=Paper=
{{Paper
|id=Vol-2920/paper_4
|storemode=property
|title=Educational Data Mining Methods for the Analysis of Student's Digital Footprint
|pdfUrl=https://ceur-ws.org/Vol-2920/paper_4.pdf
|volume=Vol-2920
|authors=Evgenia Baranova,German Shvetcov,Tatiana Noskova
}}
==Educational Data Mining Methods for the Analysis of Student's Digital Footprint==
https://ceur-ws.org/Vol-2920/paper_4.pdf
Educational Data Mining Methods for the Analysis of
Student’s Digital Footprint∗
Evgenia Baranova German Shvetsov Tatiana Noskova
ev_baranova@mail.ru shvetzoff.german@yandex.ru noskovatn@gmail.com
Herzen State Pedagogical University of Russia,
St. Petersburg, Russian Federation
Abstract
The collection and analysis of a student’s digital footprint is an integral part of the devel-
opment of education in the Russian Federation in the context of digitalization of the economy.
Among other things, such practice is associated with the assessment of the formation of students’
competencies in blended learning. The study aims to develop approaches, methods and tools for
analyzing data generated in the digital education environment (DEE), in order to identify corre-
lations between the structure and content of educational programs and students’ performance.
Educational data mining (EDM) methods as well as methods of design and development of
information systems and databases are relyed upon. The following methods of data processing
were used: correlation analysis and distillation of data for human judgement.
The experimental base of the study is the DEE of the Herzen State Pedagogical University of
Russia, which ensures the formation of data on educational programs, individual learning paths
of students and their successful development. A large amount of detailed data collected for over
10 years on various aspects of the educational process was used for the analysis.
As a result, the authors have developed a tool for collecting and analyzing the digital footprint
of students in the context of blended learning using EDM methods. In accordance with these
methods, calculations were made that allow to draw preliminary conclusions about the nature of
relationships between the selected indicators (the number of activities on the course, the partici-
pation of students in the course chat(s), the final assessment for the course, the time spent on the
course) obtained as part of the students’ work in the Moodle distance learning system (Moodle
DLS) and the results of midterm assessment. For example, the value of relationships between the
indicator of the final assessment for online education courses (OEC) and results of the midterm
assessment is 0.7108, which indicates a strong correlation. The value of relationships between the
number of activities on the course and the grade for the midterm assessment (0.314) shows a weak
correlation. On the basis of such calculations, it is possible to formulate assumptions on how par-
ticular activities may help in mastering various disciplines and on the OEC effectiveness, as well
as prepare recommendations for updating educational resources in order to improve their quality.
The experience, approaches, and results presented by the authors demonstrate the feasibility of
EDM methods for the analysis of educational process.
Keywords: student’s digital footprint, data mining, educational data mining, big data, online
learning, digital education environment
∗
Copyright c 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
1
1 Introduction
Digitalization of the economy presents a significant challenge for the entire education system. Digital
economy requires the education system not just to “digitize” individual processes, but to create an
integrated approach that would set new goals, change the structure and content of the educational
process.
An effective education involves not only the transfer of information from professor to student,
it also requires complex social interactions and adaptation to the needs of each student and their
cultural and social context. Digital transformation changes the content, forms and methods of ed-
ucation. “Digitization” primarily affects school and university education systems. AI-driven mass
online courses, chatbots and lesson plans are just a few examples of digital transformation in higher
education [Kurbatsky, 2019].
Rapid introduction of information technologies in various spheres of our life, including education,
leads to a partial or complete transfer of human activity to the virtual environment. Information
posted by users on their pages in the digital environment can be considered as a product of activity
that allows for identifying the individuality of a particular person, making assumptions about their
psychological characteristics and cognitive abilities [Tulupyeva et al., 2015].
The continuous digital transformation of society makes its own adjustments to traditional learn-
ing models, as well as to online education models [Shamsutdinova, 2020]. Building student digital
profiles based on analysis of the so-called digital footprint is one of the recent trends. In the latest
studies, the concept of digital footprint of a student is interpreted in different ways, but there is a
general perspective — a transition from a narrow, technical understanding of the footprint as a set of
IP addresses and reports on the network activity of students to a broader consideration.
According to O. P. Zhigalova: “Digital footprints considered as the result of educational and
professional activities in a digital format represent data that allow for determining the level of com-
petencies, forming a learning path, evaluating opportunities and strategies for further improvement
and professional development in a particular field.” [Zhigalova, 2019] According to V. N. Kurbatsky:
“The digital footprint is an array of data on the results of a student’s educational and project activi-
ties, including all the materials that the student creates: presentations, prototypes, audio and video
recordings, road maps, etc.”[Kurbatsky, 2019]
The prevalence and influence of social networks on the digital image of students is growing. In
the works of S. A. Vartanov [Vartanov, 2018]; G. A. Nikolaenko [Nikolaenko, 2019]; T. V. Tulupyeva
[Tulupyeva et al., 2016] and others, we can find that such information can be very different and
include hundreds of footprints: from photos and videos to comments, likes, reposts and other virtual
activities.
The digital footprint of a student starts from the moment of entering the university and con-
tains personal data, information about admission, study department, educational program, academic
performance during the entire period of study, as well as educational analytical data collected au-
tomatically when students use distance learning systems and open education platforms for online
courses.
The digital footprint in the educational space is an alienated result of a person’s educational
activity. The entire educational space that has a digital footprint must be created with the use of
pedagogical design.
The methods of processing information about the educational process, in particular the digital
footprint of its subjects, include educational data mining (EDM) methods, which provide extensive
opportunities for analyzing and interpreting the results obtained. However, despite the obvious ad-
vantages of the EDM methods, research related to their use is clearly insufficient for simulating higher
educational processes.
There are no developed methodology, tools for data analysis and data interpretation based on
these methods and designed for:
2
• identification of critical points in the process of mastering educational programs, disciplines and
modules that cause the greatest difficulties for students,
• determining the relationships and mutual influence of traditional and distance learning compo-
nents on the success of students in mastering educational programs,
• early identification of students in the group of risk who can be expelled, etc.
The study targets the development of methods and tools for analyzing data generated in the dig-
ital education environment (DEE) to identify links between the structure and content of educational
programs and results of students’ learning activities in order to optimize the educational process.
2 Literature analysis
In the Russian and foreign literature data mining (DM) is interpreted as a collective name for
a set of methods, that is also referred to as “information extraction”, “data excavation”, “knowl-
edge extraction”, “template analysis”, “knowledge discovery in databases” in other papers and books
[Ovsyanitskaya, 2013].
DM approaches are based on classification, clustering, simulation and forecasts, decision tree
construction, evolutionary programming, and fuzzy logic. Initially, the DM methods were based on
research in the field of applied statistics. To date, the development of such methods is significantly
associated with research of artificial intelligence (pattern recognition, search for knowledge represen-
tation models, neural networks, genetic algorithms), papers on information visualization, machine
learning, etc. The DM technology is supported by the concept of patterns which describe regularities
expressed in human-readable forms.
The modern research related to the application of methods of data mining, machine learning
and statistics on various types of information about the educational process is called educational
data mining (EDM) [Belonozhko et al., 2017][Zorić, et al., 2020]. Within the framework of this field
of study, models and methods of educational data processing aimed at improving the educational
process are developed. EDM specifies the source area of large data sets (Big Data, hereinafter referred
to as the BD) in relation to the educational process; aims to find patterns (samples, templates,
schemes, regularities) that are characteristic of this subject area. A detailed overview of the key
EDM components was presented in [Peña-Ayala, 2014]. A. Peña-Ayala identified three categories of
main research modules related to EDM: problems, methods, and algorithms. The use of EDM is also
considered in studies conducted to understand how students interact with educational technologies
[Angeli et al., 2017].
The paper [Manyika et al., 2011] states that EDM methods make it possible to build models
for describing various aspects of the educational process in order to identify factors that affect the
success of mastering of learning paths by students, including teaching, administrative and organiza-
tional activities. The paper [Castro, 2007] suggests that clustering and visualization methods can be
applied to analyze educational data and thus improve the process of electronic learning. Following
the study [Zamora-Musa & Velez, 2017] with the use of EDM, association rule method, based on the
data obtained from the survey, connections between the categorical variables of the survey (envi-
ronmental efficiency, environmental influence, environmental utility, control of the environment, ease
of learning in the environment) are identified to further improve the immersive environment (aug-
mented reality technology, provide the effect of full or partial presence in an alternative space). In the
study [Razaque & Alajlan, 2020], the influence of external factors (relationships, alcohol consump-
tion, parents’ education, frequency of meetings with friends, place of residence) on student academic
performance is determined using EDM methods.
According to V. A. Larionova and A. A. Karasik, data mining allows to study the behavioral
pattern of students; to learn how to take into account individual characteristics of each in the formation
3
of educational paths [Larionova & Karasik, 2019].
The study of A. Abdulmohsen suggests that the main data sources used in EDM nowadays
include the following categories:
1. Traditional learning, when the knowledge is passed on to students through personal contact.
Data can be collected by traditional methods, such as observation and questionnaires, which
allow to study cognitive skills of students and determine the success of their learning. The
statistical method and psychometry can be applied to the data.
2. Online learning and education management systems (EMS) provide students with materials,
instructions, communication, and reporting tools that enable them to learn independently. Data
mining techniques can be applied to data stored in databases.
3. Systems of Intelligent learning and adaptive educational hypermedia provide content to students
based on their profiles, so it is necessary to use data mining methods to analyze the created
user profiles.
Specifically, the authors of the following researches [Ezekiel & Mogorosi, 2020], [Rawad & Rémi, 2020]
considered the use of data mining methods to identify relationships between the interaction of stu-
dents with the DLS elements (files, tests, forums) and their final academic performance, as well as
relationships between the data of students of the mass open online course (MOOC) — age, gender,
geographical region, education, socio-professional status, and their academic performance.
Our literature analysis: [Abdulmohsen, 2016], [Baker & Yacef, 2009], [Bowers et al., 2012],
[Bowers, 2010], [Shrestha & Pokharel, 2021], allows to identify the following areas of use of EDM
methods in educational practice, the most important for higher education:
• modeling the behavior of students in the learning process in order to predict the development
of their cognitive abilities, to identify the contingent of students at risk, with a high probability
of being expelled;
• development of new models and ways of presenting knowledge in the subject area that would
correspond to diverse learning styles and cognitive capabilities identified in students using EDM
methods;
• studying the processes of interaction of students with the digital environment, identifying the
components of the environment, studying the effects that the educational environment has on
the results of learning.
3 Research Methods
Within the framework of this study, the focus is on the components of digital footprint associated
with the development of learning paths by students.
The target of the study is the components of digital footprint, represented by midterm academic
performance of students in the form of grades obtained during the study of disciplines, practical studies
and modules, as well as results of students’ educational activities in Moodle DLS in the process of
mastering the educational program and their correlation. The research is carried out with a view
to identifying critical stages for students and optimization of the structure, content and methods of
educational programs implementation in high education.
Research tools include information systems and databases for generating and collecting data,
and EDM methods for analyzing, processing, and measuring the data.
The method of analyzing the student digital footprint developed by the authors is based on
the formulated in [Lunkov & Kharlamov, 2014] stages of EDM methods application for data analysis
4
and consists of the content of the stages specified in relation to the subject area in accordance with
the study’s aim, the characteristics of means and tools for obtaining, processing, analyzing data and
presenting results.
The first stage involves setting an objective and identifying entities of the subject area under
consideration. The need to integrate various technologies of distance learning and traditional forms
of implementation of the educational process is dictated by the modern world. In this environment,
analyzing and identifying the relationships of student performance indicators in the implementation
of various forms of education becomes relevant. Let us highlight the main information sources of
data:
• an educational program, which is a set of key features of education (volume, content, targets),
organizational and pedagogical conditions and forms of assessment, presented as a curriculum,
a calendar curriculum, work programs of academic disciplines, courses, disciplines (modules)
and other components, including evaluation and methodological materials;
• the activity of the faculty, including development of teaching materials (lectures, practical
classes, tests), including in a digital format, assessment of students’ current and midterm aca-
demic performance, etc.;
• students’ learning activity, which is evaluated in the course of current, midterm and final aca-
demic performance.
Specification of the relationships between these information sources of the educational process allowed
to develop a chart of information flows, shown in Figure 1.
Figure 1: Information Flow Chart
5
The second stage involves the preparation of data for EDM and it is based on the methods of de-
signing and developing information systems [Pedersen, 2019] and databases [Campbell & Majors, 2017].
At this stage, data modeling is carried out, i.e. the definition and analysis of data requirements that
are necessary for the implementation of EDM, and the development of structures for operational,
reference and archival databases (DB) to represent data sources generated using internal and external
information systems.
To implement the information flow chart presented in Figure 1, the Herzen State Pedagogical
University has developed and rolled out an integrated educational management information system
(EMIS) in the educational process http://oio.herzen.edu.ru. The EMIS is integrated with various DEE
components, including Moodle DLS. It includes a set of information systems (IS) and web resources
based on the distributed database HERZEN, which provides storage of complete information about
various aspects of the educational process, including
• data on the main professional educational programs carried out;
• academic performance of students;
• information about the academic load of faculties;
• schedules of classes and exams;
• data on e-learning courses conducted by teachers used in the implementation of educational
programs (disciplines, modules, practical classes), etc.
Data is formed in the database for subsequent analysis by means of certain tools (information
systems) – the EMIS components, developed together with the authors. IS “Training and Work Plans”
is the tool for generating data on implemented main professional educational programs. It provides
the creation of electronic curricula of educational programs in accordance with the requirements
of the Federal State Educational Standard of Higher Professional Education and the Federal State
Educational Standard of Higher Education, the automatic formation of work plans and the volume
of training assignments of departments for all levels of education and forms of training.
To collect data on the midterm academic performance of students, the IS “Dean’s Office” is used.
It provides the formation of students’ personal data, information about characteristics of the student’s
educational program, information about the structure and composition of the educational program,
and data on individual learning paths as well as the success of their mastering [Baranova et al., 2020].
Academic load is distributed between teachers using the IS tool “Department Load”.
A system designed to create an electronic schedule of classes and exams for students in line with
work plans and distributed load of teachers is the IS “Schedule”. The generated schedule is checked
for correctness: compliance with the restrictions on the number of training hours per day for the
group and the teacher; the teacher can not be assigned classes at the same time with different groups;
the classroom can not be assigned to conduct different classes at the same time; lectures should be
conducted only by teachers with a degree, etc.
All these tools allows for generating information in databases about the core vocational education
programs (CVEP), students, the workload of the faculty for data analysis using EDM methods.
Nowadays, electronic learning and distance learning technologies are widely used. To obtain
complete and diverse information about the educational process for EDM, it is necessary to gener-
ate data on the content and results of electronic learning in DLS. Today, one of the most popular
systems for supporting the educational process in the context of distance education is Moodle DLS.
The platform’s most important advantages that make it popular are openness, mobility, portability,
extensibility, widespread use, etc.
Moodle DLS contains a wide set of tools for creating and maintaining electronic learning courses,
various opportunities for presenting educational materials, monitoring the progress, ensuring the
6
feedback between teachers and students, which allows students to post their work in DLS, receive
reviews and teacher’s advice [Zaitseva, 2012].
Training courses, simple in structure, contain educational and methodological materials for the
development of the discipline in the form of presentations, lecture texts, lists of literature, exam
questions, etc. Most of the courses published in the university’s Moodle DLS involve a wider use
of various DLS tools and include, as a rule, a working program of the discipline; educational and
methodological materials for mastering the discipline; methodological recommendations on studying
the e-learning course and preparing for various types of classes and assessment for students; a forum
for communication and discussion of questions on the course for students and the teacher; assessment
funds for monitoring students’ knowledge.
Moodle DLS, based on MariaDB DBMS (a branch of MySQL DBMS), provides storage of vari-
ous data on students’ interaction with the digital environment during the learning process, including
current academic performance. Moodle database query engine allows getting various data sets, in-
cluding
• general information about the course, teachers, and students;
• the amount of time spent by the student in the online course;
• the number of changes made by students in the various modules of the course;
• the results of completed tasks by students in the course;
• information about student activity on course forums, etc.
The tools that provide a link between the data sources presented in EMIS and Moodle DLS include
IS “Electronic Monitoring” and “Electronic Atlas” web resource — components for monitoring the
activities of the faculty related to online learning.
The systems provide an opportunity for the teacher to form an application for the creation of an
e-learning course in Moodle DLS, as well as the ability to process such an application. Such resources
allow organizing a link between the data on disciplines, courses in Moodle DLS and the teachers who
implement education. The list of disciplines and the teacher’s e-learning course are displayed on their
personal page in the “Electronic Atlas” (Fig. 2).
This connection allows establishing a correspondence between the body (students, faculty) and
their activities in Moodle DLS, provides an opportunity to obtain more detailed information about
the structure of specific courses.
The third stage includes the collection and analysis of the final data, the definition of EDM
methods for their processing and visualization of the results. Correlation analysis and the method of
Distillation of Data for Human Judgment were used as EDM methods to identify the relationships
between the entities of the subject area.
7
Figure 2: Web resource “Electronic Atlas”. The section “Teacher’s Disciplines”
The term “distillation of data” was introduced by D. Siemens and R. Baker and involves data
clustering, i.e. automatic division of elements into groups based on the “similarity” of a set of char-
acteristics in order to reduce the dimension of the original data set, and data visualization, i.e.
representation in a form that promotes human perception [Siemens & Baker, 2012].
At this stage, it is planned to use tools designed to obtain summary final data for analyzing the
quality of the educational program as a whole according to various criteria, such as the success of
the program mastering by students, the qualification characteristics of the faculty, the most difficult
components of the program to master, the demand for electronic educational courses in disciplines,
etc.
The web resource “Digital Passport CVEP” developed by the authors allows obtaining quan-
titative values of the indicators of the above criteria for analyzing the structure, composition, and
various aspects of the implementation of educational programs. For example, to generate for each
educational program:
• data on the structure of the personnel implementing the program: academic degrees and
positions of teachers, participation of teachers in the implementation of the CVEP (disci-
plines/modules, practical classes, management of the thesis and SFE), departments’ partici-
pation in the implementation of the CVEP, teacher activity in Moodle DLS (number of hours,
structure of developed courses), etc.
• data on the structure of the student body: the percentage of foreign citizens, the share of
students on a contractual basis, the movement of the student body (admission, expulsion,
graduation), employment of graduates [Shrestha & Pokharel, 2021] (availability of work, job in
one’s degree field, employment according to the field of activity);
8
• summary data on student attendance and academic performance, student ratings in the group,
learning activities in Moodle DLS, etc.
For visual representation, the generated data can be aggregated by the selected attributes:
form of study, level of education, field of study/major, orientation (scope of education), on a bud-
getary/contractual basis, etc.
The method proposed by the authors allows determining the nature of the relationships between
the selected entities of the subject area, analyzing the student’s digital footprint in order to make
changes to the content of educational programs and predicting the success of students’ mastering of
learning paths.
4 Results
The authors developed the tool named “Digital Passport of Core Vocational Education Program” (DP
CVEP). This tool provides access to analytical data on the CVEP and its implementation, allows to
track the dynamics of student expulsions, and to identify the categories of students who are at risk of
being expelled, educational programs, and training areas that are difficult to master at early stages.
The tool allows to automatically cluster data on the CVEP (faculty, student body) by certain
characteristics (staff of the faculty, academic degrees, workload by type of contract, summary student
performance, current students) and visualize them. The resource is a component of EMIS, created
on the basis of modern technologies for developing web solutions [Stauffer, 2019], currently includes a
set of reports for the university management, which allows to generate and visualize analytical data
on the implementation of CVEP, teaching staff, student body, and class attendance.
To assess the intensity of students’ work in Moodle DLS, indicators were identified, for example,
the number of actions performed with the course elements, including the number of clicks on the
course pages, loading resources, clicking on links, sending messages in forums and chats. Highlighted
indicators also included: the activity of students in the chat(s) of the course (the number of messages
sent in the chat(s)); the final grade for the course (formed on the basis of the points received when
completing tasks or tests); the time spent on the course by students.
Next, a number of disciplines were selected that play a key role in training for various educational
programs, the development of which is based on the extensive use of e-learning course in Moodle
DLS: geometry, organization theory and management of organizational policy in education, plant
systematics, ancient world history, protection and preservation of cultural heritage, etc.
Information about the success of students’ mastering of the educational program is built in the
form of grades for midterm assessment in modules, disciplines and practical classes in the Herzen
database. To connect data from two databases, an integration model was developed (Figure 3) and
implemented in the DP CVEP.
9
Figure 3: Model for integration of HERZEN DB and Moodle DB
The tool allows identifying the correlation between the selected indicators of students’ perfor-
mance in Moodle DLS and the successful results of their training. The calculation of the indicators’
values is automatic and possible thanks to the queries to MariaDB. The DP CVEP tool implemen-
tation enabled us to obtain the data on the training of five groups, each containing 20 students on
average. Correlation coefficients were calculated for each discipline, student group, and selected indi-
cator, reflecting the dependence of the indicator values on students’ grades for the disciplines midterm
assessments (Table 1).
Indicators of the number of students’ actions and their activity in the chat(s) of the course
characterize the additional activity of students on the way to achieve the final result. The final grade
for the course is formed on the basis of the quality of the student’s task performance throughout the
completion of the online course.
The calculations presented in Table 1 are part of the results of the study. Although these results
are based on a relatively small amount of data, they make it possible to formulate assumptions that,
in the future study, will be tested on large amounts of data by EDM methods.
For instance, based on the data obtained, one can assume that the values of the third indicator
have the most consistent high correlation, with an average value of 0.7108. Thus, the availability of
tools providing an evaluation of the current performance of students in the OEC (assignments, tests)
with substantial certainty allows predicting the success of the student’s mastering of the discipline.
At the same time, the first indicator has the smallest correlation, with an average value of 0.314.
It is possible to draw a preliminary conclusion that this indicator doesn’t present a significant factor
for characterizing the quality of student’s learning process in the course.
10
Table 1: Correlation r-coefficients for selected indicators
The analysis of data in the context of disciplines showed significant differences in the values of
indicators, related, according to the authors, to the specificity of the subject area and the structure
of the OEC of disciplines. On the basis of the analysis of the results of users’ interaction with
educational and methodological materials of the OEC, it is possible to formulate assumptions about
the effectiveness of the course and recommendations for its further improvement.
5 Discussion
Therefore, during the analysis of educational data, the authors identified the stages of application of
EDM methods which include: study of the nature of the subject area; design and development of tools
for the implementation of data collection and data generation; selection of approaches to identify the
relationships between these data, and implementation of developed data processing tools and tools
for a visual presentation of the results. Experimental data were obtained in DEE of the Herzen State
Pedagogical University of Russia.
Calculations of dependencies between the highlighted indicators of the student’s digital footprint
confirmed the feasibility and importance of EDM methods implementation, as it allowed to draw
conclusions about the impact of such indicators on academic results of students within the framework
of current and midterm academic performance. It would be fair to conclude that the use of EDM
methods is directly dependent on the preparedness of the educational organization: availability of
appropriate equipment and skills, as well as digital and IT competence of the students. The results
11
of further research aimed at identifying factors influencing the success of students’ mastering of the
courses will allow, according to the authors, to create recommendations for an upgrade of the structure
and content of educational programs in order to improve the quality of training.
6 Conclusion
The obtained results allow authors to move towards the next stage of the study, based on an expanded
database, and analyze the students’ performance within all the educational programs available at the
university.
Based on such data, models (patterns) describing students’ learning activities within the frame-
work of blended learning will be developed, which, according to the authors, will allow predicting the
success of students mastering of the learning path, identifying students at risk and “critical points"”
of educational programs that cause students difficulties in learning, and developing recommendations
to improve the online education courses, etc.
Thus, the use of EDM methods in the framework of the proposed methodology and implemen-
tation of the developed tools for the analysis and interpretation of detailed data on various aspects
of the learning process will improve the efficiency and effectiveness of education.
The method of analysis of the digital footprint of students suggested by the authors can be used
for informed managerial decision-making during the design, editing, and implementing university
educational programs with a view to improving the quality of training.
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