Arun C Mehta
Formerly Professor & Head of EMIS Department
NIEPA, New Delhi (India)
E-mail: acmehta100@gmail.com
The Government of India through the Department of School Education & Literacy, Ministry of Education has initiated many programmes to improve School Education in India amongst which Sarva Shiksha Abhiyan Programme (SSA) and Rashtriya Madhyamik Shiksha Abhiyan (RMSA) are the most prominent ones which are now merged into Samagra Shiksha. It has been a practice to compute indices to know the health of the state school education system which are also helpful to look into the areas which need interventions. NIEPA also computed Educational Development Index (EDI) during the period 2005-06 to 2015-16, an index one each for the primary and upper primary level of education based on a set of 24 parameters all of which were based on the information generated through the since source i.e. UDISE.
Recently two more indices, namely School Education Quality Index (SEQI) by the NITI Aayog (the first year 2016-17) and Performance Grading Index (PGI) by the Department of School Education & Literacy, Ministry of Education in consultation with the NITI Aayog were initiated. The objectives of both the SEQI & PGI being almost the same; one failed to understand the usefulness of more than one index for the same purpose.
The objective of SEQI developed by NITI Aayog was to evaluate the performance of States & Union Territories (UTs) intending to provide them a platform to identify strengths and weaknesses so that necessary course corrections are initiated. The SEQI also strives to facilitate the sharing of knowledge and best practices amongst States & UTs. On the other hand, PGI envisages that the Index would propel States & UTs towards undertaking multi-pronged interventions to pinpoint the gaps and prioritize areas for intervention. Like SEQI, PGI is also expected to act as a good source of information for best practices to share amongst the States & UTs.
Both the indices are based on a set of the same
domains (see Table 1) but the number of indicators used and weightage assigned are
different. While
the review of SEQI is presented separately, the present
article undertakes a critical review of the latest PGI 2018-19 undertaken by
the Department of School Education & Literacy.
Table
1
Domain-specific
Number of Indicators used in PGI 2018-19 & Weightage Assigned
|
Category |
Domain |
Number of Indicators |
%age Indicators |
Weightage |
%age Weightsgae |
Number of Indicators with Same
Values used in 2018-19 of 2017-18 |
Number of Indicators used in SEQI,
NITI Aayog |
|
I: Outcomes |
1: Learning Ourcomes & Quality |
9 |
13 |
180 |
18 |
08 |
03
(360) |
|
2: Access |
8 |
11 |
80 |
8 |
08 |
03
(100) |
|
|
3: Infrastructure & Facilities |
11 |
16 |
150 |
15 |
00 |
03
(25) |
|
|
4: Equity |
16 |
23 |
230 |
23 |
00 |
07
(200) |
|
|
II: Governance & Management |
1. Governance Processes |
26 |
37 |
360 |
36 |
00 |
14
(280) |
|
Total |
70 |
100 |
1000 |
100 |
16 |
30 (965) |
Source:
The table prepared is based on PGI 2018-19, DoSE&L, Ministry of Education,
Government of India.
Like 2017-18, 2018-19 Performance Grading Index (PGI) is also
based on a set of 70 indicators spread over the following five domains:
·
Learning Outcomes Government Quality
·
Access
·
Infrastructure Government Facilities
·
Equity; and
·
Governance Process
It is not known how data is updated and what the mechanism for monitoring of updated information by the states and whether data of the remaining 56 parameters/indictors are of the same year. The data used cannot be verified by independent users simply because of the reason that the raw data used in computing the indicators are not available in the public domain; even a full set of U-DISE 2018-19 is not available in the public domain.
It is not possible to verify the reliability and authenticity of data used in computing PGI 2018-19. It has been mentioned that the authenticity of data is verified internally by its officers but details have not been provided. Based on the PGI 2017-18 & 20118-19, each State/UT was provided domains on which they performed well and have had slow progress but indicator-specific details have not been made available in the absence of which it is not known how best the State/UT use the outcome in further improving their position about an indicator or a set of indicators or a domain.
A few indicators are judged based on PAB approvals details of which are generally not available in the public domain. It would be of interest to know whether State/UT has initiate activities in the light of these observations and are reflected in the Annual Work Plan and Budget in the following year. If yes, has anyone evaluated the performance of State/UT concerning a few key indicators used or PGI is an independent exercise just to know the status of State/UT concerning a few indicators/domains and practically without any follow-up exercise?
Before we analyze PGI 2018-19, first we
take a look at indicators used in computing PGI. At a glance, it looks both PGI
2017-18 & 2018-19 are based on the same set of indicators, weights assigned,
methodology used, mode of analysis & presentation. Once the set of
indicators are finalized, the same may be fixed for the next five years along
with the methodology and the same sets of weightage assigned to each indicator
parameter to see the usefulness of the whole exercise with regard to
methodology used and consistency of results.
Domain
I: Learning Outcomes
The first set of indicators we review below fall under Domain I: Leaning Outcomes which has a set of nine indicators with a total weight of 180, except one the source of the remaining eight indicators is the National Achievement Survey conducted by the NCERT in 2017. In the last part, we shall discuss the weights assigned and the methodology of assigning the weights.
The remaining lone indicator, namely the percentage of Government & Aided elementary schools which have displayed class-wise learning outcomes and reported on the Shagun portal. Is it elementary schools or elementary stage? is not specified. Since the data uploaded on the Shagun portal is not available in the public domain, it is not possible to know whether the same is reported by the schools or states that have reported the percentage of such schools? Is the percentage reported for the entire state as a whole or district-specific percentage has also been reported? It would have been better to use the percentage of districts having displayed class-wise learning outcomes which should have been converted into the state-specific indicator.
A state having 80 percent may be treated as good compared to other states but within the state, it may not present the true picture of displaying school-wise outcome. Instead of the percentage of schools having displayed learning outcomes, a better indicator would be to consider the percentage of schools having displayed student-wise learning outcomes. It would be of interest to know how this indicator was authenticated by the officers engaged in the PGI exercise which is otherwise impossible to check and there is no option to accept the information as submitted by the State/UT. Another important limitation of the entire PGI exercise is that most of the indicators used are computed only for the Government and Aided schools which is of limited use and does not present the true picture of the entire State/UT.
Percentage of schools displayed class-wise learning outcomes means
one time or concurrently in an academic session also need to be specified or
whether the same is part of the periodic evaluation or for the same a special
learning outcome on the pattern of NAS is supposed to be conducted by each of
the Government & Aided school. The
questions raised must find answers in PGI during the next round of computation.
Category 1: Outcomes
Domain I: Learning Outcomes &
Quality of Education Indicators
|
Sl.
No. |
Total
Domain Weight 180 |
Source of Information |
Weight |
|
1 |
Percentage of Elementary schools
which have displayed class wise Learning Outcomes |
Shagun Portal |
20 |
|
2 |
Average Language score in Class 3
- Government and Government schools |
NCERT (NAS) |
20 |
|
3 |
Average Mathematics score in Class
3 - Government and Government schools |
20 |
|
|
4 |
Average Language score in Class 5
- Government and Government schools |
20 |
|
|
5 |
Average Mathematics score in Class
5 - Government and Government schools |
20 |
|
|
6 |
Average Language score in Class 8
- Government and Government schools |
20 |
|
|
7 |
Average Mathematics score in Class
8 - Government and Government schools |
20 |
|
|
8 |
Average Science score in Class 8 -
Government and Government schools |
20 |
|
|
9 |
Average Social Science score in
Class 8- Government and Government schools |
20 |
NAS: National Achievement Survey
As has already been mentioned that the remaining eight indicators used in both the PGI 2017-18 & 2018-19 are related to the quality of education measured through the NCERT National Achievement Survey conducted in 2017 concerning average scores in Grades III, V, and VIII in subjects like Language, Mathematics, and Science.
Not only the same set of indicators have been used
but their values of 2017-18 have also been used in computing PGI 2018-19 which
makes no sense which should have been avoided. Better would be to use only
those indicators which have got provision for the annual collection on regular
basis and part of the administrative data. So far as possible, information that
is not available in the public domain and limited to the Samagra
authorities/State should have been avoided in computing any index, such as PGI.
Whatever
scores have been used all which relates to Government & Aided schools just
because of the reasons that NAS doesn't cover private unaided schools.
Alternative indicators should find the place during the next round of PGI
computation in the absence of which PGI will never present the true picture of
learning outcome in the entire State/UT. It may also be of interest to know
that in case of a few other indicators both the Government as well as Private
schools including the private Un-adied schools have been considered in computing
PGI 2017-18 & 2018-19. Further, it has also been observed that on the one
hand some indicators concerning elementary education have been used on the
other hand indicators concerning secondary education have also been used.
Better would have been to compute like previously computed Educational
Development Index separate indices one each for elementary and secondary level
of education. Both indices should therefore be based on a separate set of
indicators concerning the elementary and secondary levels of education.
Domain II: Access
PGI 2018-19 used a set of eight indicators under the
Domain II: Access source of which except one indicator, namely percentage
of identified Out-of-School children mainstreamed in the last completed
academic year in case of Grades I to VIII is Unified-DISE. The source of
information on Out-of-School children is state sources reported through the Shagun
portal maintained by the DSE&L, Ministry of Education. In the absence of a mechanism
for collecting information on Out-of-School children on regular basis, it is
not known on what basis state report percentage of identified Out-of-School
children who were mainstreamed in the last completed academic year? and on what
basis the information submitted by the state on out-of-school children is being
checked in the absence of which there would always be a question mark about the
reliability of indicators being used in computing PGI. Still, it would be
better to use (i) the percentage of out-of-school children identified to total
out-of-school children; and (ii) the percentage of identified out-of-school
children mainstreamed.
The rest of the seven indicators used in
computing PGI are related to enrolment which is adjusted-NER, retention, and
transition rate at the elementary and secondary level of education. It is good
to use adjusted-NER to view the participation of children in the elementary and
secondary education programmes. It would be still better to use adjusted-NER
separately for primary and upper primary levels of education instead of the entire
elementary level of education together. It may be recalled that a huge decline (59
million) in enrolment in 2017-18 was observed from its previous level in 2016-17
which continued in 2018-19; the lions share in decline in enrolment was
contributed by the primary and upper primary level of education which has got
serious implications for universal school enrolment. Enrolment in the upper
primary level cannot grow independently to the primary level of education (in
terms of graduates). Unless the primary level of education sends an adequate
number of primary graduates to the elementary level, the elementary level
cannot grow independently because of which it is important to use efficiency-related
indicators in computing any index in the future, such as PGI. Therefore, it is
suggested that at least average annual, as well as grade-to-grade drop-out rate
be considered in the future computation of PGI. Better, it would be to use
separately for boys and girls and that too for elementary, as well as a secondary
level of education. It is further observed that gender-specific rates are not
used which is otherwise essential to know participation of girls, as well as
boys in educational programmes because of which it is suggested to use Gender Parity
Index based on adjusted-NER both at the elementary as well as secondary levels
of education. Further, it has been observed that enrollment-based indicators
are used only at the state level, and as such no district-specific indicators have
been used in computing PGI which can easily be computed based on UDISE data.
Even adjusted-NER may not be free from limitations as the same need
age-specific child population in a year which is generally not readily available
from the Census of India sources in the absence of which the projections based
on up to 2001 actual Census figures are modified by the Ministry of Education
in the light of 2011 total population which is not free from limitations. It
has also been observed even revision in the projected age-specific population
in the recent past because of which even published figures were changed. In the district-specific age-specific
population, states have also been using district-specific population as per
their convenience; this does not always present the true picture of children's
participation in educational programmes. The DoSE&L had taken up the issue
of projected child population with the Office of the Registrar General of India
but without any results. Neither the Expert Committee on Population Projections
was set up by the Planning Commission (now NITI Aayog) which was otherwise a
regular exercise (up to 2001 Census) used to be initiated immediately after the
Census operations were over nor it has provided a single estimate of the age-specific
population required in the computation of enrolment based indices, such as GER,
NER, Age-SER, and Adjusted-NER for the entire period of 2012 to 2021 in the
absence of which it was left to the district to use their estimates. However, the author of this article has undertaken
the exercise based on the single-age actual child population of Census 2011 and
made available 6 to 11 and 11 to 14 years population initially up to the year
2016 both state and district-wise which were observed to be used by states in
computing district-specific GER and NER.
In view of the decline in enrolment a
few indicators, such as the percentage of schools showing increase/decline in
primary and upper primary enrolment and the percentage of blocks and districts
showing increase/decline in enrolment can be used as an alternative indicator.
Districts showing the decline in enrolment for the two consecutive years may be
dealt with separately and appropriate indicators may be added. Lastly, it may
also be observed that not a single indicators giving information on the availability
of schools have been used under Domain II: Access Indicators which otherwise
means that there is no shortage of schools in India which may not be true for
secondary and higher secondary education because of which at least the ratio of
elementary to secondary schools/sections should have found a place in PGI
indicators.
Category 1: Outcomes
Domain II: Access Indicators
|
Sl.
No. |
Total
Domain Weight 80 |
Source of Information |
Weight |
|
1 |
Adjusted
Net Enrolment Ratio at Elementary level |
UDISE |
20 |
|
2 |
Adjusted
Net Enrolment Ratio (NER) at Secondary level |
20 |
|
|
3 |
Retention
rate at Primary level |
20 |
|
|
4 |
Retention
rate at Elementary level |
20 |
|
|
5 |
Retention
rate at Secondary level |
20 |
|
|
6 |
Transition
rate from Primary to Upper Primary level |
20 |
|
|
7 |
Transition
rate from Upper Primary to Secondary level |
20 |
|
|
8 |
Percentage
of identified Out-of-School Children mainstreamed in the last completed
academic year i.e.2017-18, Grades Class I to VIII |
States through Shagun Portal of DSE&L, MoE |
20 |
UDISE: Unified District Information System
About ten indicators have been used under Domain III: Infrastructure & Facility Indicators main source of which is UDISE, Shagun, and Mid-day Meal portals all of which is being managed by the DoSE&L, Ministry of Education. Indicators concerning primary, as well as elementary and secondary education concerning only the government & aided management, have been used. The focus of this set of indicators is on the infrastructure which includes Computer-Aided Laboratories (CAL) in case of upper primary and integrated science and computer laboratory in case of secondary level, mid-day meal scheme, a host of reading material including textbooks both in case of elementary, as well as secondary education, uniforms, and functional drinking water facilities.
Category 1: Outcomes
Domain III: Infrastructure & Facility Indicators
|
Sl. No. |
Total
Domain Weight 150 |
Source of Information |
Weight |
|
1 |
Percentage
of schools having Computer-Aided Laboratory in Upper Primary Level |
UDISE |
20 |
|
2 |
Percentage
of Secondary schools having Laboratory Facility |
|
|
|
a)
Integrated Science Lab |
10 |
||
|
b)
Computer lab |
10 |
||
|
3 |
Percentage
of schools having Book Banks/Reading Rooms/Libraries |
20 |
|
|
4 |
Percentage
of schools covered by Vocational Education subject |
|
|
|
(a)
Grades IX & X |
10 |
||
|
(b)
Grades XI & XII |
10 |
||
|
5 |
Percentage
of Primary schools provided Graded Supplementary Material |
Shagun
Portal |
20 |
|
6 |
Percentage
of Elementary school children taking Mid-day Meal against target approved in PAB - Government
& Aided schools |
MDM
Portal |
10 |
|
7 |
Percentage
of days Mid-day Meal served against Total Working days – Government &
Aided Elementary schools |
10 |
|
|
8 |
Percentage
of schools having Functional Drinking Water Facility: All Schools |
UDISE |
10 |
|
9 |
Percentage
of Elementary Level Students getting Uniform within three months of the start
of previous Academic year i.e. 2017-18: Government Schools |
10 |
|
|
10 |
Percentage
of Elementary Level students getting Free Textbook within one month of the start
of the previous academic year i.e. 2017-18 |
10 |
UDISE: Unified District Information System
Table
Schools with Toilet & Functional Toilet Facility,
2018-19
|
Management |
Total
Number of
Schools |
Schools
with Toilet
Facility |
%age
Schools with Toilets |
Schools
with Functional
Toilet
Facility |
%age
Schools with
Functional
Toilets |
|
Department of Education |
835488 |
816461 |
97.72 |
795851 |
95.26 |
|
Tribal Welfare Department |
45409 |
42371 |
93.31 |
40157 |
88.43 |
|
Local body |
196530 |
191625 |
97.50 |
169981 |
86.49 |
|
Government Aided |
84623 |
78768 |
93.08 |
76586 |
90.50 |
|
Private Unaided (Recognized) |
326228 |
312252 |
95.72 |
303407 |
93.00 |
|
Other Govt. Managed Schools |
1322 |
1174 |
88.80 |
1140 |
86.23 |
|
Unrecognized |
32366 |
25960 |
80.21 |
24618 |
76.06 |
|
Social Welfare Department |
2413 |
2288 |
94.82 |
2186 |
90.59 |
|
Ministry of Labor |
356 |
288 |
80.90 |
268 |
75.28 |
|
Kendriya Vidyalaya |
1566 |
1537 |
98.15 |
1525 |
97.38 |
|
Jawahar Navodaya Vidyalaya |
505 |
499 |
98.81 |
498 |
98.61 |
|
Sainik School |
64 |
64 |
100.00 |
62 |
96.88 |
|
Railway School |
80 |
80 |
100.00 |
80 |
100.00 |
|
Central Tibetan School |
14 |
14 |
100.00 |
14 |
100.00 |
|
Madarsa Recognized (By Wakf Board/Madarsa Board) |
19150 |
17772 |
92.80 |
17043 |
89.00 |
|
Madarsa Unrecognized |
4886 |
4526 |
92.63 |
4306 |
88.13 |
|
Total |
1551000 |
1495679 |
96.43 |
1437722 |
92.70 |
It is
further observed that the exercise of PGI 2018-19 computation was undertaken
before the COVID19 pandemic maybe because of which not a single indicator
concerning electricity connection in school, availability of functional
computer and internet facility was considered which is now become essential in
future PGI computation because of online learning for last more than a year.
Still, it is not sure when normal studies through the actual classroom transactions
will resume. Since one of the main sources of PGI is UDISE+, it has also become
more important to have a look at the availability of electricity connections in
school and functional computers and internet connection which is briefly analyzed
below. In an online system, such as UDISE+, the quality of data also depends
upon the availability of an internet connection and functional computer in
school.
Percent
of Schools having Electricity, Computer and Internet Connectivity
in
School: 2017-18
|
Facility |
Primary
Only Schools |
All
Schools |
|
Electricity
Connection |
51.85 |
63.14 |
|
Computer |
12.20 |
29.57 |
|
Internet
Connection |
3.54 |
13.61 |
|
Functional
Computer |
4.19 |
13.07 |
|
Computer
Laboratory (Hr. Secondary Schools) |
- |
45.17 |
Source: U-DISE
Another crucial indicator is the availability of computers
and internet connection in schools both of which are yet to be provided in the
majority of schools in India. Of the total 1.5 million schools, only about 20
percent of schools have got a computer as against 12.20 percent such primary
schools. Unfortunately, the percentage of working/functional computers in schools
is as low as 13.07 percent in case of all schools and 4.19 percent in primary
only schools. The state-wise percentage of schools with working computers
further reveals that the same in Bihar is as low as 0.51 percent compared to 3
percent in Uttar Pradesh, about 5 percent in Jharkhand, 4 percent in Assam, 5
percent in Madhya Pradesh, and 3 percent in Odisha. On the other hand, schools
in a few states such as Andhra Pradesh, Delhi, and Gujarat have got electricity
connections in most of the schools but the percentage of schools with a working
computer, except Delhi (68.25 percent) is still very low. Schools with working
computers need not have an internet connection as only about 14 percent of
schools have an internet connection compared to only about 4 percent of primary
schools. In the light of the above
discussion, it is envisaged that the percentage of schools (all schools) with
electricity connection, functional computer, and internet connectivity in
school will be added to the list of PGI indicators in the years that follow.
Schools having
Electricity,
Computer and Internet Connectivity in Schools (All) in Selected States
2017-18
|
Facility |
Assam |
Bihar |
Jharkhand |
Odisha |
UP |
MP |
Andhra Pradesh |
Delhi |
Gujarat |
All India |
|
Electricity
Connection |
24.28 |
45.82 |
47.46 |
36.50 |
44.76 |
32.58 |
92.80 |
99.93 |
99.91 |
63.14 |
|
Functional
Computer |
3.98 |
0.51 |
4.84 |
3.22 |
3.17 |
5.99 |
24.03 |
68.25 |
38.65 |
13.07 |
Source:
U-DISE
Category 1: Outcomes
Domain IV: Equity Indicators
As many as
16 indicators have been used in computing PGI under Domain IV: Equity
Indicators of which eight indicators are based on the National Achievement
Survey conducted by the NCERT in 2017 with this the total indicators based on
NAS comes out to be 16 out of a total 70
indicators which have an aggregate weightage of 280 out of total 1,000
weightage. Since most of the school education programmes centered around
improving the quality of learners ability, it is quite natural that the
emphasis of PGI is largely on quality-related indicators but the same is not
available on the regular basis and the NAS is the only source of information that
is occasionally being conducted nation-wide the latest of which was conducted
in 2017 and the next such survey is planned to be conducted sometime in 2021
till such time there is no option but to use the 2017 data irrespective of PGI
whether it is 2017-18 or 2018-19 or 2019-20. But the moot question is whether
it is essential to use the already used indicators (with the same values) in a
year? Better it would be to use only such indicators which have a regular
source of information and is also part of the administrative survey.
The next
four indicators are related to transition rates all of which are based on UDISE
data. This set of indicators are the extension of transition rate from primary
to upper primary and from elementary to secondary level already used under
access indicators in case of the minority population, Scheduled Castes &
Scheduled Tribes category and boys and girls transition rate all of which are
important for next level of an educational level to grow.
The next
indicator used under Domain IV: Equity indicators are Gross
Enrolment Ratio of Children with Special Need (CWSN) for the age-group between
6-18 years; the main source of which is said to be Shagun, UDISE, and Ministry
of Social Justice and Empowerment. Having worked with UDISE for decades, it can
simply be said that GER for CWSN is neither required nor it is possible to
construct the same. Despite all efforts, even the number of CWSN students is
not adequately reported under the UDISE because of which it always remains an underestimate
of the total CWSN students. For computing GER for CWSN, apart from CWSN
enrolment, corresponding age-specific child population with disability in the
current year is required which is next to impossible to get the real number. It
may be observed that even the reliable annual age-specific child projected
population is not available; how one could envisage that the age-specific child
population with a disability will be available. It may even be difficult for
the data custodian i.e. Ministry of Social Justice and Empowerment to get the same in the
requisite year because of which it would be better to drop the GER for CWSN in
any future PGI computation.
Category 1: Outcomes
Domain IV: Equity Indicators
|
Sl. No. |
Total
Domain Weight 230 |
Source of Information |
Weight |
|
1 |
Difference
in Student performance in Language between Scheduled Castes (SC) and General
category in Govt. and Aided elementary schools:
Class
3, 5 & 8 |
NCERT: National Achievement Survey |
20 |
|
2 |
Difference
in Student performance in Mathematics between Scheduled Castes (SC) and
General category in Govt. and Aided elementary schools
Class
3, 5 & 8 |
20 |
|
|
3 |
Difference
in Student performance in Language between Scheduled Tribes (ST) and General
category in Govt. and Aided elementary
schools :
Class 3,
5 & 8 |
20 |
|
|
4 |
Difference
in Student performance in Mathematics between Scheduled Tribes (ST) and
General category in Govt. and Aided
elementary schools :
Class
3, 5 & 8 |
20 |
|
|
5 |
Difference
in Student performance in Language between Urban and Rural areas in Govt. and Aided elementary schools
:
Class
3, 5 & 8 |
10 |
|
|
6 |
Difference
in Student performance in Mathematics between Urban and Rural areas in Govt.
and Aided elementary schools : Class
3, 5 & 8 |
10 |
|
|
7 |
Difference
in Student performance in Language between Boys and Girls in Govt. and Aided
elementary schools:
Class
3, 5 & 8 |
10 |
|
|
8 |
Difference
in Student performance in Mathematics between Boys and Girls in Govt. and
Aided elementary schools:
Class 3, 5 & 8
|
10 |
|
|
9 |
a)
Difference between SCs and General Category’s Transition Rate from Upper
Primary to Secondary level |
UDISE |
10 |
|
10 |
b)
Difference between STs and General Category’s Transition Rate from Upper
Primary to Secondary level |
10 |
|
|
11 |
Difference
between boys’ and girls’ Transition Rate from Upper Primary to Secondary
level |
10 |
|
|
12 |
Difference
between Minorities and General Category’s Transition Rate from Upper Primary
to Secondary level |
20 |
|
|
13 |
Gross
enrolment ratio of CWSN (age group 6-18 years) |
Shagun:
UDISE & MSJE for
population |
10 |
|
14 |
%
of entitled CWSN receiving Aids and Appliances for Govt and aided schools |
Shagun
& PMS |
10 |
|
15 |
Percentage
of schools having ramp for disabled children to access school building |
UDISE |
10 |
|
16 |
Percentage
of schools having functional CWSN friendly toilets |
10 |
|
|
Percentage
of schools having a functional toilet |
|
||
|
|
a) Boys toilet |
10 |
|
|
|
b) Girls toilet |
10 |
UDISE: Unified District Information System MSJE: Ministry of Social Justice and
Empowerment
The source
of the next indicator i.e. the percentage of entitled CWSN receiving
aids and appliances for government & aided schools is reported to be the Shagun
portal and project monitoring system being maintained by DSE&L, Ministry of
Education. One fails to understand why UDISE has not been the main source of
this indicator which is otherwise being collected annually under it. The main source
of the next three indicators concerning ramp and toilet for CWSN students as
well as a functional toilet for boys & girls is UDISE all of which is termed
minimum required to assess the infrastructure been provided to CWSN students.
Category 2: Governance &
Management
Domain I: Governance Processes
The next
set of indicators that we discuss below falls under Category 2: Governance
& Management and Domain I: Governance Processes which has a set of 26 parameters/indicators
with a total weightage of 360 of the overall total weightage of 1,000 have been
used as Governance Processes indicators source of most of which is either the
UDISE or the Shagun portal being maintained by the DoSE&L; thus clearly
showing the importance of governance indicators in the overall development of
school education in India. Apart from these sources, Project Monitoring &
Project Financial Monitoring Systems internally developed for SSA/Samagra
Shiksha are the other sources of information used in case of a few other
indicators.
A cursory
look at the list of equity indicators one can get the idea that a few
indicators should have been avoided and a few others, there is no mechanism to
check and validate the information. Whatever information is provided by the
States/UTs through the Shagun portal is treated as final and there is no option
but to use it in the PGI computation which raises serious questions about the
usefulness of the whole exercise. For example. Percentage of
Children whose Unique ID is seeded in SDMIS reported through the Shagun portal
has been used. One fails to get information about what is the source of
information on this indicator especially when there are no such guidelines to
maintain the SDMIS portal from DSE&L, MoE to States/UTs. On what basis
State/UTs reported information would be of interest to know along with the
actual data reported on the Shagun portal. It may however be observed that
during 2016-17, an attempt was made through the SDMIS portal maintained by
NIEPA, New Delhi to collect information on 35 student-specific parameters in sync
with the UDISE and its varied first-year information of about 210 million
children was collected but the same was discontinued in the following years for
unknown reasons. Even before the SDMIS was put in place, with a similar purpose
a few states, such as Andhra Pradesh developed their portal which apart from a
few other states, such as Haryana is continuing. In the absence of the
guidelines from the MoE, states still maintaining SDMIS or alike portal as
their state-specific initiatives will be at the advantage stage. Another such
variable is the percentage of teachers whose Unique ID is seeded in any
electronic database of the State Government/UT Administration, percentage of
average daily attendance of students captured digitally, percentage of average
daily attendance of teachers recorded in an electronic attendance system, and
percentage of schools at elementary level displaying a photo of elementary
teachers most of which are reported to be covered only government & aided
schools. One fails to get the idea of how indicators, like the percentage of
schools at elementary level displaying a photo of elementary teachers, will
help in the attainting goal of school education in India. Is there any
notification from MoE to states to make such arrangements or the already
advanced states will again be at the advantage stage is a moot question that
must be answered.
Instead of a separate set of teacher indicators, several teacher-related indicators
have been used under Domain IV: Equity indicators which, like other indicators
are based on UDISE as per the requirement of the Right-to-Education Act 2009.
It is unfortunate that even after 12 years of RTE enactment, many schools still do not fulfill the RTE
requirement a majority of such schools are government-managed schools. A
composite indicator computed by NIEPA based on a set of 10 parameters suggested
that only 12 percent of the total schools in the country have had all the 10
facilities but the same under UDISE being managed by the DoSE&L, no such
statistics are made available in the public domain for recent years. The National Commission for Protection of Child Rights (NCPCR) must take up the issue with the States/UTs
and ensure that at least all government & aided schools must fulfill RTE
requirements. At least, the percentage of elementary schools having fulfilled
all the 10 RTE parameters must find a place in the equity or facility
indicators which can still be computed by using UDISE+ 2019-20 data.
Category 2: Governance &
Management
Domain I: Governance Processes
|
Sl. No. |
Total
Domain Weight 360 |
Source of Information |
Weight |
|
2.1.1 |
% of
Children whose Unique ID is seeded in SDMIS |
Shagun
& PMS |
10 |
|
2.1.2 |
% of
Teachers whose Unique ID is seeded in any electronic database of the State
Government/UT Administration |
10 |
|
|
2.1.3 |
% of
average daily attendance of students captured digitally (States & UTs may
set digital mechanism similar to AMS of MDM |
10 |
|
|
2.1.4 |
% of
average daily attendance of teachers recorded in an electronic attendance
system |
10 |
|
|
2.1.5 |
% of
Schools at Elementary level covered Under Twinning/ Partnership |
Shagun
Portal |
10 |
|
2.1.6 |
% of
Schools at Elementary level displaying photo of elementary teachers:
Government & Aided schools |
10 |
|
|
2.1.7 |
% of
single teacher primary schools |
UDISE |
10 |
|
2.1.8 |
% of
elementary schools having PTR as per RTE norm |
10 |
|
|
2.1.9 |
% of
primary and upper primary schools meeting head-teacher norms as per RTE |
10 |
|
|
2.1.10 |
% of
secondary schools having principals/ headmasters in position |
20 |
|
|
2.1.11
a. |
%
Upper Primary schools meeting norms of subject-teacher as per RTE |
10 |
|
|
2.1.11
b. |
% Senior Secondary Schools who have teachers
for all core subjects (classes 9 to 12) |
20 |
|
|
2.1.12 |
% of
academic positions filled in state and district academic institutions
(SCERT/SIE & DIETs) at the beginning of the given academic year i.e. 2018-19 |
Shagun |
10 |
|
2.1.13 |
Average
occupancy (in months) of District Education Officer (or equivalent) in last
03 years for all Districts |
10 |
|
|
2.1.14 |
Average
occupancy (in months) of Principal Secretary/Secreary (Education), SPD (SSA)
& SPD (RMSA) for last 03 years |
10 |
|
|
2.1.15 |
Details
of visits to the elementary schools during the previous academic year: |
UDISE |
10 |
|
|
(a) %
of schools visited at least 3 times for academic inspections |
||
|
(b) % of schools visited at least 3 times by CRC Co-ordinator |
|||
|
|
(c) % of schools visited at least 3 times by
Block Level Officer (BRC/BEO) |
||
|
2.1.16 |
a)
Average number of days taken by State Government/UT Administration to release
total Central share of funds to societies (during the financial year
2017-18)
|
PFMS |
10 |
|
b)
Average number of days taken by State Govt./UT Administration to release
total State share due to societies (during the financial year 2017-18) (not
applicable to Uts without legislature)
|
10 |
||
|
2.1.17 |
% of
teachers evaluated (during the year 2017-18) |
Shagun
Portal (State/UT/
PINDICS) |
10 |
Contd…..
|
Sl. No. |
Total
Domain Weight 360 |
Source of Information |
Weight |
|
2.1.18 |
%
of Government Head-Teachers/Principals who have completed School Leadership
(SL) training in the financial year 2017-18 -
Measured against sanctioned number by Central Government -
At a minimum, the training should include all aspects of School Leadership Development
Programme laid out by NCSL, NIEPA, New Dlhi |
Shagun
Portal |
20 |
|
2.1.19 |
%
of schools that have completed
self-evaluation and made school improvement plans during the financial year
2017-18 |
Shagun
Portal & PMS |
10 |
|
2.1.20 |
% of
teachers provided with sanctioned number of days of training during the financial year 2017-18: Government & Aided |
20 |
|
|
2.1.21 |
Number
of new teachers recruited through a transparent online recruitment system as
a percentage of total number of new
teachers recruited during 2017-18 |
20 |
|
|
2.1.22 |
Number
of teachers transferred through a transparent online system as a % of total
number of teachers transferred during 2017-18 |
20 |
|
|
2.1.23 |
Number
of head-teachers/principals recruited through a merit-based selection system
as a percentage of total number of head-teachers/principals recruited during
2017-18 |
20 |
|
|
2.1.24 |
Percent
State/UT budget share spent on scool education to total State/UT budget of
2017-18 |
Shagun Portal |
20 |
|
2.1.25 |
Funds
(including value of goods and services in kind) arranged through PPP, CSR
etc. as a percentage of State/UT budget on school education during 2017-18 |
10 |
|
|
2.1.26 |
Percentage
of each of the following registered under PFMS: |
10 |
|
|
|
(a)
Schools |
||
|
|
(b)
SCERT/SIE |
||
|
(c) DIETS |
UDISE: Unified District Information System PINDICS:
Performance indicators for elementary school teachers.
Leadership
at the top at the state level plays an important role in successful planning
and execution of large scale programmes, such as Samagra Shiksha because of
which three indicators, namely percentage of academic positions filled-in SCERT/SIE
and DIETs, average occupancy of District
Education Officer and Principal Secretary/Secretary (Education), State Project Director
for last 03 years have been used in computing PGI source of all of which is
Shagun portal. However, the same in case of SIEMATs have not found a place
maybe because of the reason that barring a few, none of the other SIEMATs are
functional in the real sense. Time has come that these institutions are made
operational as a separate body independent of the Office of the State Project
Director. Details of visits to the schools during the previous academic year
for academic inspections, and percentage of elementary schools visited by the
CRC Coordinators and Block level officer is another indicator which has been
used source of which is UDISE but over a period of time, it has been observed
that incomplete information being furnished by schools on academic and other
inspections. It is hoped that since these parameters are now part of PGI, the
quality of the same may improve in the years that follow.
The success of any programme largely depends upon
the availability of funds and that too timely release of funds, keeping this in
mind two indicators, namely (i) the average number of days taken
by State Government/UT Administration to release total Central share of funds
to societies; and (ii) the average number of days taken by State Government/UT
Administration to release total State share due to the Societies. There must be
a third indicator which must indicate whether (i) PAB was held on time to
approve the plans; and (ii) the average number of days after the PAB meeting,
the Central Government has taken to release its share to States/UTs to know on an
average how many months in a year were available to State Implementation
Society to implement its PAB approved plans. One of the other indicators used
is the percentage of teachers (self) evaluated during the previous year i.e
2017-18 source of which is Performance Indicators for Elementary School
Teachers (PINDICS) of which practically no or little information is available
in the public domain. It is not known whether self-evaluation is mandatory or
optional and whether each of the 9.4 million teachers is given login
credentials?
The next set of indicators are dedicated
to teachers transfer and recruitment in case of only government management
source of which is Shagun and PMS portal; the indicators used are:
|
· Percentage of
teachers provided with a sanctioned number of days of training during
the previous financial year i.e.
2017-18; |
|
·
Number of new teachers recruited through a
transparent online recruitment system as a percentage of the total number of new teachers
recruited during the previous year i.e. 2017-18; & Number of
Head-Teachers/Principals recruited through a merit-based selection system as
a percentage of the total number of head-teachers/principals recruited during
the previous year; and |
|
·
Number of teachers transferred through a
transparent online system as a percentage of the total number of teachers
transferred during the previous year i.e 2017-18 |
In
addition, the number of teachers provided in-service during the previous year
can also be used as an alternative indicator which is readily available from
UDISE so as the percentage of schools having trained teachers in the use of
computer and teaching through a computer can be another indicator which can be
added to list of teacher indicators. Teachers appointed through an online
system are given weightage which is also true for transfers of teachers which
may encourage states to develop an online dedicated portal to meet information
on all aspects of teachers. Emphasis is laid down on recruitment of new
teachers which can be further classified under regular and contractual teachers
which have become important because of state appointing only contractual
teachers in the recent past which is also evident in UDISE data which reveals
that about 12 to 15 percent of the total teachers at the elementary level are
the contractual teacher. Therefore the percentage of male & female contractual/para-teachers
at the elementary level along with academic and professional qualification can
be a good addition to teacher indicators. Another moot question that needs to
be answered is why the percentage of schools with educational and
professionally qualified teachers has not been used in PGI computation; may
there are specific reasons which need to be spelled out. Percent share of the state
budget on school education to the total state budget is the next indicator
used; instead, it would be better to use percent expenditure on school education
to total expenditure on education in the previously completed financial year which
may be considered a better indicator to judge the state’s commitment towards
school education so as the percentage of funds utilized at the state level
received through PPP and CSR to the total support received during the previous
year. The last indicator used in PGI is the
percentage of schools, SCERT/SIE, and DIETs registered under PFMS without
spelling out details after registration may not be considered a useful indicator.
Concluding Observations
As
has been presented above a total of 70 indicators (96 parameters including
sub-categories) falling under the categories, Outcomes and Governance & Management with a total
weightage of 1,000 have been used in 2018-19 but updated values of only 54 out
of the 70 indicators have been used and the rest, mostly based on NAS, it's
2017-18 values which had already been used in PGI 2017-18 had again been used
in computing PGI 2018-19 (see Table above). However, details of how
parameters/indicators have been selected, what methodology have been used to
identify and retain indicators, and who had identified indicators, was it
recommended by a group of experts or individuals or whether national institutions
previously engaged in computing such indices were engaged in the process of
selection of parameters. There is the scientific procedure of identifying
indicators; however initial list of indicators can be developed by the experts
based on the understanding of the school education system. National Workshop on
Educational Development Index (EDI) of Experts conducted by NIEPA views that “parameters/indicators are likely to highly
correlate with each other and therefore one needs to carefully look for
possible removal of some of these variables. It was suggested that the
correlation matrix need to be calculated that would help in identifying
variables that are highly correlated with each other and therefore some of them
can be removed and that are unique and that can be used in calculating EDI”.
The whole exercise of PGI and classification of States/UTs by levels and grades
largely depends upon how weights are assigned, each parameter was assigned
weightage of either 10 or 20 points, what methodology has been used in
assigning weights, who has assigned weights, was it an individual or a group of
experts are the details of the basic question of which must be available in the
public domain. NIEPA used Principal Component Analysis in assigning weights of
each of the 24-parameters used in the computation of EDI: 20015-16 to 2015-16.
The School Education Quality Index initiated by NITI Aayog also didn’t specify
the methodology based on which weights have been assigned in computing SEQI for
the years 2016-17 & 2017-18.
PGI
2018-19 also presents a brief analysis and distribution of States/UTs by levels
and also highlights state-specific domains with maximum and lowest improvement.
Within the domain, it would still be better to highlight parameters/indicator-specific
distribution of states that need further improvement to appropriate
indicator-specific strategies which shall eventually help a state in improving
a particular domain or a set of domains. With the state-specific indicators, it
is not possible to form appropriate strategies unless indicators used in
computing PGI are disaggregated to analyze at the district and lower levels. It
is believed that PGI is not just to know the status of a State/UT about
different domains and its score but to improve the overall school education in
India. Apart from disseminating scores & grades, it is equally important to
put the values of indicators in the public domain. Equally important would be
to thoroughly study states that have shown significant improvement in PGI
2018-19 over 2017-18 as well states those values in terms of grades/levels have
gone down. Better to ensure that indicators that have an authentic regular
source of information and are made available in the public domain should only
be sued in any future PGI computation. No point in using the same values of a
set of indicators over time or at least ensure that the indicator used is sure
to be generated during the year for which the next PGI is planned to be
computed. Since the PGI 2018-19 is based on provisional data, it is hoped that
soon PGI based on the final freeze set of 2018-19 data will see the light of
the day. At the time PGI 2018-19 was made available, a good number of states
were still finalizing their 2018-19 data.
The
following indicators may be considered in the further computation of PGI:
· percentage
of districts having displayed class-wise learning outcomes
· percentage
of schools having displayed student-wise learning outcomes
·
percentage of out-of-school children
identified to total out-of-school children
·
percentage of identified out-of-school
children mainstreamed
·
average annual, as well as
grade-to-grade drop-out rate separately for boys and girls in case of for elementary as well as a secondary level
of education
·
Gender Parity Index based on
adjusted-NER both at the elementary as well as secondary levels of education
·
percentage of schools showing
increase/decline in primary and upper primary enrolment
·
percentage of blocks and districts
showing an increase/decline in enrolment
·
the ratio of elementary to secondary
schools/sections
· percentage
of the total number of schools (including private unaided schools) with
functional toilets
· percentage of schools (all schools) with electricity
connection, functional computer, and internet connectivity in school
· percentage
of elementary schools having fulfilled all the 10 RTE parameters
the average number of days after the PAB meeting,
the Central Government has taken to release its share to States/UTs
·
average months in a year available to
State Implementation Society to implement its PAB approved plans
·
percentage of teachers provided in-service during
the previous year
·
percentage of schools having trained teachers in the
use of computer and teaching
·
indicators on recruitment of new teachers can be
further classified under regular and contractual teachers
·
percentage of male & female
contractual/para-teachers at the elementary level along with academic and
professional qualification
·
percentage of schools with educational and
professionally qualified teachers
·
percent expenditure on school education to total
expenditure on education in the previously completed financial year etc.
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