=Paper=
{{Paper
|id=Vol-3777/paper19
|storemode=property
|title=Development and Analysis of the Training Course "Organization of Databases" with AI
|pdfUrl=https://ceur-ws.org/Vol-3777/paper19.pdf
|volume=Vol-3777
|authors=Kyryl Korobchynskyi,Maksym Shapovalov,Viktoriia Seredenko
|dblpUrl=https://dblp.org/rec/conf/profitai/KorobchynskyiSS24
}}
==Development and Analysis of the Training Course "Organization of Databases" with AI==
https://ceur-ws.org/Vol-3777/paper19.pdf
Development and analysis of the training course
"Organization of databases" with AI
Kyryl Korobchynskyi1, Maksym Shapovalov1 and Viktoriia Seredenko1
1
Kharkiv National Aerospace University ‘KhAI’, 17, Chkalov str., 61070 Kharkiv, Ukraine
Abstract
This article explores approaches to teaching database organization and management, emphasizing the
importance of effective learning methods. It analyzes various teaching strategies, including project-based
learning and optimized pedagogical approaches, to enhance students' skills in database design,
implementation, and maintenance. The study highlights the significance of addressing knowledge gaps,
implementing innovative teaching methods, and evaluating learning effectiveness.
The research also examines different resources for learning database organization, from online courses to
certifications. A key focus is on the development of a web application for assessing the quality of
educational tests using the Rasch model. This application allows for the construction and analysis of test
information curves, item characteristic curves, and test score distributions.
The article describes the technical implementation of the application, including its architecture,
development tools, and functionalities. It provides a detailed overview of how the application processes test
data and generates visual representations to aid in test quality assessment, ultimately contributing to the
improvement of database education methods.
Keywords
Information technology, intelligent communication technologies, educational environment, online
education, course quality, technology in education, database learning. 1
1. Introduction
Effective training of the organization and management of databases is of great importance to ensure
optimal use of data resources and maintain system performance. In this work, several approaches to
learning in this field, aimed at improving students' skills in designing, implementing and maintaining
databases, were considered and analyzed. These approaches include various methods and tools for
providing comprehensive training [1]. By reviewing existing approaches to database organization
and management education, we can gain valuable information about the most effective strategies for
teaching students the necessary skills and knowledge for proficient database management:
1. Project-based learning: one approach to organizational learning and database management
involves the implementation of the project-based learning method. This approach focuses on
organizing learning through a course management system that allows participants to
participate in hands-on projects that simulate real-world database scenarios. Project-based
learning can be an effective way to gain hands-on experience and allow students to apply
their knowledge in real-world contexts, which is critical to developing database management
competence
2. Optimization of the approach to learning: approach to learning plays an important role in
the learning process. It is at this stage that you can prove AI, for example, to create test tasks
or automate their filling, AI copes well to create a knowledge bank in a training course. It is
also possible to use interactive methods to involve students and integrate theoretical concepts
1
ProfIT AI 2024: 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024), September 25–27,
2024, Cambridge, MA, USA
k.korobchinskiy@khai.edu (K. Korobchynskyi); maks0681912507@gmail.com (M. Shapovalov);
seredenkovikt@gmail.com (V. Seredenko)
0000-0002-3676-6070 (K. Korobchynskyi); 0009-0006-3035-3802 (M. Shapovalov); 0009-0009-7282-8046 (V. Seredenko)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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with practical applications. Thus, mentors of the curriculum may guarantee that participants
will receive a deep understanding of the course materials
3. Bridging Knowledge Gaps: One of the challenges of taking a course is bridging knowledge
gaps. To address this issue, a determined effort must be made to identify and fill any existing
gaps in participants' understanding of database concepts and technologies. Training
programs should include targeted activities aimed at explaining unclear parts of the material,
thus providing participants with a comprehensive understanding of the principles that are
taught in the training course
4. Approaches to training: effective approaches to training are essential to ensure high quality
training. Educators and mentors should use innovative and interactive learning methods to
engage participants and promote their productive outcomes. By using a variety of
instructional approaches, such as case studies, group discussions, and simulations,
instructors can facilitate the creation of diverse curriculum styles and enhance student
learning of course materials
5. Assessing the effectiveness of training: Training is of utmost importance to ensure that
participants acquire the necessary skills and knowledge. By implementing systematic
assessment methods, mentors can measure students’ performance, skills, and overall success
6. Best Practices: Applying best practices to training is essential to ensure participants are able
to learn the latest industry standards and methodologies. Collecting and organizing data, as
well as following established procedures, are integral components of any training. Training
participants in such practices will contribute to the effective assimilation of educational
materials.
When it comes to learning about database organization, there are a number of resources to
consider, from online courses to certifications [2] and training programs. These resources are
designed to provide individuals with the knowledge and skills needed to effectively manage and
organize databases, both professionally and for personal development.
Online platforms such as Coursera [3], Udemy [4], and LinkedIn Learning [5] offer a wide variety
of database management courses. These courses cover fundamental topics such as database structure
and administration, SQL queries and data security, etc. In addition, they often offer practical exercises
and projects to consolidate the studied materials.
Another valuable resource is the availability of certificates from organizations such as Oracle [6],
Microsoft [7], and MongoDB [8]. These certifications not only validate individuals' expertise in
database management, but also provide structured learning resources for exam preparation. They
cover a variety of areas such as relational database management systems, NoSQL databases and cloud
database solutions.
In addition, institutions such as edX [9] and Khan Academy [10] also offer free courses in database
management, making learning accessible to a wider audience. These courses are developed by
industry experts and cover various aspects of database management, including database modeling,
indexing, and performance optimization. They often use interactive tools and simulations to enhance
the learning experience.
In addition to online courses and certifications, professional associations and industry
organizations often offer training and workshops related to database training. These may include
live webinars, seminars and conferences where participants can learn from leading experts in the
field and stay abreast of the latest database management trends and practices.
In addition, given the constant development of technology, blogs, forums and communities
dedicated both to the specific topic of databases and to many other topics in general will help to stay
abreast of new information. Platforms such as Stack Overflow [11], Database Journal [12], and Oracle
Community [6] provide a space for professionals and enthusiasts to share knowledge, advice, and
stay abreast of best practices and new trends in the industry.
Finally, gaining hands-on experience through internships, studentships, or personal projects can
go a long way toward improving database organization and management skills. By working on real-
world database implementations and troubleshooting scenarios, individuals can gain practical
insight into the complexity of database management and improve their problem-solving skills.
In summary, the resources and approaches available for teaching organization and database
management are varied and designed for students of varying skill levels and preferences. Through
structured online courses, industry-recognized certifications, professional seminars, or hands-on
experience, individuals have ample opportunities to acquire and improve their skills in this important
field of information technology. By integrating the existing learning approaches described above into
their programs, organizations can ensure that students develop the necessary skills and knowledge
to skillfully manage and optimize databases to improve business performance and decision-making
systems.
2. Description of the educational course on the organization of
databases
DFD, IDEF0 and IDEF3 context diagrams of various approximations were created to describe the
course model. The BPWin software environment was used to create diagrams. These models visually
represent system functions and data flows, providing important information for possible further
improvement of the system and optimization of its performance:
The diagram of the Figure 1, demonstrates how various input materials and resources are
transformed into course outcomes under the guidance of certain rules and with the participation of
students and teachers. This is a high-level representation of the entire learning process within this
course. A student and a teacher interact with each other in the system.
Figure 1: General view "Organization of Databases"
This is an IDEF0 context diagram showing an overview of the "Organization of Databases"
training course.
The diagram in Figure 2 shows an IDEF0 flow chart for a course titled “Database Organization”.
The diagram illustrates the process flow of the various course components and activities. It begins
with “Lecture Materials” and “Test Materials” on the left-hand side, moving on to activities such as
“Studying or Participating in Lecture” and “Taking Tests.” The diagram progresses through practical
work, homework, and module exams, with arrows indicating the flow and relationships between the
various stages. The diagram culminates with “Feedback Writing” on the right-hand side. Throughout
the diagram, there are notations of rules, knowledge transfer, and student-teacher interactions,
represented by arrows of different colors. The overall layout provides a visual representation of the
course structure and the flow of learning.
Figure 2: Comprehensive structure of a organization of databases training course
The diagram in Figure 3 shows a process flow chart consisting of several interrelated stages. The
diagram illustrates a complex process using IDEF3 notation to model the sequence of operations and
decision-making logic. The diagram effectively displays both sequential and parallel actions, as well
as decision points in the process. For example, at the initial stage, participation in a lecture or its
elaboration is required (element 1), then two parallel units of behavior (UOB): "Completing a test
task" (element 2), "Completing a practice" (element 3). After completing several units, in accordance
with the curriculum, a module control must be passed (element 4). At the end of the course, an exam
is provided (element 6). Also, during the study, it is necessary to complete homework (element 5).
Figure 3: Sequence diagram of the learning process
The data flow diagram (DFD) depicts the interaction process between a student, a mentor, a course,
and a database within the Database Organization course. The diagram shows the main components
of the system: a student, a course, a mentor, and the STM database. A student accesses the course
learning materials and completes assignments. After completing the assignments, the student
provides feedback and grades for the course. Completed assignments, feedback, and grades are
transferred to the course. The course, in turn, sends this data to the STM database for storage and
analysis. The mentor also accesses the student learning materials and completed assignments. The
mentor can view the student feedback and grades to assess their progress and provide
recommendations. The course sends statistics and information about the student's progress to the
STM database. The STM database stores all learning materials, completed assignments, grades, and
feedback. This data is used to analyze the effectiveness of the course and improve the quality of
training. Students and mentors can access data from the database to monitor progress and adjust the
learning process. Course materials may include lectures, practical assignments, tests and other
resources. Assessments and feedback from students help the course adapt to the needs of learners.
The interaction between the student, mentor and course occurs through data exchange, which
ensures transparency and efficiency of learning. Each component of the system plays its role in
ensuring the quality of the educational process. Course materials are updated and stored in the
database, ensuring the relevance of information. The mentor plays an important role in supporting
students and providing them with feedback. The STM database is the central repository of all course
information. It allows collecting and analyzing data to improve the learning process. This diagram
helps to understand how information flows between participants in the process and how each
component interacts with each other.
Figure 4: Data flow diagram for the database organization course
The data flow diagram (DFD) depicts the interaction process between the components of the
Database Management course system, from uploading course materials to receiving feedback from
students. The diagram shows the main processes: uploading course materials, grading completed
assignments, uploading completed assignments, and writing feedback. The process begins with
uploading course materials, which are provided to students for study. These materials include
lectures, practical assignments, and other learning resources. Students upload these materials and
begin completing assignments. After completing the assignments, students upload them for grading.
The assignments are sent for grading, where a mentor evaluates their quality and correctness.
Evaluation includes checking answers, their compliance with criteria, and assigning grades. Students
provide feedback on the course and graded assignments. Feedback can include comments, reviews,
and suggestions for improving the course. This process collects information about how students
perceive the course. Each process interacts with the data flow, ensuring the transfer of information
between participants. Course materials, grades, completed assignments, and feedback move between
students and the course system. This interaction enables effective learning and assessment.
Uploading completed assignments and feedback allows the course system to store all information in
a centralized database. This makes it easier to access the data for analysis and reporting. Thus, this
data flow diagram illustrates all the stages of interaction between students and the course system. It
helps to understand how information is transferred and used to improve learning.
Figure 5: Data flow diagram of interaction of elements in the learning process
Figure 6 and Figure 7 present the structure and overall view of the training course on the STM
platform, respectively. As we can see from the graphic materials, the training course consists of 16
units (the leaves in the figure represent individual units), namely:
1. Main sections – 10 units;
2. Modular controls – 2 units;
3. Homework – 1 unit;
4. Exam – 1 unit;
5. Auxiliary section – 1 unit;
6. Additional tasks – 1 unit.
The diagram illustrates the structure of the "Database Organization" course, divided into main,
secondary and control sections. Core sections include topics such as ADO.NET, Entity Framework,
and LINQ, which cover basic concepts of working with data. Secondary sections and homework
complement the main materials and help deepen knowledge. Control sections contain modules for
checking knowledge and exams. This structure helps students study the material consistently and
assess their knowledge as they progress through the course.
The main part of the course [13], with which the most interaction takes place during training, is
10 unit parts. Figure 7 shows the view of one of the units. Each such unit of this course contains the
following elements:
1. Theoretical material for independent study;
2. Practice work;
3. The form for downloading the Practice report;
4. Useful resources and instructions for laboratory work;
5. Test task;
6. Questions for self-control;
7. Link to the general forum of the course.
Figure 6: Structure of the course "Organization of databases"
Figure 7: General view of the course on the STM platform
3. Means of evaluating the effectiveness of the course
With a systematic approach to assessment, instructors can tailor their online courses to effectively
meet the needs of students. Below are some methods that are often used to evaluate the effectiveness
of a course:
1. Exams – one of the methods of evaluating the effectiveness of the course is the analysis of
exam scores to measure success after completing the main topics of the online course;
2. Pre- and post-tests – in some cases, the use of pre- and post-tests can be an effective way to
assess the effectiveness of the training program by comparing knowledge or skills before and
after the course;
3. Feedback and surveys - collecting feedback from students using surveys is the main and
widely used technique for obtaining valuable information about the effectiveness of the
course;
4. Analysis of student data – by analyzing data related to students, the level of progress and
interaction with the course materials, it is also possible to evaluate its effectiveness.
Enabling a learning management system (LMS) – such as Moodle, Canvas or Blackboard – to
deliver a course usually includes access to analytics and reporting features. With LMS analytics, you
can gain insight into student engagement, covering aspects such as course availability, activity
duration, completion rate, and academic performance.
An important criterion for the effectiveness of the course is the assessment of the difficulty of the
test tasks. One of the options for assessing complexity is the use of psychometric paradigms.
Psychometric paradigms refer to the approaches and methodologies used to evaluate the quality and
effectiveness of tests, particularly in the fields of psychology and educational assessment. There are
specific methods used to evaluate various aspects of tests that examine key criteria such as reliability,
validity, standardized administration, and cognitive models. Psychometric paradigms include two
main ones - classical test theory (CTT) and task response theory (IRT). These paradigms serve as a
foundation for test development, quality assurance, data interpretation, and analysis, playing a key
role in ensuring the accuracy and completeness of psychometric assessments:
1. Reliability and validity – Psychometric paradigms include methods for assessing the
reliability and validity of tests, ensuring that the test qualitatively measures what it is
intended to measure (reliability) and that it actually measures the construct it is intended to
measure. These aspects provide a reliable framework for understanding the accuracy of
assessment instruments, which is critical for obtaining valid estimates of test scores.
2. Item Response Theory – Item Response Theory (IRT) is one of the major psychometric
paradigms that focuses on describing the relationship between latent traits, such as abilities,
and the probability of different responses to test items. IRT increases the accuracy and
efficiency of test scoring and analysis by providing insight into the characteristics of test
items and the abilities of test takers.
3. Classical Test Theory – Unlike IRT, Classical Test Theory (CTT) is another fundamental
psychometric paradigm that examines the relationship between observed test scores and true
scores and how measurement error affects the accuracy of test scores.
By applying these psychometric paradigms, psychologists, educators, and researchers can design,
refine, and evaluate tests to ensure their accuracy, validity, and overall effectiveness in measuring
cognitive abilities, skills, and other relevant constructs. Below is a more detailed description of test
scoring methods and test items.
The Rasch model "1 Parametric Logistic Latent Trait Model", also known as the one-parametric
logistic model (1PL), is a psychometric model for analyzing categorical data, such as answers to
questions in tests. It was developed by the Danish mathematician Georg Rasch and published in 1960.
The model is used to measure latent traits, such as attitudes or abilities, and it shows the probability
that a person will get the correct answer to a test item:
" ("#$% )
𝑃! (𝜃) = ,
#$" ("#$% )
where 𝜃 and 𝑏! are parameters of respondents' ability and task complexity, respectively.
The interaction of two sets 𝜃% і 𝑏! forms data that demonstrate a property known as conjoint
additivity. Skillful application of the Rasch model ensures complete independence of respondents'
parameters from the tasks they solve, and vice versa. This quality, called specific objectivity,
emphasizes the precision of measurement achieved by the Rasch model.
Figure 8: Task characteristic curves (ICC) in the model (1PL)
Figure 8 shows the item characteristic curves (ICC) in the 1PL model. The difficulties of tasks are
described at different levels of -2, 0 and +2 logits, where the first is the easiest, the second is medium,
and the third is the most difficult. The figure highlights that the subject's probability of success in
the task increases with his readiness level θ. Tasks can vary in probability of getting the correct
answer, with probabilities approaching one, 0.5, and near zero for the easiest, medium, and hardest
tasks, respectively.
Figure 9: Subject characteristic curves (ICC) in the 1PL model
Figure 9 presents the within-subjects characteristic curves (ICCs) in the 1PL model, which
represent the curves for subjects with readiness levels ranging from -2 logits (weakest) to +2 logits
(strongest). The illustrations emphasize that a higher level of task difficulty gives a lower probability
of providing the correct answer to the task. For example, a task with a difficulty parameter of 0
represents a serious problem for a subject with a readiness level of -2 logits, while a subject with 0
logits has a probability of completing the task of 0.5, and subjects with +2 logits have almost sure
success.
Birnbaum's two-parameter model:
" '% ("#$% )
𝑃! (𝜃) = ,
#$" '% ("#$% )
where 𝑎! - discriminability parameter;
If the test consists of tasks with different levels of differential ability (𝑎! ), then the one-parameter
1PL model is inadequate for such data characterization. To eliminate this limitation, A. Birnbaum
introduced an additional parameter - 𝑎! , identified as the element discrimination parameter or the
discriminability parameter.
Figure 10: ICC in the two-parameter 2PL model
The parameter 𝑎𝑖 determines the steepness of the characteristic curve for the i-th point. Figure
10 provides examples of characteristic curves that demonstrate that a higher value of 𝑎𝑖 results in a
steeper curve, indicating a greater differentiating capability of the element.
In Birnbaum's two-parameter model, instead of the differential ability parameter 𝑎! for the
element, the parameter 𝑎% can be introduced, which means the measure of respondents' knowledge
structuring. This parameter 𝑎% determines the steepness of the characteristic curve for the j
respondent, while 𝑏 is the respondent parameter. The model calculates the probability of a correct
answer based on these parameters using the formula:
' (" #$)
" ( (
𝑃% (𝜃) = ' (" #$) ,
#$" ( (
where 𝑎! - measure of respondents' knowledge structuring.
It is proposed to integrate both versions of Birnbaum's two-parameter model regarding tasks and
respondents, with the aim of unifying the consideration of the differential ability and structuring of
respondents' knowledge. This unified model calculates the probability of a correct answer using the
formula:
'( '%
("( #$% )
)'( * +'% *
"
𝑃!% )𝜃% , 𝑏! + = '( '% ,
("( #$% )
)'( * +'% *
#$"
In the simplified interpretation of Birnbaum's two-parameter model, the differentiated ability of
&( &%
the element or the structuring index of the respondent is replaced by the characteristic ,,
'&( * $&% *
which is constantly lower than individual values 𝑎% and 𝑎! .
This model is most suitable for scenarios involving a large number of tasks and respondents. With
the collective estimation of task and respondent parameters and potential multimodality in the
likelihood function, the risk of erroneous decisions increases due to the multiple parameters that
require estimation.
Classical testing theory postulates the existence of a true score, "T," which represents the score
that would be achieved without any measurement error. This true score, T, is essentially the expected
score in an infinite number of error-free tests. However, in practice, people never see this true
estimate; instead, they get only the observed score X, which is assumed to be the true score modified
by some error rate E:
𝑋 =𝑇+𝐸
In classical test theory, the interaction between the observed X score, the true T score, and the
error score is a central theme that focuses on these relationships in the population to assess the
quality of the test score. Central to this assessment is the concept of reliability, which is defined as
the ratio of the variance of true scores, T, to the variance of observed scores, X. The reliability of the
observed test results, denoted as ρ*() , quantifies the proportion of the variance of the true score σ*)
to the variance of the observed score σ(* :
+*
ρ*() = +*, ,
-
Based on the relation that the variance of the observed estimates is equal to the sum of the
variance of the true estimate and the variance of the error estimate, this translates into a reliability
equation that establishes the effect of the signal-to-noise ratio:
+* +*
ρ*() = +*, = +* $+
,
*,
- , .
This equation intuitively emphasizes that higher reliability of test results corresponds to smaller
percentages of error variance in test results, and vice versa. Furthermore, reliability actually
represents the proportion of variance in test scores that would be accounted for if true scores were
known. In addition, the square root of reliability represents the absolute value of the correlation
between true and observed scores.
Question response theory (IRT) is a well-known statistical technique used to measure individual
differences in fields such as education and psychology. This approach is widely used in the
development, analysis, and evaluation of tests, questionnaires, and similar instruments for measuring
latent traits. Here is a comprehensive look at the main aspects of IRT:
1. Integrated application – in an educational context, IRT plays a key role in assessing test task
parameters, such as discrimination and task difficulty, through comprehensive task analysis;
2. Models – IRT typically uses univariate models to measure a single latent trait to ensure accurate
test scoring and design test items to measure a variety of abilities;
3. Scoring - An essential aspect of IRT is its ability to produce scores that account for differences
in task difficulty across test forms.
Cognitive models and situational learning are concepts that have received considerable attention
in educational psychology. Situational learning, also known as situational cognition, was first
introduced as a learning model through the research of Paul Duguid. The theory suggests that people
learn best when they can place information in a context that emphasizes the social nature of learning.
The concept of situated learning essentially revolves around making meaning out of real everyday
activities where learning takes place in relation to a specific context or situation.
In risk perception research, the standardized administration of a psychometric paradigm plays a
fundamental role in the empirical investigation of potential terrorism and other hazards. Applying
consistent testing processes to different situations allows researchers to draw reliable conclusions
based on the data collected, allowing qualitative comparisons and analysis of risk perception.
Reliability and validity are fundamental aspects of the psychometric paradigm that play a critical
role in ensuring the accuracy and consistency of assessment instruments. Reliability refers to the
consistency and stability of measurement instruments to produce comparable results over time and
under different conditions. This embodies the ability to provide reliable, reproducible results,
allowing researchers to have confidence in the accuracy of the method's measurements.
Essentially, reliability focuses on the consistency and repeatability of measurements, while
validity focuses on the accuracy and appropriateness of conclusions drawn from those
measurements. Both reliability and validity are integral components of psychometric assessment that
work together to ensure that assessment instruments are reliable, accurate, and produce quality
results.
4. Training course support
The system supports the following main mode of operation, in which application subsystems
perform all their main functions.
In the main mode of operation, parts of the system provide:
• work in user mode - 24 hours a day, 7 days a week;
• performance of its functions - collection, processing and uploading of data, designed for the
user interface.
The product is a web application for evaluating the quality of educational tests. Includes the
following features:
• construction and review of the informativeness curve of the test;
• construction and revision of informativeness curves of test tasks;
• construction and revision of characteristic curves of test tasks;
• construction and revision of characteristic curves of test subjects;
• construction and revision of test score distribution;
• construction and review of the table of informativeness of the test.
As can be seen in Figure 11, the user goes to the appropriate page, selects a file and sends it to the
server, which, in turn, processes it and sends a response in the form of test evaluation data.
The client, having received this data, saves it and based on it calculates data for graphs and tables.
This diagram illustrates the process of analyzing test results. The algorithm includes the following
key stages:
1. Initiation of the process by the user and loading of the data file.
2. Formation of a set of analytical materials.
3. Conducting a test assessment based on the obtained data and visualizations.
4. Providing the user with the analysis results, including assessment results and data for graphs.
5. The user has the opportunity to review the results and, if necessary, start a new analysis
cycle.
Figure 11: User diagram
This algorithm provides a comprehensive analysis of test results, providing a variety of tools for
assessing the quality of the test and the level of student preparation.
5. Analysis of course support results
After entering the web application page, we immediately find ourselves on the file download page.
Click the "Choose File" button and select a JSON file with test result data previously exported from
the STM platform. Press the "Get Score" button and wait for a message about the result of the
operation.
The graph shows a curve of test information, where the X-axis shows the test values and the Y-
axis shows the informativeness (k). This graph is used to analyze the results of course support by
assessing how informative the test is at different points on the scale.
Top of the curve: The greatest information content of the test is achieved in the range from -1 to
1. This indicates that the test is most effective for students with this level of knowledge.
Symmetry of the curve: The curve is symmetrical around the central point, which indicates a
uniform assessment of the knowledge of students with both low and high levels of knowledge.
After downloading the file and receiving a message about the successful execution of the
operation, we go to the page called "Test Information Curve", the view of which is presented in
Figure 12. Here we can see the constructed curve of the informativeness of the test and more detailed
information when drawing on the graph in the place we need.
Figure 12: Test informativeness curve page
Similar to the previous step, we go to the page called "Tasks Information Curves", the view of
which is presented in Figure 13. Here we can see the constructed informativeness curves of individual
tests. It is worth noting that the curves often overlap each other.
Figure 13: The page of informativeness curves of tasks
Similar to the previous step, we go to the page called "Tasks Characteristic Curves", the view of
which is presented in Figure 14. Here we can see the constructed characteristic curves of individual
test tasks and more detailed information when drawing on the graph in the place we need.
Figure 14: Characteristic curve tasks page
We can also view it by going to the page called "Student Characteristic Curves", the view of which
is presented in Figure 15. Here we can see the constructed characteristic curves of individual subjects.
Figure 15: Subject characteristic curve page
And on the last page called "Test Informativeness", we can see a table of the main points of
severity of the indicator of the difficulty of the test task and, based on these points, the probability
of getting the correct answer and informativeness for a separate test task. This table is built on the
basis of data for graphs of characteristic curves and informativeness curves of test tasks.
This paper describes the breakdown of client software for the evaluation and analysis of tests
performed during the assessment of their effectiveness and adjustment of their complexity, as well
as their further development and implementation into operation.
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[10] Educational platform Khan Academy. URL: https://www.khanacademy.org.
[11] Questions and Answers Website Stack Overflow. URL: https://stackoverflow.com.
[12] Educational platform FreeCodeCamp. URL: https://www.freecodecamp.org.
[13] Kyryl Korobchynskyi, "Organization of databases", Training course. 2024. URL:
https://stm.korobchinskiy.com.