Information Technology Reference
In-Depth Information
Input
Effective Learning
and Teaching
Output
Effective
Learning and
Teaching
Leadership
Administrative
Setup
Institutional
Grade
improvement
(Enhancement
in overall
quality
Research
RST
Analysis
Faculty
Administrative
Setup
Industry institute
Interface
Placement
Infrastructure
Students
Fig. 3 RST based TQM model for higher education
and teaching. This may result into overburdened and disoriented faculty which
ultimately leads to quality degradation.
According to the results of RST analysis the remaining seven condition attri-
butes are lesser signi
cant and therefore does not contribute much to the decision
(Grading of the institution).
The proposed RST based TQM model emerges from the results of RST analysis
as depicted in Fig.
3
. Self assessment of quality parameters in higher education by
three different stake holders, namely, faculty, students and staff are treated with
RST analysis and two significant condition attributes emerges as the major pillars of
this model. Grading of higher education institution may be improved if these two
attributes are given maximum attention. However, this should not be interpreted
that other attributes are meaningless but the outcome of this model re
fl
ects the RST
approach of
fine tuning the vague and uncertain data.
6 Conclusion
The last 5 years have seen a phenomenal surge in the popularity of global and local
rankings of universities along with some complexities and problems (Hazelkorn
2011
). The sudden rush for prestigious rankings and grading by different rating
agencies has initiated a debate about their validity, accuracy and real worth. This
ascertains the growing importance of institutional grading as an important measure
of quality assessment in the highly competitive arena of global higher education.
This research aims to contribute through the use of the proposed model to enhance
the total quality education through a modi
ed grading mechanism which is focused
Search WWH ::
Custom Search