provides the ability
to create a system that easily adapts to domain and allows developers to use it as a building block in
services for job search, career guidance and self-assessment. ESCO classifier is available in various
formats (SKOS-RDF, CSV), so that users can integrate it into their services and other types of soft-
ware. In addition, ESCO provides local API and API of Web Services so that applications and Web
services can request real-time information from the classifier.
ESCO classification identifies and classifies skills, competencies, qualifications and occupations
important to the European labor market, education and training. Use of ESCO classifier provides con-
solidation of the labor market and the market of educational services, because, on the one hand, it
helps education providers better understand the needs of the labor market and adapt curricula to these
needs, and on the other hand – helps employers in understanding of the learning outcomes acquired by
professionals looking for work.
ESCO classifier contains three main elements: occupations; skills and competencies; qualifications.
All these elements are interconnected and allow ESCO to organize common and transparent terminol-
ogy for the European labor market and education sector.
Profession element is a description of all professions that are important for the European labor
market. Today, ESCO classifier contains description of 2,942 occupations. Each concept of the Pro-
fession describes its meaning, as well as provides useful information about the profession (metadata).
The main element that defines professions in ESCO is the basic idea or understanding of what it is
and how it differs from other professions. They are fixed in the description and the note area. ESCO
description is a text field that contains a brief explanation of the meaning of the profession and its
understanding. Most importantly, it explains the semantic boundaries of the profession. For this rea-
son, a description is always provided for each ESCO employment.
Each Profession in ESCO is associated with basic and additional skills and competencies:
- main skills and competencies are usually relevant to the profession, regardless of the work con-
text, employer or country;
- additional skills and competencies may be actual or arise when in the profession working de-
pendently on the employer, work context or country.
Professions in ESCO are structured by comparing them with the International Standard Classifica-
tion of Occupations (ISCO-08) developed by the International Labor Organization. ESCO employ-
ments and their ISCO-08 hierarchy compose the ESCO Profession element. ISCO-08 provides four
upper levels, while the professions at ESCO provide fifth and lower levels. Each ESCO employment
is assigned to one group of ISCO-08 elements (even if they are not directly related to it, for example,
if they are at the sixth or seventh level).
The Skills and Competence element (often abbreviated as Skills) of ESCO provides a complete list
of skills relevant to the European labor market:
- knowledge: a set of facts, principles, theories and practices that are related to the field of work or
study. Knowledge is described as theoretical and / or factual and is the result of information gaining
through learning;
- skills: the ability to apply knowledge and use know-how to perform tasks and solve problems.
Skills are described as cognitive (involving the use of logical, intuitive and creative thinking) or prac-
tical (involving the use of manual dexterity and the application of methods, materials, tools and
equipment);
- competencies: the ability to use knowledge, skills and personal, social and methodological abili-
ties in work or study, as well as in professional and personal development.
The concepts of "skill" and "competence" differ semantically because "skill" refers to the use of
methods or tools in specific settings and tasks, and "competence" refers to a person's ability to face
new situations and unforeseen problems, the use and application of knowledge and skills in new areas
of activity. However, these ESCO elements have similar structure.
ESCO provides metadata from the Skills group for each profession Such metadata include: pre-
ferred term; non-core terms (synonyms, spelling variants, abbreviations, etc.); hidden terms (for ex-
ample, outdated ones); sphere of the semantic boundaries of the concept; relations with ESCO profes-
sions etc. This information can be used for identifying and documenting learning outcomes and, as a
result, maintaining professional mobility.
340
Qualifications element contains information about the full list of qualifications that are important
for European labor market. This ESCO information is obtained from national databases on the qualifi-
cations of EU member states and from other qualifications provided directly by the awarding bodies.
ESCO classifier ontology consists of three elements – professions, skills and qualifications, as well
as two additional registers: bodies that award qualifications and bodies that award the context of
work, and usually refer to ESCO qualifications.
However, different subjects on the labor market describe their proposals or requirements through
different informal characteristics. Different terms can be used to describe such characteristics, and it
causes problems of their semantic comparing. Such problems can be solved by knowledge processing
and use of IISs and appropriate models, methods and ontologies that support formalization of learning
process and learning outcomes.
3. Semantic technologies
STs aimed at knowledge-level information processing can solve validation problem because they
are able to formalize, analyze and process the content of IRs that describe results of learning.
Semantics allows to define explicitly meanings and relations between domain concepts represented
by data (words, phrases, symbols, etc.) that depends on context. For the same piece of information,
semantics can be defined differently depending on ontology used to formalize the user's view of the
world. Ontologies can be used as a formal, explicit specification of conceptualization of terms at a
certain level of details.
Semantic processing of information includes: methods and means of integration and unified repre-
sentation of heterogeneous distributed knowledge and data; retrieval and processing the Web re-
sources as an universal knowledge source of about meaning of information objects.
STs in general can be described through a combination of three main components [5]: ontologies;
semantic resources; models of semantics of natural language (NL) entities. This approach is concen-
trated on NL processing. In our study we take into account more wide classes of IOs with various
structure defined by appropriate ontologies. For example, advisory systems analyze people, organiza-
tions, vacancies, learning courses, etc. that cam contain NL definitions, multimedia elements and
structured data.
Therefore from the point of view of creating semantic applications, these three basic components
form a hierarchy where: 1. ontologies and other knowledge bases (KBs) are the upper level of abstrac-
tion of the knowledge structure; 2. IO models represent the intermediary level that allows to distin-
guish typical IOs and their properties and characteristics; 3. semantic IRs are the lower level that pro-
vides information about individuals of classes.
Semantic IRs can have links between content elements (IOs of various types and structure) and
with elements of IO models (for example, links with other Wiki pages or with data). Meaning of links
is provided by means specific for IR representation and markup. For example, Semantic MediaWiki
uses semantic properties.
Semantic Wiki resources are both a provider of knowledge for building ontologies and users of
such ontologies. They are closely connected with various means of acquiring and presenting
knowledge, with Semantic Web technologies. The integration of data mining tools with semantically
annotated Web resources provides promising opportunities to effectively meet the information needs
of users in current and dynamic information about the world around them.
The ontological representation of knowledge about the domain can be automatically processed by
intelligent software agents representing the interests of students in the IAS, designed for e-learning.
One of the goals of such personal software agents in e-learning is that each student can find the course
he needs (on the one hand, that meets his information needs, and on the other hand - based on the
courses previously studied by this student and submitted to him in the most easy to learn form).
The use of agent-oriented technologies in e-learning allows you to take into account personalized
information about students and teachers and saves all users from routine operations (for example,
once determining the type of interface or the most convenient form of control, the student will always
receive a default course in in this form).
341
4. Semantic identification of learning outcomes
Tools for semantic identification and documentation of learning outcomes (including non-formal
and informal ones) should be quite dynamic. They have to take into account changes in the world
around them, use background knowledge and ensure the retrieval of new information from the Web
resources. This requires the use of technologies oriented on intelligent processing of distributed in-
formation.
Recently, developers of distributed IISs exhibit a tendency to transition from the use of relational
databases to ontological knowledge bases (KBs). This is due to the development of the Semantic Web
conception [6] that is based on tree main elements – ontologies [7], Web-services [8] and software
agents [9]. The ground of ontological KBs is ontology – a formalized, explicit description of domain,
and Web-services used by software agent need in knowledge represented by these ontologies. Onto-
logical KBs include the set of classes (concepts) and descriptions of the various relations between
them, as well as the set of class individuals. Semantics of data is defined by connection with element
of description of domain knowledge and meaning of this connection.
For example, if domain ontology describes learning outcomes then semantics of resume of potential
employee can be defined formally by some elements of this ontology [10]. Now domain ontologies
are usually represented by the OWL [11] language developed by the Semantic Web initiative that is
an add-on to the RDF language [12].
The Semantic Web technologies can be exploited for development of the Web-oriented tools for
semantic identification and documentation of non-formal and informal learning outcomes:
- software agents are used for personified and unified representation of specific needs and inter-
action of various subjects of learning process and labor market;
- semantic Web-services support various functions dealt with validation of learning outcomes
and their use in matching of vacancies and resumes at the semantic level;
- ontology represented relations between learning outcomes and professions, jobs, knowledge,
skills, competencies, qualifications etc. and various external ontologies represent domain
knowledge.
5. Ontological model of competencies
Ontological model that represents structure of relations between learning outcomes and profes-
sions, jobs, knowledge, skills, competencies and qualifications is an important element of IS for vali-
dation the learning results of is development. This model provides processing of non-formal and in-
formal learning outcomes on semantic level and can be used for their unification and matching with
existing standards and taxonomies such as ESCO. Ontologies allow to describe formally any con-
cepts, groups of classes, relations between objects and types, elements of classes, etc. Formal model
of ontology defines main elements of ontological analysis: concepts, relations and rules their interpre-
tation [13]. For example, ontologies can be used for representation of various learning courses [14]
and their characteristics, sequence of learning, links with professions and specialties.
In this work we use the enhanced formal model of ontology [15]: O X , R, F , T , that consists of the
following elements:
- X X cl X ind is a finite set of basic concepts of ontology where X cl is s set of classes, X ind is a set
a X ind A X cl , a A
of instances of these classes, such that ;
R rier _ cl {ri } { p j }
- is a finite set of relations between classes and instances of ontology clas-
rier _ cl
ses where is a hierarchical relation between ontology classes and properties of classes character-
r : X cl X cl {ri }
ized by properties such as antisymmetry and transitivity, ier _ cl ; is a set of object
ri a, a X ind b, b X ind
properties that establish the relation between instances of classes: ,
ri : X ind X ind { p j }
; is a set of data properties that establish relations between instances of classes
pi a, a X ind t , t T pi : X ind Const
and values: , , such that within sets of object properties and
342
rier _ obj : {ri } {ri } rier _ data
relation properties may also be hierarchical relations , and ,
rier _ data : { p j } { p j }
;
- F is a finite set of characteristics of ontology classes, instances of classes and their properties;
- T is a finite set of data types (for example, string, integer).
The basic classes of learning validation are competence; profession; qualification; knowledge;
as well as subjects associated with these classes: owners (potential employees with certain knowledge
and skills), customers (employers) and providers (persons and organizations that provide educational
services that allow for professional development) [16].
O learn is realized by OWL Light dialect of OWL and describes the properties of classes (both
object properties and data properties) and relation between basic terms and their subclasses. It con-
tains the following classes (the list of classes is arranged alphabetically, not by relevance):
knowledge, candidate, qualification, competence, country, course, skill, education passport, concept,
course provider, profession, job, employer, term.
O learn is used for competence management [17] for matching of individuals of these classes.
Values of their properties are defined by non-empty sets of atomic competencies [18].
Atomic competence a is defined as follows:
- a C where C is the set of information objects of the class "Competence" of O learn , and Catomic is
the set of atomic competencies, Catom C ;
- each competence is a union of a non-empty set of atomic competencies
n
c Cai Catomic, i 1, n, k ai
i 1 ;
- any atomic competence can not be a subset of any other atomic competence
a , b C, a b b Catomic.
a C are defined by individuals of relevant domain ontology: concepts x i X, i 1, n, n 1
and relations rj R, j 1, m, m 1 such that x i rj ( x i ) . Thus, matching of user competencies
a b
with vacancy at the semantic level is reduced to comparison of finite sets of atomic competencies of
individual user with competence set of tasks [19]. This approach guarantees the comparison for a time
proportional to the number of atomic competencies.
6. Advisory system AdvisOnt
Advisory system AdvisOnt (see Fig.1) defines relations between main subjects of advisory activi-
ties – applicants (persons needed in some work based on relevant competencies and skills), employ-
ers (persons or organizations needed in employees for execution of some task or work on some posi-
tion) and providers of learning services (organizations that propose various formal, non-formal and
informal learning means for expansion of personal competencies). System is oriented on agricultural
domain but it validation services based on the Semantic Web standards can be integrated with any
other learning software.
AdvisOnt is a as semantic application because it provides:
- personified user interaction based on use of personal intelligent agents;
- semantic analysis for unified representation of vacancies and resume based on NL processing and
ESCO terminology;
- comparison of resumes and vacancies at the semantic level with use of semantic similarity of do-
main terms semantic relations between professions, skills, competencies and qualifications defined by
ESCO ontology.
We distinguish the following parameters: user profile – non-formalized representation of main fea-
tures values of user proposed by applicant; formalized user profile – formalized representation (ac-
cording to selected ontological structure) of main features values of user with pre-defined data types
such as demography, skills, qualification, background, cognitive style, restrictions, etc. expressed by
the non-empty finite set of competencies; NILO (non-formal and informal learning outcomes) – non-
formalized set of additional competences and skills of user that need in formalization and adaptation
with user profile model; formalized NILO – formalized set of additional competences and skills of
343
user expressed by the non-empty finite set of competencies; task – non-formalized representation of
employer needs for work execution that can be covered by one or more vacancies; vacancy – non-
formalized representation of employer needs and demands to employees; formalized vacancy – for-
malized representation of employer needs (according to selected onto-logical structure) and demands
to employees expressed by the finite set of competencies.
Validation
service
Figure 1: General architecture of AdvisOnt advisory system
In this work we use definitions of agro-advisory services based on Service-oriented architecture
(SOA) paradigm terms and its semantic extension defined by OWL-S ontology. Functionality of Ad-
visOnt system is defined by the set of services: service for vacancy formalization; service for user
profile formalization; service for learning course formalization; service for learning course retrieval;
service for matching of vacancies and resumes. Data types of input and output of these services are
defined with the help of external ontologies: ontology of user pro-files, ESCO and ontology of agri-
cultural domain. These ontologies define the structure of analyzed data and contain information about
types of their attributes, restrictions and conditions of their use. An important feature of SOA-based
system is it`s interoperability – we can select other external ontologies instead of these ones for other
subject domain or for other view on system tasks and goals without changing of services.
We consider advisory systems used by various countries and regions, their specifics and aims, and
knowledge bases that can be used as bedrock for agro-advisory applications – the European Multilin-
gual Classifier of Skills, Competences, Qualifications and Occupations ESCO ontology, ontologies of
learning resources (on example of MOOCs - Massive Open Online Courses), user profile ontologies.
Analyses of the intelligent Web-oriented informational technologies shows an expedience of advi-
sory development based on the Semantic Web standards of knowledge representation and processing.
344
An important factor of advisory efficiency deals with semantic documentation and validation of non-
formal and informal learning outcomes typical for agriculture. We propose semantic advisory system
AdvisOnt based on ontological representation of knowledge about competencies, vacancies, training
courses, user profiles, employers and companies. AdvisOnt services match available vacancies and
applicant profiles to recommend employment or further learning of wanting competences by analysis
of their formal, non-formal and informal learning outcomes [19].
7. Conclusions
We analyze modern approaches to the interaction of the educational services market with the labor
market through recognition of non-formal and informal learning outcomes. The authors prove the
expediency of semantic technologies used for information processing at the knowledge level, analyze
the Semantic Web standards and specifics of their use for education and labor market applications.
ESCO classifier that combines services of the labor market and the educational market is examined in
detail. This classifier allows jobseekers and employers from different EU member states to handle
information on learning outcomes more efficiently. The analyzes of practical ESCO classifier usage
on example of the European Employment Service (EURES) demonstrates that this approach provides
all necessary information for jobseekers abroad.
The ontological analysis is an efficient way to model the ideas of various domains that allows to
represent formally their semantics. Hence, the research analyzes the ontological model for this infor-
mation system and its components; in particular, the authors provide the described content of the
ESCO ontological model which consists of three elements – occupations, skills and qualifications.
Original method of semantic identification and documentation of non-formal and informal learning
outcomes based on competence analysis is proposed. Main element of this method is atomic compe-
tence. Enhanced formal model of domain ontology that provides representation of competence match-
ing is developed. Labor market participants usually describe their proposals or requirements through
various informal characteristics; therefore, this research work is aimed on problem of comparing the
semantics of such descriptions on base of semantic ontologies. Analysis of publications on the topic
of the study concluded that the Multilingual Classifier of European Skills, Competencies, Qualifica-
tions and Professions ESCO can be used as an effective tool to combine the market of educational
services with the labor market. ESCO Classification identifies and classifies skills, competencies,
qualifications and occupations that are relevant to the European labor market, education and training.
Proposed approach is used efficiently in applied intelligent system that provides competence man-
agement for agro-advisory.
Information system of recognition of results of non-formal and informal training represented in this
work is oriented on the Semantic Web technologies and uses conception of the Web services and
RDF-based repositories of ontologies. The developed architecture and user interface of this system
allow semantic identification and documentation of non-formal and informal learning outcomes,
thereby increasing the efficiency of handling information about these outcomes.
In the future, we plan to use Data Mining and Inductive Inference techniques to develop the most
appropriate personal learning strategies for each student by summarizing the background of student
interaction with the e-learning system (for example, some students prefer theoretical materials and
others prefer learning on practical examples). someone prefers a graphic or textual representation of
information, etc.).
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