Spaced Learning Solution in the e-Learning Environment

Janis Kapenieks senior

and Janis Kapenieks junior

Distance Education Study Centre, Riga Technical University, Kronvalda bul. 1, Riga, Latvia

Keywords: e-Learning, Spaced Learning, Learning Management System, Personalization.

Abstract: The objective of the transdisciplinary research is to find the most effective solutions for spaced learning in

the e-learning environment and to create an information system for the implementation of spaced e-learning

in the learning management system (LMS). The method is based on short breaks (spaces) between repetitions

acquiring the learning content in the e-environment. During breaks, students disconnect from the learning by

watching engaging alternative content. During the research, the first prototype of spaced e-learning solution

was implemented in the LMS OpenEdX and brought into action with bachelor level students. Experience

from the first prototype showed the effectiveness of the method and advantages of personalization of the

content of spaces. Research showed the need to take personal interests into account for spaces’ content

personalization to motivate students to use the method in the learning process. In the light of the data obtained,

an information system (IS) for the personalization of the method was developed for use in several kinds of

LMS.

1 INTRODUCTION

The contemporary paradigm of education has

demonstrated serious changes during the last decades.

Increase of the importance of e-learning and blended

learning takes place during COVID-19 crisis. These

changes have caused serious technological

developments, challenges for motivation, and

improvements of cognitive solutions by new

pedagogical approaches to e-learning. The creation of

the “charisma” of learning management systems

(LMS) has become one of the most pressing needs for

e-learning designers, leading to the search for new

learning methods in digitalized education. One of

such methods is spaced e-learning, based on the use

of repetition effect.

Spaced learning is a pedagogical approach based

on the positive learning effect from spaced repeating

the learning content in different forms several times -

as text, video, multimedia, exercises, educational

games, and others. The first research on the spacing

effect was published by psychologist H. Ebbinghaus

(Ebbinghaus, 1885). Delays between repetition in

Ebbinghaus’s research increased as follows: the first

repetition was included after 1 hour, the second after

https://orcid.org/0000-0001-6244-2355

https://orcid.org/0000-0001-6067-0688

5 hours, the third after 1 day, the fourth after 3 days,

and so on, the tenth repetition after eight months.

Ebbinghaus found the spaced approach more

effective than massive repetition of the learning

content (Ebbinghaus, 1885). Many studies support

these findings (Pashler, Rohrer, Cepeda, & Carpenter,

2007; Quinn, 2011; Carpenter, Cepeda, Rohrer,

Kang, & Pashler, 2012; Kang, 2016). Typically, the

spacing approach takes a long time for learning.

Research showed a strong relationship between

duration of spaces and knowledge retention time

(Cepeda, Vul, Rohrer, Wixted, & Pashler, 2008).

Suspending the spaced learning study time by

shortening the duration of breaks becomes as

essential challenge for spaced e-learning design. The

latest research showed the effectiveness of short

spaces (Kelley & Whatson, 2013). It is important that

during spaces learners are offered with alternative

engaging content allowing to disconnect from the

study subject.

Implementation of the spaced learning e-learning

practice has created new challenges for learning

methods and the design of LMS.

senior, J. and Kapenieks junior, J.

Spaced Learning Solution in the e-Learning Environment.

DOI: 10.5220/0010403901690176

In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) - Volume 2, pages 169-176

ISBN: 978-989-758-502-9

169

2 EFFECTIVENESS OF THE

SPACED LEARNING

2.1 Neuroscience Considerations on the

Spaced Learning

Neuroscience findings support the spaced learning

methodology. Research shows interconnections

between the acquisition of new information and the

activation of several brain systems. Initially it was

considered that the dopamine system is a transmitter

for good feelings and euphoria, because the use of

drugs causes an increase of the dopamine level in

particular segments of the brain. In general, the

dopamine system is a group of nerve cells, most of

which are located deep in the midbrain. The

neurotransmitter dopamine is sent across the brain.

Following research shows that dopamine’s presence

in the brain cells is essential for the regulation of

movement, attention, motivation and emotional

responses (Roediger & Karpicke, 2006), including

learning and memory (Poldrack, 2010). Dopamine

secretions help to improve the function of the

working memory, because the main part of the brain

– the prefrontal cortex is associated with higher-

ordered thinking. The dopamine level in the

prefrontal cortex is extremely delicate and slight

increases or decreases of the level comparing to the

normal level may cause a significant impact on the

memory (Roffman et al., 2016).

2.2 Cognitive Sciences on the Spaced

Learning

The positive cognitive effect of spaced e-learning is

supported by cognitive neuroscience findings.

Repetition of the stimuli, separated by timed spaces

without stimuli can initiate long-term potentiation

and long-term memory encoding in the human brain

(Kelley & Whatson, 2013). Long-term potentiation in

neuroscience is defined as persistent strengthening of

synapses, based on recent patterns of activity. These

are patterns of synaptic activity that produce a long-

lasting increase in signal transmission between two

neurons. Long-term potentiation is widely considered

as one of the major cellular mechanisms that underlie

learning and memory (Cooke & Bliss, 2006).

According to the Atkinson-Siffrin memory model

(Atkinson & Shiffrin, 1968), at the beginning of the

learning process information is processed by the

sensory memory – something is perceived through

senses, as sight, hearing, sensations. Then it is

transferred to the short-term memory or forgotten,

depending on how much attention this information is

given. From the short-term memory, information is

encoded to long-term memory or forgotten, if the

encoding is not carried out. Continued rehearsal of

information strengthens the memory trace and

prevents from forgetting.

P. Smolen et.al. (Smolen, Zhang, & Byrne, 2016)

summarizes traditional cognitive theories – encoding

variability theory, study-phase retrieval theory, and

deficient-processing theory in the context of spaced

learning. Encoding variability theory interprets the

effect of spacing so that a variety of information,

stored with spaced presentations, results in a bigger

variety of information retrieval routes, considering,

that, as the lag between repetitions increases, the

memorial representations approach obtains more

independence (Bray, Robbins, & Witcher, 1976).

Contextual-variability theory explains the spacing

effect as a repeated representation in the different

contexts additionally to the representations of the

information item itself in the learner’s memory. The

effectiveness of the spacing effect is realized as the

function of the number of contexts, available in the

memory trace of spaced repetitions (Verkoeijen,

2005). Deficient processing theory predicts longer

time intervals between learning sessions, deactivating

of the item in the memory of the learner. Learners pay

more attention to spaced repetitions of an item

because the content item is not as active in memory

(Smolen et al., 2016). In the consolidation theory,

creative thinking and reflection on the previous

information are essential. Consolidation theory is

closely linked to the constructivist epistemological

approach in the spaced face-to-face learning as well,

as to the spaced e-learning methodology (Verkoeijen,

2005). Most of the theories support the viewpoint that

effectiveness of the spaced learning is related to the

duration of spaces between repeated content items.

Several experiments support this consideration

(Schimanke, Ribbers, Mertens, & Vornberger, 2016;

Kerfoot et al., 2010; Cepeda et al., 2008).

Experiments of Kelley and Whatson (Kelley &

Whatson, 2013) showed that short-spaced learning

with spaces of 10 to 20 minutes is optimal for forming

long-term memory. This approach is the most

appropriate for e-learning, so it is used in our study.

3 CONTENT OF REPETITIONS

AND SPACES

C. Pappas (Pappas, 2016) emphasizes improvement

of knowledge retention, the involvement of real-

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170

world applications in the learning process, and

reduction of cognitive overload as the most

significant advantages of the spaced e-learning

pedagogical approach. The author proposes the

following five tips for the design of the spaced e-

learning courses: (1) the use of reminders or memory

aids, (2) building knowledge on the previously

learned, encouraging learners to active recall of

knowledge, (4) integration of spaces “disconnecting”

from the learning content into course design and (5)

to find new ways of information delivery. Prasad and

Omer (Prasad & Omer, 2019) found that branched

scenarios, simulations, and game-based designs make

learners willing and active participants in the learning

experience. Branched scenarios include contexts in

real-life situations, forcing intelligent decisions.

Educational games serve for the activation of the

competitive spirit of learners, motivating to active

application of knowledge. Hanson and Brown

(Hanson & Brown, 2020) tested flashcards as an

effective application for spaced e-learning. “These

strategies can be incorporated in microlearning

modules and “pushed” to learners at regular intervals

to promote spaced learning” (Prasad & Omer, 2019).

Melamed (Malamed, 2019) emphasizes the

importance of the diversification of the content

delivery forms and design, as well as content

generated by users.

Most often the spaced e-learning was

implemented in foreign language learning (Seabrook,

Brown, & Solity, 2005; Sobel, Cepeda, & Kapler,

2011) and medical studies (Shaw, Long, Chopra, &

Kerfoot, 2011)., learning content delivery by e-mail

was used for repetitions in most cases. Our research

tested the applicability of the method in STEM and e-

pedagogy course, where it is necessary to acquire a

large amount of theory or instructions. The content of

spaces was placed in the e-learning environment. A

methodology and information system for spaced

learning in the business course also has been

designed.

4 THE FIRST PROTOTYPE OF

SPACED E-LEARNING

The first prototype of spaced e-learning was

implemented in the OpenEdX LMS, designed for the

massive online open courses approach (MOOC).

There are three essential questions to answer for

successful implementation of the method:

1. What form of repetitions’ content do students

prefer?

2. Do students prefer mandatory or voluntary

breaks?

3. What content of spaces do students prefer?

The content was delivered as text, graphics, video,

simulations, and exercises with feedback.

The content of spaces is voluntary and designed as

engaging material in different forms:

1. "Space" without offered content.

2. "Space" with a YouTube music video

3. "Space" with a YouTube video about attractive

adventures and events

4. "Space" with a YouTube beautiful nature video

5. "Space" with an easy-to-understand additional

course material video

6. "Space" with an easy-to-understand additional

course material with interactivities

7. "Space" with an interactive educational game or

interactive model

8. "Space" with a talking head on an easy-to-

understand attractive topic

9. "Space" with an optional additional material on

the subject of the course

10. "Space" with videos on topics identified in the

student survey

The prototype was implemented in two blended

learning master-level courses for Digital Humanities

program students during the first and second

semesters 2018/2019 and 2019.2020. The bachelor

level background for most students was foreign

language studies, STEM or social sciences. Spaced e-

learning was realized in the MOOC type e-learning

environment OpenEdX design of Natural Science

modelling (14 and 20 students) and E-pedagogy (13

and 17 students) courses. The blended learning

process included face-to-face classes for introduction

to the topic and discussions, and acquirement of

theory in LMS OpenEdX. During the second year of

the research learning was heavily affected by the

COVID-19 crisis and face-to-face classes replaced by

the ZOOM webinars.

5 STUDENTS’ OPINIONS

After the first study year, students were asked to

evaluate spaced e-learning solution. Most of students

- 73 % preferred voluntary spaces.

The diagram in Figure 2 shows that only 73% of

the students take the choose to discontinue learning

by the recommended built-in content of the spaces.

Spaces must “disconnect” learners from learning

content for short time, but prevent them from

distracting from the acquisition of the learning

content. 64 % of the surveyed students admitted that

Spaced Learning Solution in the e-Learning Environment

171

Figure 2: First action research cycle, students' answers

about the use of spaces.

sometimes they stopped learning after watching

content of space.

All second year students admitted that they would

be engaged to watch personalized material, suitable

to their interests.

The lecturer’s experience showed the high

importance of face-to-face interaction with students

for the explanation of the advantages of the spaced

learning method and encouraging them to use it.

Metacognitive skills of students allow to increase

effectiveness of the method. After successful

finishing of the course, students expressed

controversial opinions on the method – ranging from

“It's good to have a space!” to a desire to learn the

material without breaks. However, the vast majority

rated the learning method positively.

The small number of students does not allow to

consider the results of the survey as statistically

significant, however, it helps to draw conclusions

about the principles of using the method in a

particular group. Individual discussions with students

supports conclusions as well.

6 CONCEPTUAL DESIGN

PERSONALIZED SPACED

E-LEARNING LMS

6.1 Design of the Information System

The design of the personalized LMS is based on the

experience and students’opinions from the first

implementation of the prototype.

Figure 3 shows a flowchart of the information

system for the implementation of personalized spaced

e-Learning. Content of spaces is related to the

interests of each learner.

The system includes

• A dataset of user profiles, described by

metadata, characterizing personal interests of

the learners.

• A dataset of the content of spaces, characterized

by metadata.

Assessment of learners’ interests is carried out by

a poll at the beginning of the course and users’

behavior data from the e-learning environment.

Personal interest’s metadata are added to the profile

of each learner and stored in the database.

The content of the spaces is created by the

educator and learners and uploaded to the database.

Metadata, characterizing the content of every space,

is added to the spaces’ content database.

Figure 3: Flowchart of information system for

implementation of personalized spaced e-Learning.

The information system provides the most

appropriate choice of content of spaces during the

learning process in the e-learning environment for

each learner personally. The information system

analyses user behavior data of every learner while

browsing spaces, to improve the choice of the content

for the space by personalization or spaced e-learning.

6.2 Linking Users’ Interests with the

Content of Spaces

It is essential that the spaced learning information

system can be used in various LMS. Because of this,

an application for the personalization of the content

of spaces was designed using Google Drive tools.

The following stages of personalization were

implemented:

1. Assessment of the students’ interests and

uploading this data in the user profiles database,

2. Creation of engaging content of spaces such as

YouTube video, Prezi presentation or other,

3. Adding metadata to the content of spaces and

uploading this data to the of Spaces’ content

database,

4. Identification of the most relevant spaces’

content to the interests of each student.

5. Creation of a database for implementation of

personalized spaces’ content in the LMS.

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Google form is used for assessment of students’

interests. Interests are clustered as follows:

1. Educational interests – information

technologies, humanities, history, geography,

social sciences, ecology, arts, research

activities, engineering.

2. Professional interests: technologies, marketing,

service, organization of events.

3. Entertainment: fiction, science fiction,

traveling, participation in mass events, social

networking, driving, and tuning, hunting, fish-

catching, sports, computer gaming.

Each student rates the level of every interest on

the levels one to five points (1- not interested, 5- very

interested). The students are identified in accordance

with ID number. At the beginning of the course,

students are asked to create a five to ten minutes long

engaging material – video, Prezi presentation, Google

slides or other – corresponding to their individual

interests. An attractive form of the design of the

content is strongly recommended. In some cases, this

material can be related to the topic of the course. Each

students shares created material with others. Then

each student fills out Google form, providing a link to

it, describing the content and adding metadata.

Metadata consists of the same keywords and rating

levels as the characterization of the student’s

interests. The student’s ID, evaluation of interests,

links to engaging material, and keywords are

uploaded to the Google spreadsheet for further

processing and creation of the content personalization

database.

The relevance of a particular material (identified

by i) to the interests of each student is characterized

by coefficient C

mi1

mi2

+…+I

min

Where

is a self-evaluation level of the first

interest (1 to 5) by the student,

mi1

is the first

keywords “rating” level (rated from 1 to 5) of the

engaging material, identified with i. If each student

created an engaging material, the maximal value of i

corresponds to the number of students. If the method

is used in other courses, groups, or the next year – the

number of engaging materials increases, making the

choice richer.

An example of the calculated coefficients C

for

materials created by 13 students for the student Jānis

Bērziņš (virtual personality) is shown in Table 1. The

coefficients are listed in descending order. The

engaging YouTube video or Prezi presentations will

be placed in the course as content of spaces for the

student, starting from the second row.

For example, the course “Business

Fundamentals” includes 8 spaces, and the student

Jānis Bērziņš will watch the materials listed on rows

2 to 9.

Implementation of the spaces’ content in the

course is provided by the personalization function of

LMS. In our research, it is designed in the Moodle e-

learning environment.

Table 1: Example of the calculated coefficients C

for

materials created by 13 students for the student Jānis

Bērziņš (virtual personality) in the Google spreadsheet.

6.3 Spaces’ Content Personalization in

the Moodle LMS

In base use case Moodle LMS generates static content

from data stored in Moodle database. Content is

generated and presented to Moodle user upon content

page request. This content generation approach

provides limited options for content personalization.

The following personalization are approaches

currently available on Moodle LMS:

1) Moodle plugin FilterCodes (Moodle, 2020a)

provides customization and personalisation

options for Moodle LMS site and course content

using predefined text tags. This allows Moodle

content creator insert specific values from user

profile data such as student ID, firstname,

lastname, country, etc. This provides learning

content personalization limited to personalized

welcome messages.

2) Second approach provides more extended

personalization options. This is achieved

through "Restrict Access” feature. This feature

enables content creator to restrict access to

Moodle activities, content according to

conditions specified. The conditions can be date,

student group id, grade, etc. (Moodle, 2020b).

This feature allows creation of multiple

content/activities assigned to specific student

Spaced Learning Solution in the e-Learning Environment

173

groups and/or based on student’s level of

knowledge.

The methods mentioned above provide limited

personalization options for spaced learning approach.

"Restrict Access” feature could provide required

functionality but requires extra manual work and

scalability options are limited for large number of

students.

Moodle LMS FilterCodes plugin provides limited

personalization options but we will use it for spaced

learning content personalization tool.

Moodle LMS personalized spaced learning

content injection tool has three main components:

1) EMBED TAG in Moodle LMS content,

2) HTML Content generation script,

3) External data repository.

The first component of spaced learning content

injection tool is HTML EMBED TAG (W3schools,

2020). The EMBED TAG is included in Moodle

content page as placeholder for personalized content

and it contains all the information required for the

content generation. The current setup (spaced

learning content) requires two variables: student ID

and section ID. Any other student specific variables

can be included such as student ID, firstname,

lastname, group ID, etc. EMBED TAG calls a PHP

script located on Moodle server. This generates

content using parameters included and embeds it in

the content area enclosed by the TAG. Technically

embedded content is created externally in injected

into Moodle content.

A preinstalled Moodle plugin FilterCodes

(Moodle, 2020a) is required for the EMBED TAG

functionality. As described before FilterCodes plugin

is the first step in content personalization enabling

insertion of plain text tags in Moodle content site. The

inserted plain text tags are translated into requested

values such as students firstname, lastname, Id, and

other data from student’s profile. EMBED TAG is

inserted in Moodle learning content (by switching

Wysiwyg editor into HTML mode) in format

displayed below:

src="https://artss.mii.lv/local/customc

ontent.php?firstname={firstname}&lastna

me={lastname}&userid={userid}&spaceid=3

" width="900 " height="500">

All the values included in the TAG are sent to the

content generation script (firstname, lastname, user

id, space id). Width and height variables provide extra

formatting options. Personalized content generation

will be based on these values only. The content

generation script will not be able to access any other

information from Moodle site.

This is the script written in PHP7 (PHP, 2020)

generates personalized content in HTML based on

parameters included in the EMBED TAG. Any other

programming language or online service can be used

for this task. The only requirement is the

script/service has to be able accept variables from the

EMBED TAG and return content in HTML format.

The content generation script performs the following

tasks:

1) Content generation script receives variables

from the EMBED TAG and validates them.

2) It requests personalized data from Google

Sheets using PHP library Google PHP API

Client Services (“Github,” 2020). Data request

includes an authorization request and a data

query after.

3) It generates HTML content using data from

Google Sheets and returns it to EMBED TAG

located in learning content.

All personalized data is stored and managed by an

external data repository. We use Google Sheets as it

provides flexible environment for testing different

algorithms using formulas and data analysis tools.

Google Sheets lists all the locations of spaced

learning personalized content and assigns it to a

specific student ID using a set of Google Sheets

functions. System can be adapted to use a database

instead of Google Sheets for complete integration in

Moodle LMS.

7 EVALUATION OF THE SPACED

E-LEARNING OUTCOMES

Two essential sections of the learning outcomes must

be evaluated to understand the success of the

personalization of the spaced learning method in the

e-course:

1. Fitness of the course to the individual needs of a

particular student.

2. Fitness of the course design to its objectives.

Most appropriate to for characterizing adequacy

of the course to the learners’ needs and course

objectives is TELECI method, created by the Riga

Technical University (Kapenieks et al., 2020).

Designed in accordance with spaced e-learning

method, a graphical representation named

“Telecides” is the appropriate way for evaluation of

the course effectiveness in the e-learning

environment. The authors split each unit into small

subunits, including indicative multiple-choice

questions at the beginning of the subunit and similar

diagnostic questions at the end of each subunit. The

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174

content of the subunit was available only after

submitting the appropriate indicative test. The design

of the subunit of the course in the e-learning

environment is shown in Figure 4.

Analysis of the results enabled recording,

tracking, and visualizing the students’ activity and

their answers to the questions after each subunit.

Analysis is conducted by the calculation of the

probability from retrieved user behavior data, as

shown in Table 2.

Figure 4: Structure of subunit for implementation of the

TELECI method in the LMS.

Table 2: Analysis from the calculation of the probability

from retrieved user behavior data.

Experimental

value

Answer to

indicative

question

Answer to final

question

N-P incorrect correct

P-P correct correct

N-N incorrect incorrect

P-N correct incorrect

The authors of the method provided the following

“ideal” experimental average probability values for

the characterization of the course, if each multiple-

choice question has three answers (Kapenieks et al.,

2020):

1. Too complicated: N-P =0.222; P-P=0.111;

N-N=0.444; P-N=0.222

2. Too easy: N-P =0; P-P=1; N-N=0; P-N=0

3. Perfectly matching to learners’ needs:

N-P =0.667; P-P=0.333; N-N=0.444; P-N=0

The method allows the data to be interpreted

graphically (Figure 5).

Figure 5: Graphical interpretation of “telecides” for

evaluating course content and design (Kapenieks et al.,

2020), author’s additions.

8 CONCLUSIONS

With the growing importance of online learning, the

introduction of pedagogical findings in the e-learning

environment is becoming increasingly important.

Neuroscience and cognitive sciences findings show

spaced e-learning method as an effective approach for

improvement of knowledge acquisition. After

implementation of the method in the LMS most

students rated spacing of the content positively. The

effectiveness of the method depends on the duration

of spaces. Short spaces – 10 to 20 minutes between

repetitions of the learning content is implemented in

e-learning to improve the acquisition of the subject

matter. Metacognitive skills and face-to-face

communication with the lecturer are essential in the

spaced e-learning practice. Learning content will be

split into short units and repeated two or three times

in different forms – text, video, simulations. The way

the content is presented should be as attractive as

possible.

It is recommended to adapt the content to the

individual interests of each student. If students create

interesting content themselves, it is engaging for

other students with similar interests.

An information system for the implementation of

personalized spaced e-learning approach in the LMS

can substantially facilitate the use of the method in e-

courses. Google applications as a component of such

an information system enable using the method in

different e-learning environments.

TELECI method, implemented in the LMS, is

powerful approach for evaluation of the fitness of e-

course content and design to the needs of learner to

reach learning objectives. TELECI method is used for

testing of effectiveness of spaced learning in the e-

courses.

Proposed information system could serve as the

tool for creation of the adaptive learning content as

well. Such method for designing of adaptive learning

courses is the topic for future research.

This work has been supported by the European

Regional Development Fund within the Activity

1.1.1.2 “Post-doctoral Research Aid” of the Specific

Aid Objective 1.1.1 “To increase the research and

innovative capacity of scientific institutions of Latvia

and the ability to attract external financing, investing

in human resources and infrastructure” of the

Operational Programme “Growth and Employment”

(No.1.1.1.2/VIAA/1/16/156).

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175

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