=Paper=
{{Paper
|id=Vol-2590/paper29
|storemode=property
|title=Ontological Approach to Modeling the Current Labor Market Needs for Automated Workshop Control in Higher Education
|pdfUrl=https://ceur-ws.org/Vol-2590/paper29.pdf
|volume=Vol-2590
|authors=Elena Boldyreva,Vadim Kholoshnia
|dblpUrl=https://dblp.org/rec/conf/micsecs/BoldyrevaK19
}}
==Ontological Approach to Modeling the Current Labor Market Needs for Automated Workshop Control in Higher Education==
https://ceur-ws.org/Vol-2590/paper29.pdf
Ontological Approach to Modeling the Current
Labor Market Needs for Automated Workshop
Control in Higher Education
Elena Boldyreva[0000−0001−6357−4448] and Vadim
Kholoshnia[0000−0001−6485−6340]
ITMO University. 197101, St.Peterburg, Russia
eaboldyreva@itmo.ru
vdkholoshnia@gmail.com
Abstract. This paper is devoted to the question of current labor market
needs modeling for automated workshop control in knowledge-intensive
areas. On the basis of the ontological model of labor market needs, the
content of the workshop is systematically processed for certain profiles
in knowledge-intensive areas. An approach to the e-workshop project
control based on the pedagogical design model ADDIE is described. The
approach is implemented on the basis of an information system that takes
into account the regular updating of the technological and tool base in
professional areas. The paper proposes a method for forming and modi-
fying the list of main learning tasks - the kernel of the workshop, based
on the analysis of labor market needs. This method involves systematic
monitoring of vacancies in this profile, processing their descriptions in
order to highlight the requirements of employers. The methodology is
implemented in the framework of the information system for workshops
designing. Vacancy data is obtained from the API hh.ru, lists of the
applicants requirements and responsibilities are extracted from their de-
scriptions. The resulting lists of requirements are processed using natural
language processing and cluster analysis technologies.
Keywords: workshops design· knowledge-intensive areas · labor market
needs · machine learning · natural language processing · unsupervised
text clustering
1 Introduction
During the period of high competition in the labor market, the main task of
educational institutions is to prepare graduates who are competitive in the la-
bor market. Constant updating of the goals, learning outcomes and content of
educational programs becomes not just a requirement of regulations and educa-
tional standards, but a necessary condition for ensuring high-quality training of
students. At the level of Federal state standards, measures are being taken to
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
2 Boldyreva E., Kholoshnia V.
strengthen the influence of employers on the learning process in higher educa-
tion institutions: involving them in the development of educational standards,
creating basic departments at enterprises, and ensuring the practical orientation
of learning process [1–4].
Such actions are particularly relevant when it comes to training specialists in
knowledge-intensive areas. A feature of knowledge-intensive areas is the constant
updating of the technological and tool base, and, as a result, constantly changing
the requirements of employers.
This article is devoted to the formation and further systematic revision of
the workshop kernel for knowledge-intensive areas. The workshop kernel in the
context of this paper is a list of the main educational tasks that correspond to the
labor functions (professional tasks that are constantly faced by active specialists
in the labor market) and the competencies of the graduate. The method of
forming the workshop kernel is proposed on the basis of an information system
that constantly monitors and analyzes the requirements of the professional area -
professional standards and vacancies descriptions in online-recruitment systems.
2 Overview of existing solutions
At the moment, the direction related to information support for the implementa-
tion of the educational process is actively developing. Modeling of the curriculum
based on the ontological approach is described in the paper [5]. Graph models
and structural approaches to curriculum development are presented in the paper
[6]. Examples of works that perform modeling and expert analysis of educational
programs are also [7, 8]. However, these works do not imply a direct impact of
the professional areas requirements on the educational trajectory formation.
In order to monitor the connection between the needs of employers and the
implementation of the educational process, concepts of various intelligent sys-
tems are emerging. The following papers describe an automated system for mon-
itoring and analyzing the compliance of personnel needs based on the nomen-
clature of universities [9], a qualification-oriented expert system for managing
the educational process [10], and an intelligent system for supporting the educa-
tional programs formation based on neural network models of language, taking
into account the professional areas requirements [11]. These expert systems inter-
act with the labor market needs (its normative documents) and suggest making
recommendations for modifying the normative documents of the educational pro-
cess. The paper [11] also describes the interaction with vacancies descriptions,
but does not specify how this interaction is carried out, the emphasis is on the
analysis of professional standards.
It should be noted that the normative documents of the educational and pro-
fessional areas are inert in relation to the rapidly changing requirements for the
practical part implementation of the educational process. Therefore, the main
drawback of systems that focus on the normative documents is that all recom-
mendations are made for educational programs, but no specific recommendations
are given for the implementation of these. Such recommendations must neces-
Ontological Approach to Modeling the Current Labor Market Needs 3
sarily include the tools and technologies updates in the professional areas, if the
purpose of training is to train highly qualified specialists of this profile.
3 Approach to the formation of the actual workshop
kernel based on the analysis of the labor market needs
of knowledge-intensive areas
This article describes an approach to solving this problem, namely the system
of automated workshop design and workshop project control [12] based on the
professional area needs and the opinions of employers in knowledge-intensive
areas. On the basis of the approach, an information system for the electronic
workshop design is developed and implements the model of pedagogical design
ADDIE [13, 14] at the stages of analysis and design. A characteristic feature of
the approach is its application in knowledge-intensive subject areas that have
annual update of technologies and tools.
The approach describes the process of creating and modifying the workshop
content for the education profile of students. The model of this process is shown
in Figure 1.
Professional Educational
standard standards
Selection of the list
Required skills
...
Discip
pline N
of work tasks
Discipliine 2
Workshop kernel Workshop project Discipline
el 1
Ontology
Pa
ast year's workshop List of This year's workshop Analysis (list of main learning (main and auxiliary Modeling Workshop project
l
modeling
kernel base kernel of tasks) tasks) of for each desccipline
of
(list of professional learning (list of professional expert education
subject
tasks) tasks tasks) opinions Significance Labour-intensity process Points
area
estimate estimate estimate
e
Selection of key skills Ontology
modification Content Workshoop
from employers
modification ation
implementa
Labour market
needs Recommendations
Results Learning
g
for improving the
Analysis outcomees
workshop
Teacher
Fig. 1. Process of workshop content creating for a specific educational profile for the
current year
The approach to the e-workshop project control for knowledge-intensive areas
based on the opinions of employers involves the following steps:
1. Formation the workshop kernel for a current education profile.
During the first design, the information system forms the the workshop kernel
list of the main learning tasks for a specific profile of graduates. The main
learning tasks at this stage are the adaptation of labor tasks that the student
must be ready to solve in order to be competitive in the labor market. The
collection of labor tasks is carried out by analyzing professional standards,
4 Boldyreva E., Kholoshnia V.
normative documentation of the profile and corresponding vacancies from
online-recruitment systems. The obtained data are processed using semantic
analysis technologies [15], clustered by subject, and the most popular labor
tasks and technologies (tools) that need to be solved are identified. If the
workshop design is not the first time, then the list of learning tasks from the
previous period can also be used as a basis.
2. Formation of the main learning tasks list based on the analysis of expert
opinions.
The resulting list of learning tasks for the workshop kernel is passed to
experts to assess their significance and complexity (qualification level). The
information system offers learning tasks to the workshop kernel with an
assessment of significance above the threshold set by the workshop developer.
This stages of interaction with experts is repeated every year to update the
list of tasks, assess their significance and technologies for solving them.
3. Formation of the workshop project with calculation of labor-intensity char-
acteristics of learning tasks and division of these tasks into auxiliary ones.
The labor-intensity of learning tasks in the workshop project is determined
in accordance with the weight coefficient of each learning task. The weight
coefficient is calculated using the formula 1
Pm
j=1 Ci ∗ Kj
Wi = n Pm
P (1)
i=1 j=1 Ci ∗ Kj
where Wi – the weight coefficient of the i-th learning task, Ci – the expert
evaluation of the i-th factor of the learning task in points (from 1 to 10), Kj
– confidence level of the j-th expert, n – number of practical tasks of the
workshop, m – number of evaluation factors.
According to the value of the weight coefficient, each main learning task is
assigned a share of the total labor intensity of the workshop.
4. Formation of disciplines workshops projects of the education profile on the
basis of educational process modeling.
At the previous stage, the main learning tasks are also divided into blocks of
sub-tasks and analyzed for duplication – if the main learning task i contains
one or more identical sub-tasks with the previous (within the educational
process) learning task i − 1, then these sub-tasks are excluded from the
preparatory set i. This allows you to reduce the ”preparatory” trajectory of
the main educational task without losing the quality of students learning.
After that, the learning tasks are arranged in the optimal order for studying
and distributed among the disciplines, forming the educational trajectory of
the profile.
5. Processing the learning outcomes of the disciplines workshops and making
recommendations for modifying the workshop content.
Based on the learning outcomes, the information system generates recom-
mendations for modifying the electronic workshop content based on the re-
sults of its passage. According to the described approach, the implemen-
tation of the main learning tasks and sub-tasks is carried out using data
Ontological Approach to Modeling the Current Labor Market Needs 5
obtained at the stages of formation and expert evaluation of the workshop
kernel. Technologies and tools selected according to the labor market needs
and recommended by experts are included in the educational process. Since
we are talking about training specialists in knowledge-intensive areas where
popular technologies and tools are constantly changing, constant monitoring
of the labor market and an annual survey of specialists allow us to timely
update the workshop content implementation. In this work, we have imple-
mented an approach to ontological modeling of labor market needs based on
the results of systematic monitoring of these online-recruitment systems.
3.1 Modeling the current labor market needs within a specific
educational profile
In order to clearly define the format, purpose of the extracted data and how to
analyze it, it was decided to describe the ontological model of the professional
area for the specific educational profile. Ontology in the context of this paper
will be understood as a specific knowledge base [16–18] used for solving learning
tasks.
OP A = (EP A , RP A ), (2)
where EP A is a set of entities of the professional area, RP A is a set of relationships
between entities in a professional area.
The set of EP A = V, L, S, Req entities is divided into four main subsets:
– sub-set V (vacancies) - a subset of professions described by professional stan-
dards (PS) and vacancies. Each profession is characterized by appropriate
professional standards and a specific set of vacancies;
– sub-set L (labor tasks) entities that describe labor tasks from professional
standards or from vacancies descriptions;
– sub-set S (skills) entities includes knowledge and skill requirements. These
skills are described in the PS in relation to each labor task. The student
must master these skills in order to be a sought-after specialist;
– sub-set Req (requirements) - this set includes the employers requirements -
technologies and tools knowledge, or so-called key skills from the vacancies
description that the applicant must have in order to solve labor tasks. For
modeling knowledge-intensive areas this subset is key in the formation of
actual content for practical learning of students in the educational profile.
Figure 2 shows a generalized model for matching professional standards en-
tities and vacancies description entities in online-recruitment systems.
Figure 3 shows an ontological model of the professional area of a certain
educational profile, formed on the basis of an analysis of the labor market needs
and the competencies of the graduate.
These models show the main data that should be processed and compared in
the process of forming the actual workshop kernel of a certain educational profile
- labor tasks and skills of professional standards, requirements, labor tasks and
key skills from employers.
6 Boldyreva E., Kholoshnia V.
Profession
Profession
(Prof. standart)
- name - name
- possible posts: list - type
attach attach 1...*
1...*
Vacancy
Post confirm
- name
- name 1...1 1...*
- requirements: list
influence 1...*
Labor function determine
1...*
- name
- labor tasks: list Labor tasks
1...* confirm - name
determine - key skills: list
1...* 1...*
Skills
- competencies: list
confirm
Fig. 2. Generalized model for matching professional standards entities and vacancies
description entities in online-recruitment systems
Employee Employer
pretend Profession
-surname 1...* 1 -name -surname
-name -name
-age -type
-type
-sex 1
-expedence
1...* 1...*
determines
has
determines
Skills Vacancies
1...* attached 1
-competences : list -name
influences -requirements : list
1
1
determine
is a 1 influences 1...*
is a is a Labor tasks
Possible posts
Professional General Soft 1
skills skills skills
Main tasks Secondary tasks
Programmer Analyst
Fig. 3. Ontological model of the professional area of a specific educational profile,
formed on the labor market needs and the competencies of the graduate
3.2 Method for forming the electronic workshop kernel based on
the analysis of current labor market needs
The proposed method is based on semantic analysis of current requirements of
employers for specific projects (tasks) in knowledge-intensive areas. Based on the
Ontological Approach to Modeling the Current Labor Market Needs 7
results of this analysis, the main labor tasks that a graduate of the profile must
be able to solve in order to be competitive in the labor market are highlighted.
The collection of labor tasks is carried out using the analysis of vacancies cor-
responding to the profile from online-recruitment systems. The required skills
and responsibilities of applicants are highlighted from the vacancies description.
The data obtained are processed using semantic analysis technologies, clustered
by topic. The most popular labor tasks and technologies (tools) that need to
be solved are highlighted. If the workshop design is not the first iteration, then
the list of learning tasks from the previous learning period can also be used as
a basis.
This approach assumes the following steps:
1. determining the current labor market needs based on the analysis of online-
recruitment systems;
2. formation (addition) of an ontological model of the professional area to iden-
tify new trends in the labor market;
3. formation of the actual workshop kernel and recommendations for the imple-
mentation of the workshop content in this educational profile in accordance
with the professional area needs.
3.3 Determining the current labor market needs based on the
analysis of online-recruitment systems
Professional standards are basic normative documents that define the legally
fixed requirements of the professional area. The main drawback of such docu-
ments is their inertia – the term for changing such documents can be 5-10 years.
In addition, professional standards reflect skills, actions, and functions in gen-
eral, without delving into specific tools and technologies. However, the demand
of the labor market for specialists of a certain profile and the requirements for the
technological and tool base change faster than professional standards. In order to
track these changes in a timely manner, you need to contact online-recruitment
systems.
The texts of vacancies descriptions in such online-recruitment systems have
a similar structure – each vacancy contains a block describing the duties and
requirements for the applicant. Depending on the employer, the difference may
be that the data may be indicated by different words, such as ”Requirements”
”Required” ”It is necessary to...”. The scheme of the algorithm for extracting
and processing information from the vacancies description is shown in Figure 4.
As part of the information system, the analysis of vacancies received using
the services API hh.ru is currently implemented. First, the workshop developer
selects the required professional profile. The system sends a request to the API
and receives a list of vacancies for this profile. Since this list offers vacancies de-
scriptions in an abbreviated format, vacancies identification number is extracted
from this list. Next, the algorithm sends a request to get a full description of
each of the vacancies by its ID. Data is provided in json format, and descriptions
8 Boldyreva E., Kholoshnia V.
Selection of Clustering data using
Algorithm launch
requirements from the the DBSCAN algorithm
"description" block
and preprocessing
Creating a list of the received data. Getting data from
specializations using Preprocessing the clusters. Processing and
regular expression "key_skills" block data sorting data by the
number of cluster
elements
Downloading a list of Preprocessing the
vacancies for each received data using
specialization tokenizer and stemmer Creating a list of basic
educational tasks
Retrieving the
Formatting a list of stop
"description" and
words. The formation of Output data to a file in
"key_skills" blocks
the TF-IDF matrix the csv format
from vacancies content
Fig. 4. Algorithm for extracting and processing information from vacancies descrip-
tions
are contained in the description block. A parser was implemented in Python, ex-
tracts the ”responsibilities” and ”requirements” blocks and makes a list of Them
for further processing. In addition, the description contains the key skills sec-
tion, which describes the tools and technologies that the applicant must possess
for the position.
At the processing stage, punctuation marks (commas, dots, and semicolons)
were removed. Abbreviations like ”SW” were replaced with ”software”. Nor-
malization was carried out-bringing words to the initial form. In this task, we
decided to use stemmer, because due to the specific terminology of vacancies
descriptions and the specifics of the Russian language, the exclusion of endings
does not distort the meaning of the phrase. This system implements the Porter
stemmer [19]. At the output, we get an array of normalized tokens for employers
requirements.
Next, the TF-IDF weight matrix is created. We consider each selected request
record as a separate document. The matrix is compiled using TfidfVectorizer
(sklearn package), the block of stop words is taken from the ntlk case and sup-
plemented in accordance with the specifics of the professional area. DBSCAN,
MiniBatchKMeans, and agglomerative clustering algorithms were applied to the
resulting matrix.
The choice of the DBSCAN algorithm is due to the fact that with this spatial
clustering algorithm, you dont need to specify the number of clusters in advance -
it is difficult to predict the number of thematic clusters in the variety of employers
requirements. In addition, the list of requirements often contains requirements
Ontological Approach to Modeling the Current Labor Market Needs 9
of the type ”well understand what it is about”. These records may be considered
noise and are not included in any of the clusters. Clusters of the following types
are highlighted: corresponding to the subject area and noise requirements (the
latter are removed from the list of requirements). Table 1 shows examples of
such clusters.
Table 1. Cluster’s content examples (DBSCAN)
Cluster type Cluster content
Noise [user support, cost management, PC re-
pair, responsibility, competent speech’,
... ]
Cluster 1 [ms sql development experience from
2 years, experience with industrial
databases: oracle ms sql postgresql, ex-
perience with ms sql server from 1
year, experience with ms sql server (dis-
tributed databases), practical experi-
ence with ms sql server 2008/2016, ex-
perience with ms sql server profiler, ex-
perience with ms sql server / oracle
database, . . . ]
When selecting the clustering algorithms MiniBatchKMeans and agglomera-
tive clustering, you must specify the number of clusters. It is based on the number
of main labor tasks from the professional standards for this profile, which has
been doubled (since about 50 % of the requirements in the vacancy are insignifi-
cant for the formation of this profiles workshop). This increase allows you to take
into account ” noise” and allocate additional clusters for storing such records.
3.4 Formation of an ontological model of the professional area
At this stage, cluster data is converted to ontological entities and described using
OWL. An example of an intelligence map of an ontological model fragment for
Cluster1 from Table 1 is shown in Figure 5.
ms sql
Design
Embedded System Databases ms sql server profiler
Work
postgresql
ms sql server 2008/2016
Fig. 5. Fragment of the ontological model mind map for Cluster1
10 Boldyreva E., Kholoshnia V.
The generated ontological model is offered to the developer for analysis and
selection of the final formulation of the main learning tasks and tools and tech-
nologies for their solution. This set of learning tasks will be submitted for further
evaluation and modification to a group of experts from employers.
4 Practical application
The proposed approach and implemented tools were used to form the workshop
kernel on the profile “Embedded and cyber-physical systems”. As a basic data
set for forming a kernel fragment dedicated to embedded system software, re-
quirements and key skills for software - vacancies of 6 different specializations
were taken and analyzed - 11183 vacancies and 121698 requirements. The to-
tal execution time of the algorithm with data loading and processing was 31.5
minutes.
Visualization of the DBSCAN and MiniBatchKMeans clustering algorithms
is shown in Figure 6 and 7.
Fig. 6. Visualization of the DBSCAN clustering algorithm
Fig. 7. Visualizing the operation of the MiniBatchKMeans clustering algorithm
Ontological Approach to Modeling the Current Labor Market Needs 11
The DBSCAN algorithm showed the best results when setting the distance
between the eps clusters=0.001. With these parameters, about 270 clusters are
allocated that contain data that is significant for this profile. Using agglomera-
tive clustering 100 significant clusters and 96 clusters of the MiniBatchKMeans
algorithm were identified.
Further, the clusters were correlated with the labor tasks of professional
standards, and they were chosen as close as possible to the formulations of labor
tasks. Since it is difficult to assess the semantic proximity of texts in Russian, a
“manual” assessment of the semantic proximity of the selected clusters and the
main labor tasks was carried out. According to the results of this assessment,
the DBSCAN clustering algorithm showed better results - the selected clusters
were more “thematic” and contained fewer insignificant requirements.
5 Conclusion
The article describes an approach to creating the electronic workshop content
of a certain education profile for knowledge-intensive areas. These areas have
annualy strong changes in the issues of technologies used and requirements for
the skills of industry specialists. An ontological approach to modeling the actual
labor market needs based on a systematic analysis of online-recruitment systems
is proposed.
Ontological model of the professional area have been developed that take
into account: the relationship between professional standards and labor market
requirements, and the competence model of the graduate. This model show the
main data that must be processed and compared in the process of forming the
actual workshop kernel for a specific educational profile - labor tasks and key
skills from employers. As part of the paper, the analysis of vacancies obtained
using the services API hh.ru was implemented. For the profile ”Embedded and
cyber-physical systems” 11183 vacancies and 121698 requirements were analyzed
for the workshop kernel segment of the embedded systems software. According to
the results of the expert evaluation, the DBSCAN clustering algorithm showed
better results - the selected clusters were more ” thematic” and contained fewer
insignificant requirements.
An ontological model of the professional area of the profile was developed
based on the results of the analysis of vacancies descriptions, and a list of current
main learning tasks and tools for solving them was defined in the kernel block of
the discipline workshop “Embedded systems software” for transfer to experts-
employers for evaluation.
Based on the described approach, a software package for extracting and
processing current labor market needs from vacancies descriptions of online-
recruitment systems has been developed, and a mechanism for forming and sup-
plementing an ontological model of the professional area has been implemented.
As a direction for further research, it was decided to compare the cluster
analysis of vacancies descriptions and classify them using neural network models.
12 Boldyreva E., Kholoshnia V.
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