Mapping and Identifying Features of e-Learning Technology through

Indexes and Metrics

Elias Gounopoulos

, Stavros Valsamidis

, Ioannis Kazanidis

and Sotirios Kontogiannis

Department of Business Administration, TEI of West Macedonia,

6th National Road Kozani Grevena, 51100 Grevena, Greece

Department of Accounting and Finance, TEI of East Macedonia and Thrace, Agios Loukas, 65404 Kavala, Greece

Department of Mathematics, University of Ioannina, P.O. Box 1186, 54110, Ioannina, Greece

Keywords: e-Learning, Reach, Richness, Information Density, Indexes and Metrics.

Abstract: People' s educational needs and requirements change. At the same time, educational technologies and tools

also evolve. Therefore, contemporary educational methods are obliged to adapt to both. E-learning is the

mode of learning which serves the former while exploits the latter. As e-learning capabilities are moving

into the third decade of their implementation (Kulik et al., 1990), the necessity of thorough assessment is

imminent. Moreover, the adoption to e-learning of assessment features which were successfully used by e-

commerce is also a challenging issue. In this study, a novel approach is presented and put to test. The

approach tries to utilize applicable features of e-commerce technology to e-learning in an effort to measure

usage, user trends and knowledge affiliations. To the extent, some already tested indexes and metrics are

used for the quantification of qualitative features of e-learning. These indexes and metrics contribute to the

assessment of both educational content exposed by the educators and content usage by the learners. In this

paper the identified features are classified. Finally, an experimental case scenario that took place in a Greek

university e-learning platform is presented. From the revealed results there is evidence that these

corresponding to features variables can be used for the measurement of reach, richness and information

density of an e-learning platform system.

1 INTRODUCTION

The Internet is a relatively new technology, which

has been ever changing society since its creation.

The way people live their lives has changed and

made a big adjustment to the Internet’s features and

capabilities. People use the Internet for finding

information, conducting research, communication,

and most importantly for learning. During the last

few decades the world has observed an outstanding

growth of Internet usage. According to Internet

World Stats, on 30th November 2015 there were

more than 3,3 billion Internet users (Internet World

Stats, 2015) and this number is expected to increase

in the next years.

The explosive increase of Internet users has also

led to dramatic shifts in the way of conducting

learning. From our daily lives to traditional learning,

the Internet has profoundly impacted and changed

the way we learn. E-Learning presents enormous

opportunities for both teachers and learners in the

world. While e-Learning has proliferated with the

growth of the Internet, there have been insufficient

empirical research efforts concerning its status and

learner behavior over the Internet. There may be

some valid factors to explain the learner' s adoption

of e-Learning.

Since the current situation and needs of learner

have changed and the modern Internet user is

experienced, fastidious to offered services,

considerate, and capable to be self-addressed, it is

necessary to know well the Internet learner, to

maintain feedback with the learner, which ensures

that in the future, school which uses e-learning will

attract learner participation and increase its efforts

on the Internet (Lingyte et al., 2012). E-learning

need to be interested in every moment of learner's

behaviour: the manner of browsing website, the way

of choosing the educational content, the time and

reasons for closing the page in the process of

learning, the way to load the website of the LMS etc.

Online learners must have the opportunity to submit

Gounopoulos, E., Valsamidis, S., Kazanidis, I. and Kontogiannis, S.

Mapping and Identifying Features of e-Learning Technology through Indexes and Metrics.

DOI: 10.5220/0006399606490655

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 649-655

ISBN: 978-989-758-239-4

649

their questions, suggestions and complaints. There

should be developed an interaction between the

teacher and learner, which has to become

operational as soon as the LMS learner-browser

makes any of conscious action (comments,

complains, asks a query, etc.).

Schools have to think and explain why we have

so much interest in e-learning. These unique

dimensions of e-learning technologies suggest new

possibilities for teaching through a flexible set of

personalized, interactive and rich messages are

available for delivery to different audiences. E-

learning technologies make it possible for teachers

to know much more about learners and to be able to

use this information more effectively than was ever

true in the past. Online teachers can use this new

information to develop new information, enhance

their ability to support learners and segment the

learners into subgroups, each receiving a different

level of teaching according to their needs and

capabilities.

Although e-commerce and e-learning have

mayor differences in objectives, data and techniques

(Romero and Ventura, 2010), there are also some

similarities as web applications (Lee, 2010). Laudon

and Traver (2014) propose eight unique features of

e-commerce technology which we fully adopt and

adapt in this paper.

This paper uses indexes and metrics which firstly

proposed and used the authors in previous studies

with the innovation that they quantify 3 of the

features that Laudon and Traver proposed. The

remainder of this paper is organized into the

following four sections. The second section provides

a brief review of the background theory. The third

section describes the approach of the study. Then,

we present the results of a case study and finally we

discuss the analysis and the implications of our

study with conclusions, followed by presenting

limitations and future research directions.

2 BACKGROUND THEORY

The eight unique features of e-commerce technology

(Laudon and Traver, 2014) would be proved useful

and applicable to e-learning technology and

challenge both traditional and e-learning. The eight

unique features of to e-learning technology are

depicted in figure 1.

2.1 Ubiquity

In traditional learning, a learning place is a physical

Figure 1: The unique features of e-learning technologies.

place we visit in order to learn. For example, a school

typically motivates the learner to go someplace to

learn. E-learning, in contrast, is characterized by its

ubiquity; it is available just about everywhere, at all

times. It liberates the learner from being restricted to a

physical space and makes it possible to learn from the

desktop, at home, at work, or even from car, using

mobile e-learning. The result is called an e-learning

space—a place extended beyond traditional boundaries

and removed from a temporal and geographic location.

From a learner point of view, ubiquity reduces

participation costs—the costs of participating in

learning process. To learn, it is no longer necessary that

we spend time and money traveling to a school. At a

broader level, the ubiquity of e-learning lowers the

cognitive energy required to transact in a learning pace.

Cognitive energy refers to the mental effort required to

complete a task. Humans generally seek to reduce

cognitive energy outlays (Shapiro and Varian, 1999).

2.2 Reach

E-learning technology permits learning independently

of culture, region and nation and is more convenient

and cost-effective than traditional learning. Internet

makes much easier for online teachers within a single

country to achieve a national audience than was ever

possible in the past. The total number of learners an e-

learning can obtain is a measure of its reach (Evans

and Wurster, 1997). In contrast, most traditional

learning is local or regional. In contrast to e-learning

technology, older learning technologies do not easily

cross national boundaries to a global audience.

2.3 Universal Standards

In contrast to most traditional learning technologies

that differ from one nation to the next, the technical

standards for conducting e-learning are universal

standards and they are shared by all nations around the

world. The universal technical standards of the Internet

and e-learning greatly lower costs. Users of the

Internet, both teachers and learners, also experience

e‐

learning

Ubiquity

Reach

Universal

standards

Social

Technology

Personalizatio

Density

Interactivity

Richness

A2E 2017 - Special Session on Analytics in Educational Environments

650

network externalities—benefits that arise because

everyone uses the same technology.

2.4 Social Technology: User Content

Generation and Social Networking

E-learning technologies have evolved to be much more

social by allowing learners to create and share content

with a worldwide community. Social networks offers

new forms of communication. All previous mass media

in modern history, use a broadcast model (one-to-

many) where content is created in a central location by

experts. The Internet and e-learning technologies have

the potential to invert this standard media model by

giving learners the power to create and distribute

content on a large scale, and permit users to program

their own content consumption. The Internet provides a

unique, many-to-many model of mass communication.

2.5 Personalization/Customization

E-learning technologies permit personalization:

teachers can target their learning messages to specific

individuals by adjusting the message to a learner’s

features. Today this is achieved in short time and

followed by an instruction based on the learner’s

profile. The technology also permits customization—

changing the delivered service based on a learner’s

preferences or prior behavior. Given the interactive

nature of e-learning technology, much information

about the learner can be gathered in the learning place

at the moment of teaching. With the increase in

information density, a great deal of information about

the learner’s past results and behavior can be stored and

used by online educators. Personalization and

customization allow educators to precisely identify

learners' needs and adjust their messages accordingly.

2.6 Information Density

E-learning technologies increase information density

both quantity and quality of information available to all

learning participants, namely, learners and teachers. E-

learning technologies reduce also information

collection, storage, processing and communication

costs. At the same time, these technologies increase the

currency, accuracy, and timeliness of information

making information more useful and important than

ever.

2.7 Interactivity

Unlike any of the learning technologies of the twentieth

century, e-learning technologies allow for interactivity,

meaning they enable two-way communication between

teacher and learner and among learners. In contrast, all

of these activities are possible on an e-learning site and

are now commonplace with smart phones and social

networks.

2.8 Richness

Information richness refers to the complexity and

content of a message (Evans and Wurster, 1999). The

richness of e-learning makes them a powerful teaching

environment. Prior to the development of the Web,

there was a trade-off between richness and reach: the

larger the audience reached, the less rich the message.

The Internet has the potential for offering considerably

more information richness than traditional media

because it is interactive and can adjust the message to

individual learners.

This theory that applies to ecommerce can also be

used in e-learning with possible reductions.

3 APPROACH

The analysis of educational data may highlight

useful information and support decision making

regarding it (Romero and Ventura, 2010) . In the

educational environment, it can help teachers and to

analyze the learners’ course activities and usage

information to get a general view of a learner’s

activity.

The higher education student-evaluation data were

analyzed in (Jin et al., 2009). The number of different

pages browsed and total time spent browsing different

pages were also presented in (Hwang et al., 2008). Pahl

and Donnellan (2003) produce session statistics and

discover session patterns. Zoubek and Burda (2009)

analyzed mean values of attributes in data in order to

measure mathematical skills. Gibbs and Rice (2003)

use instructional web server logs to evaluate student

behaviour with the number of visits, origin of visitors,

number of hits, and patterns of use throughout various

time periods.

Feng and Heffernan (2006) present that statistical

analysis is very useful for assessing how many minutes

the student has worked, how many problems s/he has

resolved and his/her correct percentage and his/her

performance level. Teachers prefer pedagogically

oriented statistics such as overall success rate, typical

misconceptions, percentage of exercises tackled and

material read because it is easy to interpret. Teachers

find the statistics from log data very unwieldy to

inspect and very time-consuming to interpret (Zinn and

Scheuer, 2006). However, statistical analysis of

educational data (logs files/databases) can inform about

where students enter and exit, the most popular pages,

the browsers students tend to use and patterns of use

over time (Ingram, 1999).

Mapping and Identifying Features of e-Learning Technology through Indexes and Metrics

651

This study tries to quantify three of the unique features

of e-learning technology with the aid of indexes and

metrics. Indexes and metrics are used for the

facilitation of the course usage assessment. Firstly, the

indexes Sessions, Pages, Unique pages, Unique Pages

per CourseID per Session (UPCS) are computed with

the use of a Perl program. Then, the metrics

Enrichment, Disappointment, Interest and

Homogeneity are calculated. Some of these variables

were presented in previous works of the authors

(Valsamidis et al., 2010A; Valsamidis et al., 2010B;

Valsamidis et al., 2012A) but none of these was

mapped to any of the aforementioned features: Reach,

Richness and Information density. With the measures

of the table 1 and 2, we quantify the offered

educational material to the learners in terms of input

variables for each course. In the third column, we map

the index/metric to the unique feature of e-learning that

firstly proposed and used by Laudon and Traver (2014)

in e-commerce.

Table 1: Indexes for courses.

Index name Description

Feature

All Pages (AP)

The total number of pages per course created

by instructors

Richness

Pages (P)

The number of pages per course viewed by

users

Reach,

Richness

Unique pages

(UP)

The number of unique pages per course

viewed by users. These pages are also called

distinguished by course user pages

Reach,

Richness

Unique Pages per

Course and per

Session (UPCS)

Number of Unique Visits per course viewed

by users per session. It calculates course

activity.

Reach

Files (F) The total number of files in the course Density

Size (S)

The total size of the existing files in the

course

Density

Visits (V)

The total number of visits per course by all

users

Reach

Duration (D)

The duration of (total) visits per course by

all users

Reach

The number of sessions and the number of pages

viewed by all users are counted for the calculation of

course activity. Each session reflects when a user logs

in to the platform and, after some activity, logs out

from the platform. If there is no activity, there is a

timeout of 30 seconds. The number of pages reflects

how many pages were viewed by all users. There are

some pages of the course which were viewed by many

users but there were also some other pages not so

popular. In order to refine the situation, we define

another index which is called unique pages and

measures the total number of unique pages visited per

course viewed by all users. It counts each page of the

course only once, independently of how many times

they were viewed by the users. The Unique Pages per

Course per Session (UPCS) index expresses the

number of Unique Pages per Course visited in one

Session; it is used for the calculation of the course

activity in an objective manner. Because some novice

users may navigate in a course and visit some pages of

the course more than once, UPCS eliminates duplicate

page visits, since it considers the visits of the same user

in a session only once. The number of Unique Visits is

the average number of unique pages viewed by users in

visit intervals. The duration is the duration of (total)

visits per course by all users.

The second category of variables is related to the

courses’ online educational content. More specifically

the number of pages, the number of files and their

corresponding sizes give an estimation of the content

quantity, which is a crucial factor of online educational

content. If the number of files and their size are small,

this might be due to the weakness of the educator to

upload enough educational content into the online

platform. If the course has a lot of files with big sizes

this could lead learners to face the cognitive overload

problem and not study the course effectively.

The third category of variables helps researchers to

discover learners' activity and follow up in a course.

The number of sessions show how many times learners

have logged in. This variable could be compared with

number of visits and duration. The two later variables

show if learners find course useful and like to visit its

pages. If learners of a specific course visit more pages

for a long time, this means that course content is

interesting and useful for the learners. This could

reflect the course quality. Consequently a good course

in terms of quality may help learners at their study.

Table 2: Metrics for courses.

Metric name Description Feature

Enrichment (ENR)

(Unique Visits/Visits).

Measures the number of times

unique course information is

identified by course users

Reach, Richness

Dissapointment (DIS)

Number of sessions per course

over course visits. It reflects

how often users discontinue

viewing course pages

Reach

Interest (INT) 1-Dis

Reach

Homogeneity (HOM)

Homogeneity of unique visits

per session (Unique

Visits/Sessions). Characterizes

the percentage of LMS course

information independently

discovered by each user

participating in an LMS

Reach

Access (ACC) The rate Upages/APages Reach, Richness

Activity (ACT) The rate Visits/APages Reach, Richness

AFS Average File Size Density

VPS Visits Per Session

Reach

VPD Visits Per Duration

Reach

Enrichment is a metric which is proposed in order to

express the “enrichment” of each course in terms of

educational material. Enrichment is defined as the

complement of the ratio of the unique pages over total

number of course web pages as proposed in

(Valsamidis et al., 2010b).

A2E 2017 - Special Session on Analytics in Educational Environments

652

Enrichment = 1- (Unique Pages/Total Pages)

(1)

where Unique Pages<=Total Pages.

Enrichment values are in the range [0, 1). When

users follow unique paths in a course this is 0 while in

a course with minimal unique pages this is close to 1.

Since it offers a measure of how many unique pages

were viewed by the users, it shows how much

information included in each course is handed over to

the end user inferring that the course contains rich

educational material.

Disappointment is a metric which combines

sessions and pages viewed by users and it measures the

disappointment of the users in the course, in the sense

that when a user views few pages of the course, s/he

logs out of the course.

Disappointment= Sessions/Total Pages

(2)

In other words, the disappointment metric reflects

how quickly the users discontinue viewing pages of the

courses. Disappointment values are in the range (0, 1].

Due to the negative nature of the Disappointment

metric, it was replaced by another metric which has a

positive sounding manner, Interest. Interest metric is

defined as the complement to the disappointment.

Interest=1-Disappointment

(3)

Both disappointment and interest metrics were

proposed in (Valsamidis et al., 2010a).

Homogeneity metric is another metric, which is

defined as the ratio of unique visited course pages to

the number of sessions that visited the course.

Homogeneity =Unique pages/Total Sessions

(4)

where Total Sessions per course >> Unique course

pages.

Homogeneity metric value ranges from [0,1),

where 0 means that no user followed a unique path and

1 that every user followed unique paths. It is a course

quality index and characterizes the percentage of

course information discovered by each user

participating in a course.

Access is a metric, which expresses the “richness”

of each course in terms of educational content. Access

is defined as the ratio of the unique pages over total

number of course web pages (

Gounopoulos et al., 2016).

Access = Unique Pages/All Pages (5)

where Unique Pages≤All Pages.

Access metric values are in the range [0, 1]. When

learners follow unique paths in a course this is 1, while

in a course with minimal unique pages this is close to

0. Since it offers a measure of how many unique pages

were viewed by the users, it shows how much

information included in each course is handed over to

the end user inferring that the course contains rich

educational content.

Activity is a metric which combines visits and

pages viewed by users and it measures the usage of the

learners in the course, in the sense that when a user

views few pages of the course, s/he logs out of the

course (

Gounopoulos et al., 2016).

Activity = Visits/All Pages

(6)

VPS = Visits / Sessions (V/E)

(7)

VPD = Visits / Duration (V/D)

(8)

These measures reflect users’ behaviour related to

the educational material (Valsamidis et al., 2012B).

AFS = Size / Files (S/F) (9)

The AFS reflects the contents of the courses in the

e-learning platform.

4 CASE STUDY

In this section we present the results of applying the

approach to the data collected from the E-learning

platform during the first semester (spring semester)

of 2016. The data refer to 24 different courses of the

department of Accounting and Finance in TEI of

East Macedonia and Thrace. The students are taught

an average of 43 different subjects, each term

starting with basic subjects on Business

Organisation, Management, Mathematics,

Accounting, Banking and Finance, Computing,

Marketing, Economics, Special Accounting Issues,

Tax Accounting, Auditing and ending with advanced

subjects in various topics of Accounting and

Finance. We chose the 24 courses that have the

higher activity. More than 2000 students study in the

department but they are not all active in the

Table 3: Measures of the indexes.

CID AP P UP UPCS E F S V D

C01 22 456 20 273 304 12 750 25290 47745

C02 28 238 27 154 148 90 34575 29268 3259

C03 25 245 22 134 136 144 37383 279 34

C04 14 288 12 187 137 3 81 30273 43653

C05 29 434 27 238 192 12 363 5139 1098

C06 31 346 27 190 141 39 87870 4503 985

C07 30 321 28 208 121 42 126831 4881 9555

C08 26 378 23 207 132 24 7413 2238 5193

C09 24 423 21 253 138 30 21594 2763 573

C10 25 357 22 232 74 204 156954 5559 10905

C11 22 432 21 237 93 243 183639 4701 8673

C12 26 344 24 206 78 9 4065 2397 525

C13 27 319 25 207 75 9 9495 2952 598

C14 21 376 19 225 94 90 17829 19755 19713

C15 19 421 17 273 110 60 6195 7020 1075

C16 19 355 17 195 98 69 12525 813 212

C17 17 430 16 258 126 6 2355 6264 1061

C18 18 342 16 222 106 6 20697 13695 2289

C19 9 317 8 174 105 18 1074 4056 815

C20 14 374 12 224 133 24 1230 1779 3963

C21 16 419 15 272 182 78 6618 2814 506

C22 25 353 23 211 141 9 324 9618 1711

C23 23 316 22 173 17 42 17088 6552 1128

C24 24 147 22 115 0 51 15405 13002 2033

Mapping and Identifying Features of e-Learning Technology through Indexes and Metrics

653

e-learning platform. The institute offers traditional

learning and the e-learning is a supplementary mode.

The department uses the Open eClass e-learning

platform (GUNet, 2016).

The measures of the indexes of the collected data

are presented in table 3.

The measures of the metrics are presented in

table 4.

Table 4: Measures of the metrics.

CID Enr Dis Int Hom Acc Act AFS VPS VPD

C01 0.044 0.667 0.333 0.066 0.909 20.727 62.50

33.72

0.530

C02 0.113 0.622 0.378 0.182 0.964 8.500 384.16

0.84

0.898

C03 0.090 0.555 0.445 0.162 0.880 9.800 259.60

0.00

0.802

C04 0.042 0.476 0.524 0.088 0.857 20.571 27.00

373.741 0.693

C05 0.062 0.442 0.558 0.141 0.931 14.966 30.25

14.15

0.468

C06 0.078 0.408 0.592 0.191 0.871 11.161 2253.07

0.051 0.457

C07 0.087 0.377 0.623 0.231 0.933 10.700 3019.78

0.03

0.511

C08 0.061 0.349 0.651 0.174 0.885 14.538 308.87

0.30

0.431

C09 0.050 0.326 0.674 0.152 0.875 17.625 719.80

0.12

0.482

C10 0.062 0.207 0.793 0.297 0.880 14.280 769.38

0.03

0.510

C11 0.049 0.215 0.785 0.226 0.955 19.636 755.71

0.02

0.542

C12 0.070 0.227 0.773 0.308 0.923 13.231 451.66

0.59

0.457

C13 0.078 0.235 0.765 0.333 0.926 11.815 1055.00

0.311 0.493

C14 0.051 0.250 0.750 0.202 0.905 17.905 198.10

1.10

1.002

C15 0.040 0.261 0.739 0.155 0.895 22.158 103.25

1.13

0.653

C16 0.048 0.276 0.724 0.173 0.895 18.684 181.52

0.06

0.382

C17 0.037 0.293 0.707 0.127 0.941 25.294 392.50

2.66

0.590

C18 0.047 0.310 0.690 0.151 0.889 19.000 3449.50

0.66

0.598

C19 0.025 0.331 0.669 0.076 0.889 35.222 59.66

3.77

0.497

C20 0.032 0.356 0.644 0.090 0.857 26.714 51.25

1.44

0.449

C21 0.036 0.434 0.566 0.082 0.938 26.188 84.84

0.42

0.556

C22 0.065 0.399 0.601 0.163 0.920 14.120 36.00

29.68

0.562

C23 0.070 0.370 0.630 0.188 0.957 13.739 406.85

0.38

0.581

C24 0.150 0.340 0.660 0.440 0.917 6.125 302.05

0.84

0.639

The interpretation of the results, what we can learn

from them, what the teachers do with them can and

their relevance and usefulness are commented in the

next section.

5 DISCUSSION AND

CONCLUSIONS

The results present interesting findings in terms of

reach, richness and information density.

The courses C06, C07, C05, C02 and C13 appear

with remarkable high richness since they have the

largest values in AP (the total number of pages per

course created by instructors).

The courses C24, C02 and C03 appear with high

richness and reach since they have the largest values

in Enrichment. The courses C02, C23 and C11

appear with high richness and reach since they have

the largest values in Access. The courses C19, C20

and C21 appear with high richness and reach since

they have the largest values in Activity.

The courses C04, C02, C01, C14 and C18 appear

with remarkable high reach since they have the

largest values in visits. The courses C01, C04, C02,

C18 and C24 appear with remarkable high reach

since they have the largest values in duration.

Courses C04, C02, C01 and C18 have high values in

both indexes visits and duration.

The courses C14, C02 and C03 appear to have

also high reach since they have the top values in

VPD (visits per duration).

The courses C10, C11, C12, C13 and C14 appear

with high reach since they have the largest values in

Interest.

The courses C24, C13, C12, C10 and C07 appear

with high reach since they have the largest values in

Homogeneity.

The courses C06, C07 and C18 appear to have

remarkable high information density since they have

the largest values in AFS (Average File Size).

Teachers received the feedback regarding the

results. They were asked to improve the quality and

quantity of their course material. The position of a

particular course in the ranking of courses would

provide the motive for teachers to implement

improvements in their educational content in order

to be at the top of the rankings. Of course, many

students study the educational content just before

sitting their examinations. So, an excellent site with

outstanding content maybe can be rarely visited. On

the other hand, a poor website in terms of

educational content may have frequent visits

because visits are related to learners’ expected

grade.

Concluding, we presented an approach for

measuring through indexes and metrics three of the

features that Laudon and Traver (2014) proposed for

use in e-commerce. We made the necessary

mappings for the identification of reach, richness

and information density, namely, three of the eight

unique features. The percentage of the contribution

of each variable for the measurement of the

aforementioned features has to be defined after a

thorough and repeative analysis.

However the limitations of the study are the

sample size in terms of number of courses and

number of students. The research was conducted in

one school for one semester. Suggestions for further

research are the repeat of study with new larger

sample, to be applied in other Universities to

confirm the findings of the study.

A twofold evaluation with research based on

questionnaires both for students and teachers would

be useful for confirming the findings of the study.

However, this study is a starting point and offers a

lot of food for discussion and further work.

A2E 2017 - Special Session on Analytics in Educational Environments

654

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