=Paper=
{{Paper
|id=Vol-2630/paper_5
|storemode=property
|title=Development of Teachers' Digital Competence through Algoritmization and Programming
|pdfUrl=https://ceur-ws.org/Vol-2630/paper_5.pdf
|volume=Vol-2630
|authors=Evgenia Baranova,Irina Simonova,Tatiana Pavlova
}}
==Development of Teachers' Digital Competence through Algoritmization and Programming==
https://ceur-ws.org/Vol-2630/paper_5.pdf
Development of Teachers’ Digital Competence through
Algoritmization and Programming∗
Evgenia Baranova Irina Simonova Tatiana Pavlova
ev_baranova@mail.ru ir_1@mail.ru pavtatbor@gmail.com
Herzen State Pedagogical University of Russia,
St. Petersburg, Russian Federation
Abstract
The problem of developing students’ algorithmic competence as a basis of digital competence of
future teachers is investigated. The aim of the study is to identify the optimal learning conditions
for algorithmization and programming for students — future Computer Science teachers. As part
of the study, the problems of modular education, identification and application of Computer Sci-
ence classes of problems according to B. Bloom’s taxonomy and its teaching method, development
and application of electronic learning resources in blended learning conditions were investigated.
The methods of the system and competence approach, the Fisher method were applied to assess
statistical validity of the results and to confirm the hypothesis on the efficiency of the use of
electronic learning resources (ELR) to develop algorithmic competence of students. The results of
the study: the concept of algorithmic competence of university students is clarified as it implies
students’ readiness to develop algorithms and programs and to use them in teaching Computer
Science in the conditions of digital economy, development of ELR, self-education in Computer
Science. Classes of problems for the development of algorithmic competence with students ac-
cording to B. Bloom’s taxonomy (knowledge, comprehension, application, analysis, synthesis and
evaluation) are identified. The classes of problems correspond to ELR of a certain structure and
composition. The efficiency of application of ELR for development of algorithmic competence of
students has been statistically confirmed.
Keywords: digital competence, algorithmic competence, computer Science teacher, blended
learning, B. Bloom’s taxonomy, electronic learning resources
1 Introduction
The modern stage is characterized by the transition to a large-scale use of information technology tools
in Russian education system at all levels — the digital education stage [Baranova & Vereshchagina, 2018].
First of all, we are talking about the wide introduction of high-quality electronic learning resources
in training process, open online courses, remote access to educational materials presented in digital
format for students of all levels of education, etc.
The development of digital education involves improving the efficiency of learning by increas-
ing the motivation of students and improving the way they work with information, and developing
teachers’ digital competence. The term “digital competence” has two implications. On the one hand,
it describes requirements for people’s knowledge and skills in the digital economy, defined in any
regulations, for example, in educational standards. On the other hand, it describes the personal
∗
Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
1
characteristics (for example, of a teacher), characterizing his willingness to solve certain classes of
problems based on the digital technologies, their mechanisms and methods and the ability to select,
efficiently apply them, and to use specific tools of digital technologies.
Digital competence is characterized not only by understanding and adequate use of digital tech-
nologies and information systems in life and professional activities, but also confidence in their use
creatively, critically, independently (without assistance).
The Digital Competence Framework 2.0 by the European Commission1 distinguished 21 compo-
nents of digital competence, combined in 5 groups: Information Processing; Communication, Content
Creation, Safety and Protection, Problem Solving. The criterion for grouping is based on classes of
problems to be solved, characterized by the type of object of research, object development and its
modification with the use of digital technologies, and types of activity which are being implemented
for this.
Skills according to each component include achieving a certain level of algorithmic competence,
the basis of which is formed in the school, develop in the next stages of education and throughout life.
This determines the need for training teachers, primarily Computer Science teachers algorithmization
and programming at a sufficiently high level [Baranova & Simonova, 2018].
The aim of the study was to identify the optimal learning conditions of algorithmization and
programming for students, consisting in modular representation of content, blended learning, use
of classes of problems identified in accordance with the B. Bloom’s taxonomy on computer science
and methodology of its teaching, and application of electronic learning resources. We understand
algorithmic competence as the readiness of students to design algorithms and programs, their use
in professional activities in the process of Computer Science teaching, electronic learning resources
(ELR) development, self-education in the Computer Science field.
The development of algorithmic competence in the training of teachers is carried out on a staged
basis in various modules of the educational program. The article considers two major modules “Foun-
dations of Computer Science and Programming Techniques” and “Theories and Methods of Computer
Science Teaching at School”. The content of the first module is aimed at the establishment of student’s
theoretical knowledge in Computer Science and algorithmic competence development. The module
content is aimed at the development of methodological competence of the future Computer Science
teacher, reveals the system of concepts of teaching methods, general and Computer Science specific,
taking into account interdisciplinary connections with Pedagogy and Psychology.
The main means of developing algorithmic competence, according to the authors, is a system of
problems on Computer Science and methods of its teaching. The system of problem developed by the
authors is based on B. Bloom’s taxonomy of pedagogic objectives [Bloom et al., 1956], which includes
six categories of objectives: knowledge, comprehension, application, analysis, synthesis, evaluation.
The article describes sample problems of the determined modules, and electronic learning resources
for students to solve problems. The results of the pedagogical experiment prove the effectiveness of
the developed ELR for the development of students’ algorithmic competence.
This study and many years of practical experience in training implementation have shown the ad-
vantage of modular and blended learning. This approach allows for a flexible training system, individ-
ual educational routes and the development of information technology tools [Baranova et al., 2016].
The analysis of various studies:[Sellahewa, 2015], [Thoma, 2017], [Zylka, 2015] shows the relevance
of the development of educational learning materials for blended learning implementation, includ-
ing ELR. This approach will allow to vary the forms of blended learning: online, “flipped classes”,
etc.[Jansen, 2009]
The designed model of training is aimed at students’ algorithmic competence development and
is based on the B. Bloom’s taxonomy of pedagogical objectives. This determines the logic of the
formation of classes of problems, the readiness to their solving develops in students directed from
reproductive activity to partly searching and researching. The structure, content and functionality of
1
https://ec.europa.eu/jrc/en/digcomp/digital-competence-framework
2
ELR should correspond to the selected classes of problems. The use of ELR provides an opportunity
to individualize the rate of learning, its place and time. The inclusion of resources in the LMS
Moodle ensures ELR systematization, the ability to manage training activities, both in the classroom
and online, allows teachers to carry out tutoring, implement group discussion of problems to solve
[Baranova, 2015]. The ELR system allows each student to determine her/his initial level of algorithmic
competence development and build a comprehended individual educational route.
2 Research Methods
The activity orientated approach and the theory of the B. Bloom’s classification and blended learning
provide the methodological framework for the research. To solve the problem of the research a set
of methods based on the analysis of Russian and foreign pedagogical and psychological theory and
practice in the area of computer science were used. Those were general scientific methods, such as
modeling, association, comparison and generalization, as well as experimental methods using diagnos-
tic toolkit, expert evaluations and statistical processing of results of pedagogical experiment. F-test
was used for the assessment of statistically significant differences of the correctness of student’s actions
within monitoring and experimental groups for each of the six categories of objectives: knowledge,
comprehension, application, analysis, synthesis, evaluation.
3 Results of Research and Discussion
3.1 Foundations of Computer Science and Programming Techniques
The module content is aimed at the formation of students’ theoretical knowledge in the field of
Computer science and the development of algorithmic competence. Students study the concepts
of the Theory of Algorithms, Mathematical Logic, Formal Languages and Grammars, Relational
Algebra, Information Theory, etc. The module implements interdisciplinary connections with the
mathematical module, which studies the mathematical apparatus forming the basis of Theoretical
Computer Science [Baranova et al., 2014].
The development of algorithmic competence is carried out in the disciplines devoted to the study
of modern programming paradigms and involves students’ mastery of algorithmic and programming
methods. Students acquire knowledge of basic concepts, principles and methods of programming, on
the basis of which they develop the ability to design information models, algorithms, data structures,
databases, computer programs, information systems and web resources for solving problems from
various fields, including the field of future professional teaching activities, by means of information
technology.
The study of basic concepts, principles, and algorithms of compilation theory contributes to the
formation of students’ systematic understanding of the sources of syntax errors and skills to develop
syntactically correct programs [Kwon & Cheon, 2019].
The peculiarity of the discipline is the need for individualization of learning, taking into account
different competence levels of students in the programming field: over 50% of students do not have or
have a vague notion of this type of activity, only 10% of students usually have notion of programming
and are ready to successfully solve complex problems.
Conditions for the establishment of individual educational routes for students are provided with
a set of sample problems of different complexity levels and electronic learning resources presented in
LMS Moodle. Table 1 presents module sample problems and corresponding ELR associated with B.
Bloom’s categories.
3
Table 1: Sample Problems and Electronic Learning Resources
3.1.1 Sample problems for students
The achievement of learning objectives is carried out by using a system of sample problems designed
to develop students’ algorithmic competence, including the readiness to describe and create data
structures of different complexity levels, to develop and implement algorithms for solving problems
from different fields. The first type of problems involves performing basic data operations represented
by various structures: arrays, lists, records, object classes, database tables, etc. Such operations
include: creating, changing values, deleting. To solve these problems, students should know the
appropriate syntactic structures, the results of the implementation, including the control structures
of the programming language under studies, ways to describe data structures and to access their
elements. For example, when learning object-oriented paradigm (OOP) programming languages, the
central data structure is an object class. To solve the problems, students should know the basic
principles of OOP (encapsulation, inheritance, polymorphism), class structure, ways of description
and access to the fields and methods of the class.
The problems of the second type are focused on identifying the acquirement of algorithmic com-
petence at the comprehension level, and involve the design of computational algorithms and programs
based on the known mathematical apparatus. These are, for example, approximate calculations, cal-
culation of series sums, matrix processing, calculation of geometric objects parameters, etc. At this
stage students should be ready to interpret the mathematical model, transform it to the information
one, create a data structure adequate to nature of mathematical objects, implement a “translation”
from the language of Mathematics to the language of Computer Science.
The problems of the third type involve the development of algorithms and programs that simulate
phenomena, processes, behavior of objects from various domains. When solving such problems, the
acquirement of algorithmic competence at the level of application, the readiness of students to solve
problems in new situations based on known facts, models and rules are verified. This type includes
problems on visualisation of known algorithms operation involving new interface elements, software
implementation of simple models describing phenomena from various fields of knowledge, science and
4
technology, development of game situation fragments, etc.
At the next, fourth stage, the readiness to analyze the data, to identify the relationships between
the data elements, to determine the principles of data organization is formed. The problems of the
fourth type include development of algorithms and programs for analyzing data presented in various
structures, and formation of data according to a specified scheme: algorithms for searching and
sorting, formation of arrays, lists with a specified structure, creation of object classes with a specified
behavior, definition by means of SQL language of values characterizing data in a database table,
creation of related data sets according to a specified scheme. Particular set of problems is determined
by the data structures being learned.
The synthesis category in Bloom’s taxonomy of pedagogic objectives involves the ability of
combining elements to produce a unity that is something new, and includes the development of the
action plan, the production of a new product. When teaching algorithmization and programming
with such a new product, the result of educational activities of students is a computer program. The
problems of the fifth type involve the development of multi-modular computer programs, including
those for solving educational and professional problems connected with future professional activities.
With regard to the training of future teachers in the field of Computer Science, it can be electronic
learning resources on various topics of school subjects, information systems and web resources for the
organization and management of the educational process, testing systems, etc. It is assumed that the
sequence of actions of students in the process of information systems design is close to the stages of
the life cycle and includes the analysis of the subject area, design, software implementation, testing.
When performing these tasks, the student’s readiness to assess the quality of the product created is
checked, using her/his internal, personal assessment, which appears in the process of debugging and
testing, as well as external assessment in the process of testing with the participation of her/his group
students.
3.1.2 Electronic Learning Resources
For mastering the module, a system of electronic learning resources has been designed to improve the
efficiency of students’ learning the content of the module and achieving learning objectives. The Data
Structure resource contains a systematic description of the studied data structures, basic operations
of access to the elements of data structures, examples, illustrating the ways to access and the results
of operations. Depending on the studied data structures, programming language operators of the
selected paradigm, SQL statements, etc. are described. The resource contains tasks of a reproductive
nature: to describe the structure representing the data of a specified type, the object class performing
a certain functions, the structure of related tables representing abstraction of some subject area
[Baranova et al., 2019].
The Processing Algorithms of Data Structures resource contains a brief description of the basic
data processing algorithms presented in various structures, interactive demos — software applications
that simulate the operation of algorithms. Such computer models provide a visual display of the
algorithms operation for various data sets, allow students to understand the essence of algorithms on
the basis of experiments with different data sets. For example, to evaluate the effectiveness of sorting
methods, to understand the peculiarities of the execution of statements over the data of the database,
to learn how to understand the results of executing complex queries of the SQL language, develop such
queries, etc. The tasks included in the resource involve the modification of the considered algorithms
for use in the new environment [Baranova et al., 2016].
The Information Systems and Web Resources ELR is used at the final stage of learning and
is aimed at developing students’ readiness for independent design of algorithms and programs that
implement the specified functions. The resource contains a brief description of the design stages
including design of specifications, information model, database structure, software implementation,
testing. Professionally executed examples of software applications will help students learn how to
5
analyze the subject area, create their own software products and evaluate their quality.
The integrated Syntax Analyzer ELR developed by the following authors [Baranova et al., 2014],
includes several software modules representing valuable concepts and algorithms of Theoretical Com-
puter Science. First of all, these are models of compiler components: lexis and syntaх analyzers, code
generator. Working with models, students learn methods of analysis and syntax parsing of chains of
formal grammars that describe the sentences of the programming language. Mastering compilation
techniques allows students to learn how to design syntactically correct programs. The models use
complex data structures to represent the components of formal grammars, trees, graphs. Students
learn to analyze algorithms of Theoretical Computer Science, evaluate their effectiveness, “predict”
the results of algorithms for different data sets and develop algorithms using complex data structures.
The resource is of interdisciplinary nature. It is also used in learning process of the mathematical
module for mastering various concepts of Discrete Mathematics and methodical module as a tool for
teaching Computer Science in the profession-oriented school [Baranova & Simonova et al., 2019].
The described system of problems and electronic learning resources is used during the whole
period of training in the field of algorithmization and programming. Seniors learn more complex
structures and algorithms, and students’ performing the tasks lets their independence grow. By the
end of training, the level of algorithmic competence in students allows them to independently analyze
the subject area, design data structures and algorithms adequate to the problem being solved, create a
new software product, and evaluate its quality. The students, who are the most capable and motivated
in the field of algorithmization and programming, perform final qualification work on the creation
and use of electronic learning resources in the educational process.
3.2 Theories and Methods of Computer Science Teaching at School
The module content is aimed at the development of methodological competence of the future Com-
puter Science teacher, reveals the system of concepts of general teaching methods taking into account
interdisciplinary connections with Pedagogy and Psychology in terms of Computer Science.
It describes the stages of content development of Computer Science as a school subject in con-
nection with the development of the science of information and information processes, computer as
a universal means of information processing, software. The content highlights the concept of “in-
formation”, “algorithm”, “model” and classes of problems whose solving requires comprehension and
application of these concepts. Classifications, visual schemes, illustrations, computer models and pre-
sentations are used. Conditions for the establishment of individual educational routes for students
are provided with a set of sample problems of different complexity levels and electronic learning re-
sources presented in LMS Moodle. Table 2 presents module sample problems and corresponding ELR
associated with B. Bloom’s categories.
3.2.1 Sample problems for students
Developing methodological competence of a future Computer Science teacher implies an understand-
ing of a set of concepts which develop the structure and functions of the methodological educational
system — goals, contents, forms, methods, educational resources and knowledge on key principles of
teaching Computer Science at school, on definitions and sample problems of the school subject at all
levels of formal education: primary, secondary, high. To achieve this, in the course of training tasks
facilitating memorization and understanding of key concept logical interrelations are applied. The
tasks involve creating logical schemes of concepts for one or more topics following the material spec-
ified in secondary and high school textbooks, independent construction of working definitions for the
words “computer”, “information”, “algorithm”, “model”, etc. with the help of computer tools; analyzing
contents of Computer Science secondary and high school textbooks, and identifying changes, with
regard to the stage of training, in the volume and contents of definitions for “algorithm”, “program”,
“model”, and “information process”. The results are to be prepared with the help of computer tools:
6
logical schema editors, text and table editors, etc [Kostousov & Simonova, 2019].
The teacher is mainly involved in planning the educational process for the whole period of the
secondary or high school, drafting course schedules for the term and creating notes for lessons. Sample
problems for teaching this activities to students concern developing course schedules for Computer
Science for one secondary school term; creating task sheets to support these schedules and arrange indi-
vidual educational routes for students; writing notes for lessons on one of the school Computer Science
course topics with detailed descriptions of the lesson, educational resources and methods; analyzing
video lessons made by Computer Science teachers under suggested criteria; own assessment of goals
fulfilled, quality of the resources used, and student motivation approaches [Bocharov et al., 2019].
Students should be ready to adapt and create sets of educational problems for a Computer
Science lesson, didactic computer games, exams, including tests with the use of IT resources. Such
skills are acquired when developing sets of differentiated tasks for secondary school students so that
they strengthened knowledge on algorithmization, and implementing tasks with the help of training
executor while facilitating future transfer of this knowledge to other areas of programming.
Table 2: Theories and Methods of Computer Science Teaching at School
Spread of network communities has led to the need of developing the skills to participate in
network discussions and come up with solid arguments with regard to Computer Science education
at school. The future teacher should be able to develop and describe a plan and scenario of a network
discussion on topical computer science issues for secondary or high school.
3.2.2 Electronic Learning Resources
Electronic learning resources have been developed to support students’ independent work and in-
dividual educational routes during the development of module material. These resources comprise
of systematized theoretic material by topics and presentations which focus on the main aspects of a
7
topic, conclusions and questions for self-reflection, and tests. Students’ practical activity in mastering
the subject is supported by laboratory work. The resources are implemented in LMS Moodle.
Methodical System of Computer Science Teaching at School contains lectures, scientific articles
on the methods of Computer Science teaching, articles with the concepts by authors of Computer
Science textbooks for school, videos with speeches by scientists and practitioners at thematic confer-
ences, links to video Computer Science lessons, implemented in various conditions of equipping with
IT tools and students’ needs using various pedagogical technologies, bibliography to be studied by
students and links to sources, solving problems examples. These materials can be used to perform
sample tasks, the list of which is presented in the resource [Kostousov & Simonova, 2019].
Educational Learning Materials for the Computer Science Lesson at School contains examples
of Computer Science teachers’ lesson plans, links to video lessons with explanations on how to solve
complex problems, links to Internet resources with relevant information on new methods and means of
Computer Science teaching at school, a set of computer presentations for lessons dedicated to certain
topics, a set of computer models in support of solving cross-subject problems, analysis examples of
Computer Science lessons according to specified criteria.
Network Services for Creating Multimedia Educational Content contains detailed instructions for
creating graphic illustrations, animations, processing audio and video objects, using freely distributed
network services, examples of objects created by students in previous years of study, examples of their
use in Computer Science lessons, test tasks for each topic for self-reflection, tasks for independent work.
The resource is implemented as a website with free access. The teacher interacts with students via the
group in the VKontakte network community. The resource is used by students throughout the whole
period of training to create educational learning materials for lessons [Simonova & Ustyugova, 2017].
3.3 Experiment
Two groups of students of Years 2 – 4, trained in the study field of Teacher Education, specialization
Computer Science and Information Technologies in Education, Herzen State Pedagogical University,
Saint Petersburg, took part in the experiment to assess students’ readiness to solve the allocated
classes of the problems aimed at algorithmic competence development. The control group consisted
of 48 students, while the experimental group numbered 52.
Prior to the experiment, both groups received initial training in Computer Science and Teaching
Methods. At the beginning of Year 2, both groups had to solve problems in Computer Science and
Methodology, focused on the achievement of various categories of achievement of B. Bloom’s pedagogic
objectives. Table 3 presents the results of the tasks performed by the students of the control and
experimental groups. The analysis of the results (Table 3) shows that there were no significant
differences between the readiness levels of students in both groups to solve sample problems. It
should be noted that the problems related to the analysis, synthesis and evaluation of data are quite
challenging for students. Further training in the experimental group was carried out in the form of
blended learning and was based on the application of the described ELR classes. ELRs were not used
in the control group. After the modules study completion, the final test was carried out, including the
same tasks for both groups. The tasks of the control group were aimed at checking the achievement
of B. Bloom’s pedagogic objectives [Baranova et al., 2019].
The analysis of the results of the final test (Table 3) showed that the results to achieve all peda-
gogic objectives in the control and experimental group increased. The increment in the experimental
group is significantly higher. On the basis of the data given in Table 3 we obtained the following
Fisher’s ratio test (F-test) values which are presented in Table 4 that shows differences in students’
readiness to solve problems in Computer Science and Methodology focused on achieving different B.
Bloom’s categories between the experimental and control groups before and after the experiment.
The critical Fisher’s ratio test value for 1% of the significance level ϕcr = 2.31 and for 5% of the
significance level ϕcr = 1.64. Before the experiment, there were no statistically significant differences
8
Table 3: Experimental data on the development of students’ readiness to solve classes of problems
Table 4: Fisher’s ratio test value for assessing differences in students’ readiness to solve classes of
problems between the experimental and control groups
in all four categories of the studied features in the control and experimental groups at the specified
significance level, since all empirical Fisher’s ratio test values before the experiment were lower than
the critical value.
The value of the Fisher’s angular transformation after the experiment is higher than the critical
value equal to 2.31 to achieve the first three pedagogic objectives and higher than the critical value
equal to 1.64 for the fourth objective. Together with the fact that before the experiment there were
no statistically significant differences in the control groups, and after the experiment such statistically
significant differences appeared, we can claim that — with a probability of 99% — statistically sig-
nificant differences in the students’ training after the experiment were established for the categories:
knowledge, comprehension and application, analysis; and — with a probability of 95% —statistically
significant differences in the students’ training after the experiment were established for the synthesis
and evaluation category.
9
Conclusions
The present study showed that the development of algorithmic competence of future teachers of
Computer Science is contributed by:
• the content presented in the modular structure and implemented in a mixed form,
• classes of problems in Computer Science and Methods of its Teaching focused on students’
achievement of the pedagogic objectives in B. Bloom’s taxonomy,
• the system of electronic learning resources, whose structure and content correspond to the
selected classes of problems in Computer Science.
The directions of the research development may be connected with the transfer of the proposed
approach to other modules of Computer Science training of teachers, as well as to Computer Science
training of specialists of other spheres.
Acknowledgements
The research was supported by the Ministry of Science and Higher Education of the Russian Federa-
tion (project No. FSZN-2020-0027).
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