Stages of Adaptive Learning Implementation by Means of Moodle LMS

Nataliia V. Morze

, Liliia O. Varchenko-Trotsenko

and Tetiana S. Terletska

Borys Grinchenko Kyiv University, 18/2 Bulvarno-Kudriavska Str., Kyiv, 04053, Ukraine

Keywords:

Adaptive Learning, E-Learning, Microlearning, Students’ Needs, Moodle.

Abstract:

Adaptive learning is a methodology that allows to identify the level of students’ knowledge and their learning

styles and transform materials, tasks and ways of their delivery according to the needs of learning process

participants. The interest of higher education institutions (HEI) to use adaptive learning as an innovative data-

driven approach to the educational process is increasingly growing. However, the level of its actual use in HEIs

is not high. The main reason is that a university has to overcome a lot of challenges in the process of adaptive

learning implementation including technological, pedagogical and management-related ones. The authors

address the problem of the possibility of adaptive learning integration into existing learning management

systems (LMS) on the basis of Moodle as one of the most popular LMS for e-learning arrangement. The

research is focused on the study of activities and resources that can be used as solutions at different stages of

adaptive learning development in an e-learning course (ELC). This is an up-to-date question as it allows to cut

the costs on innovation implementation and at the same time simpliﬁes its introduction for teachers as they

have already got experience in the system. Although Moodle LMS is not an adaptive learning system, it can be

used as a compromise for adaptive learning implementation at higher education institutions, claim the authors.

It provides administrators and teachers with tools to vary all stages of a learning process starting with delivery

of information and ending with assessment. Learning materials can be adopted through choosing different

types of delivery of the same information as well as through the choice of the level at which students are

able to gain the knowledge. The sequence of information delivery also can be adjusted to the students’ needs

through using settings in lectures and other types of materials. Adaptive assessment can be achieved through

adaptive quizzes tools. In the paper it is offered to look at the perspective of adaptive learning implementation

through the stages of its development in an electronic learning course (ELC), activities and resources that can

be used to provide those stages. Microlearning is highlighted as a means of adaptive learning implementation.

1 INTRODUCTION

Modern e-learning platforms are able to support the

creation and sharing of educational content and build-

ing collective intelligence. Students can look for such

content online and decide whether it is suitable for

achieving their learning objectives. However, search-

ing and organising suitable content can easily make

learners lose their focus on learning (Huang and Shiu,

2012). Therefore, open and ﬂexible approaches and

the establishment of adaptive systems are required to

ensure better delivery of educational content and pro-

vision of high quality education for a large number

of higher education institutions (HEI) students (Pel-

letier et al., 2021). The interest of higher educa-

https://orcid.org/0000-0003-3477-9254

https://orcid.org/0000-0003-0723-4195

https://orcid.org/0000-0002-8046-423X

tion institutions to use adaptive learning as an inno-

vative data-driven approach to the educational pro-

cess is increasingly growing. However, the actual use

of adaptive learning by HEIs remains rather limited

in spite of promising results of recent studies on its

effectiveness (Mirata et al., 2020). The main types

of challenges faced by HEIs in the process of adap-

tive learning implementation include technology, ped-

agogy, and management-related issues (Mirata et al.,

2020). Among them there are dealing with real time

data, difﬁculties in integrating adaptive learning so-

lutions into existing learning management systems

(LMS), the need to change e-learning courses design

and content etc. In particular, in the process of adap-

tive learning implementation teachers often struggle

with modifying learning content, because they have

lack of experience with adaptive technologies. Most

higher education institutions still have uniﬁed learn-

ing materials which do not consider students’ learn-

476

Morze, N., Varchenko-Trotsenko, L. and Terletska, T.

Stages of Adaptive Learning Implementation by Means of Moodle LMS.

DOI: 10.5220/0012065500003431

In Proceedings of the 2nd Myroslav I. Zhaldak Symposium on Advances in Educational Technology (AET 2021), pages 476-487

ISBN: 978-989-758-662-0

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

ing styles, knowledge level difference, needed depth

of study, time frameworks for the course completion

etc.

In the process of knowledge consumption students

tend to divide knowledge arrays into small parts, and

then put them in order and format that is easy to pro-

cess for them. This is also proven by the results of the

survey conducted at Borys Grinchenko Kyiv Univer-

sity. Learners then develop links between these pieces

until they fully grasp the knowledge (Huang and Shiu,

2012). This corresponds to one of the recent educa-

tional trends – microlearning. It is a learner-centred

teaching and learning approach which is result ori-

ented and provides division of the material into seg-

ments that are easy to be consumed at a time (Grevt-

seva et al., 2017). Microlearning components of-

ten remove any inconsequential and unrelated content

and focus only on what a student needs to know. This

reduces learners’ cognitive load and increases reten-

tion since they are able to process information more

effectively (Giurgiu, 2017). When material is split

into smaller sections it is much easier to be adapted

to students’ needs. Thus, the authors claim that mi-

crolearning can be used as a means to implement

adaptive learning in HEIs.

The aim of the research is to determine whether

learning management systems (LMS) can be used as

a platform for implementing adaptive learning as they

are already used for e-learning arrangement in HEIs.

For this purpose activities and resources in e-learning

courses (ELC), that allow the adaptation and person-

alisation of materials in a way which is relevant to

students’ individual needs, are studied. The authors

offer to look at the perspective of adaptive learning

implementation through the stages of its development

in an ELC (initial stage, pre-test stage, path gener-

ation stage, learning stage, post-test stage), activi-

ties and resources that can be used to provide those

stages. The e-learning system of Borys Grinchenko

Kyiv University based on Moodle LMS is taken as a

background for testing adaptive learning implementa-

tion in ELC.

2 ANALYSIS OF RESEARCH AND

PUBLICATIONS

Skinner (Skinner, 1968), who is considered to be a

founder of personalised (adaptive) learning, stated in

his book “The Technology of Teaching” that one of

effective ways of teaching is dividing material into

small parts and adapting learning tasks to current level

of students’ knowledge. Elements of adaptive learn-

ing were reﬂected in (Bloom, 1994; Pashler, 1998;

Cronbach, 1967; Bondar and Shaposhnikova, 2013;

Fedoruk, 2010; Osadcha et al., 2021).

The deﬁnition of adaptive learning by Skinner

(Skinner, 1968) led us to considering microlearning

as a means of adaptive learning implementation. Mi-

crolearning has got a lot of attention from scientists

recently. According to Leong et al. (Leong et al.,

2020) 476 relevant publications have been identiﬁed

during 2006–2019. Hug (Hug, 2007) in his book “Di-

dactics of Microlearning” is covering a vast variety

of questions on the topic, including those considering

adaptive learning cycles. In particular, the question

of adaptive microlearning is addressed by Gherman

et al. (Gherman et al., 2021), Sun et al. (Sun et al.,

2016).

Among the tools for implementation of adap-

tive learning in HEIs learning management system

is noted. One of such systems that gained popular-

ity in universities due to its ﬂexibility and free dis-

tribution is Moodle LMS. That makes the question

of implementation of adaptivity elements in Moodle

relevant and many researchers have paid attention to

this topic in recent decade among whom there are

Surjono (Surjono, 2011), Caputi and Garrido (Ca-

puti and Garrido, 2015), Kukhartsev et al. (Kukhart-

sev et al., 2018), Gaviria et al. (Gaviria et al.,

2009), Akc¸apınar (Akc¸apınar, 2015), Nikitopoulou

et al. (Nikitopoulou et al., 2017), Jurenoks (Jurenoks,

2017), Rollins (Rollins, 2017).

3 THEORETICAL BACKGROUND

AND PRACTICAL

IMPLEMENTATION

One of promising educational technologies according

to NMC Horizon Report 2018 is adaptive learning –

adaptation of content and choice of means for its im-

plementation according to the needs of educational

process participants to increase the effectiveness of

activities. Personalization of the approach to learn-

ing cannot be made without understanding educa-

tional technologies implemented in HEIs. Many HEIs

use e-learning systems for provision of distant learn-

ing, blended learning and independent study. Moodle

LMS is a widely used e-learning system as it is open

source and can be adapted to HEIs’ needs. Moodle

LMS is used at Borys Grinchenko Kyiv University,

therefore it is chosen by the authors as a platform for

innovation implementation.

Adaptive learning is a technique that involves pe-

riodically gathering information about students’ level

of knowledge and learning styles, and conﬁguring

Stages of Adaptive Learning Implementation by Means of Moodle LMS

477

learning resources, tasks, and assessment accordingly

(Edmonds, 1987). Thus, e-learning developers are

challenged to take into account the needs of users to

ensure better learning outcomes. The main factors

that inﬂuence the quality of ELCs according to the

survey are the choice of the diversity of presentation

formats, the tasks and tests complexity, the level of

complexity of the course and the sequence of study of

the material (ﬁgure 1). Implementing adaptive learn-

ing can ensure that these needs are met.

Adaptive design of the e-learning platform also

plays an important role under the current conditions

as students use various devices among which are PC,

tablets and smartphones. Moodle LMS is able to pro-

vide required adaptivity of the design.

According to the the Deming or Plan-Do-Check-

Act (PDCA) cycle for higher education (Gueorguiev,

2006) (ﬁgure 2) it is vital to analyse the factors that

inﬂuence the effectiveness of the educational process,

current situation in a HEI and tendencies in educa-

tional technologies on the international level prior to

integration of any innovative tools and methodologies

into the educational process.

All adaptive learning systems follow a similar

PDCA architecture (ﬁgure 2) that gathers data from

the learner and then uses that data to estimate the

learner’s progress, recommend learning activities, and

provide tailored feedback. The adaptive learning al-

gorithm is designed to make such decisions by refer-

ring to a learning plan (the knowledge to be learned),

a student model of learners’ background character-

istics (knowledge level, learning style, individual

needs, etc.), and a task model that speciﬁes features

of the learning activities (such as questions, tasks,

quizzes, dynamic hints, feedback, prompts, and rec-

ommendations) (Lee and Park, 2008).

The goal of responsive e-learning is to provide stu-

dents with the tools they need to absorb the material

they need to the best of their ability. Requirements

for tailored educational materials are tailored to the

goals of the educational process (van Velsen et al.,

2008). Consideration should be given to students’

prior knowledge as well as differences in learning

styles and individual needs. Among the objectives of

the appropriate learning system is to ensure the same

efﬁciency of the educational process for students who

are not familiar with the ﬁeld of knowledge as those

who have previous academic experience.

Adaptive learning tools are technologies that can

be synchronised with the learning process and, based

on machine learning technologies, can adapt to the

progress of each student and independently adjust the

learning content in real time. Adaptability can be

manifested in one or more elements of technology:

content, evaluation, consistency.

Content adaptation is the presentation of educa-

tional materials in a form that will allow the student

to navigate his own educational trajectory. Content

adaptation includes contextual clues, content branch-

ing, material partitioning, volume selection and ma-

terial format. For example, when giving a lecture

online, you can use the question system to assess

whether a student has mastered the relevant material

at a sufﬁcient level, and if necessary, return it to cer-

tain information again, or allow them to skip some of

the material as previously learned.

Sequence adaptation involves the automatic selec-

tion of relevant content, the level of complexity and

the order of study of the material based on the analysis

of the results of its educational activities. Adaptive-

sequence tools are the most complex, because they

analyse the data and compile and adjust the student’s

individual trajectory in real time.

Data collection is not limited to accumulating in-

formation about correct and incorrect answers. Adap-

tive programs take into account many different indi-

cators to make a personal learning trajectory:

• correct answer;

• number of attempts;

• use of additional tools or resources;

• interests of the student (for example, what re-

sources the student prefers).

The adaptive sequence is implemented in three

stages: to collect the data, to analyse it and to adapt

the sequence of the material submission to the needs

of the particular student. The main advantage of

a learning tool with adaptive consistency is to ﬁll

knowledge gaps. If a student has missed a class or has

not yet mastered the topic and now this impedes the

learning of new material, the sequence of tasks and

topics changes. So the student ﬁrst ﬁlls in the knowl-

edge gap and then moves on to the current topic.

The adaptation of the assessment assumes that

each subsequent question depends on the answer

given by the student to the previous one. The better

it is, the more difﬁcult the tasks are, and vice versa –

if it is too difﬁcult for the student, the questions will

be easier until the material is mastered. Adaptive as-

sessment tools are commonly used for periodic moni-

toring every few months. Students receive a relatively

voluminous test assignment, the purpose of which is

to test how well they have mastered the material per

module, semester, etc. After monitoring, data is anal-

ysed, and the results are used to further adjust the

program and the individual learning trajectory of each

student. Therefore, one of the advantages of adaptive

tests is detailed statistics.

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478

Figure 1: Factors inﬂuencing the quality of ELC (survey results).

Figure 2: PCDA cycle for innovation implementation in HEI.

The adaptive learning implementation process can

be classiﬁed into the following stages: initial stage,

pre-test stage, path generation stage, learning stage,

post-test stage (Huang and Shiu, 2012).

Initial stage. Learners login to the e-learning sys-

tem and select a course to study. In Borys Grinchenko

Kyiv University this stage is organised by integration

of educational programmes in the e-learning system.

Every student is enrolled in all the courses of their

educational program and each course is bound to a

speciﬁc semester(s). For this stage such activities as

Subcourse, Assignment and Page are used to provide

students with information on all ELCs (disciplines),

their forms of control in each semester and students’

progress in each discipline and in general (ﬁgure 3).

Pre-test stage. Learners are provided with a pre-

test and/or a survey to determine their level of knowl-

edge, learning styles, intended learning outcomes.

The testing results become the basis for learning path

generation. At this stage gaps in students’ knowledge

are identiﬁed as well. In Moodle the stage can be im-

plemented by such activities as Quiz, Survey, Ques-

tionnaire. The choice of the activity depends on its

aim.

Thus, the activity Survey is pre-populated with

questions and a teacher cannot create own questions

there. The Attitudes to Thinking and Learning Sur-

vey (ALLTS) Survey resource allows you to assess

the level of collaboration of a learning community

(group). This will help determine the optimum bal-

ance of individual and group work in the course.

The Questionnaire module is aimed at collecting

data from users. Unlike the Survey activity, it allows

teachers to create a wide range of questions and mod-

ify them to the needs of the course. However, the pur-

pose of these two modules is similar – to gather in-

formation and not to test or assess students. It can be

used to determine learning styles for further selection

and gradation of materials.

The Quiz resource lets you rank students’ level of

knowledge through standard testing. With the Over-

all feedback setting (ﬁgure 4), boundaries are set for

each level of knowledge and the student receives a

corresponding feedback. For example, students with

a score of 80% and above may be offered an advanced

course, with results of 60-80% a standard course, and

Stages of Adaptive Learning Implementation by Means of Moodle LMS

479

Figure 3: Example of disciplines arrangement in the educational program “E-learning management in the intercultural space”.

a basic course for those who scored less than 60%

Path generation stage. At this stage a student has

to receive an individual learning path based on the re-

sults of the pre-test stage. Moodle LMS does not con-

tain automated mechanisms to provide this stage be-

ing a learning management system, but not an adap-

tive learning platform. Therefore, alternative ways of

the stage implementation must be found to provide

students with their own learning trajectory.

Topics can be used to separate materials for stu-

dents with different knowledge levels. By changing

course layout to the section per page format and plac-

ing all materials of the corresponding level into the

relevant section (ﬁgure 5) we simplify navigation in

the course.

Another option is to use the Checklist module to

form lists of themes or tasks that have to be fulﬁlled

to ﬁnish the course. The items can be added to the

list from the current section, from the whole course

or manually created. The status of the items in the list

is updated automatically as students complete the re-

lated activity. A checklist can be edited so that only

activities or resources that contain tasks were listed as

obligatory ones. Thus, a teacher can customise check-

lists to the needs of a group and create them either

for the whole course or for each module/theme sepa-

rately. If different items can be completed by students

with different levels of knowledge or learning styles,

a teacher can set up an amount of items to be checked

off to complete the Checklist (ﬁgure 6).

An individual learning path in Moodle LMS can

be provided to a student as a list of to-do items to

complete the course based on the pre-test results. It

might require individual teachers recommendations

or be partly uniﬁed for a speciﬁed level of knowledge.

Learning stage. At this stage recommended ma-

terial is identiﬁed and a student deals with the learn-

ing content of a course. To provide ﬂexibility of the

content microlearning is used. The material separated

into small logically complete parts can be easily used

in any activity or resource used at the learning stage.

Microlearning has a variety of advantages including

better implementation of students’ needs, wider diver-

sity of materials for different knowledge levels, lower

time expenses for material consumption, a possibility

for knowledge gaps ﬁlling, increased motivation etc.

(Varchenko-Trotsenko et al., 2019). Such materials

are also easier renewable when needed as a teacher

is able to change it by small pieces. According to

the results of the survey they also correspond better to

students’ needs who indicated materials divided into

micro modules, short videos, visual materials and pre-

sentations as the most effective formats for theoretical

materials (ﬁgure 7).

Among the activities used at the learning stage the

most popular are Assignment, Book, Chat, File, Fo-

rum, Glossary, Lesson, Page, Quiz, Wiki and Work-

shop. In our work we are going to pay attention to

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Figure 4: Overall feedback setting for introductory testing in ELC.

the activities which are the most beneﬁcial from the

perspective of adaptivity implementation, i.e. Lesson

and Quiz modules.

A teacher can use Lesson activity to provide con-

sequent theoretical materials (that is a set of pages

with lecture materials) or to organise learning activi-

ties where different trajectories of a lesson are offered

using transactions between pages, adding extra clus-

ters and pages with questions (multichoice, matching,

short answer questions, etc.) (ﬁgure 8). Depending

on the given answer and the way a teacher uses Les-

son activity, a student can either go to the next page or

return to the previous page or be directed in another

way that corresponds to the student’s needs.

If it is required, a Lesson can be assessed, de-

signed in different difﬁculty levels, and can be a part

of adaptive assessment.

A type of the lesson can be chosen by a lecturer

depending on the educational needs and the way it

will be used – for support of in-class activities or for

self study.

One of the activities through which an assessment

Stages of Adaptive Learning Implementation by Means of Moodle LMS

481

Figure 5: Arrangement of learning materials according to the level with the help of the topic sections.

Figure 6: Activity completion settings in Checklist module.

can be organised is Quiz, its ﬁlling and display for

students depends on the setting of different parame-

ters. We can change the Question behaviour param-

eter to select the best student passing test mode. Se-

lecting Adaptive mode and Adaptive mode (no penal-

ties) allows students to make multiple attempts before

moving on to the next question. That is, if students

are unsure of their answers, they can check it directly

during the attempt and change their answers, but the

repeated answer is indicated by taking into account

the appropriate penalty indicated by the teacher in the

parameters of the question (Fig. 9).

Penalties are established for each question sepa-

rately in the Multiple tries section of editing a ques-

tion. Hints are added in the same section. Both op-

tions are used only in the correspondent modes which

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482

Figure 7: Effective ways of theoretical material delivery (survey results).

Figure 8: Logical scheme of an adaptive lesson.

allows teachers to use the same question in tests with

different modes. For example, a test for formative as-

sessment might have multiple tries and hints, whereas

for a summative assessment test Deferred feedback

mode can be chosen.

In Interactive with multiple tries mode after sub-

mitting one answer and reading the feedback, the stu-

dent must click the “Try Again” button before at-

tempting a new answer.

The teacher can provide students with tips to help

answer questions. Once a student has correctly an-

swered the question, he can no longer change his an-

swer. After a student has made too many mistakes

with the question, the answer is evaluated as incorrect

(or partially correct) and receives feedback. A student

may have different feedback after each attempt. The

number of attempts a student receives is the number

of tips in determining the question plus one. The use

of this mode gives a student an opportunity to deter-

mine whether to use the tips or not and adjust their

assessment.

Deferred feedback or Immediate feedback mode

with Certainty-based marking (CBM) are the modes

where a student not only answers the question but

also indicates how conﬁdent they are: not very sure

(less than 67%); average conﬁdence (between 67%

and 80%) or very conﬁdent (more than 80%).

When the answer is assessed, both accuracy and

the level of certainty are considered by the sys-

tem. For example, if the answer is correct, but only

Stages of Adaptive Learning Implementation by Means of Moodle LMS

483

Figure 9: A test with penalties in the Interactive with multiple tries or Adaptive mode.

guessed, the score is adjusted from 1 to 0.33. If the

answer is incorrect and high level of conﬁdence was

indicated, the score can be from 0 to -2 points (ﬁg-

ure 10).

Using this mode provides the following beneﬁts

for students:

• they have to evaluate the correctness of our own

answer;

• encouraging a solution to a problem, as opposed

to answering questions immediately;

• adds conﬁdence in your own knowledge;

• get a more objective rating.

To encourage students to ﬁll the gaps in their

knowledge, Combined feedback option can be used in

questions for Quiz. For each incorrect or partly cor-

rect answer a teacher can indicate a related topic to

study or/and give links to the corresponding activities

and resources in the course.

Post-test stage. After the learner has ﬁnished the

entire learning path, it has to be checked whether

the learning process was successful or not and needs

some changes to be made. The summative assessment

can be arranged in the form of a test, a project (indi-

vidual or group), a speech etc. Thus, such activities

as Quiz, Workshop, Wiki or Assignment are prevail-

ing at this stage. The results of summative assessment

must be analysed to ﬁnd out strengths and weaknesses

of the e-learning course and plan improvements for its

next PDCA cycle. It is also essential to get feedback

from students on the course to see whether there was

enough material on each topic and whether it was un-

derstandable, diverse and easy to use. The feedback

collection can be arranged with activities Question-

naire, Feedback, Forum.

Feedback lets you create surveys with different

types of questions, including multiple choice, yes /

no, or text input to determine the level of satisfaction

in the learning process, gaps in the course arrange-

ment, etc. This resource allows you to view statistics

in the form of diagrams, tables, and download them

for further processing.

4 CONCLUSION

The survey of students carried out at Borys

Grinchenko Kyiv University indicated that there is a

need for personalisation of the learning environment

and individual learning path arrangement. Adaptive

learning is an educational approach that can meet the

needs.

AET 2021 - Myroslav I. Zhaldak Symposium on Advances in Educational Technology

484

Figure 10: An example of answered question in a mode with CBM.

Analysis of Moodle LMS activities and resources

presented in the research paper has shown that adap-

tive learning can be implemented in HEIs with the

help of already used learning management systems.

Each stage of adaptive learning implementation (ini-

tial stage, pre-test stage, path generation stage, learn-

ing stage and post-test stage) is possible to be ar-

ranged by means of Moodle LMS. Microlearning

plays an essential role in adaptive learning implemen-

tation as learning materials divided into small parts

are easier to meet individual educational needs of a

learner, to navigate in an ELC and to update when re-

quired.

The paper is dedicated mostly to technological

challenges of adaptive learning implementation. Fur-

ther research of the topic might include pedagogi-

cal and management-related issues such as learning

materials modiﬁcation, teacher training, adaptive e-

learning courses and educational programs correla-

tion, etc.

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