=Paper=
{{Paper
|id=Vol-2590/short17
|storemode=property
|title=Applying Blockchain Technology for Improvement of the Educational Process in Terms of Data Processing
|pdfUrl=https://ceur-ws.org/Vol-2590/short17.pdf
|volume=Vol-2590
|authors=Dina Zimina,Dmitry Mouromtsev
|dblpUrl=https://dblp.org/rec/conf/micsecs/ZiminaM19
}}
==Applying Blockchain Technology for Improvement of the Educational Process in Terms of Data Processing==
https://ceur-ws.org/Vol-2590/short17.pdf
Applying Blockchain Technology for
Improvement of the Educational Process in
Terms of Data Processing1
Dina Zimina[0000−0001−9044−7292] , Dmitry Mouromtsev[0000−0002−0644−9242]
ITMO University, Saint-Petersburg 197101, Russia
dina.zimina@gmail.com, d.muromtsev@gmail.com
Abstract. The paper considers some new methods of using blockchain
technology as a tool of improving educational process management. Two
e-learning problems are described. The first one is low ratings of com-
pleting MOOCs. The second one is the need for more detailed and accu-
rate reports. Blockchain technology and its applications in education is
considered. Blockchain’s ability of documenting events is reviewed par-
ticularly. Two cases of using blockchain in this way are performed. The
first one is making educational reports more detailed and easier to de-
sign. Another one is using blockchain for creating any student model in
adaptive education to make electronic educational courses more effective
and to increase ratings of completing MOOCs. A blockchain’s option
of documenting timestamps and users’ addresses automatically is spe-
cially mentioned. Both cases are described mathematically and united
in one model. Using Ethereum blockchain for the evaluation because of
its smart-contract’s mechanism is justified. Sequence diagram showing
application workflow, is presented. Input data from the real e-learning
course are processed with using statistics to get the most frequently
used system events. Application tools for working with the Ethereum
blockchain are presented. The process of developing, deploying and min-
ing smart-contract is shown. Output report is presented. Implementation
is described, the ways of future learn are reported.
Keywords: blockchain, · adaptive learning, · report, · smart-contract, ·
education, · Ethereum.
1 Introduction
Today online education becomes more and more massive [1–3]. There are a lot
of students, therefore, a lot of data. To analyze and control educational process,
we need a lot of parameters to handle. Every learning management system has
a module for generating and analyzing reports. But as MOOCs become more
popular, new methods of manage and control educational process are required.
1
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 Dina Zimina, Dmitry Mouromtsev
Another problem is low rating of completing courses. One of solutions is adaptive
learning. When learning is adaptive, every student has his own learning path
with his own preferences and features. Adaptive learning can make electronical
education more effective [4].
Blockchain technology is a popular research topic. It is a distributed database
which based on peer-to-peer network. Every transaction in the net must be con-
firmed with all the nodes. Every new confirmed transaction needs to be recorded
to every node of the network. All the confirmed data is packed in special blocks;
every block has a reference to previous block; so, there is a chain of blocks as all
data storage.
Blockchain technology has some advantages of use in many areas like medicine,
realty and education [5, 6].
Initially, blockchain was created for operation with cryptocurrency, but later
it became independent platform for open and reliable data change in some peer-
to-peer network. Bitcoin blockchain is for cryptocurrency operations only, while
Ethereum blockchain allows various transactions using smart contracts mecha-
nism. A smart contract is a program, which describes some operations in the
Ethereum blockchain, Operations become automated transactions between the
nodes in the network. It allows to use blockchain in electronical education.
EduCTX project was developed from the Ethereum platform. EduCTX pro-
vides internal tokens, which acts like virtual educational currency. Every student
has his own electronical ectx-wallet, which appends while learning process [7].
“Learning as earning” is another concept. It means building a chain, using all the
education results as building blocks. This chain forms student’s digital portfolio
[8].
Nicosia University uses blockchain for certificates’ digital signature. Also, it
runs blockchain courses and accepts bitcoin as a payment currency [9]. Finaly,
blockchain is used in the education reputation system. Reputation acts as a
currency [10].
Now there are some studies about certificates’ verification applying blockchain.
Confirmation records are automatically stored in the blockchain [11, 12].
One of the most useful blockchain features is its ability to register and store
all the events in the system. All these events are available for future processing.
The paper considers two cases of applying blockchain in the e-learning.
2 Method
2.1 Grade reports
The first case is generating reports. Today there are only determined options
for saving and reporting events in existing LMS [13]. Let’s consider a functional
block in the LMS, which makes reports. Input data is heterogeneous and consists
mostly of four parts. Let the course have N students and M tasks. A = {ai } is an
array of integer students’ identifiers, where i = 1, 2, .., N . T = {ti,j } is a matrix
of integer timestamps. Every ti,j means that student ai solves task number j,
Applying Blockchain Technology for Educational Data Processing 3
where j = 1, 2, . . . , M . If student ai didn’t solve task number j yet, ti,j = 0.
Then, K = {ki,j } means a matrix of grades. Every element ki,j is a student ai
integer grade for the task number j, or NULL if the student didn’t solve the
task yet.
So, grade report is a matrix:
U = {ui,j }, (1)
where every element is a dataset obtained from the function:
ui,j = f (ai , ki , j, ti,j ) (2)
Function means program code, which are necessary to create for processing
all the parameters.
When using blockchain, students form peer-to-peer network, so all their in-
teractions are recorded into blockchain. Time parameters and users’ identifiers
are registered automatically by blockchain; therefore, grade report is matrix:
U 0 = {u0i,j }, (3)
where every element is a dataset obtained from the function:
u0i,j = f (ki,j ) (4)
Any report in every LMS can be considered similarly. Every report uses time
parameters and users’ identifiers. Blockchain makes reports more detailed and
universal and doesn’t require any data scheme.
2.2 Adaptive learning
Another case of using blockchain in education is student modelling in adaptive
learning. The student model is the central concept of adaptive learning [14, 15]. It
allows the system to choose proper learning path for every student. It denotes a
set of characteristics that have different values for different students. Depending
on these values, each student belongs to a particular group with different edu-
cational path. The characteristics of students are collected and processed both
before the start or during the educational process. Such parameters as academic
performance, time for solving tasks, asking for reference material, etc. are pro-
cessed. Regardless of chosen model, to build it it’s necessary to get some data
about the behavior of the student. Some parameters are time related; anyway,
student’s identifier is required.
If D is mentioned before input data for processing in the student modelling,
then output dataset for the student model is a result of function:
f (D) = f (A, T, K), (5)
where A stands for an array of students’ identifiers, like in the previous case; T
stands for an array of all students’ actions’ timestamps, like in the previous case;
4 Dina Zimina, Dmitry Mouromtsev
K stands for all the other parameters, required for modelling including grades,
actions, uploaded files, comments, viewed pages etc.
When using blockchain, every timestamp and user identifier are registered
automatically, similarly to the previous case, so student model is a function:
f 0 (D) = f 0 (K). (6)
So, blockchain using allows partially automate student modelling and makes
input data more detailed. Also, no matter what type of adaptive model us used.
Similar to previous case, no special data scheme is required. Blockchain is a
universal tool for heterogeneous data registering and storing. Therefore, different
models can be designed and implemented.
2.3 Both cases in one model
Both cases can be described in one model (see Fig. 1, Fig. 2).
Task
number
Student
Grade model
Action Grade
report
Time
Actions
report
User ID
Fig. 1. Registering events in existing LMS
It also can be presented mathematically. The first case describes existing
LMS:
U = f (A, T, K), (7)
Applying Blockchain Technology for Educational Data Processing 5
Task Blockchain
number
Possibility
Time to create
any
Grade student
model or
User ID report
Action
Fig. 2. Registering events in blockchain-based system
where U means output dataset to form report or student’s model, A means an
array of users’ identifiers, T means an array of all user actions’ timestamps,
main data K contains all the other user actions’ parameters of learning process
including grades, actions, uploaded files, comments, viewed pages etc.
Blockchain-based system simplifies the process:
U 0 = f (K) (8)
Now, system has to register only K to generate U 0 , because arrays A and T
are registered by blockchain automatically.
It is important to say, that in both cases no special data scheme or structure
is required. Any parameter can be added or selected for processing during system
runtime.
The blockchain supports several properties that are considered in both cases:
1. Traceability. It can be easily tracked how an assessment was formed using a
detailed report of all student’s actions
2. Security. Data cannot be distorted.
3. Reliability. It can be found out that the assessment was obtained precisely
by the methods provided for in the curriculum.
6 Dina Zimina, Dmitry Mouromtsev
3 Evaluation
Both models were implemented in a developer’s version of the Ethereum blockchain
using the smart contract mechanism. As it was said before, Ethereum is blockchain
application platform, which allows to develop blockchain-based applications.
Input data was obtained from the educational center of Design in ITMO
University. The system is based on the Moodle platform. At the moment, there
are running more than 20 courses. Webmaster offered three log files with results
of studying “Design and layout using CorelDraw”. Since the course started,
there were about 270 students studied and their activities were recorded into
three logs. The files contained following data:
– First and last login of all users in the system, including date and time.
– All students’ grades. The students had to complete 11 home tasks and the
final test. Home tasks had to be loaded in the form of a file and were accom-
panied by a grade and teacher’s comment. The grades were presented in a
five-point scale, as well as a percentage of the maximum possible value. The
final test consisted of 30 questions. Test grades were presented as a number
of correctly answered questions, as well as a percentage of the maximum
possible value.
– All students’ actions with date and time. The actions contained data on the
following events: a student enrolling in a course, a student uploading a file,
giving grades, test attempts, viewing a course section, as well as some system
events like assigning a role to a user.
Every file had its own structure. So, a lot of heterogeneous data was available
for processing with blockchain. There were more than 200 records with grades
and about 40,000 actions.
This data was analyzed for types of events with their parameters and fre-
quency. It is not necessary to model all the events from the input data. This
paper needs only the most common types of students’ actions:
– task solving with grade and number of attempts;
– page viewing;
– file uploading with teacher’s and student’s comments.
Smart contract was created with Solidity language and Remix online com-
plier. It contained event descriptions with a set of parameters for every event
type.
There was a handler method for every event. Methods initiated events and
recorded them into blockchain with event timestamp and author’s address.
The contract was deployed and mined in the “dev” Ethereum network, avail-
able through “geth” console.
Event data was available through the “geth” console too (see Fig. 3, Fig. 4).
Dataset was exported into text file. It allows us to create any student’s model
and any educational report (see Fig. 5).
Applying Blockchain Technology for Educational Data Processing 7
LMS
User Smart-contract Blockchain
Educational event
Calling smart-contract method
Logging event into blockchain
Fig. 3. Recording event into blockchain
LMS
User Blockchain
Data request
Searching for event
Event object with parameters
Generating dataset
Dataset
Fig. 4. Extracting data from blockchain
Fig. 5. Event report
8 Dina Zimina, Dmitry Mouromtsev
The output is a log exported into a .txt file. Here is a excerpt from the log
with four entries of two types. The first type is task assessment. It contains the
”grade” parameter with a student grade as a percentage of the maximum value
and the ”task” parameter with an integer task number.
The second type is a student’s action. It contains the ”action” field for the
event’s name.
Both types of records also have two parameters. ”Time” is an integer that the
blockchain registers. This is the time of adding the new block, which contains
the transaction with this event. The ”owner” parameter is the address of the
node in the blockchain that triggered the event. This is a hexadecimal number.
It serves as the user identifier in this dataset.
The difference between input and output data can be presented in the table.
Table 1. The difference between input and output datasets
Property Initial data Processed data
Number of logs 3 1
Log files format .xls .txt
Data structure Every log has a table structure, Log is a list of records with dif-
it’s impossible to add new data ferent set of parameters, any new
format data format can be added
Events’ se- No unified sequence for all events, Events are in one sequence
quence every log needs to be considered
separately
Traceability Need to form every student’s path Easy to trace every students’
from different logs learning path
Security Reports are stored in one place Records are stored in distributed
network
Reliability Reports are stored in the editable Every event has its owner in the
database network, so, actions cannot be
changed
4 Conclusion and future work
As it was shown, smart contract logged heterogeneous data of learning events
and students’ actions to the blockchain without using special data structures.
The blockchain stored data in a unified registry and stores event timestamp and
event author’s address by itself.
As a result of the study we can say, that blockchain technology is a possibly
good tool for electronic massive education. The next research task is to measure
advantages of blockchain’s implementation. The next practical task is to expand
the system to the full version, including user interface, and also estimate the
cost of using this model.
Applying Blockchain Technology for Educational Data Processing 9
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