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Evaluation of Policies on Ban of Mobile Phone Usage by SHS Students on Campuses using
an Analytical Hierarchy Process (AHP).
Frank Opoku1, Dawuni Mohammed2, Abdul-Aziz Ibn Musah3 and Abdulai Abdul-Hanan4
1Department of Information Systems and Decision Sciences, KNUST School of Business, Kwame
Nkrumah University of Science & Technology, Ghana. 2,3,4Department of Statistics, Mathematics and Science, Tamale Polytechnic, Tamale-Ghana.
2Correpondence: [email protected]
Received: 21st June, 2014 Revised: 25th July, 2014 Published Online: 30th July, 2014
URL: http://www.journals.adrri.org/
[Cite as: Opoku, F., Mohammed, D., Musah, A.A.I. and Abdul-Hanan, A. (2014). Evaluation of Policies on Ban of
Mobile Phone Usage by SHS Students on Campuses using an Analytical Hierarchy Process (AHP). Africa
Development and Resources Research Institute Journal, Ghana: Vol. 10, No. 10(2).]
Abstract The study seeks to evaluation of policies on ban of mobile phone usage by SHS students on campuses; an
analytical hierarchy process (AHP). The possible policy directions opened to school authorities in
achieving student discipline on mobile phone usage on campuses, alternative policies to the enforced
policy, reasons for noncompliance of students to the ban on mobile phone usage on campuses and an
alternative policy that will be benefit both school authorities and students are looked at. Questionnaires
were used to collect primary data from respondents, mainly educational experts (administrators), parents
and students of seven senior high schools in Ashanti Region and the results analysed using Expert’s
Choice for Windows 2007 and Excel. 36% of SHS students have stopped using their phones while on
campuses of which 16% stopped because of the ban. Allow students to carry specified cell phones on
campus and taught how to use them responsibly at specific time and places is beneficial to all players.
The effort of educational authority should be focused towards making cell phone usage on campuses
more friendly in a more responsible way rather than the current no cell at all policy which by all standard
unsustainable. Authorities must also realize that day in day out cell phones are becoming powerful than
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most of the personal computers stocked in our various learning institutions across the country and when
implemented effectively can help school authorities serve costs such as inventory, maintenance and
purchase.
Keywords: evaluation, policies, mobile phone, Senior High School (SHS) students, analytical hierarchy
process (AHP)
INTRODUCTION
A number of questions have arisen following the student unrest which took place at the Ghana
SHS (GHANASCO) in Tamale, Aduman SHS in Ashanti Region and the Navrongo SHS
(NAVASCO) in Navrongo in early 2010 all in relation to ban on mobile phone usage. A
publication captioned, ‚GES ban of mobile phones in SHS is unnecessary‛ (Modern
Ghana.com), June 2, 2010. , Radio and television discussions on the above problem show the
level of public interest in the matter at the time.
The importance of mobile phones cannot be overemphasized. Phones are use as calculators,
flash lights, web browsers, multimedia devices, cameras, television, radio, recorders, storage
devices, calendars, diaries, alarm clocks, date keepers, timers, broadcast devices, outside
broadcast tool or news gathering tool, crime monitors, aid to parents to keep touch with kids
and many more. Notwithstanding, these positives can easily be eroded by the negatives;
promotion of pornographic materials, increase of incidence of theft among students,
impersonation, over spending of time on phones rather on their books and many more if care is
not taken especially with the age bracket, 14 to 20 years pupils by (Mullen, 2006 cited in Clarene and William, 2007) .
Opinions are sharply divided on whether to ban the usage of mobile phone in the SHS or not,
even within school administrators, parents, and teachers. While many sees mobile phone as a
modern tool needed to develop students skills other think they are just to guided by the adults
such as their teachers and family to used the device in a more responsible manner of which they
will use anyway now or in the future.
In order to arrive at an optimal policy alternative so as to reduce the adverse effect of mobile
phone usage by SHS students, the study seek to adopt a scientific approach, analytic hierarchy
process (AHP) by Saaty (1980) to decomposes the decisional process in a hierarchy of criteria,
sub-criteria, attributes and alternatives through a set of weights that reflect the relative
importance of these alternatives. Lack of certainty of the effect of mobile phone usage on
students in SHS and lack of consensus among experts and stakeholders makes the use of AHP
more profound. AHP has become a significant methodology due to its capability for facilitating
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multi-criteria decision-making Ramanathan (2001). According to (Alphonce, 1997; Tiwary et al.,
1999; Duke et al. 2002; Ferrari, 2003), AHP has been widely applied to numerous complex
environmental and economic problems. Opinion of experts with an in-depth understanding of
the educational policies and their effects on students conduct is required. These experts do not
have to agree on the relative importance of the criteria or the rankings of the alternatives, but
each expert enters his judgments and makes a distinct, identifiable contribution to the issue.
In today’s democratic environment, management of organizations are increasingly in favour of
consensus based decisions rather than imposition of ideas on employee to promote high
productivity. Hahn (2002) stressed the need for a structured approach to decision making which
allows trade-offs after all perspectives are considered. Multi-criterion methods such as scoring
models, preference based methods; outranking methods, goal programming, and analytical
hierarchy process are effective in this scenario.
It is more practical to try to contain this inconsistency within reasonable limits, rather than to
eliminate it completely for a truer representation of the actual decision. A decision model which
is not overtly complex and uses simple human judgment to make the decisions is thus needed.
AHP is such a tool which breaks down a problem into smaller parts which can be easily
handled by a human mind.
Gray (1999) observes that, when scales for decision making are not consistent (units differing)
making decisions based on multi-criteria becomes complex and risky. However, Saaty (1999)
suggests that the use of a model that is not overtly complex, legitimately aggregates across
scales and addresses consistency in judgments from multiple participants. Common to most of
the multi-criterion methods require participants of high deductive capacity, and a human mind
would find it challenging to take into consideration the different trade-offs between the various
criterions under different circumstances. This consistency is difficult to maintain in the real
world.
The AHP process has been used for strategic evaluation of emerging technologies May et al.
(2006) and selection of R & D projects Duke and Aull-hyde (2002). In both evaluation and
selection there is considerable uncertainty about the future, and little statistical data is available.
The ultimate goal is to meet the strategic objective of the company. It can be said that these cases
are similar to the problem at hand: evaluate policies to eliminate the negative impact of mobile
phone usage on SHS students.
May et al (2003) demonstrate the application of AHP incorporating stakeholder preferences in
the complex task of forest planning. Bevilacqua M. and Petroni, (2002) described applying the
AHP to select the best maintenance strategy. Chan and Chan (2004) detailed the development of
a material handling equipment selection system involving AHP. AHP has also been used in
combination with other tools. . Chan and Chan further suggested that a decision support tool
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that includes a data structure to monitor the effectiveness of a decision, through the use of AHP,
cost-benefit analysis, and statistical analyses. Davis and Hersh (1998) used AHP in design
improvement by integrating it with Failure Mode Effect Analysis (FMEA). Lee et al. (2006) used
AHP in combination with fuzzy set theory for enterprise waste evaluation problem. Due to its
relative simplicity, AHP has been used in numerous applications with consistent results.
METHODOLOGY
This study adopted survey as its strategy. Questionnaires were administered to experts (SHS
administrators, formal administrators and lecturers) from randomly selected SHS, some parents
and students within K.M.A to score the relative importance of predetermined factors over the
aother. Secondary data was also collected mostly from the internet to facilitate the analysis of
the results using Excel for Windows program called Expert Choice for Windows. The study
focused on the Kumasi Metropolitan Assemble (K.M.A.) of the Ashanti Region. The region has
102 Senior High Schools (SHSs) and Secondary Technical Schools of which about 26 are private.
This study also adopted both qualitative and quantitative methods like stated in chapter one.
The nature of the problem is such that the necessary information needed were basically
opinions and views of experts and stakeholders. These views and opinions are then quantified
using a scale describing the relative importance of one factor over the other. This makes it
necessary to adopt both qualitative and quantitative methods to be able to answer the set
objectives. The results are presented quantitatively.
Simple random sampling technique was used to select seven (7) SHSs within K.M.A. 5 out of
about 22 public SHSs and 2 out of about 11 private SHSs. In each school, the
Headmaster/Headmistress, Assistant Headmaster/Headmistress Academic and
Domestic/Senior Housemaster/Housemistress were interviewed. Ten (10) students, five boys
and five girls who have at least used a cell/mobile phone in school before were interviewed in
each school. Two parents from each of the seven schools selected schools were also surveyed.
Were we did not get the two parents on campus to interview a contact number was solicited
from school’s administration.
Data was analysed by AHP using Microsoft Excel for Windows and Expert Choice for
Windows. AHP is a multi-criteria decision method that uses hierarchical structures to solve
complicated, unstructured decision problems, especially in situations where there are important
qualitative aspects that must be considered in conjunction with various measurable quantitative
factors.
AHP Axioms
AHP is founded on the following set of axioms for deriving a scale from fundamental
measurements and for hierarchical composition Saaty (1986).
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Axiom 1: Reciprocal
If element A is x times as important than element B, then element B is 1/x times as important
than elements A.
Axiom 2: Homogeneity
Only comparable elements are compared. Homogeneity is essential for comparing similar
things, as errors in judgment become large when comparing widely disparate elements.
Axiom 3: Independence
The relative importance of elements at any level does not depend on what elements are
included at a lower level.
Axiom 4: Expectation
The hierarchy must be complete and include all the criteria and alternatives in the subject being
studied. No criteria and alternatives are left out and no extra criteria and alternatives are
included.
AHP Procedure
Pairwise comparisons among n elements in each level lead to an approximation of each
iij
j
wa
w , 0ija , 1iia and ji <<<<<<<<<<<..<<<<<<.. (3.1)
Following Saaty (1980, 2000), the priorities of the elements can be estimated by finding the
principal eigenvector w of the matrix A (comparison matrix). The estimated weight vector w is
found by solving the following eigenvector problem:
maxAw w <<<<<<<<<<<<<<<<<<<<<<<<<<<. (3.2)
where the matrix A consists ofija ’s, and max is the principal eigenvalue of A. lambda ( ) is the
average of the consistency vector. When the vector w is normalized, it becomes the vector of
priorities of elements of one level with respect to the upper level. In cases where the pairwise
comparison matrix satisfies transitivity for all pairwise comparisons it is said to be consistent
and it verifies the following relation: If there is consistency between the pair of elements, then
ij ik kja a a and 1
ij
ji
aa
for all i j and k…<<<<<<<<<<<<<< (3.3)
The result is that max n and we have Aw nw , where n is the number of elements in each
row. Table 3.1 reports the pairwise comparison scale used in the AHP developed by Saaty
(1977). It allows converting the qualitative judgments into numerical values, also with
intangible attributes.
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Table 3.1 The AHAP pairwise comparison scale
Numerical
values
Verbal scale Explanation
1 Equally important for both elements Two elements contribute
equally
3 Moderate importance of one element over
another
Experience and judgment
favour
one element over another
5 Strong importance of one element over another An element is strongly
favoured
7 Very strong importance of one element over
another
An element is very strongly
dominant
9 Extreme importance of one element over
another
An element is favoured by at
least
an order of magnitude
2,4,6,8 Intermediate values Used to compromise between
two judgments
Source: Saaty (1977, 1980).
Saaty (1980) has shown that to maintain reasonable consistency when deriving priorities from
paired comparisons, the number of factors being considered must be less or equal to nine. AHP
allows inconsistency, but provides a measure of the inconsistency in each set of judgments. The
consistency of the judgmental matrix can be determined by a measure called the consistency
ratio (CR), defined as:
CICR
RI <<<<<<<<<<<<<<<<<<<<<<<<<<<<< (3.4)
where CI is called the consistency index and RI is the Random Index. Furthermore, Saaty (1980,
2000) provided average consistencies (RI values) of randomly generated matrices Table 3.3. CI
for a matrix of order n is defined as:
max
1
nCI
n
<<<<<<<<<<<<<<<<<<<<<<<<<<<. (3.5)
In general, a consistency ratio of 0.1 or less is considered acceptable this threshold is 0.08 for
matrices of size four and 0.05 for matrices of size three. If the value is higher, the judgments
may not be reliable and should be elicited again.
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Table 3.3. The average consistencies of random matrices (RI values)
Size (n) 1 2 3 4 5 6 7 8 9 10
RI 0.00 0.00 0.58 0.89 1.11 1.25 1.35 1.40 1.45 1.59
Source: Saaty (1980, 2008)
Once the local priorities of elements of different levels are available, in order to obtain final
priorities of the alternativesia , the priorities are aggregated as follows:
i k k iikS a w s a <<<<<.<<<<<<<<<<<<<<<<<<<. (3.6).
Assessment of alternative policies on Cell phone ban by SHS students on campuses
In order to evaluate alternative policies on cell phone ban by SHS students on campuses to
minimize the adverse impacts of cell phone on their lives, we have investigated the opinions of
twenty one educational experts, twenty eight parents and fourteen students by means of a
survey questionnaire. Experts do not necessarily have to agree on the relative importance of the
criteria of the rankings of the alternatives. Each expert entered his judgement and gave a
distinct, identifiable contribution to the issue.
For the case study, a four-level analytic hierarchy process has been applied, as shown in Figure
3.1. The first level is composed of the final goal one wishes to attain in carrying out the project:
minimization of the adverse impact of cell phone usage on SHS students in Ashanti region. The
second and third levels represent the criteria and sub-criteria on the basis of which the policy
options are to be evaluated:
C1 – Promotion of exam malpractice
C2 – Cyber bullying of fellow students
C3 – Allocations of too much time to calls specially deep into night to the neglect of
their books
C4 – Breakdown of good moral standing (misapplication of the device like viewing and
taking of pornographic materials)
Management of Ghana Education Service (GES) is aware that despite all these potential adverse
impact of cell phone usage, the device very important functions and uses and if care is not taken
these students would be denied off the benefits. Four important uses were broadly identified as
why students in SHS might need cell phones on campuses:
SubC1 – As aid for students to keep in touch with parents and relations
SubC2 – As aid during emergencies (example financial needs, health needs, etc.)
SubC3 – As a device for learning (example as device for internet accessibility, storage
device, picture taking etc.)
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SubC4 – As a device for developing the technology capabilities of students
The third level presents the policy options, which are:
A1 – Outright ban of all personal cell phones own by students on campuses and install
phone boots in dormitories and campuses
A2 – Allow students to carry specified cell phones on campus and taught how to use
them responsibly at specific time and places
A3 – Allow students to carry models of their choice and taught how to use them
responsibly to facilitate their learning at restricted places and time
A4 – Allow students to carry models of their choice and taught how to be responsibly to
facilitate their learning
Figure 3.1 Analytic Hierarchy Process of Policy directions which will reduce the adverse
impacts of cell phone usage by SHS students on campuses
Source: Adopted from Saaty’s cited in Qureshi & Harrison
RESULTS
Demography of student respondents
Of the 70 students’ respondents, 74.3% are within (18 – 20) years, 21.4% are within (15 – 17)
years and the rest of the 4.3% are within (21 – 23) years.
Goal (Policy direction which will reduce the adverse
impacts of cell phone usage by SHS students on campuses)
C1 C4 C3 C2
SUBC2
A1
SUBC1 SUBC3 SUBC4
A2 A3
A4
Goal
Criteria
Sub-criteria
Options
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Of the 70 students’ respondents, 84.4% of the students said their cell phones were important to
them academically and 15.6% said their cell phones were not important (not use for academic
purpose) to them academically.
What students use their cell phones to do?
Again, 70 out 70 respondents use their cell phones for both ‘voice calls’ and ‘text messaging’
each. 52 out of 70 use their cell phones for ‘internet browsing’. 39 out of the 70 use their cell
phones for ‘radio/audio recording’ and 28 out of 70 use their devices to take ‘pictures and
capture video’.
Awareness and effects of cell phone usage.
Of the 70 respondets 33 think cell phone usage has some negative effects on their lives and 53%
do not think cell phone usage has any negative effects on their lives.
Of the 33 respondents who think cell phone usage has some negative effects on their lives, 24
mentioned ‘less sleep due to calls made deep into the night’ 20 mentioned ‘extra financial
burden’18 mentioned addiction to ‘watching of pornographic pictures and videos’, 9 mentioned
‘health consequences (itching of ears, impaired hearing)’ 3 mentioned ‘encouragement of theft
among students’
22 of the 33 respondents have ‘made calls deep into night’ before, 20 out of the 33 have suffered
‘extra financial burden’, 15 out of the 33 have ‘watch pornographies’ with their cell phones
before, and 5 said they have experienced ‘health problems’.
Parents
Of the 35 parents 31.4% strongly agreed with the ministry’s directive on the ban, 37.2% agreed,
20% disagreed and 11.4% strongly disagreed with the directive.
Experts Views (Educational administrators)
Of the 19 school management respondents, 42% described the rational of the ban as very
important, 37% described it as important, and 11% each described it as normal and
unimportant.
On the issue of compliance, all of the 19 respondents said the ban has not been complied with
by students.
Reasons for noncompliance
At their age students always want to be in touch with parents and family members
while in school (unedited). Many others fall under this category.
Students use their phones to engage in examination malpractices (unedited). Many other
responses fall under this category.
School authorities are not committed enough to enforcing the ban by turning blind eyes
to culprits(unedited)
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Students always protect each other by failing to expose culprits (unedited)
Unavailable of suitable alternative communication modes on various campuses and
dormitories (unedited)
Students are generally recalcitrance always want to go in the opposite direction to the
pursuit of administration (unedited)
It is their era. There is little we can do about it we rather have to concentrate on
educating them on how to be more responsible with the devices (unedited)
Some of these students are adults and they know what they are about. We all know how
difficult it is to stay without your cell phone once one begin using one (unedited)
Students claim they need them for emergency situations. Many responses fall under this
category
Some parents want to keep in touch with their wards all the time while they are in
school
Students need cell phones for unknown ill-motives
The cost of using these installed phone boots is higher than using one cell phone
(unedited)
Sometimes teacher – student relationships makes it difficult to enforce the ban. Many
other responses fall within this category
Some teachers do not share the opinion of the ministry view
The device aid students in their academic work
They use the phone to entertain themselves rather than learning
From the above information, it follows that the reasons for non compliance to the ban varied.
While some are attributable to students own agendas, parents and teachers interest also
accounts for the non compliance. Others are seen as good reasons while others are seen as ill
motives.
When respondents were asked if they are in fully support of the current ‚no cell phone at all‛
policy subscribed to by GES, 11out of 19 said ‘no’ they do not and 8 out of 19 said ‘yes’ they are
in fully support to the ban.
Policy alternatives opened to school authorities on mobile phone usage on campuses.
Of the experts and school administrators sampled, the various policy directions opened to
management for the enforcement sanity on cell phone usage by SHS students on campuses are:
1. Outright ban of all personal cell phones own by students on campuses and install phone
boots in dormitories and campuses (Alternative A).
2. Allow students to carry models of their choice and taught how to use it responsibly to
facilitate their learning at restricted places and time (Alternative B).
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3. Allow students to carry specified cell phones on campus and taught how to use them
responsibly at specific time and places (Alternative C) and.
4. Allow students to carry models of their choice and taught how to be responsibly to
facilitate their learning (Alternative D).
Relative weights of the alternative policies
Total weighted evaluations are shown on Table 4.2. The policy option A3 receives the highest
importance (more than 50%); A4 is the second-best option (19%), slightly more important than
A1 (16%) and A2 (13%).
Table 4.2 Factor Weights for alternatives
Policy Alternative Total Weighted evaluations
Alternative A1 0.1617
Alternative A2 0.1343
Alternative A3 0.5174
Alternative A4 0.1866
Source: Survey, 2012
From the above results, there is an indication that the highest importance is to the alternative A3
‚Allow students to carry specified cell phones on campus and taught how to use them
responsibly at specific time and places‛ (51.7%); the other two criteria ‚Outright ban of all
personal cell phones own by students on campuses and install phone boots in dormitories and
campuses‛ and ‚Allow students to carry models of their choice and taught how to be
responsible with the device to facilitate their learning‛ have almost equal priority (about 20%)
and the final alternative ‚Allow students to carry models of their choice and taught how to use
it responsibly to facilitate their learning at restricted places and time‛ has priority choice off
about (10%).
Sensitivity analysis to test under which conditions the ranking of alternatives may change. The
method has involved specifying a certain number of experiments, which set different possible
combinations of the criteria’ weights (Harrison et al., 1993). In particular, the weight of any
criteria i,iw , has been supposed to evolve according to the stochastic differential equation:
ii i idw w dt w dz
This equation implies that iw are changing according to a process of geometric Brownian motion
(GBM). The term dt is the mean or expected percentage change in iw for the increment dt , and
μ is called the mean drift rate. The term dz introduces a random component to the drift,
because dz= t dt , where t is a normally distributed random variable with 0 mean and
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standard deviation of 1. A discrete approximation of the equation shown above is given by the
stochastic difference equation:
, 1 , , 11i t i t i t tdw w w
Where 1t the standard normal variants and the implied increment are is 1dt . Given the base
weights vector of the criteria in table 3 and the values for μ and σ, selected from the standard
normal distribution defined for the 95% confidence interval, we have generated sample paths of
100 random numbers for any criteria.
The sensitivity results indicated that all possible combinations of increasing and decreasing,
reported in Table 4.3, which reports the mean percentage change in each alternative and the
standard deviation across the 100 random samples, suggests that the results are relatively
robust to different combinations of the weights’ values. In fact, the mean percentage change is
very low, as well as the standard deviation tend to be quite small. Moreover, analysing the
sample probability of the ranking of alternatives, we have found that A3 is always the best
option, and the change in the ranking of alternatives is due mainly to the fact that A1 becomes
slightly more important than both A4 (30% of cases) and A2 (10% of cases).
Table 4.3 Sensitivity analysis of policy options
Mean % Standard deviation
A1 4.065 0.012
A2 4.124 0.006
A3 4.006 0.034
A4 4.232 0.009
Source: statistics computed from survey, 2012
Dantzig (1963) the benefit of doing a sensitivity analysis is too paramount to be ignored in
applications of the weighted sum model (WSM), the weighted product model (WPM), and the
analytic hierarchy process (AHP) to real-life problems. As stated it: "Sensitivity analysis is a
fundamental concept in the effective use and implementation of quantitative decision models,
whose purpose is to assess the stability of an optimal solution under changes in the
parameters."
DISCUSSIONS OF RESULTS
Students who are the subject of discussion think cell phones are of important to their education.
Uses of the handsets included; making voice calls, sending and receiving of text messages,
browsing of the internet, data storage and as an audio/radio. It is important to mention that the
students themselves admitted the devices are abused sometimes. The vices they mentioned
included; financial burden on the addictions to watching of pornographies, health problems
and increase of phone theft cases among students.
Majority of parents support the band but up to 31% of the parents are still willing to buy cell
phones to students. This means some children still has the support of their parents on using cell
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phones on campuses. Against the ban policy is bound not to succeed because the implemented
of the policy are not in full support of the ban policy. The 58% school administrators who do not
fully support the current cell phone usage by students cannot be seen to be effectively enforcing
the ban. Some parents want their wards to own their personal cell phones in SHS and will
therefore provide for their wards with cell phones.
Whatever the reasons are, all these players must buy into the decision of dealing with the
problem of ownership of cell phones students in secondary schools.
School administrators’ reasons for the non compliance are follows:
The assertion by school administrators that students will always want to contact parents and
family members while in school indicates they do acknowledge the need for them owing cell
phones.
Again, they believe that students always want to use their phones to engage in examination
malpractices should not be too much a reason for the ban. Instead proactive measures should be
employed to counter any attempt to cheat in examination. To hit the nail on the head some
school administrators accepted that they themselves are not committed enough to enforcing the
ban by turning blind eyes to culprits.
Unavailability of suitable alternative communication modes on various campuses and
dormitories are also the reason for the noncompliance of the current no cell phone at all policy
been articulated by GES. Students are generally recalcitrance and will always want to go in the
opposite direction to the pursuit of administration.
The ranking of the policy options with respect to the ultimate goal of evaluating the ban of cell
phone usage on SHS campuses by students indicate that the most important policy alternative
to the current policy is A3. That is ‚Allow students to carry specified cell phones on campus
and taught how to use them responsibly at specific time and places‛ (51.7%); the fourth
alternative, A4‚Allow students to carry models of their choice and taught how to be responsible
with the device to facilitate their learning‛ (18.7%) is also ranked better than the current
‚Outright ban of all personal cell phones own by students on campuses and install phone boots
in dormitories and campuses‛ (16.2%) and the final alternative ‚Allow students to carry models
of their choice and taught how to use it responsibly to facilitate their learning at restricted
places and time‛ (13.4%) is ranked less important to the current policy.
Sensitivity analysis indicated that the results are consistent. It implies that the results are stable
over a certain range of variation of the weights values assigned to the various criteria. It also
means that respondents were consistent in expressing the views. The mean percentage change
in each alternative and the standard deviation across the 100 random samples, suggests that the
results are relatively robust to different combinations of the weights’ values. In fact, the mean
percentage change is very low, as well as the standard deviation tend to be quite small.
AFRICA DEVELOPMENT AND RESOURCES RESEARCH INSTITUTE (ADRRI) JOURNAL
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Moreover, analysing the sample probability of the ranking of alternatives, we have found that
A3 is always the best option, and the change in the ranking of alternatives is due mainly to the
fact that A1 becomes slightly more important than both A4 (30% of cases) and A2 (10% of
cases).
CONCLUSIONS
The reasons for students noncompliance with cell phone ban in various SHS campuses are:
students seeing the device to be useful for their academic career, parents not been generally
supportive of the directive and SHS adminsitrators not in total agreement with the current ‚no
cell phone at all‛ policy.
The various policy directions opened to school authorities are: Outright ban of all personal cell
phones own by students on campuses and install phone boots in dormitories and campuses,
allow students to carry specified models of cell phones on campus and taught how to use them
responsibly at specific time and places, allow students to carry models of their choice and
taught how to use it responsibly to facilitate their learning at restricted places and time and
allow students to carry models of their choice and taught how to be responsibly to facilitate
their learning.
Amongst the alternative policies, allow students to carry specified cell phones on campus and
taught how to use them responsibly at specific time and places has been assessed as the best
option to pursue to reduce the adverse impacts of cell phone usage on students of SHS.
Authorities of GES must come to the realization that the issue of technological development of
the nation has reach a stage where both adults and children are finding it even more difficult to
do without it. The effort should therefore be gear towards making cell phone usage on
campuses more friendly in a responsible way rather than the current no cell at all policy which
by all standard unsustainable.
Authorities must also realize that day in day out cell phones are becoming powerful than most
of the personal computers stocked in our various learning institutions across the country. There
are cell phones with higher processors and speed than our PCs. Ensuring that students use
these devices in the best possible ways would help the institutions in a number of ways. First
cost of acquiring and maintenance of these devices would be saved. Cost of providing the
infrastructure for housing of these PCs would also be saved. Information would have been
closer to the students using their cells than PCs which are likely to be locked up somewhere
after a certain period of the day.
ACKNOWLEDGMENT
Our first appreciation goes to Dr. Bashiru Imoro Ibn Saeed, Statistics Department, Kumasi
Polytechnic for his constructive criticisms, suggestions and advice that led to the completion of
this paper.
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We also appreciate the contributions of all staff of the Statistics, Mathematics and Science
department of Tamale Polytechnic.
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