Network Learning Platform Evaluation System Design and Case

Capital Budget

Zhenghua Yue

The College of Alameda, U.S.A.

Keywords: E-Learning Platform, On-Line Study System, On-Line Education Material, E-Learning Platform Evaluation

System, Analytic Hierarchy Process (AHP).

Abstract: In order to comprehensively evaluate the explosive growth of e-learning platform in recent years, this paper

constructs the evaluation system design and case capital budget of e-learning platform on the basis of

extensive research, uses analytic hierarchy process (AHP) to construct the judgment matrix of evaluation

index, determines the weight of each index, tests the consistency of the judgment matrix, and calculates the

weight of secondary index. Based on this index system, an online learning platform in China is analyzed and

evaluated. The results show that the evaluation system and method are effective.

1 INTRODUCTION

In the National Medium and Long-term Educational

Reform and Development Project Summary (2010-

2020) promulgated 2010, the construction of

lifelong education system and learning-oriented

society is overall planned and the goal of “basic

realization of education modernization, basic

formation of learning-oriented society and becoming

a human resource power” is established [see State

council 2010]. E-learning platform is undoubtedly

an important carrier in the construction of a learning

society, as well as an important means of practicing

lifelong education and lifelong learning. In recent

years, as internet-study is surging countrywide,

various E-learning platforms are emerging.

Scientific and reasonable evaluation on the E-

learning platform can improve the customer

satisfaction, the quality and efficiency as well as the

sustainable and healthy development of the platform.

The existing website platform evaluations fall into

two categories: the evaluation aims to general

website platform and the evaluation aims to specific

types of website platform. The researches on the

latter mostly focus on the e-commerce website

platform evaluation, government website platform

evaluation, and university website platform

evaluation. However, the researches on the

evaluation of E-learning platform websites are rare.

2 RELATIVE STUDY

Currently, the main body of the website platform

evaluation mainly includes the evaluation agencies

and scholars, whose evaluation criteria are not

uniform. Lots of world famous evaluation agencies

like comScore, Nile Company, Argus Associate can

provide website evaluation services. ComScore [see

comScore] is the world's leading internet and

digital media data statistic analysis agency. Based on

its MediaMetrix index, the agency releases the

website traffic rankings, and provides the most

comprehensive digital measurement solutions to the

industry website. Nile Company [see Nielsen ratings]

is a well known American internet market survey and

statistics company. The Nielsen ratings system

evaluates a website through its navigation, response

time, credibility, and content, etc., and provides

reference data for a company to formulate its media

delivery plan. Argus Associate [see Argus Associate]

proposed the evaluation of a website should base on

the site resource description, subjective evaluation,

design level, organization, and resource guidelines.

Scholars from all over the world put forward

website platform evaluation methods from different

perspectives. Some view website as a information

system and evaluate the site from the perspective of

internet information resource. Richmond [see

Richmond] proposes “10C” indices for internet

information resource evaluation. The “10C” means

204

Yue, Z.

Network Learning Platform Evaluation System Design and Case Capital Budget.

DOI: 10.5220/0012028200003620

In Proceedings of the 4th International Conference on Economic Management and Model Engineering (ICEMME 2022), pages 204-213

ISBN: 978-989-758-636-1

Copyright

c

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

Content, Credibility, Critical thinking, Copyright,

Citation, Continuity, Censorship, Connectivity,

Comparability and Context. Some evaluate a

website from the perspective of usability. Aziz and

his team [see Aziz et al.]

suggest evaluate a website

from the aspects of utility, benefit, learnability,

satisfaction, equal accessibility, etc. Ke Qing and her

team [see Ke Qing et al.2011] construct an index

system to evaluate the usability of the website

platform, which includes website size, website

quality, website promotion, website assistant

function, user emotion and so on. Some evaluate a

website from the perspective of user-perception. Pei

Ling and her team [see Pei Ling et al. 2009] design a

user-oriented measurement site information service

quality evaluation system based on the principle of

service quality management. The system includes

general impression, customer service indices, future

forecasts, technical indices, information indices,

interface indices and functional indices. While some

of the scholars evaluate a website form the

perspective of integrity. Gan Liren and his team [see

Gan Liren and Cai Lei 2003] evaluate a website

from its organization, symbol, website navigation,

retrieval system design, etc., based on information

architecture (IA) theory. Feng Yingjian [see Feng

Yingjian 2016] indicates that a complete corporate

website, no matter how complex it is, can be divided

into four components: structure, content, function

and service, and the website can be evaluated from

the four aspects.

The existing evaluation systems are mostly set

up from the manager’s point of view. Most of them

are based on the following foundations: one is the

actual data. For example, the resources the site has,

the number of students registered (rate), students’

participation in learning, course selection status quo,

etc., these data are generally provided by the

respondents. The other is expert grading. The

experts grade every evaluation index according to

the grading requirement after the establishment of

stratified evaluation index, such as resource type,

function provision [see Sun Meng 2015].

3 EVALUATION INDEX SYSTEM

AND THE WEIGHT SETTING

OF E-LEARNING PLATFORM

This research is based on the four website

components scholar Feng Yingjian putting forward-

structure, content, function and service. Based on

wide investigation and experts interviews and

combined with the characteristics of E-learning

platform, we set the evaluation index system and the

weight of each index by AHP.

3.1 Index System Setting

The evaluation index system built in this thesis

includes six primary indices and the corresponding

twenty five secondary indices. Show in diagram 1.

Diagram 1: The Evaluation Index model of E-learning

Platform.

3.1.1 Platform Structure

Platform structure reflects the E-learning platform

overall structure. It embodies in organization system,

navigation system and symbol system. Table 1 is the

meaning and key points of evaluation of each index.

Network Learning Platform Evaluation System Design and Case Capital Budget

205

Table 1: Platform structure index.

Name of index Index interpretation Key points of evaluation

A1 Organization

reasonability

The organization system is

responsible for the

classification of information,

and hierarchical division of

the website content. It is the

basis for building a navigation

system.

Whether the organization structure is clear,

the classification logic is scientific and

reasonable, and whether the hierarchical tree

structure is employed in information

organization, whether the information

guidance is of high efficiency.

A2 Navigation

reasonability

Navigation system allows the

users to know their location

and path. The navigation

system is divided into global

navigation, local navigation,

contextual navigation and

supplementary navigation [see

Chen Lanjie 2007].

Whether the content of global navigation is

comprehensive and the location is consistent.

Whether the content of local navigation is

comprehensive and the location is consistent.

Whether the content of contextual navigation

is abundant and relevant. Whether the

supplementary navigation is comprehensive

and how is the consistency with other

navigations.

A3 Symbol

reasonability

Symbol is the description of

the information represented by

navigation elements with an

appropriate vocabulary [see

Wang Yongfang and Hu Yaolei

2014].

The consistency of symbol; the intelligibility

of symbol; the accuracy of symbol.

3.1.2 Platform Content

Platform content is the core and soul of E-earning

platform. The assessment of the platform content is

reflected in five aspects: comprehensiveness,

timeliness, authority, uniqueness, form diversity.

Table 2 shows the key points of evaluation of each

index.

Table 2: Platform content index.

Name of index Key points of evaluation

B1 Resource

comprehensiveness

Whether the platform is comprehensive or professional, and whether

resources type coverage is comprehensive.

B2 Resource timeliness

How is the update frequency of platform resource, whether the content is

out of date

B3 Resource authority

Whether the platform resource publishing agency is authoritative. How is

the authority level of the content interpreter.

B4 Resource uniqueness Whether the platform has a certain resource exclusively owned by itself.

B5 Resource form diversity

Resources provide the versions information, such as the video, audio, text,

PPT, etc,.

3.1.3 Platform Technology

Platform technology is the guarantee of the normal

operation of the network learning platform. It’s

necessary to consider the following aspects on the

technical side: stability, security, using convenience,

using fluency, retrieval system reasonability, and

mobile terminal compatibility. Table 3 shows the

key points of examination of each index.

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206

Table 3: Platform technology index.

Name of index Key points of evaluation

C1 Stability

With professional maintenance and disaster recovery function; without

missing chain and broken chain; can identify human error, with less

coding error.

C2 Security

Provide a security platform for the users and resources; maintain and

upgrade regularly.

C3 Using convenience

It’s easy to use the platform and with high operational efficiency, do not

deliberately remember its function and process.

C4 Using fluency

Platform information and resources can be downloaded fast, after

corresponding processing, the users feel good.

C5 Retrieval system

reasonability

The searching interface is good, with diversify searching forms, fast

searching speed, and error correction capability.

C6 Mobile terminal

compatibility

With mobile platform, can log in with different kinds of social platform

membership accounts.

3.1.4 Platform Service

The network learning platform need interact with

learners in order to play its function better. The

service of platform embodies in the following three

aspects: communication interaction, personalized

service, charge reasonability. Table 4 shows the key

points of evaluation of each index.

Table 4: Platform service index.

Name of

index

Key points of evaluation

D1

Commu

nication

interacti

on

Platform provide the communication

interface for the first users to log in,

provide the communication platform for

learners, such as learning group, forum,

etc., provide the communication interface

between learners and teachers.

D2

Personal

ized

service

Set up personal learning file for the

learners, recommend personalized

learning materials. The learners can

customize their own learning content.

D3

Charge

reasonab

ility

The ratio of the free resource, the price

reasonability of the charged resource.

3.1.5 Platform Operation

Platform operation is the guarantee of the

sustainable and healthy development of E-learning

platform. Platform operation embodies in the visits

amount growth, the search engine performance, the

platform PR value and the number of registered

users. The key points of examination of each index

are shown in table 5.

Table 5: Platform operation index.

Name of

index

Key points of examination

E1 The

visits

amount

situation

The platform visits amount, the growth

rate of PV values in fixed cycle, the

amount of independent identity visitors,

the quantity of per capita page visit,

average page visit depth, the bounce

rates.

E2 The

performan

ce of

search

engine

Whether be included by the main search

engine and ranks in the front places.

The

platform

PR value

Whether the PR values or weight are

greater than 4.

E4 The

number of

registered

users

The overall number of registered users;

the growth rate of registered users.

Network Learning Platform Evaluation System Design and Case Capital Budget

207

3.1.6 Platform Benefit

Platform benefit directly reflects the use situation of

E-learning platform. It is reflected in the resource

click rate, download rate, collection rate and the user

average learning duration. The key points of

evaluation of each index are shown in Table 6.

Table 6: Platform benefit index.

Name of

index

Key points of evaluation

F1 Resource

click rate

The click frequency of resource in

fixed cycle; the average click duration;

the click rate.

F2 Resource

download

rate

The download frequency of resource in

fixed cycle; the download rate.

F3 Resource

collection

rate

The collection frequency of resource in

fixed cycle; the collection rate.

F4 Users

average

learning

duration

The average learning duration of

registered users.

3.2 Index Weight Setting

The index weight of this evaluation system was set

by AHP based on the investigation. The procedures

are as follows:

3.2.1 The Construction of Judgement

Matrix

Take the secondary indices under “B Platform

Content” as an example. We set the evaluation

factors as {B1, B2, …, Bm}, and B represent the

target, then the judgement matrix is:

In the weight calculation of platform content B

(B1, B2, B3, B4, B5), the respondent has to make a

judgement on Bi and Bj, to decide which is more

important and how much it is important. And the

importance of each index is evaluated according to

the 1-9 proportion scale [see Qiu Junping 2009]

shown in Table 7. The final judgment matrix is

given in Table 8.

Table 7: 1-9 Proportion scale.

Scale b

ij

Meaning

1

The two factors have the same

importance

3 Factor i is slightly important than factor j

5

Factor i is obviously important than

factor j

7

Factor i is strongly important than factor

j

9

Factor i is extremely important than

factor j

2,4,6,8

The median of the two adjacent

judgments

Reciproc

al

b

ji

refers to the comparative judgment of

factor i and j, there are b

ji=

1/b

ij

Table 8: “B platform content” judgement matrix.

B platform

content

B1 Resource

comprehensivene

ss

B2

Resource

timeliness

B3

Resource

Authority

B4

Resource

uniqueness

B5

Resource

form

diversit

y

B1 Resource

comprehensiven

ess

1 1/4 1/5 1/2 1/3

B2 Resource

timeliness

4 1 1/2 3 2

B3 Resource

Authorit

y

5 2 1 4 3

B4 Resource

uniqueness

2 1/3 1/4 1 1/2

B5 Resource

form diversit

y

3 1/2 1/3 2 1

11 12 1

21 22 2

12

m

mm

bb b

B

b b bmm





=











ICEMME 2022 - The International Conference on Economic Management and Model Engineering

208

3.2.2 Calculate the Relative Weights of the

Lower-Level Indices to the

Upper-Level Indices

(1) Normalize every column of judgement matrix B

(2) Add the normalized judge matrix according

to rows

(3) Normalize the vector

The resulting vector is the

required weight vector.

(4) Calculate the maximum eigenvalue of the

matrix

For any ( ),

is the i

th

component of vector AW.

The results obtained after the above-mentioned

processing on judgment matrix are shown in Table 9.

Table 9: The weight and the maximum eigenvalue of judgement matrix B.

B B1 B2 B3 B4 B5

Row

sum

Normalized

weight W

AW

B1 0.0667 0.0612 0.0876 0.0476 0.0488 0.3119 0.0624 0.3140 5.0345

B2 0.2667 0.2449 0.2190 0.2857 0.2927 1.3089 0.2618 1.3372 5.1080

B3 0.3333 0.4898 0.4380 0.3810 0.4390 2.0811 0.4162 2.1291 5.1154

B4 0.1333 0.0816 0.1095 0.0952 0.0732 0.4929 0.0986 0.4952 5.0234

B5 0.2000 0.1224 0.1460 0.1905 0.1463 0.8053 0.1611 0.8150 5.0603

column

sum

1.0000 1.0000 1.0000 1.0000 1.0000 5.0000

Sum: 25.3416

the maximum

eigenvalue:

5.0683

The weights and the maximum eigenvalues of

the overall matrix T and the judgement matrix A, C,

D, E, F can be calculated according to the above

method, as shown in Table 10, Table 11, Table 12,

Table 13, Table 14 and Table 15 respectively.

Table 10: The weights and the maximum eigenvalues of the overall matrix T.

T A B C D E F

Row

sum

Normalized

weight W

AW

A 0.0923 0.1020 0.0778 0.0706 0.1290 0.1429 0.6146 0.1024 0.6218 6.0694

B 0.3692 0.4082 0.4669 0.4235 0.3226 0.2857 2.2761 0.3794 2.3562 6.2111

C 0.2769 0.2041 0.2335 0.2824 0.2581 0.2381 1.4930 0.2488 1.5459 6.2126

D 0.1846 0.1361 0.1167 0.1412 0.1935 0.1905 0.9626 0.1604 0.9864 6.1486

E 0.0462 0.0816 0.0584 0.0471 0.0645 0.0952 0.3930 0.0655 0.3952 6.0335

F 0.0308 0.0680 0.0467 0.0353 0.0323 0.0476 0.2607 0.0434 0.2634 6.0639

column

sum

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 6.0000

Sum: 36.7392

the

maximum

eigenvalue:

6.1232

1

, 1, 2, ,

ij

m

kj

k

ij m

b

=

==





1

1,2, ,i

m

i

j

j imbW

=

==





{

}

12 m,,

T

W

ωω ω

= 

1

i

m

j

w

=



1, 2 ,()

T

m

Wwww= 

max

λ

1

()

max

m

i

AW i

m

i

w

λ

=



1, 2, ,im= 

()iAW

i()

i

AW

w

i()

i

AW

w

Network Learning Platform Evaluation System Design and Case Capital Budget

209

Table 11: The weights and the maximum eigenvalues of the judgement matrix A.

A A1 A2 A3 Row sum

Normalized

weight W

AW

A1 0.1667 0.1818 0.1429 0.4913 0.1638 0.4921 3.0044

A2 0.5000 0.5455 0.5714 1.6169 0.5390 1.6248 3.0147

A3 0.3333 0.2727 0.2857 0.8918 0.2973 0.8943 3.0085

column

sum

1.0000 1.0000 1.0000 3.0000

Sum: 9.0276

the maximum

ei

g

envalue:

3.0092

Table 12: The weights and the maximum eigenvalues of the judgement matrix C.

C C1 C2 C3 C4 C5 C6

Row

sum

Normalized

weight W

AW

C1 0.0923 0.0706 0.1020 0.0778 0.1429 0.1290 0.6146 0.1024 0.6218 6.0694

C2 0.1846 0.1412 0.1361 0.1167 0.1905 0.1935 0.9626 0.1604 0.9864 6.1486

C3 0.3692 0.4235 0.4082 0.4669 0.2857 0.3226 2.2761 0.3794 2.3562 6.2111

C4 0.2769 0.2824 0.2041 0.2335 0.2381 0.2581 1.4930 0.2488 1.5459 6.2126

C5 0.0308 0.0353 0.0680 0.0467 0.0476 0.0323 0.2607 0.0434 0.2634 6.0639

C6 0.0462 0.0471 0.0816 0.0584 0.0952 0.0645 0.3930 0.0655 0.3952 6.0335

column

sum

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 6.0000

Sum: 36.7392

the

maximum

ei

g

envalue:

6.1232

Table 13: The weights and the maximum eigenvalues of the judgement matrix D.

D D1 D2 D3 Row sum

Normalized

weight W

AW

D1 0.5455 0.5000 0.5714 1.6169 0.5390 1.6248 3.0147

D2 0.1818 0.1667 0.1429 0.4913 0.1638 0.4921 3.0044

D3 0.2727 0.3333 0.2857 0.8918 0.2973 0.8943 3.0085

column

sum

1.0000 1.0000 1.0000 3.0000

Sum: 9.0276

the maximum

eigenvalue:

3.0092

Table 14: The weights and the maximum eigenvalues of the judgement matrix E.

E E1 E2 E3 E4

Row

sum

Normalized

weight W

AW

E1 0.2609 0.3077 0.3000 0.2400 1.1086 0.2771 1.1201 4.0416

E2 0.1304 0.1538 0.2000 0.1600 0.6443 0.1611 0.6469 4.0160

E3 0.0870 0.0769 0.1000 0.1200 0.3839 0.0960 0.3853 4.0152

E4 0.5217 0.4615 0.4000 0.4800 1.8633 0.4658 1.8872 4.0513

column

sum

1.0000 1.0000 1.0000 1.0000 4.0000

Sum: 16.1242

the maximum

eigenvalue:

4.0310

i()

i

AW

w

i()

i

AW

w

i()

i

AW

w

i()

i

AW

w

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210

Table 15: The weights and the maximum eigenvalues of the judgement matrix F.

F F1 F2 F3 F4

Row

sum

Normalized

weight W

AW

F1 0.2609 0.3000 0.3077 0.2400 1.1086 0.2771 1.1201 4.0416

F2 0.0870 0.1000 0.0769 0.1200 0.3839 0.0960 0.3853 4.0152

F3 0.1304 0.2000 0.1538 0.1600 0.6443 0.1611 0.6469 4.0160

column

sum

1.0000 1.0000 1.0000 1.0000 4.0000

Sum: 16.1242

the

maximum

eigenvalue:

4.0310

3.2.3 Consistency Test

Consistency test is the weight reasonable test of

each judgement matrix after the weights are

calculated.

The Calculation formula for CR (Consistency

ratio):

The Calculation formula for CI (Consistency

index) (m is the order of the judgement matrix):

RI refers to the average random consistency

index, the value table shown in table 16 [see Qiu

Junping 2009].

Table 16: The average random consistency index.

order 1 2 3 4 5 6 7 8 9 10 11

RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51

When CR ≤ 0.1, that the weight order of the

level has a satisfactory consistency, otherwise the

elements value of the judgement matrix need to be

re-adjusted.

Based on the formula, the CR of matrix T, A, B,

C, D, E, F are 0.0199, 0.0079, 0.0152, 0.0199,

0.0079, 0.0115, 0.0115 respectively. The weight

order of the level obviously has a satisfactory

consistency.

3.2.4 The Synthetic Weight Calculation

The calculation of synthetic weight of each level

index to the system target is the secondary index

weight multiplied by the primary index it belonged.

The results are shown in table 17.

Table 17: Evaluation index weight table.

Primary index

(weight)

Secondary index

relative weight

Synthetic

weight

The certain

platform grade

A Platform

structure

(0.1024)

A1 Organization

reasonabilit

y

0.1638 0.0168 90 1.512

A2 Navigation

reasonabilit

y

0.539 0.0552 80 4.416

A3 Symbol

reasonabilit

y

0.2973 0.0304 80 2.432

B Platform

content

(0.3794)

B1 Resource

comprehensiveness

0.0624 0.0237 88 2.0856

B2 Resource

timeliness

0.2618 0.0993 85 8.4405

B3 Resource Authorit

y

0.4162 0.1579 90 14.211

i()

i

AW

w

CI

CR

R

I

=

max

1

m

CI

m

λ

−

=

−

iW

iY

iiWY

Network Learning Platform Evaluation System Design and Case Capital Budget

211

B4 Resource

uni

q

ueness

0.0986 0.0374 70 2.618

B5 Resource form

diversit

y

0.1611 0.0611 70 4.277

C Platform

technology

(0.2488)

C1 Stabilit

y

0.1024 0.0255 90 2.295

C2 Securit

y

0.1604 0.0399 85 3.3915

C3 Using convenience 0.3794 0.0944 88 8.3072

C4 Using fluency 0.2488 0.0619 85 5.2615

C5 Retrieval system

reasonabilit

y

0.0434 0.0108 87 0.9396

C6 Mobile terminal

com

p

atibilit

y

0.0655 0.0163 90 1.467

D Platform

service

(0.1604)

D1 Communication

interaction

0.539 0.0865 86 7.439

D2 Personalized

service

0.1638 0.0263 80 2.104

D3 Charge

reasonabilit

y

0.2973 0.0477 80 3.816

E Platform

operation

(0.0655)

E1 Platform visits

amount

0.0106 0.0182 90 1.638

E2 Search engine

p

erformance

0.1611 0.0106 90 0.954

E3 Platform PR value 0.096 0.0063 86 0.5418

E4 Number of

registered users

0.4658 0.0305 90 2.745

F Platform

benefit

(0.0434)

F1 Resource click rate 0.2771 0.0120 85 1.02

F2 Resource download

rate

0.096 0.0042 80 0.336

F3 Resource collection

rate

0.1611 0.0070 85 0.595

F4 Average learning

duration

0.4658 0.0202 88 1.7776

Total 84.6203

4 CASE STUDY ANALYSIS

According to the established evaluation system

design of e-learning platform and the case capital

budget, evaluate a popular e-learning platform in

China. For the four indicators A, B, C and D, ten

people who often study on the platform are invited

to score the second-level evaluation indicators with

full marks, and the average value is calculated as the

score of each indicator. For the two indexes E and F,

we interviewed the management and maintenance

personnel of the platform, searched relevant data

through professional tools, and comprehensively

gave the scores of each secondary index. As shown

in Table 17, the comprehensive score of this

platform can be calculated as 84.6203, which is also

consistent with our subjective judgment.

5 CONCLUSION AND

PROSPECTS

This paper draws on the theoretical and methodical

achievements of the academic circles at home and

abroad in the evaluation of the website platform,

especially the online education website platform.

According to the characteristics of network learning

platform and the intensive research, an online study

platform index system is built from six aspects and

each index weigh is set by Analytic Hierarchy

Process. Then a case study based on one certain

online study platform is conducted according to the

system.

For the survey sample data is limited in the

process of index setting and case study, the accuracy

of the weight and grade needs to be improved in the

future. In the next step, we will expand the survey

and interview samples to have a further research.

Meanwhile, we will develop automatic evaluation

ICEMME 2022 - The International Conference on Economic Management and Model Engineering

212

tools to improve the efficiency of the evaluation and

make the process of the evaluation more exercisable.

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