IT
Based KM In Indian Higher Education System:
Addressing
Quality Concerns And Setting The Priorities Right
Ashish Kumarą, Arun Kumar˛,
Indian Institute of Information Technology-Allahabadą, Allahabad
ABSTRACT:
The Quality of Education being offered in institutions of Higher Education
is a question being debated widely. With the growing cost of Higher Education
in
Keywords: Information
Technology, Knowledge Management, Indian Higher Education, Quality of Service (QoS)
1. Introduction
Declining resources allocation for Higher Education (HE) & increasing
competition among HE Institutions together with the growing awareness about
value for money among public at large have all made the quality of Higher
Education being offered in
¨ Students: for choice of an Institution for studying
¨ Parents: for worth of personal investment on the education of their wards.
¨ Governments: for accountability & policymaking
¨ Funding Agencies: for deciding the quantum & extent of fund allocation
¨ Society: for value of taxpayer’s money
¨ Industry: for Industry–Institution partnership & also as employers for graduate recruitments
In the present scenario therefore, when all stakeholders of Indian Higher Education System are concerned about the education offered in its Institutions of HE, against the back drop of declining funds for HE, it becomes pertinent to look for technological options which can make an impact on the existing system in a most cost effective & user friendly manner. Such options & tools must also be in keeping with the all round socio economic development of the country and must also be relevant in the present day knowledge based society. A blend of Information Technology (IT) & Knowledge Management (KM) techniques therefore seem to be the helping tools to meet this challenge (Kumar & Kumar, 2005) , against this backdrop.
2. Higher Education System
In
HE in
Deemed-to-be-Universities are institutions conferred with the status of a University owing to their long tradition of teaching / specialization / excellence in a particular area of knowledge (viz. Tata Institute of Social Science, Mumbai, IIIT-Allahabad etc.). Institutes of National Importance are so designated by an Act of the Parliament of India and as a special case empowered with all the rights & privileges of a University (viz. IIT’s)(Universities Handbook, 2002).
Higher Education at grass root level in
The complexity with the Indian Higher Education (IHE) System is that while
initially it was established with deep colonial roots, for the purpose of
producing literate & semi-skilled manpower to largely look after the
subordinate services, in the past five decades after independence, when the
system has moved on to become an egalitarian one, it has been pressed immensely
to meet the growing aspirations of a developing and vibrant democracy. To meet
this growing demand, while the number of Indian HE institutions has grown from
18 universities in 1947 to over 305 in 2005, the quality offered by HE institutions
has fallen terribly short of the expectations. This has been because of two
basic reasons. Firstly, the population explosion in
It is therefore that institutions of HE in India, today are hard pressed to accept the modern management and computerized interventions into their systems in order to ensure value for money ( as desired by the investors of the system) and superlative quality of services offered ( as desired by its stakeholders, some of whom are also its constituents).
Over the years, though the HE system has been benefited by the examination & recommendations of a number of education committees & commissions, the system today is observed to be the one with lot of short comings viz :
¨ Lack of focused planning at Institutional Level
¨ Variable quality of HE in different institutions across the country (Powar, 2000)
¨ Inflexibility of academic structure that inhibits innovation (Powar, 2000) & excellence
¨ Non-productive research being conducted
¨ Lagging quality of curriculum due to lack of enthusiasm in revision & development of new curriculum
¨ Mis- as well as under-utilization of already scarce resources viz. equipments etc., due to ignorance as well as apathy of all concerned
¨ Low standard services being offered to students & alumni
¨ Very low consistency in decision making coupled with slow pace of its delivery
Kumar and Kumar (2005), have already explained the advent of Information
Technology (IT) based Knowledge Management (KM) as a modern day
techno-management tool, with the help of which, institutions of HE in
3. IT Based KM And Its Role
In Higher Education
While various agencies have in their own perspective attempted to define the landscape for technologies and interventions as IT & KM, Government of India (2000) interprets IT as a set of media devices and services, out of which proper solution can be configured, based upon the needs and affordability of the target clientele in the country. Further, owing to its dynamicity, it lends a strong potential to improve & manage several facets of HE, viz. office automation, decision support system (DSS), access to & availability of reliable and updated information, etc. All these avenues influence the overall productivity and efficiency of the Indian HE system, which are important constituents of a Quality System.
Kidwell et al (2000) viewed KM as a process of transforming information & intellectual assets into the ones of enduring value, while Holsapple and Joshi (2004) view it as an entity’s systematic and deliberate effort to expand, cultivate and apply available knowledge in ways that add value to the entity, in the sense of positive results in accomplishing its objectives or fulfilling its purpose. The entity’s scope may be individual, organizational, Trans organizational, national, etc. Burstein and Linger (2003), define KM as a broad concept that addresses the full range of processes by which an organization deploys knowledge. This involves the acquisition, retention, storage, distribution and use of knowledge in an organization. Jennex (2005), proposes KM as the practice of selectively applying knowledge from previous experiences of decision making to current and future decision making activities with the express purpose of improving the organization’s effectiveness. Knowledge management is the explicit and systematic management of vital knowledge and its associated processes of creating, gathering, organizing, diffusion, use and exploitation. It requires turning personal knowledge into corporate knowledge than can be widely shared throughout an organization and appropriately applied (Skyrme, 2003).
According to Petrides and Nodine (2003) Educational Institutions however seem to be working in a more complex way, as these organizations are adaptive and are social systems where people co-operate with technologies to evolve processes to achieve common goals. Just as ecosystems rejuvenate themselves through cycles & seasons, educational organizations grow & revitalize themselves through the knowledge they create, their processes facilitate passing that knowledge on to others and the exchanges and relationships that they foster among people.
Knowledge Management therefore, very aptly brings together
three core organizational resources – people, processes and technologies
– to enable the educational institutions use and share information most
optimally & effectively (Figure 1). KM in education can therefore be
thought of as a framework or an approach that enables people within the
institution to develop a set of practices to collect information & share
what they know leading to actions that improve services and outcomes (Petrides & Nodine, 2003
As an inference from the ongoing research of the authors, it
is not ‘Technology’ in general, but ‘Information
Technology’ (IT) in particular, that facilitates interaction between
people & processes. It does so, to such an extent that today not KM alone,
but IT based KM seems to be a major & potent techno management tool, the
interventions of which aim to revitalize the existing HE system in
4. Methodology
To obtain the view of persons who had a fair experience about the IHE system, based upon the earlier work of Kidwell et al (2000), a questionnaire was developed specially by the authors, to examine whether the Indian academia also believed that IT based KM interventions could be applied to Indian Higher Education Institutions for their benefit. And if yes, then what should be the order of priority of each intervention. The questionnaire was sent generously for response. The analysis of 301 responses received was done using statistical techniques and MATLAB (Ver. 7). The respondents included persons who were largely academics with over ten years of experience in IHE System and also academic administrators. Wholly the cross-section of respondents included persons from government institutes of national eminence to state government owned universities as well as Government and privately funded HE institutions. The results were since significant at 95% confidence level, on the whole it represented the Indian opinion fairly .
The questionnaire framed consisted of two sections. Section 1, solicited
marking the options as Yes or No, while Section 2 solicited scores from the
respondents in the six areas, identified to be important from a Quality
Conscious System point of view for HE system. These areas were adopted for
exploration, to see if the concept of IT based KM could be developed for
application in IHE System through these areas, as was proposed in the earlier
work of Kidwell et al (2000). The identified areas for the study accordingly,
were:
i. Institutional Planning & Development
ii. Institutional Research & Development Process
iii. Curriculum Development Process
iv. Institutional Administration
v. Finance & Accounts Departments
vi. Students Affairs
5. Analytical Environment
In the current research, following Statistical methods were used for data analysis:
¨ Measures of Central Tendency
¨ Measures of Dispersion
Though the measure of central tendency viz. Mean, Mode and Median for the data obtained in the current work were calculated, they have not been relied upon totally for drawing the conclusion, since statistics relies more on the other type of summary statistics i.e. Measures of Dispersion (Variation).This is because the measure of Dispersion of values around the central tendency are more important than the mean, mode, etc. (Averages, 2005).
This paper takes into assumption the Normal Distribution, which is also
called Gaussian Distribution or a
Accordingly therefore, the statistical parameters used for analysis in this paper are:
¨ Spread of distribution
¨ Skewness (The Third Moment)
¨ Kurtosis (The Fourth Moment)
Spread of
Distribution: The spread or width of a process is the distance
between minimum and the maximum measured value. If the distribution is narrower
than the specification limits; it is an indication of small variability in the
process.
Skewness:
A "normal" distribution of variation results in a specific
bell-shaped curve, with the highest point in the middle and smoothly curving
symmetrical slopes on both sides of center. The characteristics of the standard
normal distribution are tabulated in most statistical reference works, allowing
the relatively easy estimation of areas under the curve at any point. Many
distributions may be skewed, or they may be flatter or more sharply peaked than
the regular normal distribution. A "skewed" distribution is one that
is not symmetrical, but rather has a long tail in one direction. If the tail
extends to the right, the curve is said to be right-skewed, or positively
skewed. If the tail extends to the left, it is negatively skewed
This can be summarised
as follows (
If
the chart looks like this: It
indicates that the distribution has: Right Skew - If the plotted points appear
to bend up and to the left of the normal line that indicates a long tail
to the right. Left Skew - If the plotted points bend
down and to the right of the normal line that indicates a long tail to
the left. Short Tails - An S shaped-curve indicates
shorter than normal tails, i.e. less variance than expected. Long Tails - A curve which starts below
the normal line, bends to follow it, and ends above it indicates long tails.
That is, you are seeing more variance than you would expect in a normal
distribution. While comparing two or more data sets, the one’s
having least skewness are given priority.
Kurtosis:
Kurtosis is the measure of peakedness as well as the
length of the tails of a distribution. For example, a symmetrical distribution
with positive kurtosis indicates a greater than normal proportion of product in
the tails. Negative kurtosis indicates shorter tails than a normal distribution
would have (Histograms, 2005). Accordingly, a data having high kurtosis value
is preferred over the one having a lower value. For same data sets kurtosis
being the fourth moment assumes significance only when both data are equally or
almost equally skewed (the third moment).
6. Observations And Inference
6.1. Institutional Research And Development
Fig. 3 Illustrates the benefits that
IT based KM interventions can play in Institutional Research & Development.
91.06% respondents agreed that the tools can lead to improved utilization of
institutional resources & facilities, while 90.75% believed that they would
facilitate interdisciplinary research. Over 84% respondents opined that the
benefits would also accrue by way of increased competitiveness and responsiveness
for research grants and commercial activities, leveraging Research &
Proposals efforts apart from bringing down the turnaround time for research.
Reduced administrative costs were anticipated by 81.13% while that these
interventions shall lead to minimising the devotion
of Research Resources towards Administrative tasks was believed by 72.52 %
respondents.
6.2. Curriculum Development
Process
That benefits of IT based KM interventions can cast an impact over the curriculum development process of higher education institutions and thus ease the complaint of students, a major stakeholder in Indian HE system, is depicted by Fig.4. 91.4 % respondent believed that these interventions could lead to enhanced quality of curriculum and programmes. 86.4% academics agreed that such interventions would lead to improved speed of curriculum revision as well as improved monitoring and feedback from all stakeholders. Also while 86.1% people agreed that interventions shall lead to improved Administrative services related to teaching & learning with Technology, 79.14% believed that it shall also assist in improving the pace of New Faculty Development.
6.3. Institutional
Administration
Fig. 5 clearly shows that Institutional Administration shall be marked by improved effectiveness & efficiency, if the IT based KM interventions were applied to Higher Education Institutional Administration (as per responses received by over 91% respondents).Enhanced responsiveness & communication capabilities together with increased consistency in decision making shall also occur (88.42 % & 85.76% responses in affirmation).Impact would also be felt on the parameters of improved compliance with administrative policies & practices (84.44%) and also on lowering the decision taking time (81.46%).
6.4. Students Affairs
An indication that Student Affairs of an institution of Higher education shall be benefited significantly by the perceived interventions is visible from Fig. 6. Improvement in the overall quality of services offered to students (92.72%) apart from that in the accessibility to institutional resources & facilities (89.74 %) shall also be obtained. In the opinion of 87.42 % respondents, service quality to alumni shall be improved. Further, 86.43 % academics agreed that the IT based KM interventions shall improve the service capabilities of Faculty, Officers and Staff of the Higher Education Institutions.
6.5. Institutional Planning and
Development
Impact of IT based KM interventions in the area of Institutional Planning is as evident from Fig.7. Over 83% respondents opined to expect enhanced ability to develop more relevant & focused policies besides increased efforts of all stakeholders being directed towards achievement of common institutional goals. The interventions were also believed to be stabilising good human relationships amongst various constituents of the institution by 77.49% academics.
Table 1 shows that data is negatively skewed in all the four cases 1A, 1B, 1C and 1D. However, it is also observed from the same that 1D is the least skewed. This implies that the parameter 1D is favored by most respondents followed by 1C, 1A & 1B, in the decreasing order of their priority. The extent of skewness in all the cases is elaborated from corresponding curves as shown in Fig. 8. Fig. 8 represents a Normal Probability Distribution Function Plot from Matlab Ver.7 for four proposed IT based KM interventions that can assist in Institutional Planning and Development. Accordingly therefore, for the best benefits of IT based KM in the area of institutional planning and development highest priority should be accorded in the maintenance of repository for data related to accountability & outcomes by monitoring & assessment mechanisms, performance indicators, etc. This must be followed by repository containing reports on benchmark studies, competitor data, links to researchers & research groups. Further, reports & presentations made by various officials of the Institute & review committees is also important. Data on reviews made by external agencies about the Institute & its activities, is though important, received the least scores.
Table 1
IT Based KM tools can
assist in Institutional Planning & Development:
|
|
1A |
1B |
1C |
1D |
|
Cumulative of 301 scores |
1924.5 |
1858.2 |
1942.0 |
1954.5 |
|
Mean (Mu) |
6.394 |
6.173 |
6.452 |
6.493 |
|
Median |
7.000 |
7.000 |
7.000 |
7.000 |
|
Mode |
8.000 |
7.000 |
8.000 |
8.000 |
|
S.D. (Sigma) |
2.294 |
2.182 |
2.149 |
2.263 |
|
Variance |
5.264 |
4.762 |
4.617 |
5.122 |
|
Skewness |
-0.617 |
-0.590 |
-0.612 |
-0.826 |
Fig. 8
1. IT based KM interventions can assist in Institutional
Planning & Development, by way of maintaining repository of : A reports
& presentations made by various officials of the Institute, review
committees, etc. B reviews
made by external agencies viz. funding agencies, accreditation agencies,
media, etc. about the Institute & its activities. C reports
on benchmark studies, competitor data, links to researchers & research
groups etc. D data
related to accountability and outcomes by monitoring & assessment
mechanisms, performance indicators, etc
As per data in Table 2, the order of priority for various interventions of IT based KM for improving institutional R&D, on the basis of skewness is as follows: 2J, 2E, 2F, 2D, 2C, 2B, 2H, 2G, 2I and 2A. However, since the level of skewness of 2E & 2F was very close, the decision was taken on the basis of relative kurtosis values. The priorities of various interventions are thus modified as 2J, 2F, 2E, 2D, 2C, 2B, 2H, 2G, 2I and 2A, signifying that best results of applications of IT based KM in the area of institutional R&D can be had by maintaining data of related researchers & research groups, followed by knowing the budget under various schemes and project proposal routing policies & procedures. This is followed by information availability on commercial opportunities for research results, project funding agencies & in-house research results, project award notifications. Availability of Technical & Financial report templates, Audit reports on internal resources & services of the institution and data about potential vendors etc. was also considered relevant in the order of decreasing priority. Fig. 9 represents a Normal Probability Distribution Function plot from MATLAB Ver.7, for ten IT based KM interventions that can be applied to improve the Institutional Research and Development Process
Table 2
IT Based KM tools can be
applied to improve Institutional Research and Development Process
|
|
2A |
2B |
2C |
2D |
2E |
2F |
2G |
2H |
2I |
2J |
|
Cumulative |
1707.0 |
1885.2 |
1889.2 |
1934.3 |
1880.7 |
1904.1 |
1631.0 |
1777.9 |
1633.3 |
2009.7 |
|
Mean (Mu) |
5.671 |
6.263 |
6.276 |
6.426 |
6.248 |
6.326 |
5.419 |
5.907 |
5.426 |
6.677 |
|
Median |
6.000 |
7.000 |
7.000 |
7.000 |
7.000 |
7.000 |
6.000 |
6.000 |
6.000 |
7.000 |
|
Mode |
6.000 |
7.000 |
7.000 |
8.000 |
8.000 |
8.000 |
7.000 |
7.000 |
8.000 |
8.000 |
|
S.D.(Sigma) |
2.491 |
2.349 |
2.326 |
2.449 |
2.379 |
2.315 |
2.628 |
2.460 |
2.867 |
2.805 |
|
Variance |
6.203 |
5.516 |
5.411 |
5.999 |
5.659 |
5.358 |
6.907 |
6.050 |
8.218 |
7.869 |
|
Skewness |
-0.176 |
-0.402 |
-0.487 |
-0.602 |
-0.611 |
-0.610 |
-0.293 |
-0.307 |
-0.259 |
-0.842 |
|
Kurtosis |
|
|
|
|
2.715 |
2.909 |
|
|
|
|
Fig. 9
2. IT Based KM
tools can be applied to improve Institutional Research and Development
Process by maintaining data about: A Potential
vendors/research & Scientific equipment suppliers B In
house research results C Project
funding agencies D Commercial
opportunities for research results E Budget
under various schemes F Project
proposal routing policies & procedures G Project
Award Notifications H Technical
and Financial Report Templates I Audit
reports on Internal Resources & Services of the Institution J Related
researchers & research groups
That all parameters 3A through 3H are relatively skewed is clear from the values shown in Table 3. Accordingly, the order of priority for various IT based KM parameters in the area of curriculum development process is: 3A, 3D, 3B, 3C, 3E, 3F, 3G & 3H. A Normal Probability Distribution Function plot, for eight IT based KM interventions that can assist in Institutional Curriculum Development Process is represented in Fig. 10. A database on curriculum revision efforts that includes data on research delivered, best practices & lessons learned followed by clusters of information in various, disciplinary areas and modularized content reposition have highest significance for good curriculum development process. Information related to teaching & learning with technology, is then followed by maintenance of a repository of pedagogy & assessment techniques. Analysed updated data on student evaluations, guides for developing curriculum and a repository of corporate relationships to identify curriculum advisory task forces then assumes significance.
Table 3
IT Based KM tools can
assist Curriculum Development Process
|
|
3A |
3B |
3C |
3D |
3E |
3F |
3G |
3H |
|
Cumulative |
2030.5 |
1932.9 |
1913.7 |
2016.6 |
1762.7 |
1861.1 |
1876.0 |
1750.8 |
|
Mean (Mu) |
6.746 |
6.422 |
6.358 |
6.700 |
5.856 |
6.183 |
6.233 |
5.817 |
|
Median |
7.000 |
7.000 |
7.000 |
7.000 |
6.000 |
6.000 |
7.000 |
6.000 |
|
Mode |
8.000 |
8.000 |
8.000 |
8.000 |
6.000 |
8.000 |
8.000 |
7.000 |
|
S.D. (Sigma) |
2.590 |
2.251 |
2.290 |
2.320 |
2.202 |
2.293 |
2.478 |
2.546 |
|
Variance |
6.708 |
5.067 |
5.245 |
5.381 |
4.849 |
5.257 |
6.139 |
6.482 |
|
Skewness |
-0.776 |
-0.595 |
-0.567 |
-0.728 |
-0.496 |
-0.486 |
-0.451 |
-0.366 |
Fig. 10
Table 4 displays the relative values for various statistical parameters for the scores obtained by various interventions for application in the area of Administration section of an Indian HE institution. The order of preference on the basis of least skewness is 4G, 4D, 4H, 4E, 4A, 4B, 4C and 4F implying highest priority to the maintenance of a database on HR policies & practices for trainings & promotions, followed by repository on rules & regulations for staff facilities etc. Maintenance of repository of minutes of meetings of statutory bodies and that of trainings etc. for Faculty, Officers & Staff figure the next important priorities. A repository on Frequently Asked Questions (FAQ’s), about institutions & its policies, templates of all forms & formats, updated data on merit lists of entrance exams etc. followed by a repository on disciplinary conduct on campus are also found important in the decreasing order of their priority. The relative priority is also seen from the Normal Distribution plots as shown in Fig.11
Table 4:
IT Based KM can be
applied in Administration Section
|
|
4A |
4B |
4C |
4D |
4E |
4F |
4G |
4H |
|
Cumulative |
1811.5 |
1999.2 |
1806.1 |
1898.2 |
1941.1 |
1676.0 |
1940.4 |
1848.1 |
|
Mean (Mu) |
6.018 |
6.642 |
6.000 |
6.306 |
6.449 |
5.568 |
6.447 |
6.140 |
|
Median |
6.000 |
7.000 |
6.000 |
7.000 |
7.000 |
6.000 |
7.000 |
7.000 |
|
Mode |
6.000 |
6.000 |
7.000 |
8.000 |
8.000 |
7.000 |
8.000 |
8.000 |
|
S.D. (Sigma) |
2.639 |
2.345 |
2.483 |
2.349 |
2.246 |
2.420 |
2.376 |
2.505 |
|
Variance |
6.962 |
5.501 |
6.165 |
5.519 |
5.044 |
5.856 |
5.644 |
6.277 |
|
Skewness |
-0.466 |
-0.401 |
-0.250 |
-0.531 |
-0.493 |
-0.221 |
-0.545 |
-0.526 |
Fig. 11
4.
IT Based KM can be applied in Administration
Section of an Institution of Higher Education by maintaining: A a
repository on FAQ’s B templates
of all applications, forms & formats C updated
data about merit list of Entrance Examination & Results. D repository
of rules & regulations for on campus housing etc. facilities E database
on trainings/ workshops/ seminars & symposiums attended & to be
attended by all constituents of the Institution. F a
repository on disciplinary conduct on campus G database on HR policies & practices
for trainings & promotions H repository of minutes of all meetings
of all statutory bodies of the institution
Table 5, followed by Fig. 12, considers six interventions for application in the IHE institutions’ Finance & Accounts departments. The observed order of priority on the basis of ascending order of skewness is 5A, 5F, 5C, 5B, 5D & 5E signifying that maintenance of up to date budgeting & accounting is most important. Database on income, taxation etc. status of each employee followed by that on approach procedures & policies are the next desired focus. A repository on FAQ’s about financing & accounting policies, templates of all related forms & formats followed by an updated database on fees & fines status of every constituent are the desired priority areas in order of their importance.
Table 5
IT Based KM can be
applied in Finance & Accounts department
|
|
5A |
5B |
5C |
5D |
5E |
5F |
|
Cumulative |
2137.6 |
1891.3 |
2033.5 |
1931.2 |
1876.3 |
1975.1 |
|
Mean (Mu) |
7.102 |
6.283 |
6.756 |
6.416 |
6.234 |
6.562 |
|
Median |
8.000 |
6.250 |
7.000 |
7.000 |
6.000 |
7.000 |
|
Mode |
8.000 |
6.000 |
8.000 |
8.000 |
6.000 |
8.000 |
|
S.D. (Sigma) |
2.641 |
2.119 |
2.072 |
2.151 |
2.191 |
2.390 |
|
Variance |
6.974 |
4.490 |
4.294 |
4.628 |
4.800 |
5.714 |
|
Skewness |
-0.997 |
-0.509 |
-0.587 |
-0.443 |
-0.411 |
-0.682 |
Fig. 12
5. IT Based KM can be applied in
Finance & Accounts department of an IHE by maintaining: A up
to date Budgeting & Accounting B a
repository on Frequently Asked Questions (FAQ’s) C a
database on approved procedures & practices D templates
of applications, forms & proposals E an
updated database on fees & fines status of every constituent F an updated
database on income, taxation, dues, etc. status of each employee
Students are the most important constituents-cum-stakeholders of any HE system and also that of IHE. IT based KM interventions are observed to be of direct assistance in management of student affairs of the institution by at least four ways. Table 6 shows the same. The indicated order of priority for the four interventions on the basis of skewness of the data obtained from the respondents is as 6A, 6B, 6C and 6D. The fact stresses the presence of an updated database on entire institutions resources, policies & procedures related to aspects as admissions, examinations, financial aids, fees, student counseling etc. A portal for career placement services followed by a repository on various student affairs & services available to them are the next important things.
Table 6
IT Based KM tools can
benefit Students
|
Intervention
code |
6A |
6B |
6C |
6D |
|
Cumulative |
2242.0 |
2178.4 |
1988.9 |
1898.6 |
|
Mean (Mu) |
7.449 |
7.237 |
6.608 |
6.308 |
|
Median |
8.000 |
8.000 |
7.000 |
7.000 |
|
Mode |
8.000 |
8.000 |
7.000 |
8.000 |
|
S.D. (Sigma) |
2.430 |
2.295 |
2.144 |
2.209 |
|
Variance |
5.903 |
5.268 |
4.595 |
4.882 |
|
Skewness |
-1.182 |
-1.087 |
-0.855 |
-0.464 |
Fig.13
6. IT Based
KM tools can benefit Students in an IHE by way of providing: A an
updated database on entire institutions resources, policies &
procedures related to admissions, examinations, financial aids, fees,
student counseling facilities, etc. B a
portal for career placement services hosting information about probable
employers, their contact details, packages offered, etc. C a
repository of student affairs, services for faculty and staff to ensure all constituents
understand existing services & can provide proper counseling. D a portal for
alumni to keep track of their professional growth, etc.
A portal for alumni to keep track of their professional growth is though the fourth priority, but an important one. Fig. 13 presents the Normal Distribution curve for the four stated interventions for the benefit of Students.
7. Conclusion
A far sighted planning at Institutional level, supported by good R & D as well as Curriculum Development activities are important quality parameters in any institution of Higher Education looking forward to a satisfied stakeholder base in Higher Education System. A concerned and sensitised institutional administration towards the special needs of bright young men & women as its immediate constituents as well as stakeholders, is also an essential requirement of a quality conscious Higher Education System. From the results as discussed above, IT based Knowledge Management interventions seem to be promising techno – management tools to help cast an impact over all the vital areas of the Indian Higher Education System viz. Institutional Planning, Curriculum Development Process, R & D activities of the HE institutions, etc. and thus provide a quantum leap in the “Quality of Service (QoS)” being currently offered by them.
The identified interventions in selected areas are if taken up by appropriate agencies viz. Governmental (for policy making) and institutional (for implementation), are bound to rationalize the investment in higher education system as well as lead to more responsive Higher Education System with optimized resources utilization. These modern interventions of IT based KM could also lower the overall investment in the existing higher education system by carefully identifying the key areas where these interventions could be applied.
8. Acknowledgement
The authors are thankful to Dr. M.D.Tiwari,
Director Indian Institute of Information Technology,
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Contact the authors:
Ashish Kumar, Assistant Registrar, Indian Institute
of Information Technology, Devghat, Jhalwa, Allahabad-211011,
Arun Kumar, Reader, MONIRBA, Allahabad University,
Allahabad, Uttar Pradesh, INDIA; Tel: +91-9415217841; Email:arun@shuchita.com