The Ambivalence of Strengths and Weaknesses of E-Le arning Educational [617694]

55
The Ambivalence of Strengths and Weaknesses of E-Le arning Educational
Services

Venera3Mihaela Cojocariu
Vasile Alecsandri University of Bacau, Calea Marase sti 157, 600115 Bacau, Romania
Phone: +40w747w066462
[anonimizat]

Iuliana Lazar
Vasile Alecsandri University of Bacau, Calea Marase sti 157, 600115 Bacau, Romania

Gabriel Lazar
Vasile Alecsandri University of Bacau, Calea Marase sti 157, 600115 Bacau, Romania

Abstract
This paper represents a thorough phase in the effor t to identify and assort the strengths and
weaknesses of ewlearning educational services. This paper reviews a synthesis of the assessments on
the ewlearning educational services through a surve y of the specialized literature from 2000 to 2012
in order to identify the strengths and weaknesses o f ewlearning educational services which were
reported during the past decade. The steps of our a pproach are the following: 1. The identification of
a large number of specialized studies that analyze the above mentioned issue; 2. A basic theoretical
review of the research from the perspective of iden tifying the strengths and weaknesses of the ew
learning educational services and some of their imp lications on the intellectual development of the
beneficiaries; 3. A descriptive statistical data an alysis which is carried out in order to extract
information about strengths and weaknesses relevant to the literature taken into consideration; 4.
Results classification and interpretation; 5. Formu lating practical suggestions for the notion of ew
learning educational services considering the devel opment of studies on the impact of their use on
the intellectual development of the beneficiaries. The study results highlighted that strengths and
weaknesses are not 'pure', but ambivalent, simultan eously incorporating meanings and limits with
different weights. A predictive model of future ewl earning educational services can be designed on
the basis of the results obtained in the research. This predictive model is based on a pedagogical
concept that takes into account the ambivalence of the higher indices which have been identified.
Keywords : interactive learning environment; pedagogical iss ue; teaching/learning strategy;
intellectual development of the beneficiaries

1. Introduction
The concept of ewlearning can hardly be the subject of a comprehensive and universally
accepted definition, due to the different forms it takes now and to the multiplicity of variables
involved, which have a large and permanent dynamic. A relevant study (Mata, Lazar, & Lazar,
2012) highlights some interesting and useful aspect s for our study, as a response to the question
“What are the ewlearning educational services?”:
• Ewlearning is commonly referred to the intentional use of network ed information and
communications technology in teaching and learning (Naidu, 2006).
• Ewlearning is instruction delivered on a digital device such as a computer or mobile device
that is intended to support learning . This definition has several elements concerning t he
what , how , and why of ewlearning.
w What. Ewlearning courses include both content (informatio n) and instructional methods
(techniques) that help people learn the content.
w How. Ewlearning courses are delivered via digital device s such as computers and
smartphones using words in the form of spoken or pr inted text and pictures such as
illustrations, photos, animation, or video. Some fo rms of ewlearning called asynchronous ew

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

56 learning are designed for individual selfwstudy. It may be noted that the two components of
ewlearning analyzed here (content and strategies) c orrelates with both the development of
higher cognitive processes (thinking, memory, imagi nation, language) and formation /
development of new techniques of intellectual work, different from those classic but
complementary to them (data search, selection, stor age, processing, graphic representation,
realization of illustrations, animations, presentat ions, transfer) (Naaji et. al., 2015).
w Why. Ewlearning lessons are intended to help learners re ach personal learning objectives or
perform their jobs in ways that improve the bottomw line goals of the organization. In short,
the 'e' in ewlearning refers to 'how'wthe course is digitized so it can be stored in electronic
form(Clark & Mayer, 2008).
• Ewlearning is an Internet3based learning process, using Internet technology to design,
implement, select, manage, support and extend learn ing, which will not replace traditional
education methods, but will greatly improve the eff iciency of education (Masud & Huang,
2012).

Ewlearning educational services represent a distin ct category of formative services. It is about
those services which offer instruction, training, r etention, transfer, consolidation, evaluation, revi ew,
systematization by mainstreaming the 'e' dimension in teaching and learning. Our analysis will focus
only on ewlearning educational services in formal l earning (although they can be integrated and
exploited in any training which is more or less for mal or nonwformal) and on some of the benefits of
intellectual nature that appear.
This paper represents a thorough phase in the effor t to identify and assort the strengths and
weaknesses of the ewlearning educational services. In order to identify which strengths and
weaknesses of ewlearning educational services were reported during the past decade, this paper
reviews a synthesis of the assessments on the ewlea rning educational services, through a survey of
the literature from 2000 to 2012.
The spreading of ewlearning educational services, w hich could hardly be thought of 25 years
ago, has led to enhancing the research which aimed at analyzing this type of services in terms of
their essence and their qualitative dimensions. Lea rning new technology, in particular those related
to the Web 2.0, offers new opportunities to create and share content and to interact with others. Also
known as 'social media', Web 2.0 encompasses tools that allow individual and collective publishing;
sharing of images, audio and video files; the creat ion and maintenance of online social networks
(Nedelea & Costea, 2016). It is stated that new pra ctices and attitudes have come with these new
tools (Bennett et al., 2012; Jomah, et al., 2016).
After two decades of actual educational practice in ewlearning educational services, after
overcoming the initial methodological and action en thusiasm, a deeper research was generated with
regard to the pedagogical richness of these service s and, especially, the real meaning of this phrase,
which is still unclear (Middleton, 2010; Slevin, 20 08).
This aspect is visible in the exponential growth of the research allocated to this problem,
especially after 2000. Our previous studies led to the identification of9 most representative
strengths, respectively 5 weaknesses of ewlearning educational services, as follows:
/head2right strengths: 1. It is a process in full development i n agreement with some of the defining
characteristics of the learners in the third millen nium; 2. Flexibility; 3. Customization of
learning related to the needs of the learner; 4. A great methodological diversity ; 5. A
particular intuitive character; 6. Interactivity ; 7. Collaborative learning; 8. It is motivating ; 9.
Focus on the learner . All these strengths are considered as an expressi on of current trends in
the area of psychology and science of education, es pecially the differentiating learning, and
the focus on learning beneficiary, according to its individual cognitive peculiarities.
/head2right weaknesses: 1. Insufficient compatibility between the technological design of the service
and the psychological component of the learning pro cess; 2. Flexibility and autonomy in
learning are relative and fragile; 3. The limited, inadequate or unattainable character of the

V.M. Cojocariu, I. Lazar, G. Lazar – The Ambivalence of Strengths and Weaknesses of E-Learning Educatio nal Services
57
learning customization ; 4. A possible superficiality in learning which is induced by a wide
variety of methodology ; 5. A certain kind of reduction of the relations among learners,
between them and the teacher, a possible loss of direct communication and immediate
collaboration. All these weaknesses are considered as expression of some insufficient (yet)
developments and psychowpedagogical adaptations of the ewlearning, especially on the
intellectual training component.

A very interesting thing we have found since the ti me of the survey is that some of the
identified strengths were also on the weaknesses li st, highlighting both their inherent limitations an d
their ambivalence.
The main aim of the study is to identify the ambivalent character of a set of strengths and
weaknesses of ewlearning educational services. Thei r highlighting and analysis can provide the basis
to design an optimal model of creating ewlearning e ducational services to be used in higher
education. The term of ambivalence has not been use d so far, to our knowledge, with respect to such
services. It means, in our view, the real possibili ty that the same feature of ewlearning educational
services, (for example, flexibility, customization or methodological diversity) can be analyzed and
interpreted unilaterally, monovalently, by authors, either as a strength (flexibility(Smetana & Bell,
2012))or as a weakness (methodological diversity(Cu thbert & Slotta, 2004)) or some of them can
analyze them simultaneously from two points of view .This study has been developed on the basis on
these results.

2. Research Methodology
The documentation and data systematization were pe rformed through a similar procedure as
the one reported by Liao, Chu et al.(2012). Keyword indices, “ewlearning services”, “strong points”,
“weak points” and article abstracts were used to id entify 203 articles. The research methodology is
based on a twowstage analysis of a package includin g bibliographic studies published between 2000w
2012 in the literature which is accessible to the g eneral public in the following databases: Science
Direct (Elsevier), ProQuest, and Google Scholar:
1. a quality study consisting of identifying and group ing the strengths and weaknesses of ew
learning educational services in the selected liter ature;
2. astatistical quantity analysis, consisting of ident ifying the occurrence frequencies of those
strengths and weaknesses in the literature. The sta tistical analysis was carried out in SPSS®
version 20.0 that allowed us to subsequently identi fy those ambivalent strengths and
weaknesses and the frequency of their being simulta neously entered under both categories.
The following hypotheses and research objectives we re established:
Hypothesis 1: Are there features of ewlearning educ ational services which may be considered
strengths and weaknesses (ambivalent) at the same t ime?
Hypothesis 2: If such a set exists, is the number o f ambivalent features of ewlearning
educational services enough from a statistical poin t of view so as to base the design of a model of ew
learning educational services with a higher pedagog ical level?
O1. identifying a relevant bibliography on the set issue, published between 2000 and 2012;
O2. content quality analysis of selected literature , which results in identifying strengths and
weaknesses;
O3. Quantitative data analysis, classifying the str engths and weaknesses characteristic to ew
learning educational services on a statistical basi s;
O4. selecting and highlighting the ambivalent stren gths and weaknesses, statistical analysis of
their weight.
O5. making a system of distinct categories of ambiv alent indices to develop a model of vectors
required in the generation and operation of specifi c services, which is optimal from a pedagogical
perspective.

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

58 Multiple responses analysis allows us to study the composition of the bibliographic package
on which the research was conducted. This is a usef ul operation for the description of percentages of
texts which specify different assessments. In this study, texts are represented by the studied article s,
and assessments correspond to strengths, respective ly weaknesses highlighted in these publications.
The significant bibliographic package was generated gradually by successive operations as
follows:
/head2right creating the primary bibliographic packagew203 stud ies were identified;
/head2right creating the final bibliographic packagew192 studie s were selected from the initial
bibliographic package (by excluding the ones which were in both packages);
/head2right constituting the three components of the final bibl iographic package: 1.102 articles outlining
strengths, referred to from now on as 'strong artic les'; 101 articles outlining
weaknesses,referred to as 'weak articles'; 102 arti cles from their total, containing the
ambivalence idea of some of the strengths and weakn esses, referred to as 'common articles',
presented in a separate section of the article's re ferences.

The analysis of multiple responses allows us to stu dy multiple choice items. In our case,
each item is represented by the strengths and weakn esses which have been identified in the studied
literature.
The chosen multiple answers analysis method corresp onds to the dichotomous variables for
each answer. For example, in the case of the articl e of Ossiannilsson (2012), the response to the item
'flexibility' was coded as 1 for yes, if this featu re is specified, and 0 if it is not specified.
The statistical analysis was carried out by applyin g the dichotomous multiple responses
analysis by the SPSS®software, version 20.0. This a nalysis was also done separately, according to
the levels of 'categories' variable, namely the per iod of the appearance of the article, respectively:
2000w2004, 2005w2008and 2009w2012.
This type of methodology, inevitably, has a number of limitations(Liao, et al., 2012). Firstly,
making a review of the literature is a difficult ta sk, both from a quantitative and a qualitative poin t
of view. Secondly, the selection of texts and analy sis relied significantly on the keywords index and
article abstracts, which mean, within certain limit s, the researcher’s subjective touch. Finally, the
number of analyzed articles, even if it is importan t, does not cover, for sure, the whole sphere of
publications, being the expression of the author's limited knowledge of this subject.

3. Results and Their Analysis
From the multitude of the obtained data only those that serve the hypotheses and objectives
set out aboveare present.

Figure 1. Frequencies of strengths and weaknesses t hrough a survey of literature from 2000 to 2012

V.M. Cojocariu, I. Lazar, G. Lazar – The Ambivalence of Strengths and Weaknesses of E-Learning Educatio nal Services
59
Step I/Objective 1- identifying a relevant bibliogr aphy on the set issue, published between 2000
and 2012
Identifying, selecting, organizing and analyzing t he studies represent an extremely complex,
but absolutely necessary process, providing the the oretical basis for the concept of ambivalence for
the strengths and weaknesses of ewlearning educatio nal services. The most relevant aspects are: the
comparative analysis of the perspectives offered by the selected studies; identifying the direction th e
studies evolve to and the interest of the people wh o use these services; the general rate for the
studies’ publication; comparative frequency of repo rting the beneficiaries to the strengths and
weaknesses during the covered period.
We identified 192 specific studies which analyze, d irectly or indirectly, the subject of the
strengths and weaknesses of ewlearning educational services, respectively those containing the idea
of some of their strengths and weaknesses ambivalen ce. The frequencies of strengths and
weaknesses reported to intervals corresponding to t he years when they were published are shown in
Figure1.
The above presented data analysis shows the followi ng:
1. an accelerated and increasing evolution of studies on the issue of strengths, which has a rate
increased by 22.5% in 2005w2008 compared to 2000w20 04, by 7.8% in 2009w2012 compared
to the previous period and by 30.39% compared to th e initial period. Although the
increasing interest for strengths is obvious during 2009w2012, it was 2.9 times less than in
the previous period. Along the whole interval when studies related to strengths analysis
appeared, the growth rate was an average of 33.3% f or that period and 8.3% for the year;
2. an accelerated and increasing evolution of studies on the issue of weaknesses, which has a
rate increased by 18.49% in 2005w2008 compared to 2 000w2004, by32.8% in 2009w2012
compared to the previous period and by 51.3% compar ed to the initial period. The
increasing interest for weaknesses is permanent and it is the biggest in the interval 2009w
2012. It increased by 2.1times compared to the prev ious period. Along the whole interval
when studies related to weaknesses analysis appeare d, the growth rate was an average of
33.3% for that period and 8.33% for the year;
3. an identical average growth rate of the interest fo r the analysis of both strengths and
weaknesses for the entire period was 33.3% on each of the three periods in which studies
were grouped and 8.3% for each year;
4. a 1.5times greater interest of the authors for the strengthsanalysisbetween2000w2004
(15.7%) than for the weaknesses analysis(10.1%);
5. a 1.3 times greater interest of the authors for the strengths analysis between 2004w2008
(38.2%) compared to the weaknesses analysis (28.6%) , with a 0.2 times decrease compared
to the previous interval;
6. a much greater interest of the authors for the weaknesses analysis between 2009w
2012(61.3%) compared to the strength sanalysis (46.1%), a 1.3times growth, almost double
when compared to the previous interval;
7. a 4.1% greater interest of the authors for strength s than for weaknesses between 2005w2008
compared to 2000w2004, but an extraordinarily high interest of authors for weaknesses,
22.9% more in 2009w2013 compared to the previous pe riod;
8. a significant and more rapid increase of the weight of studies analyzing weaknesses (51.3%)
compared to those which analyze strengths(30.4%) al ong the whole period (2000w2012),
which means 20.9%, i.e. 1.7 times more;
9. the trend of strengths and weaknesses analysis is a n upward one, but it increases faster for
weaknesses studies, especially between 2009w2012, w hen the growth of studies focused on
strengths recorded the slowest rate in the whole in terval (7.8%).

In terms of linking these data with the practice of ewlearning educational services, it must be
concluded that as they work, it begins to be more o bvious what shortcomings their manifestation has
and how interested the creators of these services a re in identifying and reducing their weaknesses.

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

60 Step II/Objective 2-Content quality analysis of sel ected literaturewhich results in identifying
strengths and weaknesses
The focused reading of the192 studies and the data centralization of the identified strengths
and weaknesses, in the chronological order of their appearance was performed. Their
systematization was done along the process of their identification by means of indices categories.
For example, flexibility, interactivity, studentwce ntered learning, accessibility, saving costs – for
strengths; reduced social interaction, lack of inst ructors, cost, long time for preparing online cours es,
resistance to (new) technology – for weaknesses.

Step III/ Objective 3-Quantitative data analysis, c lassifying the strengths and weaknesses
characteristic to e-learning educational services, on a statistical basis
We ordered the obtained data for the identified str engths and weaknesses. This was done by
giving a point for each presence of an index, or no t giving it for its absence. Thus, we quantified a
total of 63 strengths and 91 weaknesses. In additio n to finding a great diversity of views captured in
the selected texts, the primary data analysis allow ed us to set the parameters with the highest
frequency. So, the most important for strengths is flexibility representing 29.7% of the total of
studied bibliography and an occurrence frequency of 13.6% from the total of identified strengths
(Figure 2).

Figure 2. Frequency of principal strong indices rel ated to 102 'strong articles'

Figure 3. Frequency of principal week indices relat ed to the 101 'week articles'

V.M. Cojocariu, I. Lazar, G. Lazar – The Ambivalence of Strengths and Weaknesses of E-Learning Educatio nal Services
61

Reduced social interaction is the most important for weaknesses, representing 9.4% of the
total of studied bibliography and an occurrence fre quency of 5.5% (Figure3).
A significant frequency difference of the weaknesse s indices compared to the strengths
indices was observed. If the top 5 strengths have f requencies ranging between 13.6% and 7%, none
of the weaknesses has an occurrence frequency over 6%, all barely ranging between 5.5% and
3.4%, relative to the middle of strengths frequency range. For the practice of ewlearning educational
services, this may show either a still insufficient detection or theoretical analysis of weaknesses, o r,
indeed, the superiority of these services.

Step IV/Objective 4-selecting and highlighting the ambivalent strengths and weaknesses,
statistical analysis of ambivalent strengths and we aknesses weight
Based on the data presented in Figure 2 and Figure 3 and carrying out a comparative and
cumulative analysis to identify ambivalent strength s and weaknesses, the 102 ambivalent
strong/weak indices related to the 'common articles ' in Figure 4 were highlighted.

Figure 4. Frequency of strong/weak indices related to the 102 'common articles'

Their analysis shows that:
1. There are at least 13 ambivalent indicators iden tified in the studied literature (Table 1);
2. The strengths weight is 62% while the weight of weaknesses is 38.9%, resulting in a
pretty big difference in favor of underlining and s upporting the strengths, 23.1% more than in favor
of weaknesses. These data indicate a significantly higher perception and approach in favor of
appreciating the strengths of ewlearning educationa l services, even in the case of their ambivalence.
3. In this context, the data illustrate the following three cases :
3.1. a huge gap between the perception and the interpret ation of an index as strength and as
weakness (e.g., flexibility is regarded 7.5 times m ore a strength rather than a weakness).
It can be seen that this category of indices defini tely belongs to strengths, acknowledged
and validated by a large number of studies. In rela tion to these, efforts will be made for
the development, improvement, elevation and obtaini ng superior parameters.
3.2. a relative correspondence between the perception an d the interpretation of an index as
strength and as weakness (e.g., the time required t o design and implement educational
services is considered a strength at a rate of 3.2% and a weakness at a rate of 3.3%). It
results that this category of indices has to be stu died thoroughly and watched in

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

62 experimental studies, to replace the uncertainty ar ea in their analysis, to determine
which their area of predominance is, to what extent their identified limits and
shortcomings have been reduced to allow their conve rsion into strengths or not;
3.3. a very large gap between the perception and interpr etation of an index as a weakness
and as a strength (e.g., lack of instructional deli very is considered as being a weakness
20 times more than a strength).This shows that this category of indices comes into focus
as weaknesses which need to be analyzed, studied an d experimented in order to reduce
their negative impact.

Table 2. System of common indices on categories and frequencies related to 102
common articles
Common indices Categories Frequency (%)
1. flexibility A. Technical support 18.6%
2. interactivity “ 15.4%
64.1% 3. accessibility “ 12.4%
4. usability “ 6.2%
5. connection “ 4.3%
6. time “ 7.2%
7. cost B. Financial support 12.7% 12.7%
8. develop skills C. Educational
approach 4.3%

11.8% 9. quality “ 3.9%
10. diversity “ 2.6%
11. delivery “ 1.0%
12. anxiety/reduce social
impact D. Social impact 7.2% 11.4%
13. responsibility “ 4.2%

Step V/Objective 5-Making a system of distinct cate gories of ambivalent indices
An additional and very interesting perspective is p rovided by the analysis of common
indices (ambivalent), based on the 102 workswcommon articles type, and their frequencies (Figure
5).

Figure 5. Frequency of common indices related to 10 2 'common articles'

V.M. Cojocariu, I. Lazar, G. Lazar – The Ambivalence of Strengths and Weaknesses of E-Learning Educatio nal Services
63
The data reading indicates 13 ambivalent indices as percentages in descending order: 1.
flexibility, 19%; 2. interactivity, 15.4%; 3. cost, 13%; 4. accessibility, 12%; 5. time, 7%; 6.
anxiety/reduce social impact, 7%; 7. usability, 6%; 8. connection, 4%; 9. develop skills, 4%; 10.
responsibility, 4%; 11. quality, 4%; 12. diversity, 3%; 13. delivery, 1% (Figure 5).
The first position occupied by flexibility, an inde x belonging to the technical support
category, represented by a percentage of 19%, compa red to the last position occupied by delivery,
an index belonging to the educational approach cate gory, represented by a percentage of 1% are
both appreciated as being relevant. At this level of analysis, we may generalize considering that the
two parts of the fight for the better use of new le arning technologies are between these (technical)
advantages and (pedagogical) limits.

Table 2. Weight of indices categories through a sur vey of literature from 2000 to 2012
No. Category of
common
indices Frequency of
common indices
category related to
102 'strong articles' Frequency of
common indices
category related to
101 'week articles' Fr equency of
common indices
related to 102
'common
articles'
1. technical support 47.7% 11.7% 64.1%
2. financial support 6.4% 4.6% 12.7%
3. educational approach 5.7% 14.4% 11.8%
4. social impact 2.2% 8.2% 11.4%
total 62% 38.9% 100%

The effort to fit common (ambivalent) indices into categories and frequencies generated the
system from Table 2.
We systematized the four categories of indices and presented the following in the order of
frequencies in Table2: 1. technical support; 2. fin ancial support; 3. educational approach; 4. social
impact. We proceeded, also intrawcategorically, to creating a hierarchy of indices based on their
frequency. The prevalence of indices from the techn ical category can be remarked in an
overwhelming proportion of 64.1%, so that from the total of the first 7 indices (Figure 5), 6 indices
totally cover technical support. The other three ca tegories of indices are positioned far away from it
and the weights are relatively close to each other: financial support, 12.7%; educational approach,
11.8%and social impact, 11.4%. It can be seen that the amount of their weight, 35.9%, is slightly
above half the weight of the technical support cate gory. The data confirm the findings of previous
partial analyses according to which the technical a spect of educational services represents the
researchers and practitioners’ greatest interest.

Correlating the data presented and analyzed on the basis of figures 2w5, we undertook a
systematic synthesis of categories of indices and w e obtained the following results:
1. Of the whole amount of the consulted literature, 62 % of sources highlight strong indices
while only 38.9% highlight weak indices, showing a superior interest for the advantages of
ewlearning educational services;
2. It is interesting that both the largest weight (47. 7%)w technical support and the smallest
weight (2.2%)w social impact , belong to strong indices;
3. The difference between the highest and lowest weigh t recorded for strong indices is very
high, 45.5%, marking almost a break between the tec hnical support (47.7%) and the other
three categories of indices (financial support, edu cational approach and social impact) which
together cover only 14.3% of the strong indices. Th e data clearly show a higher interest in
the technical performances of these services, even users’ exuberance related to their novelty
and attractiveness.

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

64 4. The difference between the highest and lowest weigh t recorded for weak indices is 9.8%,
and by reporting it to the same difference of stren gths, it is 4.9smaller. At the same time,
analyzing differences by category of indices, we ca n see a more balanced approach, without
breaks, even relatively homogeneous, the difference from one category of weak index to
another being an average of 3.3%. Considering the p eriod when the analyzed studies were
published, it can be appreciated that, relative to strengths, users are becoming more
interested in technical performance, while in relat ion to weaknesses they become more
cautious and demanding in many fields;
5. For strong indices, the first two categories which become obvious as weight arethe technical
support (47.7%) and the financial support (6.4%), s howing the manifest support type
advantages through which the analyzed services are detached from classical education
services. This strong interest for technical and fi nancial aspect is convertible, paradoxically,
in a weakness in terms of psychowpedagogical intere st for adapting the ewlearning services to
cognitive peculiarities of the beneficiaries. These data correlate well with those to be
analyzed at point 6;
6. For weak indices, the first two categories which be come obvious are the educational
approach (14.5%) and the technical support (11.7%). The first weight indicates the serious
and profound pedagogical shortcomings of this categ ory of educational services. This result
is confirmed by the relatively small weight obtaine d by the educational approach category as
strength (5.7%), 2.6 times smaller than the one hig hlighted for weaknesses (14.5%).From a
theoretical perspective, these data reinforce the p revalence of the technicist approach of ew
learning educational services, with strong emphasis on the advantages of this type and with
a relative delay in getting aware of the individual and social limits and dangers included into
this perspective. The 4.1 times smaller weight of t echnical support (11.7%) as a weakness
indicates that although the strengths of this categ ory of indices are clear, there are also at
least 25% problems which should be solved in order to optimize them;
7. Data relating to the educational approach are of pa rticular importance. They reflect a
paradoxical situation: while the indices considered strengths only cover 5.7% of the total of
62%, i.e. a weight of 9.2% of strengths, their weak nesses, being highlighted much better
(14.4%), express 31% of the total of weaknesses. It is confirmed the prevailing perception of
pedagogical shortcomings of ewlearning educational services. By reporting educational
approach weight to the weaknesses of the technical support category (11.7%), it seems
natural and relatively understandable (meaning that pedagogical limitations of these serv ices
are obvious, given the constant innovation and deve lopment of the field and the difficult
reported to a human resource characterized by fluct uation and subjective parameters), and
even higher,with a percentage of 2.7%.However, the weight of strengths awareness of this
category of indices is extremely low (5.7%), both i n relation to all categories (62%) and
especially to the technical support as a strength ( 47.7%) (addressed 8.36 times more like a
strength than the educational approach).These data raise serious questions about the
pedagogical effectiveness of these services, or abo ut the interest for the studies addressing
this issue.
8. The lowest weight for strong indices belongs to soc ial impact (2.2%) which, together with
the weight located in the third position for weak i ndices (8.2%), 3.7 times bigger than the
weight of social impact considered as a strength, d etermines the understanding of this index
(even if ambivalent) as being, in reality, mostly a weakness;
9. The l owest weight for weak indices belongs to financial support (4.6% ), 1.8 times less than
the social impact and 1.4 times less than the recog nition of the same category of indices as
strength. These data indicate that the increasing c osts arising from the creation and
improvement of these services are recognized, and a lso the availability of public and private
companies and institutions to invest in this area.

V.M. Cojocariu, I. Lazar, G. Lazar – The Ambivalence of Strengths and Weaknesses of E-Learning Educatio nal Services
65
The data reconfirm the researchers and practitioner s overriding interest for the technical
dimension of educational services, seen as a repres entative category both for strengths (47.7%) and
as an ambivalent category (64.1%). Some debatable p erspectives are also stated on the pedagogical
side of these services which is the leader when it comes to weaknesses (14.4%) and just the third as
an ambivalent index (11.8%) because of its poor ide ntification as strength.
We are going to fathom the study of ambivalent indi ces categories by analyzing multiple
dichotomous responses. This will allow us to study the variation of common indices, with the
following response options: 1 for yes (the index wa s identified) and 0 for no (the index was not
identified).In this article, we will analyze the mu ltiple dichotomous responses by the grouping year
variable.

Table 3. Common indices of technical support catego ry grouping by stages years
Stage years
Tota
l 2000w
2004 2005w
2008 2009w
2012
Technica
l support a 1. flexibility Count 12 17 28 57
% within Technical support 21.1% 29.8% 49.1%
2.
interactivity Count 7 23 17 47
% within Technical support 14.9% 48.9% 36.2%
3.
accessibility Count 7 10 21 38
% within Technical support 18.4% 26.3% 55.3%
4. connection Count 3 5 5 13
% within Technical support 23.1% 38.5% 38.5%
5. usability Count 3 6 10 19
% within Technical support 15.8% 31.6% 52.6%
6. time Count 4 7 11 22
% within Technical support 18.2% 31.8% 50.0%
Total Count 19 32 48 99
Percentages and totals are based on respondents.
a. Dichotomy group tabulated at value 1.

The evolution of common indices from the technical support category grouped by stage
years can be followed in Table 3. What do these dat a show?
− Firstly there is speeding growth of analyses target ing these indices.
− Of the total of six indices, four of them record gr owth on each separate period (flexibility,
accessibility, usability, time). One index (interac tivity) records growth on the first period,
and it has a slight setback in the second, even if it is a higher percentage compared to the
first period. The last index (connection) is the on ly one which records growth on the first
period and then it stagnates.
− The 'usability' index has the most accelerated grow th throughout the period 2000w2012,
which is 3.3 times.
− For the period 2005w2008 compared to 2000w2004, the most significant growth is given by
the 'interactivity' index, 3.3 times, which then wi ll record a decrease of 0.7 times compared
to the previous quarter. For the last period, the m ost representative growth of 2.1 times is
given by the 'accessibility' index.

Overall, we can say that there is still a growing i nterest from researchers and practitioners
for all indices ranging from the technical support category. And these data confirm the previous
analysis that emphasized the priority and impact of the technical aspect of ewlearning educational
services. Moreover, unfortunately, confirm their lo w interest for psychowpedagogical indicators,
with relevance for the cognitive training of the be neficiaries.

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

66 The evolution of common indices from the financial support category grouped by stage
years can be followed in Table 4. What do these dat a show?
− There is an increasing number of studies aiming at both indices belonging to this category.
− While the growth of the 'not evidenced financial su pport' index is a continuous one, from
one time slot to another, the growth recorded by th e 'evidenced financial support' index is a
twowsteps process in which the initial impact of st udies on the topic is slightly decreasing by
7.7% and then, on the second interval, there is a l arger increase, by 15.4%.
− The most accelerated growth, 4.1 times, throughout the period 2000w2012 is recorded by the
index 'not evidenced financial support'.
− For the period 2005w2008 compared to the period 200 0w2004, there is a 2.3 growth in the
'not evidenced financial support' index and a 1.3 t imes decrease in the 'evidenced financial
support' index. For the last interval, the increase d interest in studies of the two indices is
relatively close, slightly higher for the 'no evide nced financial support' index which is 1.8
times, compared to the growth of the 'evidenced fin ancial support' index which is 1.6 times.

Table 4. Financial support indices grouping by stag es years
stages years
Total 2000 w
2004 20 05 w
2008 2009 w
2012
Financial
support No
evidenced Count 13 30 53 96
% within cost 13.5% 31.3% 55.2% 100.0%
evidenced Count 13 10 16 39
% within cost 33.3% 25.6% 41.0% 100.0%
Total Count 26 40 69 135
% within cost 19.3% 29.6% 51.1% 100.0%

Overall, we can say that there is still a growing i nterest from researchers and practitioners
for all indices belonging to the financial support category.
The evolution of common indices from the educationa l approach category grouped by stage
years can be followed in Table 5. What do these dat a show?
− Firstly, there is a rhythmic growth of the analyses related to only two indices (develop skills
and diversity strategies) of the total of four.
− One of the other two indices (delivery) registers a growth of 33.4% on the first interval,
which is almost double, and on the second interval it marks a complete drop, as no studies
have been revealed to address this issue. The last index (quality) is the only one which has
the most fluctuating evolution, marking an 8.3% dec rease in the first period and a 41.6%
increase over the last period, which means 3.5 time s.
− The 'diversity' index has the most accelerated grow th, 5 times, throughout the whole period
2000w2012.
− For the period 2005w2008, compared to the period 20 00w2004, the growth of all three indices
that are in progress (develop skills, diversity, de livery) is equal, respectively 2times. For the
last interval, the 'quality' index gives the most r epresentative growth of 3.5 times of all 3
indices in progress.

Throughout the whole period, we can say that there is a fluctuating interest from researchers
and practitioners for indices belonging to the educ ational approach category. Studies related to
develop skills and methodological diversity have a sustained growth. At the same time, they have
not been identified (statistically significant) art icles that address the impact of the ewlearning
educational services on the stimulation/development of higher cognitive processes (thinking,
memory, imagination, language). Studies related to the quality of these services record a fluctuating

V.M. Cojocariu, I. Lazar, G. Lazar – The Ambivalence of Strengths and Weaknesses of E-Learning Educatio nal Services
67
evolution, initially marked by a decrease, followed then by the most significant growth,
emphasizing the more and more serious concern about this aspect in the last 3 years. Delivery seems
to be the index for which the researchers’ interest seems to have totally decreased lately.
Data for (ambivalent) common indices belonging to t he social impact category are presented
in Table 6.

Table 5. Common indices of educational approach cat egory grouping by stages years
stages years
Total 2000w
2004 2005w
2008 2009w
2012
Educational
approach develop
skills Count 2 4 7 13
% within
Educational_benefits 15.4% 30.8% 53.8%
diversity Count 1 2 5 8
% within
Educational_benefits 12.5% 25.0% 62.5%
quality Count 3 2 7 12
% within
Educational_benefits 25.0% 16.7% 58.3%
delivery Count 1 2 0 3
% within
Educational_benefits 33.3% 66.7% 0.0%
Total Count 6 8 16 30
Percentages and totals are based on respondents.
a. Dichotomy group tabulated at value 1.

Table 6. Common indices of social impact category g rouping by stages years
stages years Total
2000w
2004 2005w
2008 2009w
2012
Social
impact a anxiety Count 6 6 10 22
% within Social impact 27.3% 27.3% 45.5%
responsibility Count 2 4 7 13
% within Social impact 15.4% 30.8% 53.8%
Total Count 8 8 17 33
Percentages and totals are based on respondents.
a. Dichotomy group tabulated at value 1.

The cumulated data of the entire analysis confirm h ypothesis no. 1 of the research. There is a
set of ambivalent strengths and weaknesses of ewlea rning educational services, which we have
identified on the basis of quality and quantity ana lysis of a package of 192 papers published
between 2000 and 2012. This set has 4 important cat egories of indices: technical support,
educational approach, financial support, social imp act. A set of 13 ambivalent indicators were
identified by reporting them to the above mentioned categories : 1. flexibility, 19%; 2. interactivity ,
15.4%, 3. cost, 13%; 4. accessibility, 12; 5. time, 7%; 6. anxiety/reduce social impact, 7%; 7.
usability, 6%; 8. connection, 4%; 9. develop skills , 4%; 10. responsibility, 4%; 11. quality, 4%; 12.
diversity, 3%; 13. delivery, 1%.
According to the data presented and analyzed in Fig ures 1w4 and Tables1w5we can conclude
that ewlearning educational services are interestin g, as practice and also as theory, especially due
rather to the prevailing technical and financial be nefits (54.1%) than to those of sociowpedagogical
and human type (7.9%). Accordingly, the difficult i ssues the beneficiaries of these services are most

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

68 worried about are related mainly to technical suppo rt and educational approach (26.1%) and much
less to social impact and financial support (12.8%) .

4. Conclusions
The analysis of the studied bibliography has not le d us to identify a systematization of
strengths and weaknesses, much less to the idea of their ambivalence or their systematization from
this point of view. Through their approaches most r ecent studies (Bhuasiri et al., 2012; Elster, 2010;
Lin, 2011; Smetana & Bell, 2012; Sun et al., 2008)m ention, both our previous statement and the
results we obtained from the analysis of the releva nt literature.In general, there are developed
bibliographic studiesor practical, experimental per spectives(Bellefeuille et al., 2008; Lee & Yoon,
2009; Székely & Nagy, 2011), related to designing a nd implementing ewlearning solutions in
education. Most of them are about drawing some conc lusions on the strengths and weaknesses of
these services; they may also have optimization sug gestions, unlike our study which emphasizes
their ambivalence.
Based on these results we consider that the hypothe sis 2 of our study is also validated. We
have had sufficient data to generate in a future st age of our efforts, a predictive model of ewlearnin g
educational services with higher educational level, given the ambivalence of the identified indices.
In the same time, in future studies, an additional attention should be paid to the identification of
different categories of cognitive effects (superior cognitive processes: thinking, with its forms,
memory, imagination, language; specific skills of i ntellectual work but also cognitive motivation)
produced by the use of the ewlearning services.
Compared to the areas of uncertainty identified at the beginning of the study, we have been
able, on the basis of the effective study of 192 te xts, to give the following responses by
systematizing the highlighted perspectives:
/head2right Ewlearning educational services are necessary, for the moment, rather due to the technology
they objectify than to the pedagogy they incorporat e and promote. An approach from a
pedagogical and also a technological perspective is necessary.(Cuthbert & Slotta, 2004).
/head2right Strengths and weaknesses are, to a large extent, no t 'pure', but ambivalent, simultaneously
incorporating meanings and limits with different we ights.
/head2right The reaction related to those dimensions of ewlearn ing educational services which were
identified simultaneously as strengths and weakness es, must be discerning, cautious and
within bounds, so that the maximum impact can be do uble, both for the positive accents to
be valued and the negative ones to be reduced.
/head2right Programmers of ewlearning educational services must become aware of the ambivalence of
their services'features, and be supported to adapt their predominantly technical perspective
to an interdisciplinary one, with pedagogical and s ociowhumanistic accents.

5. Acknowledgements
This research was financially supported by the Exec utive Unit for Financing Higher
Education, Research, Development and Innovation (Gr ant PNwIIwPTwPCCAw2011w3.2w1108,
“Networked interactive ceramic whiteboards with int egrated sound (ENO) for teaching and learning
science and technology”).

V.M. Cojocariu, I. Lazar, G. Lazar – The Ambivalence of Strengths and Weaknesses of E-Learning Educatio nal Services
69
References

Bellefeuille, G., McGrath, J., & Jamieson, D. (2008 ). A pedagogical response to a changing world:
Towards a globallywinformed pedagogy for child and youth care education and practice.
Children and Youth Services Review, 30 (7), 717w726.
Bennett, S., Bishop, A., Dalgarno, B., Waycott, J., & Kennedy, G. (2012). Implementing Web 2.0
technologies in higher education: A collective case study. Computers & Education, 59 (2),
524–534.
Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J. , & Ciganek, A. P. (2012). Critical success
factors for ewlearning in developing countries: A c omparative analysis between ICT experts
and faculty. Computers & Education, 58 (2), 843w855.
Clark, R. C. & Mayer, R. E. (2008). E3learning and the science of instruction: proven g uidelines for
consumers and designers of multimedia learning (3rd ed.): Pfeiffer.
Cuthbert, A. J. & Slotta, J. D. (2004). Research Re port: Designing a webwbased design curriculum
for middle school science: the WISE ‘Houses In The Desert’ project. International Journal
of Science Education, 26 (7), 821w844.
Elster, D. (2010). Learning Communities in Teacher Education: The impact of ewcompetence.
International Journal of Science Education, 32 (16), 2185w2216.
Jomah, O., Masoud, A. K., Kishore, X. P., & Aurelia , S. (2016). Micro Learning: A Modernized
Education System. BRAIN. Broad Research in Artificial Intelligence an d Neuroscience,
7(1), 103w110.
Lee, B.C. & Yoon, J.O. (2009). Learners’ acceptance of ewlearning in South Korea: Theories and
results. Computers & Education, 53 (4), 1320w1329.
Liao, S. H., Chu, P. H., & Hsiao, P. Y. (2012). Dat a mining techniques and applications – A decade
review from 2000 to 2011. Expert Systems with Applications, 39 (12), 11303–11311.
Lin, K.wM. (2011). ewLearning continuance intention : Moderating effects of user ewlearning
experience. Computers & Education, 56 , 515–526.
Masud, A. H. & Huang, X. (2012). An Ewlearning Syst em Architecture based on Cloud Computing.
World Academy of Science, Engineering and Technolog y, 62 , 74w78.
Mata, L., Lazar, I., & Lazar, G. (2012). The current framework of e3learning concept
representation. Paper presented at the International Conference “E ducation and Creativity
for a knowledge based society”, Bucuresti
Middleton, D. (2010). Putting the learning into ewl earning. European Consortium for Political
Research, 9 (1), 5w12.
Naaji, A., Mustea, A., Holotescu, C., & Herman, C. (2015). How to Mix the Ingredients for a
Blended Course Recipe. BRAIN. Broad Research in Artificial Intelligence an d
Neuroscience, 6 (1w2), 106w116.
Naidu, S. (2006). E3learning. A Guidebook of Principles, Procedures a nd Practices :
Commonwealth Educational Media Center for Asia (CEM CA).
Nedelea, A. M., & Costea, M. (2016). The Presence a nd Activity on Facebook of the Informative
Travel Organizations in Romania. BRAIN. Broad Research in Artificial Intelligence an d
Neuroscience, 7 (2), 41w48.
Ossiannilsson, E.S.I. (2012). Quality enhancement o n ewlearning. Campus3Wide Information
Systems, 29 (4), 312 w 323.
Slevin, J. (2008). E ‐learning and the transformation of social interacti on in higher education.
Learning, Media and Technology, 33 (2), 115w126.
Smetana, L. K., & Bell, R. L. (2012). Computer Simu lations to Support Science Instruction and
Learning: A critical review of the literature. International Journal of Science Education,
34 (9), 1337w1370.
Sun, P.C., Tsai, R.J., Finger, G., Chen, Y.Y., & Ye h, D. (2008). What drives a successful ew
Learning? An empirical investigation of the critica l factors influencing learner satisfaction.
Computers & Education, 50 (4), 1183w1202.

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

70 Székely, L. & Nagy, Á. (2011). Online youth work an d eYouth — A guide to the world of the
digital natives. Children and Youth Services Review, 33 (11), 2186w2197.

References used for ambivalence study
Abdelaziz, M., Kamel, S. S., Karam, O., & Abdelrahm an, A. (2011). Evaluation of Ewlearning
program versus traditional lecture instruction for undergraduate nursing students in a faculty
of nursing. Teaching and Learning in Nursing, 6 , 50w58.
Abouchedid, K. & Eid, G. M. (2004). Ewlearning chal lenges in the Arab world: revelations from a
case study profile. Quality Assurance in Education, 12 (1), 15w27.
Adams, J. & Morgan, G. (2007). "Second Generation" EwLearning: Characteristics and Design
Principles. International Journal on ELearning, 6 (2), 157w185.
Alptekin, S.E. & Karsak, E.E. (2011). An integrated decision framework for evaluating and
selecting ewlearning products. Applied Soft Computing, 11 , 2990w2998.
Andersson, A. (2008). Seven major challenges for ew learning in developing countries: Case study
eBIT, Sri Lanka. International Journal of Education and Development using Information
and Communication Technology, 4 (3), 45w62.
Bahreininejad, A. (2006). EwLearning and Associated Issues in Iran. International Journal of
Distance Education Technologies, 4 (4), 1w4.
Barber, T. C. (2011). The Online Crit: The Communit y of Inquiry Meets Design Education. Revue
de l'Education a Distance, 25 (1), 2w16.
Barbour, M. K. & Hill, J. (2011). What are They Doi ng and How are They Doing It? Rural Student
Experiences in Virtual Schooling. Revue de l'Education a Distance, 25 (1), 2w14.
Baylari, A. & Montazer, G. A. (2009). Design a pers onalized ewlearning system based on item
response theory and artificial neural network appro ach. Expert Systems with Applications,
36 , 8013–8021.
BeatonwGarcia, S., Casper, C., & Tidwell, J. (2012) . Ewlearning SupportwA Library Perspective.
Distance Learning, 9 (2), 36w41.
Bell, M., Martin, G., & Clarke, T. (2004). Engaging in the future of ewlearning: a scenarioswbased
approach. Education + Training, 46 (6), 296w307.
Berge, Z.L. & Giles, L. (2008). Implementing and Su staining EwLearning in the Workplace.
International Journal of Web3Based Learning and Tea ching Technologies, 3 (3), 44w53.
Biasutti, M. (2011). The student experience of a co llaborative ewlearning university module.
Computers & Education, 57 , 1865–1875.
Bıhmová, P. (2012). Educational and professional co mpetence of experts in the prevention of
socialwpathological phenomena. Technology of Education, 20 (2).
Bowen, S. T. (2000). Ewlearning tested. InfoWorld, 22 (44), 38w39.
Cappel, J. J. & Hayen, R. L. (2004). Evaluating ewl earning: a case study. The Journal of Computer
Information Systems, 44 (4), 49w56.
Costa, G. J. M. & Silva, N. S. A. (2010). Knowledge versus content in ewlearning: A philosophical
discussion. Inf Syst Front, 12 , 399–413.
Costea, O. (2011). The logic of social development and ewlearning environments. Euromentor
Journal, 2 (1), 119w133.
Dunn, P. (2001). Has ewlearning truly arrived in Eu rope? Training Journal , 34w36.
Ehlers, U. D. (2009). Web 2.0wewlearning 2.0wqualit y 2.0? Quality for new learning cultures. Quality
Assurance in Education, 17 (3), 296w314.
ELwDeghaidy, H. & Nouby, A. (2008). Effectiveness o f a blended ewlearning cooperative approach
in an Egyptian teacher education programme. Computers & Education, 51 , 988–1006.
Ferretti, S., Roccetti, M., Salomoni, P., & Mirri, S. (2009). Custom EwLearning Experiences:
Working with Profiles for Multiple Content Sources Access and Adaptation. Journal of
Access Services, 6 , 174–192.

V.M. Cojocariu, I. Lazar, G. Lazar – The Ambivalence of Strengths and Weaknesses of E-Learning Educatio nal Services
71
Freriks, T. A. (2004). ROI and Justification of EwL earning. LIMRA's MarketFacts Quarterly, 23 (1),
30w33.
Fry, K. (2001). Ewlearning markets and providers: s ome issues and prospects. Education +
Training, 43 (4), 233w239.
Gierłowski, K. & Nowicki, K. (2009). A Novel Archit ecture for EwLearning Knowledge
Assessment Systems. International Journal of Distance Education Technol ogies, 7 (2), 1w19.
Githens, R. P. (2006). Cautions: Implementing Inter personal Interaction in Workplace Ewlearning.
TechTrends, 50 (5), 21w27.
Gonzalez, J. F., Rodrıguez, M. C., Nistal, M. L., & Rifon, L. A. (2009). Reverse OAuth: A solution
to achieve delegated authorizations in single signw on ewlearning systems. Computers &
Security, 28 , 843 – 856.
Granic, A. & Adams, R. (2011). User sensitive resea rch in ewlearning: exploring the role of
individual user characteristics. Univ Access Inf Soc, 10 , 307–318.
Greenhow, C. & Belbas, B. (2007). Using activitywor iented design methods to study collaborative
knowledgewbuilding in ewlearning courses within hig her education. Computer3Supported
Collaborative Learning, 2 , 363–391.
Gunasekaran, A., McNeil, R. D., & Shaul, D. (2002). Ewlearning: research and applications.
Industrial and Commercial Training, 34 (2), 44w53.
GuriwRosenblit, S. & Gros, B. (2011). EwLearning: C onfusing Terminology, Research Gaps and
Inherent Challenges. Revue de l'Education a Distance, 25 (1), 1w12.
Hagen, J. M. & Albrechtsen, E. (2009). Effects on e mployees' information security abilities by ew
learning. Information Management & Computer Security, 17 (5), 388w407.
Halkett, R. (2002). Ewlearning and how to survive i t. Industrial and Commercial Training, 34 (2),
80w82.
Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring ewlearning systems
success in universities. Expert Systems with Applications, 39 , 10959–10966.
Heshmatpanah, J. & Neyestanak, A. A. L. (2011). EwL earning Effects on Teaching at ALBORZ
High School (Iran). Creative Education, 2 (2), 71w75.
Hișmanoğlu, M. (2011). Ewlearning Practices in Nort h Cyprus Universities: Benefits, Drawbacks
and Recommendations for Effective Implementation. International Education Studies, 4 (4),
149w159.
Huddlestone, J. & Pike, J. (2008). Seven key decisi on factors for selecting ewlearning. Cogn Tech
Work, 10 , 237–247.
Inglis, A. (2008). Approaches to the validation of quality frameworks for ewlearning. Quality
Assurance in Education, 16 (4), 347w362.
Ion, A. M. (2012). Influence of ICT Development on Education. Informatica Economică, 16 (1),
154w163.
Islam, A., Rahim, N. A. A., Liang, T. C., & Momtaz, H. (2011). Effect of Demographic Factors on
EwLearning Effectiveness in A Higher Learning Insti tution in Malaysia. International
Education Studies, 4 (1), 112w121.
Jones, O., Saunders, H., & Mires, G. (2010). The Ew learning revolution in obstetrics and
gynaecology. Best Practice & Research Clinical Obstetrics and Gy naecology, 24 , 731–746.
Jung, I. (2011). The dimensions of ewlearning quali ty: from the learner’s perspective. Education
Tech Research Dev, 59 , 445–464.
Kang, M., Heo, H., Jo, I.wH., Shin, J., & Seo, J. ( 2010). Developing an Educational Performance
Indicator for New Millennium Learners. Journal of Research on Technology in Education,
43 (2), 157w170.
Kenney, J., Hermens, A., & Clarke, T. (2004). The p olitical economy of ewlearning educational
development: strategies, standardisation and scalab ility. Education + Training, 46 (6), 370w
379.
Khirwadkar, A. (2009). Research and Development in Ewlearning: A Vision. International Forum of
Teaching and Studies, 5 (1), 44w72.

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

72 Kidney, G., Cummings, L., & Boehm, A. (2007). Towar d a Quality Assurance Approach to Ew
Learning Courses. International Journal on ELearning, 6 (1), 17w30.
Kistow, B. (2009). Ewlearning at the Arthur Lok Jac k Graduate School of Business: A Survey of
Faculty Members. International Journal of Education and Development using Information
and Communication Technology, 5 (4), 14w20.
Koven, J. (2003). Tracking trends in ewlearning. Canadian HR Reporter, 16 (20), G5.
Lam, P. & McNaught, C. (2008). A ThreewLayered Cycl ic Model of EwLearning Development and
Evaluation. Journal of Interactive Learning Research, 19 (2), 313w329.
Landsberger, J. (2002). Accessibility: how easy, or even possible… TechTrends, 46 (5), 65w67.
Laxton, R. & Applebee, A. C. (2010). Developing Com munities of Practice around ewLearning and
Project Management. Revue de l'Education a Distance, 24 (1), 123w142.
Lee, J.wY. & Reigeluth, C. M. (2009). Heuristic tas k analysis on ewlearning course development: a
formative research study. Asia Pacific Educ. Rev., 10 , 169w181.
Leman, J., Trappers, A., Brandon, E., & Ruppol, X. (2008). Migration Related Sociowcultural
Changes and ewLearning in a European Globalising So ciety. Stud Philos Educ, 27 , 237–251.
Lin, K.M. (2011). ewLearning continuance intention: Moderating effects of user ewlearning
experience. Computers & Education, 56 , 515–526.
Loh, C. S. (2008). A Suitable Textbook for the Clas sroom. However … Educational Researcher,
36 (9), 573–578.
Marković, M.R. (2010). Education through ewlearning : Case of Serbia. Journal of Business
Economics and Management, 10 (4), 313–319.
Marshall, S. (2010). A Quality Framework for Contin uous Improvement of ewLearning: The ew
Learning Maturity Model. Revue de l'Education a Distance, 24 (1), 143w166.
McCombs, B.L. & Vakili, D. (2005). A LearnerwCenter ed Framework for EwLearning. Teachers
College Record, 107 (8), 1582w1600.
McKee, K., Maureen (2011). The emperor’s new comput er: ICT, teachers and teaching. Revue de
l'Education a Distance, 25 (1), 1w2.
Mele, J. (2006). The Three E'sw Eletronic, Educatio n and Essential. Fleet Owner, 101 (3), 118w121.
Meredith, S. & Burkle, M. (2006). EwLearning: Encou raging International Perspectives. A Mexicanw
UK Comparative Case Study Analysis. International Journal on ELearning, 5 (4), 469w491.
Mihhailova, G. (2006). Ewlearning as internationali zation strategy in higher education: Lecturer's
and student's perspective. Baltic Journal of Management, 1 (3), 270w284.
Morgan, G. (2001). Thirteen "must ask" questions ab out ewlearning products and services. The
Learning Organization, 8 (5), 203w211.
MorterawGutiérrez, F. (2006). Faculty Best Practice s Using Blended Learning in EwLearning and
FacewtowFace Instruction. International Journal on ELearning, 5 (3), 313w337.
Mungania, P., & Hatcher, T. (2004). A Systemic, Fle xible, and Multidimensional Model for
Evaluating EwLearning Programs. Performance Improvement, 43 (7), 33w39.
Munro, R. A. (2005). Understanding the Buzz Around EwLearning: "Searching for … ASQ World
Conference on Quality and Improvement Proceedings, 59 , 131w143.
Mwambazambi, K. (2011). La Pertinence de l’Universi té Ouverte et d’Études à Distance en
Afrique. Revue de l'Education a Distance, 25 (1), 1w8.
Nafukho, F. M. (2007). The Place of EwLearning in A frica’s Institutions of Higher Learning. Higher
Education Policy, 20 , 19w43.
Neville, K., Heavin, C., & Walsh, E. (2005). A case in customizing ewlearning. Journal of
Information Technology, 20 , 117–129.
O’Neill, K., Singh, G., & O’Donoghue, J. (2004). Im plementing eLearning Programmes for Higher
Education: A Review of the Literature. Journal of Information Technology Education, 3 ,
314w323.

V.M. Cojocariu, I. Lazar, G. Lazar – The Ambivalence of Strengths and Weaknesses of E-Learning Educatio nal Services
73
Ozkan, S. & Koseler, R. (2009). Multiwdimensional s tudents’ evaluation of ewlearning systems in the
higher education context: An empirical investigatio n. Computers & Education, 53 , 1285–
1296.
Özpolat, E. & Akar, G. B. (2009). Automatic detecti on of learning styles for an ewlearning system.
Computers & Education, 53 , 355–367.
Pinto, A., Brunese, L., Pinto, F., Acampora, C., & Romano, L. (2011). Ewlearning and education in
radiology. European Journal of Radiology, 78 , 368–371.
Pop, C. (2012). Evaluation of Ewlearning Platforms: a Case Study. Informatica Economică, 16 (1),
155w167.
Roffe, I. (2002). Ewlearning: engagement, enhanceme nt and execution. Quality Assurance in
Education, 10 (1), 40w50.
Savidis, A., Grammenos, D., & Stephanidis, C. (2007 ). Developing inclusive ewlearning and ew
entertainment to effectively accommodate learning d ifficulties. Univ Access Inf Soc, 5 , 401–
419.
Servage, L. (2005). Strategizing for workplace ewle arning: some critical considerations. Journal of
Workplace Learning, 17 (5), 304w317.
Sharma, K., Pandit, P., & Pandit, P. (2011). Critic al success factors in crafting strategic architectu re
for ewlearning at HP University. International Journal of Educational Management, 25 (5),
423w452.
Shurville, S., O'Grady, T. B., & Mayall, P. (2008). Educational and institutional flexibility of
Australian educational software. Campus3Wide Information Systems, 25 (2), 74w84.
Šimonová, I. (2012). Ewlearning in engineering educ ation: FIM UHK contribution to engineering
pedagogy. Technology of Education, 2 , 1w8.
Singh, B., Kapil, R., & Katare, O. P. (2009). Pharm aceutical EwLearning: Precepts, Retrospect and
Prospects. J Young Pharm, 1 (2), 99w109.
Smith, K. (2007). Supporting ewlearning in enterpri se: the TE3 project. Education + Training,
49 (8), 656w671.
Sørebø, Ø., Halvari, H., Gulli, V. F., & Kristianse n, R. (2009). The role of selfwdetermination theory
in explaining teachers’ motivation to continue to u se ewlearning technology. Computers &
Education, 53 , 1177–1187.
Stocker, D. K., Griffin, P. M., & Kocher, C. J. (20 11). Evaluating the Knowledge and Use of Web
2.0 Technology among Criminal Justice Students at T wowYear and Graduate Level
Institutions of Higher Learning. Sociological Viewpoints, 27 , 76w93.
Suanpang, P., & Petocz, P. (2006). EwLearning in Th ailand: An Analysis and Case Study.
International Journal on ELearning, 5 (3), 415w438.
Sung, Y.wT., Chang, K.wE., & Yu, W.wC. (2011). Eval uating the reliability and impact of a quality
assurance system for Ewlearning courseware. Computers & Education, 57 , 1615–1627.
Taylor, L., McGrathwChamp, S., & Clarkeburn, H. (20 12). Supporting student selfwstudy: The
educational design of podcasts in a collaborative l earning context. Active Learning in
Higher Education, 13 (1), 77–90.
Torres Maldonado, U. P., Khan, G. F., Moon, J., & R ho, J. J. (2011). Ewlearning motivation and
educational portal acceptance in developing countri es. Online Information Review, 35 (1),
66w85.
Trotter, A. (2002). Ewlearning goes to school. Education Week, 21 (35), 13w18.
Turvey, K. (2010). Pedagogicalwresearch designs to capture the symbiotic nature of professional
knowledge and learning about ewlearning in initial teacher education in the UK. Computers
& Education, 54 , 783–790.
Udo, G. J., Bagchi, K. K., & Kirs, P. J. (2011). Us ing SERVQUAL to assess the quality of ew
learning experience. Computers in Human Behavior, 27 , 1272–1283.
Uhomoibhi, J. O. (2006). Implementing ewlearning in Northern Ireland: prospects and challenges.
Campus3Wide Information Systems, 23 (1), 4w14.

BRAIN. Broad Research in Artificial Intelligence an d Neuroscience
Volume 7, Issue 3, August 2016, ISSN 2067-3957 (onl ine), ISSN 2068 – 0473 (print)

74 Waight, C. L. & Stewart, B. L. (2005). Valuing the adult learner in ewlearning: part twowinsights
from four companies. Journal of Workplace Learning, 17 (5), 398w414.
Waller, V. (2004). The State of the Nation: ewlearn ing in the 21st century. Training Journal , 30w36.
Wang, Q., Zhu, Z., Chen, L., & Yan, H. (2009). Ewle arning in China. Campus3Wide Information
Systems, 26 (2), 77w81.
Wang, T. J. (2010). Educational Benefits of Multime dia Skills Training. TechTrends, 54 (1), 47w57.
Wu, H.wY. & Lin, H.wY. (2012). A hybrid approach to develop an analytical model for enhancing
the service quality of ewlearning. Computers & Education, 58 , 1318–1338.
Yunus, M., Salehi, H., & Chenzi, C. (2012). Integra ting Social Networking Tools into ESL Writing
Classroom:Strengths and Weaknesses. English Language Teaching, 5 (8), 42w48.
Zaharias, P. & Koutsabasis, P. (2012). Heuristic ev aluation of ewlearning courses: a comparative
analysis of two ewlearning heuristic sets. Campus3Wide Information Systems, 29 (1), 45w60.
Zahner, J. (2002). Teachers Explore Knowledge Manag ement and Ewlearning as Models for
Professional Development. TechTrends, 46 (3), 11w16.
Zhang, D. & Nunamaker, J. F. (2003). Powering EwLea rning In the New Millennium: An Overview
of EwLearning and Enabling Technology. Information Systems Frontiers, 5 (2), 207–218.