Data Acquisition Model for an Intelligent System Integrating the
Creation of Educational Programs and Professional Standards
DINARA KAIBASSOVA, ARAILYM ASHIMBEKOVA
Department of Computing Engineering,
Astana IT University,
Mangilik El avenue, 55/11, Astana,
KAZAKHSTAN
Abstract: - The Digital Kazakhstan program ensures that higher education meets the needs of modern
industrialization by developing students' professional skills and competencies. These qualities guarantee that
students will be able to quickly adapt to the professional environment and remain competitive in the national
and global labor markets after graduation. The aim of this research is to improve the content of educational
programs by developing models, methods, and algorithms for an intelligent system for designing educational
programs that consider the interrelationships between the subjects studied and the competencies formed
through natural language analysis and processing. To achieve the goal, several tasks were solved, including the
creation of a data model of educational content (subject programs) and professional content (requirements of
professional standards) to study their structural components. This paper presents a model of the professional
competencies database and the concept of creating an educational program corresponding to the required skills.
This model is based on the analysis of unstructured texts describing the content of educational courses. The
work performed has led to two important results. First, the successful prototyping of an intelligent system that
dynamically links course content to the required professional competencies. Second, the development of an
algorithm that improves the accuracy of matching learning outcomes to industry standards. These achievements
prove that educational programs can be designed to better meet the changing demands of the labor market,
equipping graduates with the necessary skills to succeed.
Key-Words: - data analysis, natural language texts, frequency matrix, education content, education program,
intelligent system, natural language, professional competencies, professional standards,
matching learning.
Received: February 16, 2024. Revised: August 12, 2024. Accepted: September 7, 2024. Published: October 4, 2024.
1 Introduction
To achieve the required educational results,
regulatory and advisory documents, such as
competency and qualification frameworks, and
educational and professional standards, are
developed and implemented. In this regard, the task
of creating modern methods of planning and
organizing the educational process that considers the
needs of employers and labor market trends become
especially urgent, [1]. As employer requirements
are constantly changing and evolving, the methods
developed must support the development of
professional skills through specialized training
programs.
The 2020 rating of educational programs at
higher education institutions in RK, conducted by
the National Chamber of Entrepreneurs “Atameken”,
demonstrated that there is a 48% demand for
graduates and that employers in RK are satisfied
with the quality and relevance of the educational
programs. The criteria are united in three main
blocks: career prospects of graduates, quality of
educational programs, and achievements of students.
"The rating methodology was adopted as a result of
open discussion with the public of higher education
institutions and experts. Accordingly, the
methodology of the rating is constantly being
improved. The Ministry and National Chamber of
Entrepreneurs "Atameken" hold open discussions
with the heads of universities and the rating is aimed
at protecting the interests of students, who should
and will understand what career prospects are open
to them after graduation. The authors [2], [3] assert
that the key stakeholders in developing an
educational program aimed at developing
professional competencies should be students,
representatives of the labor market (employers), and
teaching staff.
In the process of developing educational
programs, analysis of the content of academic
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disciplines, their relationship with the requirements
of educational and professional standards, as well as
considering the demand of the labor market for
specialties and areas (profiles) of education,
becomes crucial. Consequently, curriculum design
includes the process of creating a basis for
professional competencies based on professional
standards, which will be the basis for the process of
creating and updating educational programs.
For studying this subject area, the main
document is state standards in the field of
information technology and professional standards
[4] developed by the National Chamber of
Entrepreneurs «Atameken» under the project
"Development of Labor Skills and Job Incentives"
within the framework of the Partnership Agreement
between the Government of the Republic of
Kazakhstan and the International Bank for
Reconstruction and Development.
2 Subject Area
In the context of academic freedom in universities,
the quality of preparing specialists directly depends
on the relevance of educational programs. Moreover,
the professional competencies of future specialists
should be shaped in accordance with the labor
market demands in specific regions of the country.
When developing educational programs, one of the
primary tasks for educational institutions is to
identify a set of disciplines that align with the
required competencies. However, there is a lack of
software applications in the market that would allow
for a comparative analysis of the content, objectives,
and outcomes of different disciplines and courses,
irrespective of their subject area, to update and
develop educational programs in line with the latest
requirements of educational and professional
standards.
In this regard, the possibility of integrating
components of data analysis into a wide range of
applied software becomes relevant. Designing an
educational program is a multi-step process, as it
reflects the dynamics of student learning and should
allow for timely adjustments in the teaching process,
[5].
The research aims to enhance the quality of
educational program content by developing models,
methods, and algorithms for an intelligent system
that shapes educational programs, considering the
interrelation between studied disciplines and the
formation of competencies, based on the analysis
and processing of natural language texts.
To achieve the stated goal, several tasks need to
be addressed, and one of them is as follows:
Develop data models for educational content
(syllabus thematic plans) and professional content
(requirements of professional standards) to study
structural elements.
In essence, the research seeks to create an
intelligent system capable of analyzing and
processing textual information in natural language
to optimize and improve the design of educational
programs. By considering the interaction between
different disciplines and aligning them with the
requirements of professional standards, the system
aims to ensure that the educational content remains
relevant and meets the demands of the labor market.
The object of the research is professional
standards and discipline curricula (syllabus). In
other words, the main subjects of the study are
normative documents defining the requirements for
professional training of specialists, as well as
educational programs that include information about
the content and organization of education for
individual disciplines.
The subject of the research is natural language
text models designed for text data search,
classification, and clustering. The research aims to
develop and apply methods of natural language text
analysis and processing to structure and systematize
the information contained in professional standards
and discipline curricula.
The practical significance of this research lies in
the fact that the obtained results and software can be
used to optimize and shape educational programs.
By utilizing the developed methods and algorithms
for text analysis, researchers and educational
institutions can better understand the interrelations
between disciplines and competencies, adapt the
content of educational programs to changing labor
market demands, and improve the quality of
specialist training. For the successful completion of
the task of researching a method for selecting
textual documents necessary for organizing
education based on the identified competencies of
the educational program, complex intelligent
algorithms need to be developed. The first algorithm
aims to extract terms characterizing the given
domain of scientific knowledge, while the second
algorithm aims to determine the similarity of these
terms with the competency base of the subject area
developed in this work, [6].
Natural language texts have an informal
structure, consisting of sentences based on the rules
of natural language and using the full diversity of its
vocabulary, [7]. Unlike fixed constructions in
programming languages, the absence of such fixed
structures in natural language makes data extraction
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highly relevant and often serves as the first step in
text processing.
Data extraction is the process of finding,
collecting, and storing information in various
formats. Specific characteristics of this process
include:
− processing individual texts or collections of texts;
− extracting data relevant to specific problems,
questions, or topics.
To address this task effectively, the use of
sophisticated intelligent algorithms is essential in
order to extract relevant data and information for
organizing education and shaping educational
programs based on the identified competencies.
3 Materials and Methods
To address the research task of selecting textual
documents necessary for organizing learning based
on the identified competencies of the educational
program, sophisticated intelligent algorithms need to
be developed. These algorithms will facilitate
efficient information extraction and processing from
the text documents.
Let's describe the model for creating an
educational program that meets the specified
competencies. The problem statement can be
formulated as follows:
Let's assume we have a collection of syllabuses
n
dddD ,...,,21
and a dictionary of terms from
the competency database
n
sssS ,...,,21
, which
interacts with the user's query. Consider a document
from the collection of syllabuses
Ddi
and
represent it as a vector in the space. Then this vector
will have the form as in (1):
iniii tftftfd,...,,21
(1)
where
ij
tf
- is the number of occurrences of the word
from the query
j
s
in the document
i
d
. In the vector
model, the document is considered as an unordered
set of terms.
The goal is to obtain a matrix:
ij
xX
,
where
ijij dstfx,
- represents the frequency of
terms occurring in the document.
Let's consider the sequence of actions for organizing
the search
j
s
in the document
i
d
:
− select a text document from the document
collection;
− remove stop words from the text;
− considering the word morphology, calculate the
frequency of occurrence of each term.
Rank the terms in descending order of their
occurrence frequency.
Form the term-document matrix.
Thus, we obtain the term-document matrix, where
the rows correspond to documents from the
collection, and the columns correspond to terms, [8].
As a result, there might be zero rows corresponding
to syllabi that do not match any competency, as they
are not considered for further analysis, and such
rows are removed, [4], [7]. There might also be zero
columns, representing uncovered competencies, in
which case it is necessary to add syllabi covering
those competencies.
For vectorizing the corpus, various methods are
used, such as frequency-based methods, direct
encoding methods, term frequency-inverse
document frequency methods, and distributed
representation methods. These methods allow
transforming documents from the transform corpus
into the vector space model to make predictions
later.
The frequency vector is the simplest method of
vectorization, which involves filling the vector with
frequencies of word occurrences in the document
[9]. The encoding of each document is represented
as a set of its constituent lexemes, and the value for
each word position in the vector is the counter of the
corresponding word. This method is suitable for
models based on the Bayesian approach, [10]. The
drawback of this method is that as the result of
vectorization, lexemes that occur very frequently
may appear to be more "significant" than those
occurring much less frequently, [11].
In turn, professional competencies describe a set
of essential typical characteristics of a particular
specialty, defining the specific direction (profile) of
an educational program. They manifest in a
specialist's ability to address a set of professional
tasks within their chosen field of activity based on
specific knowledge, skills, and abilities. The list of
professional competencies is structured in
accordance with the main types of professional
activities that a graduate should be prepared for,
such as scientific research, project management,
production and technological skills, and
organizational and managerial competencies.
The main elements of a professional standard
are as follows:
− Direction of Professional Activity: this describes
the field or area of work where the standard
applies.
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− Job Title: The official designation of the
profession for which the standard is defined.
− Qualification Level: This specifies the
requirements regarding education and work
experience within the National Qualifications
Framework.
− Labor Function: A set of interconnected work
actions that contribute to achieving a specific
task or objective.
− Requirements for Skills and Knowledge: The
necessary skills and knowledge needed to
perform a particular labor function effectively.
In this context, the present work focuses on the
development of a database of professional
competencies, which will be described in more
detail below.
a. Professional Competencies Database Model
The problem statement is to extract data from the
database to construct a query, which consists of the
following procedures:
Processing the query, including:
− selecting from directions;
− selecting professions;
− selecting functional tasks;
− selecting competencies.
Viewing, creating, moving, editing, and adding
competencies, functional tasks, professions, and
directions.
The database structure consists of 5 main tables
(Figure 1), interconnected by parent-child
relationships.
Fig. 1: Database model «Professional
competencies»
The «Directions» table is intended for storing
information about educational program directions:
direction identifier (id) and direction name (name).
The «Professions» table contains information
about professions related to the corresponding
directions: profession identifier (id), a field
(direct_id) serving as a foreign key, and the name of
the profession (name). It's important to note that a
foreign key is a column or a combination of
columns whose values correspond to the primary
key in another table. In this case, the values of the
«direct_id» column (foreign key) correspond to the
values of the «id» column (primary key) from the
"Directions" table.
The other tables «Functions», «Tasks» and
«Knowledges» are created using the same scheme.
Building the database based on a relational DBMS
allows for utilizing built-in full-text search,
regulating relationships between competencies, and
implementing certain linguistic algorithms using
stored procedures and functions.
The interaction between the database and the
visualization module is organized using a retrieval
command, which, in turn, performs the function of
transforming the input text into a matrix, where the
values represent the occurrences of a given key
(word) in the text.
b. The structural elements of an educational
program
According to the Working Educational Program
(Syllabus), include the following components:
− Information about Instructors: Details about the
teaching staff involved in delivering the course.
− Description of the Studied Discipline: An
overview of the subject or course being taught.
− Objectives and Goals of the Discipline: Clearly
stated aims and objectives of the course.
− Brief Content, Topics, and Duration of Study:
An outline of the course content, the topics
covered, and the duration of each segment.
− Assignments and Requirements: Guidelines for
self-study assignments and the expectations set
by the instructor.
− Evaluation Criteria and Schedule: The criteria
for assessment, timelines for task completion,
and submission deadlines for assignments
related to the discipline.
− Student Assessment Criteria: The criteria used
for evaluating students' knowledge and
performance.
− List of Main and Additional Literature and
Information Resources: A compilation of
primary and supplementary reading materials
and other resources relevant to the course.
Additionally, the working educational program
for a discipline may include variations of
examinations, course projects, and assignments
tailored for students in distance learning programs.
From the perspective of educational program
development, the following crucial concepts can be
highlighted:
1. Educational Goals and Objectives of the
Course: The competencies that the educational
program aims to develop and the expected learning
outcomes for the students after completing the
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discipline. The course's place within the educational
program is determined, specifying the entry
requirements (prerequisites) for students and the
subsequent preparation (post requisites) for the
educational program.
2. Course Structure: This includes a breakdown of
the sections, key topics, objectives, and content of
lectures and practical sessions, as well as
assignments for independent student work.
Much of the information in educational
programs and discipline syllabuses is presented in
large blocks of loosely structured text. Some
sections are formatted as tables (e.g., workload of
the course, thematic plan of the discipline, list of
practical and laboratory work) or lists (e.g.,
objectives and goals of mastering the discipline,
etc.).
This shortcoming can be elucidated in greater
detail by examining several pivotal factors.
1. The prevalence of frequently occurring words.
Words that occur frequently, such as prepositions,
conjunctions, and general terms, are assigned high
values in the frequency vector. This results in an
overestimation of their significance. Consequently,
words that are significant but infrequent may be
eclipsed.
2. The inability to distinguish between synonyms
and polysemantic words. The Frequency Vector
method is unable to distinguish between synonyms
and polysemantic words. In the event that a word
possesses a similar denotation yet disparate forms,
the same word will be represented by distinct vector
elements. Furthermore, the same word in disparate
contexts will have an identical representation.
4 Results and Discussion
The primary findings of the content analysis of
educational programs (subject programs) are
presented in the form of frequency distributions of
keywords and phrases, as well as identified
connections between disciplines and competencies.
1. Analysis Methodology: The analysis of
curriculum content was conducted in several stages,
including:
− the texts comprising the curriculum were then
collected and prepared for analysis;
− the texts were then parsed, and keywords and
phrases were highlighted;
− the frequency distribution of keywords and
phrases was determined;
− the identification of connections between
disciplines and competencies.
2. Analysis Results: The results of the analysis are
presented in the form of frequency distributions of
keywords and phrases, as well as identified
connections between disciplines and competencies.
The frequency distribution of keywords and phrases
demonstrates which terms are most frequently
encountered in curriculum texts. This enables the
identification of the principal topics and areas of
focus within each discipline that are of greatest
importance and relevance.
Connections Between Disciplines and
Competencies: the identification of connections
between disciplines and competencies enables the
determination of which disciplines contribute the
most to the formation of specific competencies. This
facilitates the optimization of educational program
structures and ensures their alignment with
professional standards.
5 Conclusions
In summary, the work involved analyzing the
development of educational programs in accordance
with current educational and professional standards.
It also addressed the challenges of aligning the
professional domain with the educational program.
Additionally, the study explored the structural
elements of educational programs and described
models for organizing and representing knowledge
in intelligent systems, specifically for educational
and professional content. Finally, a formal and
structured model was developed to represent the
elements of educational and professional content
and their relationships.
This study makes a significant contribution to the
existing research in this field.
1. An innovative solution for designing educational
programs has been developed. This solution
comprises the creation of an intelligent system that
dynamically links course content with professional
competencies. This allows educational programs to
be more accurately adapted to the requirements of
the labor market.
2. The algorithm developed for this project increases
the accuracy of educational programs' alignment
with professional standards, thereby enhancing the
quality of student training and their competitiveness
in the labor market. Furthermore, the introduction of
natural language text analysis algorithms allows
educational institutions to more effectively update
and adapt their programs to meet changing
requirements.
3. The practical significance and potential
applications of this research are twofold. Firstly, the
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Volume 12, 2024
results obtained and the developed software can be
used to optimize educational programs at
universities and other educational institutions.
Secondly, the study demonstrates how modern
methods of data analysis and natural language
processing can be integrated into educational
processes to improve their quality and meet the
requirements of the labor market.
By implementing this method and algorithm, it
becomes possible to efficiently extract relevant data
from large volumes of loosely structured texts and
create valuable resources in the form of a
competency database and a corpus of documents
enriched with competencies. These resources can
significantly aid in curriculum development,
competency-based education, and research in the
field of education and professional development.
Declaration of Generative AI and AI-assisted
technologies in the writing process.
During the preparation of this work the authors used
ChatGPT (Generative Pre-trained Transformer) in
order to improve the readability and language of
their manuscript. After using this tool, the authors
reviewed and edited the content as needed and took
full responsibility for the publication's content.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed to the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This research has been funded by the Science
Committee of the Ministry of Science and Higher
Education of the Republic of Kazakhstan (Grant No.
AP19677319).
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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