Medical Students' Online Compatibility

Web-based Survey and Regression Analysis to
Determine USAIM Students’ Online
Compatibility – A Pilot Study

Dr S. Sanyal
MBBS, MS (Surgery), ADPHA, MSc (Health Informatics)
Associate Professor, Faculty of Gross Anatomy and Neurosciences
University of Seychelles American Institute of Medicine (USAIM), Seychelles

Original research conducted in USAIM

May 2008

Web-based Survey and Analysis of USAIM Students’ Online Compatibility – Pilot Study

COPYRIGHT AND DISCLAIMER

Copyright Notice

Attention is drawn to the fact that copyright of this project rests with its author and USAIM. This copy of

the project has been supplied on condition that anyone who consults it is understood to recognise that its

copyright rests with its author and USAIM, and that no quotation from the project and no information

derived from it may be published without the prior written consent of the author or USAIM.

Restrictions on Use

This project may be made available for consultation within the USAIM Library and may be photocopied or

lent to other libraries solely for the purposes of education, research and consultation.

Disclaimer

The opinions expressed in this work are entirely those of the author except where indicated in the text.

Disclosures and conflicts of interest
The author discloses no incentives, financial or otherwise, and no conflicts of interest during conduct of this
study and production of this treatise.

Signature

2008-05-1

******

USAIM Online Survey; Dr S. Sanyal, Assoc. Prof., Faculty of Anatomy & Neurosciences, USAIM, Seychelles May 2008 i


ACKNOWLEDGEMENTS
The author wishes to thank all those who helped in the research project and the production of this treatise,
either directly or indirectly. The first and foremost is Dr Fauzia Alkhairy, MD; President of University of
Seychelles American Institute of Medicine (USAIM), Fort Wayne, Indiana, USA. Without her permission
the whole project would not have taken off in the first place. Next are Mr Tariq Alkhairy, Managing
Director of USAIM, and Dr Rana Shinde, PhD; Dean of USAIM, whose tacit support during the conduct of
the student survey within the USAIM campus was invaluable. Thank you to all personalities.

Then the author would like to thank the entire student body of USAIM for being such enthusiastic
participants in the Web-based survey. Such was the enthusiasm that many students completed the survey
from home, from their personal Internet connections, due to paucity of time during regular college hours.
Such a response was heart-warming, to say the least.

Next the author would like to acknowledge the tacit cooperation of other staff members and colleagues in
the faculty, notably Dr Sanjay Kulkarni, MD, Department of Microbiology and Immunology, USAIM; he
was a great morale-booster during the process of the survey, by being there when it was needed most. Dr
Justin Gnanou, MD, Department of Biochemistry, USAIM, though he is no longer with us in USAIM,
deserves special thanks for making the SPSS v.10 software package available to the author.

There are two researchers who the author has never met. They are Franz Faul and Edgar Erdfelder of the
Department of Psychology, University of Bonn, Germany. They deserve thanks in absentia for taking the
pains to make the G*Power power analysis software package available free of charge to researchers all over
the world.

The author also gratefully acknowledges M/s eLearners™Advisor for enabling the use of an adaptation of
their questionnaire for the purpose of this Web-based survey.

Finally, how can the author overlook the silent contribution of his lovely spouse? During the trying months
of the project, she bore with his infrequent phone calls, taciturn monosyllabic responses and pre-occupations
with the project with silent fortitude and patient forbearing, which only the deep unspoken understanding
capabilities of a woman can bring forth.

***********

USAIM Online Survey; Dr S. Sanyal, Assoc. Prof., Faculty of Anatomy & Neurosciences, USAIM, Seychelles May 2008 ii


TABLE OF CONTENTS

TITLE PAGE
COPYRIGHT AND DISCLAIMER Page i
ACKNOWLEDGEMENTS Page ii
ABSTRACT Page iv
CHAPTER 1: PRELIMINARIES AND LITERATURE REVIEW Page 1 – 7
CHAPTER 2: MATERIALS AND METHODS Page 8 – 15
CHAPTER 3: STATISTICAL ANALYSIS AND RESULTS Page 16 – 40
CHAPTER 4: DISCUSSION Page 41 – 57
REFERENCES Page 58 – 61

USAIM Online Survey; Dr S. Sanyal, Assoc. Prof., Faculty of Anatomy & Neurosciences, USAIM, Seychelles May 2008 iii


ABSTRACT
Immediate objective: To identify technical glitches (problems) in the newly-devised Web-based
questionnaire and try to devise a future-proof system through troubleshooting

Short term goals: (1) Determine USAIM students’ online learning preparedness against 5 set parameters;
(2) Devise a mathematically objective scoring system for each parameter; (3) Determine overall online
preparedness (Compatibility Score) of USAIM students; (4) Determine robustness of LS questionnaire
currently being used for the study; (5) Identify relationships between students’ personal and general
characteristics vis-à-vis online learning; (6) Suggest improvements to questionnaire and survey; and (7)
Suggest ways of overcoming barriers to online learning

Long term goals: (a) Use Compatibility Score as baseline for future studies in USAIM and elsewhere; and
(b) Render online learning and examinations a regular and feasible option for USAIM

Design: It was designed as a Web-based questionnaire survey of USAIM students. It was a one-shot, cross-
sectional, non-experimental pilot study.

Setting: The study was conducted within the campus of USAIM.

Participants / Data sources: Thirty-five students from PC-1 to PC-5 were the participants. Their feedback
from the questionnaire provided the data for statistical analysis

Main outcome measures: The following mathematical outcomes were generated: (A) Weighted scores for
technology access parameters; (B) Weighted scores for personal parameters; (C) Weighted scores for
technical proficiencies; (D) Weighted scores for online LS preferences; (E) Weighted scores for students’
general considerations; (F) Overall Compatibility Score of USAIM students; (G) Correlation, internal
consistency and factor analysis scores of online LS questionnaire items; (H) Correlation and regression
analysis scores of personal factors vs. general considerations; (I) Predictive model and formula of students’
online learning characteristics; and (J) Power analysis scores vis-à-vis sample size.

Results: Identification of 5 problems and their tentative solutions; Weighted scores (expressed as percentage
of maximum) of measured parameters were; Type of Internet access (63.7%); Primary computer (80%);
Motivation (70%); Schedule (58.5%); Hours of online study (57.8%); Technical proficiencies (73.9%);
Online LS preferences (64%); Online concerns (64%); Education level (54.3%); Age (73.3%); Overall
Compatibility Score of USAIM students (64%); Pearson’s correlations (‘Pro-Yes’ vs. ‘Anti-No’) r = 0.3; (p
= 0.48; 2-tailed; N = 8); Reliability coefficients (Intra-class, Cronbach α, Guttman, Spearman-Brown) 0.42
to 0.45; PCA factor analysis – Component-1 (‘Anti-onlineness’ factor); Pearson’s correlation (‘Concerns’
vs. ‘Age’) r = -0.963; (p = 0.037; 2-tailed; N = 4); Regression analysis (‘Concerns’ vs. ‘Age’) ‘Concerns’ =
80.261 – 0.898(‘Age’); Regression analysis (‘Concerns’ vs. ‘Motivation’) ‘Concerns’ = 80.261 +
0.638(‘Motivation’); Predictive model ‘Concerns’ = Constant + [0.638(‘Motivation’)] – [0.898(‘Age’)]; Post
hoc power analysis (N = 35) – Power = 0.43 (1-tailed), 0.30 (2-tailed); Compromise power analysis (N = 35)
– Power = 0.77 (1-tailed), 0.68 (2-tailed); A priori analysis – Required N = 102 (1-tailed), 128 (2-tailed)

Conclusions: Glitches in the Web-based questionnaire are attributable to excessive ‘hits’ on Google site
server from a single user-session. Average online readiness and overall online Compatibility of USAIM
students are in the ‘Good’ category. Learning style questionnaire needs to be re-structured. The
questionnaire as a whole needs to be rendered more robust from research perspective. Online concerns of
students are directly proportional to their motivation and inversely proportional to their age. Subject
recruitment for a formal study needs to be at least 3.7 times more than this pilot study. This would render the
results of a robust statistical analysis more valid. Overall, USAIM students are poised on the threshold of
introduction of online courses and examinations. Once they are introduced, the natural progression of
learning curve would take care of the ongoing hurdles.

***********

USAIM Online Survey; Dr S. Sanyal, Assoc. Prof., Faculty of Anatomy & Neurosciences, USAIM, Seychelles May 2008 iv


CHAPTER-1: PRELIMINARIES AND LITERATURE REVIEW
“The voyage of discovery is not to seek for new landscape, but to install a pair of new eyes.”
~~Anon~~
1. Introduction
Implementing online technologies towards imparting learning constitute the next big wave to hit the
educational arena, after the chalk and blackboard. This is not so surprising, considering the ubiquity of
computers, Internet and the inexorable progression of related technologies.[1] The Sloan Consortium (Sloan-
C™) defines Online Courses as those where 80% or more of the course content is delivered online. There
are usually no face-to-face (F-2-F) interactions between faculty and students. Other types of imparting
education, based on decreasing proportion of course content delivered online are; Hybrid / blended Course
(30-79% course content delivered online, there are both online discussions and face-to-face meetings); Web-
facilitated Course (1-29% course content (assignment, syllabus etc) delivered through course management
system (CMS) or Web pages, uses Web technologies to facilitate an essentially F-2-F course delivery
program); and Traditional (no course content delivered online, only orally or in writing).[2]

2. Models of online education
A radically different classification identifies 5 new ‘Models’ for online learning, aimed towards improving
learning at affordable costs. In the Supplemental Model the basic structure of traditional course (number of
class meetings etc) is retained; only some technology-based out-of-class activities are added to encourage
greater student engagement with course content. In the Replacement Model the key characteristic is a
reduction in class meeting time, replacing face-to-face time with online, interactive learning activities by
students. The Emporium Model is based on the premise that a student learns best when he wants to learn
rather that when the instructor wants to teach. This model therefore eliminates all class meetings and
replaces them with a learning resource center featuring online materials and on-demand personalized
assistance. In the Fully Online Model, the single-handed, monolithic, repetitive, labor-intensive task of a
professor of traditional course has been transferred to the online scenario. This model assumes that the
instructor must be responsible for all interactions, personally answering every inquiry, comment or
discussion. However, newer software systems have been developed (viz. Academic Systems software) that
present the course content so effectively that instructors do not have to spend any time delivering content.
These 4 course-redesign models treat all students as same; therefore they represent ‘one-size-fits-all’
approach. In contrast, the Buffet Model offers students an assortment of interchangeable paths that match
their individual learning styles (LS), abilities and tastes at every stage of the course. Even the best ‘fixed-
menu’ of teaching would fail for some students. In contrast, the ‘buffet’ strategy suggests a large variety of
offerings that can be customized to fit the needs of the individual learner.[3]

3. Growth of online education

USAIM Online Survey; Dr S. Sanyal, Assoc. Prof., Faculty of Anatomy & Neurosciences, USAIM, Seychelles May 2008 1


Over the last five years there has been a progressive increase in online courses in universities worldwide and
in USA in particular. This increase pertains to all aspects educational continuum; in terms of numbers of
courses being offered, number of colleges and higher education schools offering them, and numbers of
students enrolling for online courses, both in absolute terms as well as in proportion to those enrolling for
traditional courses. From 1.6 million online students in 1998 in the US, the number had escalated to 3.48
million by 2006. [2,4] Among all colleges offering distance learning, the proportion using the Internet had
grown from 22% in 1995 to 60% in 1998.[4] Overall, students in USA who were taking at least one online
course in 2006 represented 20% of total enrollments in higher education. This represented a jump of nearly
10% over 2005.[2] It is projected this growth will continue, albeit at a slower rate, into the future.[1,2]

4. Advantages of online education
More and more universities and colleges worldwide are jumping on the online bandwagon. The reasons
cited by Sloan-C™ for adopting online courses, in order of importance are to; increase student access, attract
students from outside traditional service areas, grow continuing and / or professional education, increase rate
of degree completion, enhance value of college/university brand, provide pedagogic improvements, improve
student retention, increase the diversity of student body, optimize physical plant utilization, improvement
enrollment management responsiveness, increase strategic partnerships with other institutions, reduce /
contain costs, and enhance alumni and donor outreach. Therefore these are the purported advantages of
online education.[2] Improving student retention is a contentious issue. Statistics of Foothill College, Los
Altos, CA showed that students in on-line computer classes had a drop rate of 30% compared to a drop rate
of 10-15% in on-ground classes.[5] On the other hand, the University of North Carolina (UNC) School of
Public Health has cited 10 essentially different reasons why online learning excels over traditional
education; Student-centred learning; Writing intensity; Highly interactive discussions; Geared to lifelong
learning; Enriched course materials; On-demand interaction and support services; Immediate feedback;
Flexibility; Intimate learner community; and Faculty development and rejuvenation.[6] Here again there is an
apparent contradiction. Low acceptance of online instruction by faculty has been cited as one of the barriers
to online education.[2]

5. The online framework
In terms of engagement in online courses and their attitudes towards same, institutions have been classified
into 5 categories by Sloan-C™. These are; (A) Fully engaged: those that have online courses that they have
fully incorporated into their formal strategic long term plans; (B) Engaged: Those that have online course(s)
that they believe are strategic to their long term plans but have not yet incorporated them into the formal
long term strategy; (C) Not yet engaged: Those that do not have any online courses yet but believe they
critical to their long term strategy, and are therefore expected to implement some form of online courses in
the future; (D) Non-strategic online: Those that have some online course(s) but do not believe that it is



important for their long term strategy; and (E) Not interested: Those that do not have any online courses and
do not believe that it is important for their long term strategy.[2]

6. USAIM in online framework
The University of Seychelles American Institute of Medicine (USAIM) is a pre-clinical medical school in
Seychelles that was established in 2001. Commensurate with its progressive-minded philosophy it believes
in adopting the latest technologies in imparting education. In collaboration with another organization,
Boolean Education from Mumbai, India, USAIM introduced its online M.Ch (Orthopedics) Certification
program as part of its AACME-accredited (American Academy of Continuing Medical Education) CME
activity (Figure-1). This is a 6-month course, 5 of which are entirely online, covering one module every
month; and the sixth month includes a 3-day F-2-F Instructional Course Lecture Series (ICLS).[7] Thus, as
per the Sloan-C™ definition (and according to its self-declaration) it is a Hybrid / blended course. But since
more than 80% of the M.Ch Orthopedics certification course content is delivered online, it is closer to the
definition of a true Online Course.[2] Apart from all examinations of M.Ch certification program, which are
fully online, USAIM is also on the verge of introducing fully online and automated examinations for its
routine Pre-clinical semesters (of which the author is an Associate Professor) on a regular basis. Therefore,
since USAIM already has an online course and it has fully incorporated online activities into its formal
strategic long term plans, it conforms to Sloan-C™ categorization of a ‘Fully Engaged’ institution.[2]

Figure-1: Screenshot from the Website showing online M.Ch Certification course offered by USAIM, Seychelles and Boolean Education, India

7. Barriers to online learning
In spite of all the purported benefits and advantages of online course, they are not without their barriers.
Some of the identified hurdles to widespread adoption of online learning are; students need to have more
self-imposed discipline in online courses, variable / low acceptance of online instruction by faculty, lower
student retention rate in online courses, high costs of developing online courses, high costs to deliver online
courses, and lack of acceptance of online degrees by employees. These are the identified barriers from the



institutional perspective. Not all these are barriers are given identical weightings by all institutions; in fact
some institutions do not consider some of them as barriers at all.[2]

Of arguably greater importance are those potential barriers that could be identified from the students’
perspective. Eight barrier factors were determined by a factor analytic study in 2005; (a) administrative
issues, (b) social interaction, (c) academic skills, (d) technical skills, (e) learner motivation, (f) time and
support for studies, (g) cost and access to the Internet, and (h) technical problems. Independent variables that
affected student ratings of these barrier factors included; gender, age, ethnicity, type of learning institution,
self-rating of online learning skills, effectiveness of learning online, online learning enjoyment, prejudicial
treatment in traditional classes, and the number of online courses completed.[8]

8. Background of present pilot study
The findings from the aforementioned 2005 study provided the impetus to try to determine how many of
those factors applied to USAIM students, and in what way, on the theoretical assumption that they were all
to be enrolled for online courses in the near future. A more focussed search of the Web-based literature,
based on the factors identified by the 2005 study, corroborated that technical skills, learner motivation, and
access to Internet (technologies) were of special significance from the online learning perspective.[5,9,10]
Another factor that had not been addressed in the abovementioned study pertained to students’ learning style
(LS) preferences; more specifically their online LS preferences. And finally, prior academic skills and age
also have an indirect role in students’ online learning endeavours.[8-10]

Technology access: Having access to technology (viz. computer and Internet) is to the online learner what
pen and paper is to the traditional student. For the former, computer and Internet access are the primary
instruments of learning. Having to use a computer with inadequate computing power or an erratic / slow
Internet connection can impede the online learner significantly. Consequently the capabilities of the
technology used by the online learner, and access to the same, play important roles in the overall success in
online learning.[10]

Self-motivation: Implicit within the structure of most traditional forms of learning is a certain level of
external motivation. Online learning is more loosely structured and relies more heavily on internal
motivation of the learner. They must schedule time for learning on their own and then stick to that schedule,
not for external impositions by others but because they have to meet their self-imposed personal goals. To
that extent they must be sufficiently internally motivated and must be able to put in sufficient numbers of
hours of self-study without exhortation from anyone.[5,8-10]

Techno-skills: Technical skills are also a key factor in the success of an online learner. This does not imply
advanced computer skills. However, a minimum level of technical ability is essential, which can make all
the difference between success and failure. The determining factor in what constitutes this ‘minimum’ level
is simply having enough technical knowledge to ensure that the technology does not become a barrier in the



learning process. If the online student has to submit a paper electronically he/she should spend most of their
time towards writing the paper and not figuring out how to attach the file to an outgoing e-mail.[10,11]

Learning styles: Research has revealed that everyone has different preferences in how they learn. These
preferences are called ‘learning styles’ (LS). There is considerable confusion about the exact definition of
LS.[12] In one review there were 7 definitions / descriptions of LS. The most ‘accurate’ definition appears to
be that of Keefe, who described LS as characteristic cognitive, affective, and psychological behaviours that
serve as relatively stable indicators of how learners perceive, interact with and respond to the learning
environment.[13] Grasha defined LS as personal qualities that influence a student’s ability to acquire
information, to interact with peers and the teacher, and otherwise participate in learning experiences.[14]
Theis described LS as a set of biological and developmental characteristics that make identical instruction
for learners either effective or ineffective.[15] LS pertains to a preference of the student. Some students find
that they have a dominant LS, which they utilise most frequently (or prefer to do so), and use other styles
less frequently. Other students find that they use different styles in different circumstances. Everyone has a
mixture of LS. There is no right mixture; nor are they fixed.[16] Some empirical evidence suggests that
learners also have different preferences when it comes to online learning. Some prefer to learn through
lectures while others find that project-based learning better suits abilities and interests.[14,17] Knowing one’s
‘online LS’ can be important in ensuring that they would select a style of online learning delivery (e.g.
synchronous or asynchronous) in which they would excel.[10,18]

Considering all these parameters, the present study was narrowed down to focus on 5 factors. These
pertained to students’ access to technology, personal issues, technical competencies, online LS preferences
and some general aspects of students. Further perusal of the literature revealed several resources that had
considered some or all of these factors in determining students’ readiness for online learning.[10,19-24]
Therefore these considerations formed the basis for the present study.

It was decided to incorporate the parameters identified in the preceding paragraphs into a newly-devised
Web-based questionnaire system. The details of creation of a Web-based questionnaire system are described
in the next chapter. It was decided to pilot this new Web-based system among the students of USAIM at the
time of conducting the survey. Therefore, the present study was actually based on two background issues;
the issue of online compatibility of USAIM students, and the piloting aspect of a newly-introduced Web-
based questionnaire system for the study. The two were to go hand in hand during the course of the study.

9. Selection criteria for questionnaire – based on literature review
The survey instrument (questionnaire) and the questions themselves had to conform to the requirements of
the study that had been planned, apart from fulfilling the precepts of a good questionnaire (described in
Chapter-2). Therefore a set of parameters and a scoring system was applied to the various survey
instruments that were available. The parameters were; (a) Number of question items: Between 30 and 40 was



considered ideal (reason described in Chapter-2 and Chapter-4); therefore it scored 1 point, anything less
scored 0; (b) Ease of administration: This depended on the format and presentation of the questionnaire,
whether script-based or plain .doc format; (c) Easy questions referred to the wording, sentence construction,
user-friendliness and ease of understanding; it was graded from 1+ to 5+; (d) Mixed response items referred
to bimodal (Yes/No), scaled (Very/Somewhat/Not) or multi-option question items. Question items should
not ideally be mixed too much (elaborated under ‘Discussion’); (e) Dimensions referred to all the groups
described earlier; whether they were deficient or whether there were any extra groups in the questionnaire.
Each dimension scored 1 point; absence scored negative point(s); (f) Scoring method and interpretation of
results: If it was automatically performed by the parent organization, it was better than manual scoring; (g)
Validity: This referred to the accuracy (how accurately it measures what it is purported to measure) or
otherwise of the items in the questionnaire; ideally questionnaire items should be independently peer-
validated. Based on the overall score, the questionnaire from eLearners™Advisor[10] was selected for this
study because it scored the maximum points (Table-1). The questionnaire was not being piloted; rather the
Web-based system that was being introduced for the first time was being piloted.

Pace AASU OLE eL DVC ION DuPa
Number of Qs items 31 (1) 35 (1) 23 (0) 40 (1) 32 (1) 12 (0) 10 (0)
Ease of administration Yes (1) No (1) Yes (1) Yes (1) No (0) Yes (1) Yes (1)
Easy Qs items 5+ 3+ 2+ 4+ 1+ 4+ 4+
Mixed response options No (1) No (1) No (1) Yes (0) No (1) No (1) No (1)
No. of dimensions 4 3 4 5 0 0 0
Extra dimension(s) Time Mx None (0) None (0) None (0) ? ? ?
(1)
Deficient dimension(s) LS, Gen (- Personal, Gen (-1) None (0) ? ? ?
2) Gen (-2)
Scoring method Self (0) Parent Parent org Parent org Parent org Parent Self (0)
org (1) (1) (1) (1) org (1)
Automatic result No (0) Yes (1) Yes (1) Yes (1) Yes (1) Yes (1)
No (0)
interpretation
Instrument self-validated Yes (1) Yes (1) Yes (1) Yes (1) Yes (1) Yes (1) Yes (1)
Instrument peer-validated No (0) No (0) No (0) No (0) No (0) No (0) No (0)
Total score 12 10 10 14 6 9 7
Table-1: Score for each parameter is given in parenthesis and colored green; See text for details.
Pace[24]; AASU[19]: Armstrong Atlantic State University; OLE[20]: OnlineLearning.net™; eL[10]: eLearners™Advisor
(acknowledged in ‘Acknowledgements’ section); DVC[21]: Diabolo Valley College; ION[22]: Illinois Online Network, University
of Illinois; DuPa[23]: College of DuPage

10. Objective and goals of study
The immediate objective of the pilot study was to identify technical glitches (problems) in the newly-
devised Web-based questionnaire survey system and try to devise a future-proof system through
troubleshooting. During the course of this pilot, the following additional goals were fulfilled. These
pertained to the online compatibility issue alluded to earlier.

1. Determine USAIM students’ online learning preparedness against 5 set parameters
2. Devise a mathematically objective scoring system for each parameter
3. Determine overall online preparedness (Compatibility Score) of USAIM students
4. Determine robustness of LS questionnaire currently being used for the study



5. Identify relationships between students’ personal / general characteristics vis-à-vis online learning
6. Suggest improvements to questionnaire and survey
7. Suggest ways of overcoming barriers to online learning
8. Use Compatibility Score as baseline for future studies in USAIM and elsewhere (long term goal)
9. Render online learning and examinations a regular and feasible option for USAIM (long term goal)

11. Study outcomes measured
The following mathematical outcomes were generated:

A. Weighted scores for technology access parameters
B. Weighted scores for personal parameters
C. Weighted scores for technical proficiencies
D. Weighted scores for online LS preferences
E. Weighted scores for students’ general considerations
F. Overall Compatibility Score of USAIM students
G. Correlation, internal consistency and factor analysis scores of online LS questionnaire items
H. Correlation and regression analysis scores of personal factors vs. general considerations
I. Predictive model and formula of students’ online learning behaviour characteristics
J. Power analysis scores vis-à-vis sample size

12. Summary and usefulness of research
This preliminary chapter provided the introduction, background information and current status of on online
education, provided the background of USAIM, its role in online education, the basis of this study, the
rationale behind questionnaire selection, and the objectives, goals and expected outcome measures from this
study. This research would be useful from a number of perspectives. It would tell us how the innovatively-
designed Web-based survey system performs. It would provide baseline data about USAIM students’ online
learning potential; the so-called ‘Compatibility Score’. It would identify deficiencies or lacunae in students
that would require to be addressed. The Web-based nature of the survey itself would inform us about
students’ online potentialities. If they can successfully undertake the online survey, it would automatically
mean they possess basic online skills. Finally it would pave the way for implementation of future online
courses and examinations in USAIM. The next chapter would describe the methodology involved in creating
the Web-based questionnaire and its pilot administration to students of USAIM during the course of survey.

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CHAPTER-2: MATERIALS AND METHODS
"Don't be afraid to take a big step. You can't cross a chasm in two small jumps."
~~David Lloyd George~~
1. Introduction
Following up from the selection of questionnaire that was described in the previous chapter, this chapter
describes the contents and structure of the questionnaire, the method of creation of the new Web-based
survey system (and troubleshooting its attendant glitches), piloting the Web-based questionnaire and
conducting the survey to its successful conclusion. The quotation from David Lloyd George aptly reflects
the ethos of this chapter insofar as it relates to the Web-based questionnaire itself.

2. Survey preliminaries
This study was conducted in USAIM, Seychelles from February 2008 to March 2008. A preliminary round
of discussions with the President, Managing Director and Dean of USAIM culminated in their collective and
tacit approval for the study. This was followed by submission of the study proposal in the form of a
preliminary abstract, which was accepted by the Dean. Then a notice was inserted in the student notice
board, detailing the purpose, scope and depth of the study, and the approximate time it would take to
complete the questionnaire. It also contained an FAQ to clear common anticipated doubts and allay
apprehensions. It was stressed that there were no wrong or right answers so that students would respond
honestly, without any misgivings. The students were also informed that all results would be statistically
aggregated and no individually identifiable data would be asked for or displayed. Informed consent of study
participants was implicit.

3. Study design, setting, participants and data sources
The study was conducted within the campus of USAIM. It was designed as a Web-based questionnaire
survey of USAIM students. It was a one-shot, cross-sectional, non-experimental study, with data collected at
a single point in time to reflect a cross-section of the current student population. Therefore the current
students from PC-1 to PC-5 were the participants. There were 35 students at the time of conducting the
survey, all of whom were included in the study. Their feedback from the questionnaire provided the data for
statistical analysis.

4. Questionnaire
It was decided at the outset that, unlike the previous surveys conducted by the author in USAIM, which were
paper-based, this study would utilize a Web-based questionnaire.[25,26] There were several reasons for this.
Firstly, the study itself was about students’ online preparedness. Therefore it made logical sense to have
Web-based questionnaire. If the students could access and answer the questions online, it would be a
significant reflection on their online capabilities. Secondly, online questions are easy to administer, less
time-consuming, more efficient, and are eco-friendly insofar they do not entail any usage of paper.[25]



Thirdly, the software in the online system allowed automatic percentage calculation, thereby reducing the
time and effort required to manually perform the same.

The contents of the questionnaire were selected from several questionnaires that were available on the net,
which had been used to test for students’ online preparedness.[10,19-24] The selection criteria for the
questionnaire was described in Chapter-1. Forty questions were selected and sorted into 5 matches for online
learning question groups; numbered sequentially from A to E. Group A had 2 questions pertaining to
technology access. Group B had 3 questions pertaining to personal facts (insofar as they impacted the
students’ online learning capabilities). Group C had 16 technical proficiency-related questions. Group D
contained 16 questions to determine students’ learning style (LS) preferences. Eight questions in this group
were worded in such a way that a ‘Yes’ response indicated pro-online LS preference. The other 8 reflected
anti-online LS preference. The pro-online and anti-online LS preference questions were alternately arranged
in Group D. Finally group E contained 3 questions of a general nature. All questions had provision for only
one answer except the question about motivation for online learning (first question in group B), which
permitted students to upload up to 3 options. Appendix-1 gives a sample of the questionnaire. The purpose
behind adapting the questionnaire from existing ones rather than creating a fresh questionnaire from scratch
was these had already been tried and tested on student populations elsewhere; i.e. they were self-validated, if
not entirely peer-validated. This obviated the time-wastage on piloting the questionnaire itself, which a
newly-generated questionnaire would have entailed.[26,27]

4.1 Precepts of good questionnaire-design

While preparing the questionnaire, every effort was made to keep within the principles of good
questionnaire design, both paper and Web-based.[27-30] It was within 2 pages, as per the stipulations of good
questionnaire.[27] Estimated time of completion was not more than 20 minutes. It had 40 questions,[30] with
easy wordings and user-friendly sentence constructions. All required single option selection except third
question, which required up to 3 selections. Sixteen question-options were ranked (Very / Some / None) and
16 had bimodal (Yes / No) options. Among the rest, the number of available options ranged from 3 to 5.
There were no open-ended questions, as advocated by some.[27] Though strictly not within the rule,
differently ranked questions were somewhat mixed.[27] At the outset, a short introduction mentioning the
background and aims of the evaluation was given. Users were told what to expect so that they would be
mentally prepared and were informed that they would be anonymous.[30] The questions had been piloted
elsewhere; that was one of main reasons for their selection. Therefore it was not considered necessary for the
author himself to pilot the questionnaire again, as suggested by some.[26,27] There were certain drawbacks in
the questionnaire that have been discussed in Chapter-4.

5. Generating a Web-based questionnaire
Perlman described Web-based questionnaire creation using customizable Web-based PERL (Practical
Extraction and Report Language) CGI (Common Gateway Interface) script. It was based on established



questionnaires and automated the process of data collection. It was hard to completely customize,
questionnaire terminology did not closely match the required domain for this study and there were no
analysis tools.[25] Therefore, a different technique was employed in this study. The Google-based blog-site;
URL: http://www.blogger.com was used as the platform for creating the Web-based questionnaire survey
system. A new blog-site was created in February 2008. First, an introduction to the survey and USAIM logo
(Figure-1a); and essentially a repetition of the earlier paper-based instructions (Figure-1b), was entered in
the main ‘Blog Posts’ box (Figure-1c). In the main blog posts page, below the ‘Blog Posts’ box was an
option; ‘Add a Page Element’ (Figure-1c). Clicking on this opened a dialog box that enabled one to ‘Create
a poll’ (Figure-1d). This allowed entry of a question followed by as many answer-options as desired, set the
limit of selection of options (single / multiple options), and also set the time limit for the poll (Figure-1d).
Clicking on the ‘Save’ button of ‘Create a poll’ dialog box saved the question in the ‘Page Element’ section
of the main blog page. Forty of these poll-creating ‘Page Elements’ were added in succession to constitute
40 questions of the questionnaire. For each question the student had to select one of the options through the
radio buttons and click ‘Vote’ in order to save his/her poll (Figure-1e). The process had to be repeated for
each of the 40 questions. Clicking on the ‘Show results’ link for any question (Figure-1e) revealed the
percentage scores for that question (Figure-1f). The blog-site was published for public viewing. The author
personally accessed the blog-site and checked it several times for usability; till opening, loading and
refreshing of the page elements was satisfactorily achieved, and all buttons, options and links were found to
be successfully operating. Finally, The URL: http://sanyalonlineusaimsurvey.blogspot.com/ was made
available to the students through a general notice.[30]

a b



c d

e f
Figure-1a,b: Shows creation of a blog post for the online survey, with USAIM logo, introduction and instructions for the survey; Figure-1c,d: Shows method of
creating an online question (with options) through the ‘Create a poll’ dialog box. Figure-1e,f: Shows the resultant online questionnaires and responses by students
automatically expressed as percentage of total respondents.

6. Sequence of survey
After the preliminary notice, the URL of the blog-site containing the survey was released to the students and
they were given 2 weeks to complete the questionnaire. Throughout the release period the author regularly
visited the site to keep track of the numbers of students responding to the questionnaire. Moreover, the
researcher was always available to solve doubts, queries and troubleshoot glitches. After 1 week a reminder
notice was issued for those who had not visited the site or attempted the survey. After all students had
completed the questionnaire, the author visited the site and manually extracted the percentage scores for
each option (through the ‘Show results’ link) for each question, which had been automatically calculated by
the blog-site server. The raw data were entered on an MS® Excel® worksheet and tabulated for further
analysis. The result scores were analyzed with a specific view towards arriving at the stated goals of the
study. This is detailed in the next chapter. At the conclusion of the analysis, a summary of the aggregated
results, without identifying anybody, and USAIM students’ online Compatibility Score was put on the
student notice board for everybody’s information.

7. Troubleshooting technical glitches in Web-based questionnaire
This was a study to pilot the newly-devised Web-based questionnaire, and identify the technical glitches in
the system. They required ongoing identification and correction throughout the process of the survey. In



fact, it was part and parcel of the survey process itself. Therefore it is apt to describe the glitches and the
measures taken to circumvent them in this chapter itself. Technical explanations are dealt in Chapter-4.

Several glitches were encountered that required ongoing attendance. Firstly, the full blog-site page required
considerable time to open fully, after numerous (~ 40) ‘clicking’ sounds. This was because each question
was entered as a separate poll in a separate page element in the blog-site, as per the feature of that site.
Therefore during opening of the page, each question (element) required a separate response from the site
server. There was nothing that could be done about this, except to warn the students about this and
encourage patience. Secondly, the most serious problem encountered was the en masse error message, “This
page cannot be displayed”, corresponding to each question, even though the Internet connection was stable.
This occurred when several students simultaneously tried to access the site from different machines on the
USAIM server. Investigation revealed that the Google server, which was the main site server for the blog-
site, interpreted these simultaneous hits as suspected virus attacks on its server, and therefore tried to shut
out the USAIM server. Therefore, perforce the students had to access the site one at a time when they were
within the USAIM network. The third problem was when students tried to progress rapidly through the
questions; after certain time, the last few questions tended to display the same error message, possibly as a
result of the same erroneous interpretation by the Google server. A fourth situation, similar to the previous,
was encountered when students clicked on the ‘Vote’ button and, without waiting for the page to refresh,
progressed to the next question and clicked on its option radio-button. The same error message was
displayed. Therefore the students had to be told to progress through the questions at a moderate, but not too
rapid, pace. They were instructed to wait for the page to refresh after each ‘voting’. Another problem was
encountered when one student immediately followed the next (usually, but always, on the same machine).
The page used to open with the questions displayed in the post-voting mode of the previous student, asking
if the student wanted to change his poll opinion. If the next student clicked on this, the page got refreshed
but the previous student’s response got erased. This also required that there should be a sufficient time gap
between two students’ access to the blog-site. Because of these problems many students had to complete the
questionnaire in more than one sitting. Not all students encountered all these problems, however. Quite a
few managed to complete the whole questionnaire without encountering a single glitch, especially those who
accessed it from home on their personal laptops through their own Internet connection provided by their
personal ISP.

8. Conclusion
Three aspects of this study were covered in this chapter. Firstly, the nuts and bolts of the whole survey
process (questionnaire, Web-based system and the survey proper) were exhaustively described. Secondly, it
described the fulfilment of the immediate objective of this study, namely to assess the functioning and
identify the problems in the newly-devised Web-based system. Thirdly, the process described herein led to
the generation of data that led to the fulfilment of other goals of this study. These are discussed in Chapter-3.
***********



APPENDIX-1: Text of the online questionnaire

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Available at URL: http://sanyalonlineusaimsurvey.blogspot.com/ (Totally 40 questions)

Q1) What type of Internet access will you have at home during your online studies?
No Access
Dial-up/Modem Access
High-Speed/Wireless Access

Q2) Please select the option that best describes the primary computer you will be using
Purchased 1 - 2 years ago
Purchased 3 - 4 years ago
Purchased > 4 years ago
I plan to buy a new PC soon
I'm unsure what PC I will use

Q3) Which of the following would be your motivation(s) for undertaking the online course? (Select up to 3)
A) It seems like the fastest and easiest way to study
B) To increase my earning potential in my future career
C) To qualify for a good position or career
D) A personal interest or goal (I like this method of learning)
E) Outside influences rather than my own goals or needs (My lecturer is telling me to do it!)

Q4) My schedule is...
A) Predictable (I can devote regular blocks of time for online study)
B) Somewhat Unpredictable (My schedule changes often, but I can usually put in some time for online study)
C) Very Unpredictable (My schedule is rarely the same; however, I shall see what I can do)

Q5) Each day I could dedicate the following number of hours for online study:
A) 1 to <2
B) 2 to <3
C) 3 to <4
D) 4 to <5
E) 5 or more

Q6) Fast and accurate typing on a computer keyboard
A) Very skilled
B) Some skills
C) No skills

Q7) Open files saved on a floppy disk, hard drive or CD
A) Very skilled
B) Some skills
C) No skills

Q8) Save a file with a new name, file type or file location
A) Very skilled
B) Some skills
C) No skills

Q9) Copy, cut and paste text/files between windows/programs
A) Very skilled
B) Some skills
C) No skills

Q10) Format fonts and document layout using a word processor
A) Very skilled
B) Some skills
C) No skills

Q11) Insert a picture/object into a word processing document
A) Very skilled
B) Some skills
C) No skills

Q12) Solve basic computer problems (e.g. computer freezes)
A) Very skilled
B) Some skills
C) No skills

Q13) Learn new software programs or applications
A) Very skilled
B) Some skills
C) No skills

Q14) Visit a web site (if you are given the address/URL)
A) Very skilled
B) Some skills
C) No skills



Q15) Send and receive e-mail messages
A) Very skilled
B) Some skills
C) No skills

Q16) Send and receive attachments/files through e-mail
A) Very skilled
B) Some skills
C) No skills

Q17) Use search engines to find answers and resources
A) Very skilled
B) Some skills
C) No skills

Q18) Use "message boards" or "forums" or "newsgroups"
A) Very skilled
B) Some skills
C) No skills

Q19) Use a "chat room" or "instant messaging"
A) Very skilled
B) Some skills
C) No skills

Q20) Download and install software or a "plug-in"
A) Very skilled
B) Some skills
C) No skills

Q21) Protect your PC from threats (viruses, spyware, hackers)
A) Very skilled
B) Some skills
C) No skills

Q22) Socializing with my classmates is important for my education
A) Yes
B) No

A) Yes
B) No

Q23) I am comfortable building online relationships and networking online.
A) Yes
B) No

Q24) I always need to share my knowledge, thoughts and experiences with others.
A) Yes
B) No

Q25) I am a disciplined student and I can usually stick to my study plan.
A) Yes
B) No

Q26) I have difficulty completing assignments on time, and sometimes need extension dates.
A) Yes
B) No

Q27) I prefer to learn through independent projects instead of structured assignments.
A) Yes
B) No

Q28) I prefer lecture-based learning rather than discussion-based / project-based learning
A) Yes
B) No

Q29) I have decent computer reading speed and I can learn well that way.
A) Yes
B) No

Q30) I do not participate much in group discussions unless specifically called upon to do so.
A) Yes
B) No

Q31) I prefer working alone on assignments instead of in study-groups.
A) Yes
B) No

Q32) I prefer verbal discussions rather than submitting my ideas in writing.
A) Yes
B) No

Q33) I prefer structuring my own projects instead of being given specific directions.



A) Yes
B) No

Q34) I prefer hearing verbal explanations instead of reading written ones.
A) Yes
B) No

Q35) I have good writing skills and can effectively communicate my ideas in writing.
A) Yes
B) No

Q36) I am much more comfortable communicating face-to-face rather than with email.
A) Yes
B) No

Q37) I am good at structuring my own learning; independent study courses are right for me

Q38) What is your age?
A) <18 years
B) 18 to <19 years
C) 19 to <20 years
D) 20 to <21 years
E) = or > 21 years

Q39) What is the highest level of education that you have completed till date?
A) Class 10 or equivalent
B) High school (10 + 2)
C) Some college courses (e.g. Pre-med)
D) Bachelor’s degree

Q40) Do you have any concerns about the quality of online courses?
A) No concerns at all (Hey, cool man!)
B) Some concerns (I’m just a wee bit worried!)
C) Many concerns (Gee, I’m highly worried!!)
D) Unimaginably concerned (It ain’t for me dude!)

*******************



CHAPTER-3: STATISTICAL ANALYSIS AND RESULTS
“You are never granted a wish without the power to make it come true. You have to work for it, however.”
~~Anon~~

1. Introduction
Continuing from the data collection described in the previous chapter, this chapter deals with univariate and
bivariate statistical analysis of the data and the output generated there from. The results of the survey
pertained to four broad set of student-specific parameters that were considered relevant for online learning.
These were; technical access parameters (type of Internet access, primary computer that would be used for
online courses); personal parameters (motivation for online learning, scheduling ability for same, ability to
devote certain numbers of hours of self-study per day); technical proficiency (which included 16 items);
learning style preferences insofar as they pertained to online learning (which also included 16 items); and
general considerations (concerns about online learning, previous education levels, age groups).

A. UNIVARIATE STATISTICS
2. Weightings and score-generation
All results were collected as percentages of students responding to various item-options in each question. In
order to render the crude percentage results more meaningful, they were fitted into a scoring system.[10,23]
Moreover, since each options for the questions were not of equal importance, there also has to be logical
weighting system for the options, based on their relative importance.[10] Each item of the parameters under
study was given a weighting factor that ranged from ‘0’ to ‘5’. ‘0’ was the weighting for the response that
was not at all useful, and ‘5’ weighting was allotted to the most important response-option in the online
learning context. The intermediate weightings ranged sequentially between the two extremes, depending on
their relative order of importance from online learning point of view. However for each parameter the actual
weighting range was variable; some had ranges 0-3, others had ranges 0-4, 0-2 1-3, 1-4 or 1-5, etc;
depending on the number of response-options for that parameter.

3. Technology access parameters
3.1 Type of Internet access
This parameter pertains to type, speed and bandwidth of Internet connection that students would have for
their online course. The weighting for items in this parameter ranged from ‘0’ to ‘3’; ‘0’ being for ‘No
access’ and ‘3’ being for ‘High-speed / Wireless access’, this latter being considered ideal for online
courses.[11] ‘Cable modem’ and ‘Dial-up’ received weightings of ‘2’ and ‘1’ respectively, the former being
considered superior to latter. Slightly less than half (48%) of the students had access to high-speed or
wireless Internet at home. The weighted score collectively secured by the students for this parameter was
191, out of a theoretical maximum (Max) of 300. This worked out to 63.7% of Max for the parameter ‘Type
of Internet access’ (Table-1, Figure-1).



60
Type of Internet Access
Type of Internet access % W P Max 50

No Access 19 0 0 48
40
Dial-up modem Access 19 1 19

% Stuudents
30
Cable modem access 14 2 28
High-Speed/Wireless Access 48 3 144 300 20
19 19
Total 100 191 300 10 14

Final score 63.7% 100%
0
Table-1: Shows percentage of students with various types of Internet No Access Dial-up modem Access Cable modem access High-Speed/Wireless
Type of Access Access
access at home. The items have been weighted (W) from 0 to 3 based on
their relative importance. Next column gives the product (P) of previous 2 columns, the collective score for this parameter and the % of maximum (Max) possible
score. Figure-1: Shows histogram of percentages of students with various types of Internet access at home for online studies.

3.2 Primary computer
This parameter refers to the main computer that students claimed they would use at home for their online
courses. Weightings for items in this parameter ranged from ‘0’ (those who were unsure which PC to use) to
‘4’ (those possessing latest PCs). Older PCs secured lesser weightings. Two-thirds (68%) of the students had
purchased their primary computer within the last 1 or 2 years. The collective weighted score for this
parameter was 320, out of a theoretical Max of 400, giving students a score of 80% of Max for the
parameter labelled as ‘Primary computer’ (Table-2, Figure-2).
80
Duration of Primary Computer
Primary computer % W P Max 68
70
Purchased 1 - 2 years ago 68 4 272 400 60
Purchased 3 - 4 years ago 11 3 33 50

Purchased > 4 years ago 5 2 10
% Students

40

Plan to buy new PC soon 5 1 5 30

Unsure what PC to use 11 0 0 20
11 11
Total 100 320 400 10 5 5

Final score 80% 100% 0
Purchased 1 - 2 Purchased 3 - 4 Purchased > 4 years I plan to buy a new I'm unsure w hat PC I
Table-2: Shows percentage of students with various types of years ago years ago ago PC soon w ill use

primary computer at home for online studies. The items have Duration

been weighted (W) from 0 to 4 based on their relative
importance. Next column gives the product (P) of previous 2 columns, the collective score for this parameter and the percentage of maximum (Max) possible
score. Figure-2: Shows histogram of percentages of students with various types of primary computer at home for online studies.

4. Personal parameters

4.1 Motivation
This was considered as the single most important parameter under personal factors, which has the capacity
to determine whether a student would be able to pursue an online course successfully or not.[5,8-11,24] The
weightings employed for items in this parameter were thus; ‘Outside influences’ carried ‘0’ weight because
they were not students’ internal motivations. ‘Personal interest’ carried the maximum weight of ‘2’ for the
converse reason. Other response items (‘Fast easy learning’, ‘Earning potential’, ‘Good position’) were
equivocal response-options; they may or may not be applicable to a given situation. Therefore they could all
be considered as motivations under certain, but not all, circumstances. They carried a uniform weighting-
factor of ‘1’ each. Personal interest was the most frequent (50%) motivating factor for pursuing online
studies. One-third or more (33% - 44%) cited other reasons also, because students were permitted to tick up
to three response options in this parameter. A small proportion (16%) admitted being influenced by outside
sources, viz. lecturers. The collective weighted score for this parameter was 210, out of a theoretical Max of



300, giving them a score of 70% for the parameter labelled as ‘Motivation’ to pursue online courses, as one
of the personal factors (Table-3, Figure-3).

Motivation % W P Max 60
Motivation for Online Study
Fast, easy learning 44 1 44 100
50
Earning potential 33 1 33 100 50

40 44
Good position 33 1 33 100

% Students
Personal interest 50 2 100 200 30 33 33

Outside influences 16 0 0 0
20
Total * 210 300
16
(3/5 of 500) 10
0
*Students were permitted to tick up to 3 choices Fast, easy learning Earning potential Good position Personal interest Outside influences

Table-3: Shows percentage of students with various Motivating Factors

motivating factors for online studies. The items have been
weighted (W) from 0 to 2 based on their relative importance (see text). Next column gives the product (P) of previous 2 columns, the collective score for this
parameter and the percentage of maximum (Max) possible score. Max of 300 for this parameter was computed as three-fifths of 500 (3/5 x 500), because students
were permitted to tick up to a maximum of 3 response-options. Figure-3: Shows histogram of % of students with various motivating factors for online studies.

4.2 Schedule
This parameter, also under personal factors, refers to how predictably students could devote a fixed number
of hours of self-study at home during their online course. Ability to maintain predictable hours of study was
also considered important for successful online studies.[10,11,24] Therefore the weighting scheme for items in
this parameter, which ranged from ‘0’ to ‘2’, was straightforwardly based on this aspect of the schedule,
with ‘0’ for ‘Unpredictable’ ‘1’ for ‘Somewhat unpredictable’ and ‘2’ for ‘Predictable’ study schedules.
Somewhat less than half (47%) admitted that they had somewhat unpredictable schedules insofar as it
pertained to devoting notional self-study time for online course. However, more than one-third (35%)
indicated they had the ability to maintain predictable study hours. The collective weighted score for this
parameter was 117, out of a theoretical Max of 200, giving them a score of 58.5% of Max for the parameter
labelled as ‘Schedule’ to pursue online courses (Table-4, Figure-4).

Schedule for Online Study
Schedule % W P Max 50
45
Predictable 35 2 70 200 47
40
Somewhat unpredictable 47 1 47 35
35
Unpredictable 18 0 0 30
% Students

Total 100 117 200 25
20
15 18
Table-4: Shows percentage of students with predictability of 10
various schedules for online studies. The items have been 5
weighted (W) from 0 to 2 based on their relative importance. 0
Next column gives the product (P) of previous 2 columns, the Predictable Somew hat unpredictable Unpredictable
collective score for this parameter and the percentage of Prdictability

maximum (Max) possible score. Figure-4: Shows histogram
of percentage of students with predictability of various schedules for online studies.

4.3 Hours of online study
This parameter pertains to the number of hours that students can devote daily as part of their online studies.
A 3-credit course requires 9 to 12 hours (some say 10 to15 hours) of study per week.[10,11,24] The options in
this study pertained to numbers of hours/day; the rationale being those who could study more hours/day
were more likely to complete their scheduled weekly credits. The weighting for this parameter ranged from
‘5’ (capable of 5+ hours of study/day) to ‘1’ (capable of 1-2 hours/day). Majority (42%) of students stated
they could devote 3-4 hours of online study/day. About one-third (32%) could devote 2-3 hours/day. Since



no student could put in 5+ hours, it was considered realistic to peg the Max possible score for this parameter
at 400. The collective weighted score for this parameter was 231; considered out of a Max of 400, it gave a
score of 57.8% for the parameter labelled as ‘Hours of online study’ per day (Table-5, Figure-5).
Hours of Online Study Possible
Hours of online study % W P Max 45

1 to <2 21 1 21 40 42
35
2 to <3 32 2 64
30 32
3 to <4 42 3 126
25

% Students
4 to <5 5 4 20 *400 20 21
5 or more 0 5 0 15

Total 100 231 400 10

Final score 57.8% 100% 5
5 0
0
Table-5: Shows percentage of students capable of various 1 to <2 2 to <3 3 to <4 4 to <5 5 or more
hours of online study per day. The items have been Hours / day
weighted (W) from 1 to 5 in ascending order of
importance. Next column gives the product (P) of previous 2 columns, the collective score for this parameter and the percentage of maximum (Max) possible
score. [*Since no student could put in the maximum number of hours as per the response items, it was considered realistic to consider the next highest (400) as the
maximum possible score.] Figure-5: Shows histogram of percentage of students capable of various hours of online study per day.

5. Technical proficiency
This parameter considered 16 items to determine how proficient the students were in handling computers.
They had to grade their capability for each item in terms of ‘Very skilled’, ‘Some skills’ and ‘No skills’.
Therefore it was logical to allocate weights of ‘2’, ‘1’ and ‘0’ respectively to each of these skill levels,
according to a modified Likert scale. Students proved to be most proficient in visiting Websites and
Emailing messages; achieving weighted scores of 98% in each. Using search engines scored 90%. They
were moderately weak in downloading / installing software (58%), and protecting PCs from virus (60%).
Using message board and basic problem-solving was their Achilles heel; achieving only 48% and 50%
weighted scores respectively. There were very few items where students professed no skills; they were the
same two items that was their Achilles heel (24% and 23% respectively). The collective weighted score for
this parameter was 2366, out of a theoretical Max of 3200, giving them a score of 73.9% for the parameter
labelled as ‘Technical proficiencies’ to pursue online courses (Table-6, Figure-6).

Very W1 Score1 Some W2 Score2 No W3 Score3 Σ Score Max %
skilled skills skills
(%) (%) (%)
Keyboard typing 32 2 64 58 1 58 10 0 0 122 200 61%
Opening files 53 2 106 47 1 47 0 0 0 153 200 77%
Saving files 72 2 144 28 1 28 0 0 0 172 200 86%
Copying-pasting files 63 2 126 32 1 32 5 0 0 158 200 79%
Formatting document 47 2 94 47 1 47 6 0 0 141 200 71%
Inserting picture/object 70 2 140 20 1 20 10 0 0 160 200 80%
Basic problem solving 23 2 46 54 1 54 23 0 0 100 200 50%
Learning new software 42 2 84 48 1 48 10 0 0 132 200 66%
Visiting website 95 2 190 5 1 5 0 0 0 195 200 98%
Emailing messages 95 2 190 5 1 5 0 0 0 195 200 98%
Attaching files to msgs 73 2 146 27 1 27 0 0 0 173 200 87%
Using search engines 80 2 160 20 1 20 0 0 0 180 200 90%
Using message board 19 2 38 57 1 57 24 0 0 95 200 48%
Using chat room 59 2 118 36 1 36 5 0 0 154 200 77%
Download/install software 33 2 66 50 1 50 17 0 0 116 200 58%
Protecting PC from virus 35 2 70 50 1 50 15 0 0 120 200 60%
Total 2366 3200


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