=Paper=
{{Paper
|id=Vol-2834/Paper6.pdf
|storemode=property
|title=Adaptive Learning Using Artificial Intelligence in Distance Education
|pdfUrl=https://ceur-ws.org/Vol-2834/Paper6.pdf
|volume=Vol-2834
|authors=Petr V. Chetyrbok,Marina A. Shostak,Lennara U. Alimova
}}
==Adaptive Learning Using Artificial Intelligence in Distance Education==
https://ceur-ws.org/Vol-2834/Paper6.pdf
61
Adaptive Learning Using Artificial Intelligence in
Distance Education*
Petr V. Chetyrbok1[0000-0002-0115-9158], Marina A. Shostak1 [0000-0002-3834-0559],
Lennara U. Alimova 2 [0000-0001-7815-5262]
1 V.I. Vernadsky Crimean Federal University, Simferopol, Russia
2 State Budgetary Educational Institution “Crimean Engineering and Pedagogical University”
named after F. Yakubov, Simferopol, Russia
petrchetyrbok@gmail.com
Abstract. Reviewed and analyzed is the use of artificial intelligence (AI) in dis-
tance education. The authors highlight several areas of artificial intelligence’s
application in education. Adaptive learning is perhaps one of the most promising
lines of artificial intelligence’s application in education.
Keywords: artificial intelligence, distance education, neural networks, platform,
course, adaptive learning
1 Introduction
The distant method allows organizing the learning process online in the real-time
mode. Students and teachers communicate via the Internet. Teachers transmit
knowledge to students who receive new information and assignments and pass tests. At
the same time, teachers can be removed from the student at any distance, they can live
in different countries and on different continents.
Under the current conditions, the quality of education depends on information trans-
mission and the methods of its presentation, including the use of artificial intelligence
elements.
We can distinguish the following features and properties of artificial intelligence
systems distinguishing them from conventional automation systems:
1) a purpose or group of purposes;
2) planning their actions and search for solutions to problems;
3) learning and adapting their behavior patterns within the operating process;
4) working in a poorly formalized environment, under the conditions of uncertainty
and following fuzzy instructions;
5) self-organization and self-development;
6) understanding natural language texts;
* Copyright 2021 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
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7) data generalization and abstraction.
The purpose of the article is to review and analyze how the directions of artificial
intelligence application in adaptive learning could be used in the educational system.
2 Related Works
Adaptive learning is the learning mode taking into account learners’ abilities,
knowledge, skills, and moods. In the 1950-60s, programmed learning algorithms
started to develop actively. The programs created on their basis controlled students’
current knowledge, which facilitated the increase in the effectiveness of educational
materials’ gradual mastering. Adaptive testing appeared in this area. In his writings,
Gordon Pask subsequently outlined the main provisions of adaptive learning. In his
theory, he substantiated the idea that a high-quality curriculum should be individualized
and based on learners’ knowledge, taking into account their activity and attitude to the
subject. The need for algorithms taking into account learners’ characteristics has be-
come urgent nowadays. It is essential to develop services implementing these algo-
rithms and using artificial intelligence elements. Jose Ferreira, the founder of Knewton,
notes: “In April 2013, I wrote a post in which I predicted that over the next several
years, all educational materials would become digital and adaptive. Knewton played an
important role in this revolution. We want to create a world in which each learning
application is a priori adaptive. But it is not so simple. Nowadays, there are numerous
applications in the market having rich educational experience, beautiful interfaces, in-
teresting content, and well-thought-out methodology. But a significant drawback of
many of these applications is the inability to adapt to learners’ knowledge”.
Knewton is known to be one of the first companies to actively apply data analysis
technologies in the field of education. As a result, an adaptive educational platform was
created that can be connected to any modern educational process management system
(LMS). The Knewton platform allows creating universal algorithms and developing an
extensive infrastructure for collecting, analyzing, and using information about learners’
performance, such as:
1. Collecting data on learners’ knowledge and the degree of certain concepts’ master-
ing.
2. Conclusions based on the collected data on learners’ characteristics and their reac-
tions to changes in learning.
3. The choice of the optimal learning strategy for every student based on personaliza-
tion. The Knewton algorithms based on the personalization of learning success fore-
casts offer the optimal structure and complexity level of adaptive learning courses.
Knewton provides an infrastructure platform offering software developers the algo-
rithms to adapt educational resources and helps them create unique, personalized,
and flexible educational courses. “Analytics” is linked to the Knewton platform us-
ing the API and allows answering the following questions:
− what do learners know?
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− why has a student made a mistake?
− what topics and disciplines are best studied by a certain student at a given time?
− what are the chances that the student will solve the problem correctly?
Being of particular relevance, personalized learning is a wide range of educational
programs in which the method and pace of learning depend on learners’ needs, special
interests, and preferences.
Artificial intelligence adapts the learning process to the individual learning rate of
every student and offers tasks of increased complexity.
Adaptive learning technologies are applied in commercial projects in the sphere of
Human Resources (HR). Сompetentum, Ispring, E-mba are available in the Russian
market. Artificial intelligence is introduced in language teaching (“Skyeng”, “Lin-
gualeo”, “Websoft”), as well as programming and design (“Geekbrains”, “Netology”).
The Moodle learning management system is a distance learning system distributed
in the open-source code, which makes it possible to customize it to the characteristics
of a particular educational project using certain elements of artificial intelligence.
Moodle is a platform used in distance education providing both teacher-student and
student-student types of communication. It offers the following services:
− exchanging files of any format;
− newsletters allowing to quickly inform all the course participants or individual
groups about current events;
− creating and storing each student’s portfolio containing all the tasks submitted by
him or her, all the assessments and teacher’s comments on the tasks, all the mes-
sages in the forum.
The teacher creates his or her assessment system within the framework of the course,
which allows issuing summary statements of performance, controlling students’ attend-
ance, activity, and the time they spend in the educational network. The modular struc-
ture of the system makes it easy-to-use by students and teachers.
Educational institutions pay attention to students’ attitudes towards teachers and
conduct surveys. Even though digital ones have replaced paper questionnaires, the feed-
back process has not changed much. However, the time has come to revise it because
students’ feedback is an important source of information.
Artificial intelligence offers several interesting opportunities to optimize this pro-
cess:
− Chatbots can collect information using a dialogue interface simulating real inter-
views. This process does not require much effort from students.
− Conversations can be adapted to students’ individual features of character and
changed depending on their answers.
− Chatbots can filter out rude comments and personal insults that sometimes occur in
feedback forms.
The “Smart Campus” answers any student inquiries related to studying and living on
campus: how to find a lecture hall, register for a chosen course, get assignments, find a
free parking space, or contact a professor.
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The University of Western Australia (UWA) already has such a “Smart Campus”. It
is operated by the Watson supercomputer system created by IBM.
3 Results
The Optimal Model. In this article, the models will include:
1. Measuring students’ cognitive and emotional state;
2. Creating an open student model to stimulate students’ thinking and reasoning;
3. Providing dynamic assistance (effective feedback) to increase students’ motivation
and involvement;
4. Using models of social modeling, e.g. so that language learners could more success-
fully interact with native speakers of the language they learn, as well as understand
cultural and social norms.
Century Tech Platform. Teachers try to pass the same knowledge to students at
different levels, but often fail to do it. Artificial intelligence helps to adapt lessons to
the individual characteristics of every student. Within the following five years, Century
Tech will be deployed in all 700 Flemish schools. It is the first time that the authorities
have decided to introduce artificial intelligence in schools to such an extent. The goal
is to replace the standard model, in which teachers try to pass knowledge to students at
different levels, but often fail to do it, with a fundamentally new one, in which artificial
intelligence helps to adapt lessons to the individual characteristics of every student.
This includes identifying areas of knowledge they are familiar or unfamiliar with,
where they feel confident or lack confidence, and an assessment of how much they like
certain activities. The artificial intelligence system constantly analyzes every student’s
performance, identifying trends in knowledge mastering, and adapting lessons by them.
In other words, if your third-grade student cannot understand the equation, he will not
have to get ahead of the rest of the class and listen to you explaining “what is X” in the
evenings. Artificial intelligence will determine his problem and let him master the ma-
terial in the form of micro tricks.
The introduction of artificial intelligence systems in schools will become possible
when students have constant access to computers at school and the educational tech-
nologies collected in them. When every student starts using his or her digital device, it
will be possible to say that the introduction of a full-fledged digital school has begun.
This is the only way to get a digital educational footprint and an initial technological
foundation for forming individual educational trajectories using artificial intelligence.
And then teachers will begin to acquire and adapt their knowledge about students
thanks to the data obtained during each interaction with the system. What have our
schools managed to implement so far? Moscow electronic school is actively used in
Moscow. It is important to understand that it is not an artificial intelligence system yet,
but rather the first attempt to automate the learning process.
A national project called “Education" has been developed in Russia. Its overarching
goal is to create a unified educational platform allowing every student to receive a high-
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quality quality education, including the use of adaptive learning and individual educa-
tional trajectories. The model of a unified digital educational environment was devel-
oped in 2019. Artificial intelligence as technology can become part of this ecosystem.
On the other hand, in our country, there are already examples of the use of artificial
intelligence in education. For example, the Russian startup “Parla” created an applica-
tion for learning English. The application is based on a program that is taught alongside
students and adapted to their tasks and progress. Already at the registration stage in the
application, the program can analyze data from social networks and offer an individual
learning program based on a particular person’s interests. It is a commercial project,
but most technologies appear primarily as a commercial product.
According to British developers, their platform is used 20 minutes a week in ele-
mentary schools and 40 minutes a week in high schools (on average). Applicants claim
that the system maximizes six hours a week. This time can be spent on creative activity
and physical education, which are often “neglected” in comparison with academic dis-
ciplines due to a serious examination load on students.
The platform also applies the basic principles of neurophysiology to determine stu-
dents’ abilities, levels of knowledge and endurance, individual pace and appropriate
time for learning, as well as the speed of information transfer from the short-term
memory to the long-term memory. These analytical data are later incorporated into au-
tomated learning algorithms.
The use of artificial intelligence can solve the problem of accompanying students
along their educational path (IEP) at a new qualitative level. The fact is that the real
IEP is dynamically rebuilt as the student develops. To do this, you need to conduct
regular monitoring, as well as monitor any educational activities (reading and watching
educational materials and problem-solving). This is what we call “an experienced
teacher’s intuition”. But in fact, we imply the processing of the resulting large data
array, which can only be done by artificial intelligence. The role of human teachers
must be transformed: from the carrier of knowledge to the carrier of the philosophy of
the studied subject. Conceptual things can be comprehended and mastered by people,
not by artificial intelligence systems. Teachers will pass them to children in small
groups due to enhanced free time. “It seems that the Belgian initiative may well become
a Russian reality, but not very soon. On the other hand, individual educational trajecto-
ries are already being introduced to higher education institutions. Universities such as
the Higher School of Economics provide their students with online courses provided
by other universities according to students’ specialties. It means that students can al-
ready change the curriculum to some extent and independently choose were to master
the material and up to what extent.
Big data are just an archive in which the accumulated information is stored. This
archive is very large containing millions of records or data points/sets of data, such as
students’ names, their annual grades, absence due to illness, and the number of lost
textbooks or goals scored in physical education. Previously, no one collected such data,
because there would not be enough resources for accounting, storage, and, especially,
for analysis. At the moment, the easiest way of collecting data is used by developers of
electronic textbooks, online courses, or mixed-type learning systems. They are used
immediately, helping to set up the system for on-the-fly learning: the more data, the
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more accurate it is. Big data analysis can speed up the solution of scientific, research,
and educational problems. Studying statistics, one can work with both individual tra-
jectories and global educational systems. The methods used in big data analysis are
based on computer-aided learning, image recognition, psychometry, and statistics. The
most popular PSLC Data shop repository stores information collected over 250,000
hours spent by students on educational programs – about 30 million actions, answers,
and results.
Trial and Error Method. Using neural networks based on this method, it has been
possible to solve complex problems in education. For example, a system can improve
students’ essays and indicate answers to questions [8].
Neural Network Duelists. The structure is as follows: one system consists of a net-
work generating new data after training, and another system divides the data of the
previously specified network into “true” and “false”. Such a set can ultimately produce
very realistic synthetic data, and so far such “duels” of neural systems are excellent for
creating landscapes in computer games, improving pixel videos, or for applying stylis-
tic changes to a design that was created using a computer. Duelists can also be useful
for improving the quality of educational materials. For example, in studying foreign
languages there is a huge segment for filling dictionaries with current word meanings,
where the filter is most often used by other users or native speakers [9], [10].
Data Collection and Personalization. Using geolocation data and our previous
searches, artificial intelligence can already offer us the perfect cafe nearby or, for ex-
ample, give directions to the nearest store of our favorite comic books. Nowadays,
smartphones can hold a charge longer, because they keep a record of our interaction
with applications and prioritize, quitting those that we hardly use. Most commonly used
applications start up faster and issue notifications.
Now let us imagine how we learn a grammar rule based on the examples only from
the area we are interested in, and all educational materials are adapted to meet our
needs. This is the type of content we will continue to search for.
The Main Models of Big Data. The most interesting model of working with big
data is forecasting, where a combination of the known data allows predicting something
unknown that a person is looking for. The known data are collected from records of
school systems, online services, surveys, and observations during experiments. It is
very important to collect such data. One needs to know what to pay attention to and be
able to determine the necessary information. The model is used to predict the future
(we calculate whether the student can solve the following problem and what result he
or she is likely to achieve) or the present (judging from the statistics over the last hour,
we find out whether a student is interested in viewing online materials now). The esti-
mated value may be a number: for example, the time spent on solving a problem, the
number of sentences used, the percentage of video views, or the test result in points.
There may be a category: quit / not quit, ask, try to solve/ask for a hint, which will be
A / B / C / G. In such cases, the classification method and various algorithms, such as
a decision tree or clustering, are used (Fig. 1) [11], [12].
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Fig. 1. Clustering
Advanced algorithms take into account error costs and the effectiveness of proper
system intervention. For instance, if a student covers 0.05% of the course per minute,
the wrong forecast “costs” him 1 extra minute of learning, and the correct one – 0.03%.
Forecasts should be checked. Are there dependencies in the data, or just random coin-
cidences? To find it out, one can separate the data and see whether the dependency
relevant for one group is repeated in all the others. The main question is as follows: is
the determined dependency applicable to new data? There are cases when the results
obtained in a sterile laboratory do not coincide with the results shown by students in
real life. Unlike the forecast, where it is known what needs to be determined, the struc-
ture determination method is used to identify unknown patterns and subsequent data
clustering. Another method, called “network analysis”, considers all the participants in
the educational process as “nodes” interconnected by ties, which can be stronger or
weaker depending on the intensity and frequency of communication.
The system includes various types of interaction: teamwork with one resource, lead-
ership, help, criticism, or even insult. The interaction data are determined by the fol-
lowing important parameters:
− Density: how many possible ties are established among students? This parameter
shows up to what extent a group of students may be called a real class. Sometimes
everyone communicates with everyone, and sometimes only a few students in the
entire class make up the active core, and the rest prefer to listen passively.
− Accessibility: are there students who are not contacted? Some students do not com-
municate with anyone, they exist in the system “on their own."
− Distance: how many “nodes” go along the route from one student to another? The
principle of five handshakes works here. The shorter the chain, the more active the
class.
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− Flow: how many possible routes (through different “nodes”) from one student to
another exist? Numerous routes stipulate more diverse ties.
− Centrality: how important is each student? Who is the most influential student in
the class? This parameter is determined using the following three components. The
first one is the number of ties leading to the learner. Incoming and outgoing ties are
taken into account separately: a student who wants to be friends with everyone does
not always look like the one with whom everyone wants to be friends. The second
one is proximity, which is the sum of students’ distances from each other. Strong
ties are considered closer here. And the third one is the number of routes passing
through the student among other “nodes”.
Fig. 2. Network analysis
Reciprocity: the number of bidirectional ties in all pairs of students (Fig. 2).
Proper vector: A parameter calculated mathematically and based on the number and
strength of links. This algorithm is also used by Google to generate PageRank and de-
termine the order in which results are displayed on the search page.
Assess students’ knowledge to expand it competently. Also, these data will help
evaluate teachers’ work. Finally, following on from such data, the system can inde-
pendently make pedagogical decisions. Academic performance is constantly being rec-
orded. These data help to form templates by which one can estimate every student’s
knowledge. After all, students may not know something, but correctly answer a ques-
tion (there is always a chance to guess) or, conversely, accidentally make a mistake.
Big data allow understanding of how students behave when they are bored. The sys-
tem can determine one of the models of such behavior.
− Students try to “outsmart” the system, i.e., to succeed without any preparation. For
example, they go through all possible answers until the correct answer appears.
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− Students are distracted by other tasks, such as communicating with the teacher or
commenting on Facebook.
− Students answer thoughtlessly and make random choices, without even trying to
think.
− Students behave unpredictably.
There are other metacognitive problems. For example, students often do not use hints
and help, even if it takes a long time to solve a problem. In another case, having made
a mistake, they linger on the correct answer for a long time, trying to understand its
origin, instead of moving on. And sometimes they quickly look through the answer
choices and answer the question correctly, but then stop and slowly analyze it, thus
wasting time. The same data on students’ behavior can be viewed in different ways.
After analyzing students’ behavior patterns for a week, one can tell which student tried
to “outsmart” the system (this student will have additional classes), which day of the
week was the least effective, and which lesson was the most boring. To determine a
student’s behavior pattern, it is important to collect the correct data.
It is not appropriate to ask students direct questions in such a situation. One cannot
ask students: “Are you lying to me now? Yes / No”. Instead, one needs to observe
students’ behavior, record their actions, analyze the sequence of their responses, or even
use the PrintScreen function. There are also turnkey solutions offered by Neuromation
using synthetic data. The main idea of synthetic data is to model the missing data to
create a model of students’ behavior. In other words, one needs to model a dataset for
a training set of deep neural networks. Such a neural network will be able to predict
students’ behavior patterns depending on their specific actions during the test proce-
dures.
Big data allow:
− creating methods adapted for a large number of students;
− personalizing educational materials;
− choosing a teacher.
4 Conclusion
Nowadays, distance education is not only popular but also quite a promising form of
education. However, to use it with maximum efficiency, its technical and theoretical
basis must be up to the proper level. Moreover, the process of teacher-student interac-
tion should be characterized by mutual interest and the use of artificial intelligence el-
ements.
This article discusses the possibilities of using artificial intelligence in education. Of
particular importance are such features as adaptive learning using artificial intelligence
elements, personalized learning, automatic assessment, interactive learning, students
assessing their teachers, smart campuses, and smart agents.
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