Education For All in India: June 2021

Thursday, June 17, 2021

Is Fifty Percent GER at Higher Education Level by 2035 Possible?

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Arun C Mehta
Formerly Professor & Head of EMIS Department
NIEPA, New Delhi
E-mail: acmehta100@gmail.com

Introduction

Much before the National Education Policy (NEP) 2020, the Government of India through the University Grants Commission and the Department of Higher Education, Ministry of HRD/Education resolved to achieve a Gross Enrolment Ratio (GER) of 30 percent at the higher education level by the year 2020. Rashtriya Uchchatar Shiksha Abhiyan of the Ministry of Education also set a GER target of 32 percent at higher education level by the year 2022. Apart from 50 percent GER at the higher education level by the year 2035, the following are some of the other important policy resolutions as specified in the National Education Policy 2020 which was approved by the Union Cabinet in its meeting held on 29th July 2020 under the Chairmanship of the Prime Minister of India.

·        Multi-disciplinary holistic education at the undergraduate level

·        Under Graduate degree to be of either 3 or 4 years, with a provision to issue certification after completion of each year

·        Higher Education Body Commission of India to be the single umbrella for higher education

·        Aim to achieve 100 percent youth and adult literacy by 2030

·        Education sector to reach 6 percent of GDP at the earliest (from its present 4.6 percent)

·        Multidisciplinary Education & Research Universities are to be set up in each district

·        Students from underprivileged classes to be incentivized per merit

·        Expansion of Open and distance learning across the country

·        Graded accreditation & autonomy to achieve excellence in the next 15 years

·        Comprehensive National Curriculum Framework for Teacher Education to be formulated

·        To promote the use of technology, National Educational Technology Forum to be formed; etc.

 

Tables 50% Increase in Higher Education Enrolment by 2035 Arun C Mehta

 

In addition to the target to achieve a 100 percent Gross Enrollment Ratio at school education level by 2030, the following proposals have also been made in NEP 2020 concerning school education in India; instead, the target would have been fixed to achieve a 100 percent adjusted-Net Enrolment Ratio (Adjusted-NER) which is considered a better indicator of participation. In this brief note, we examine whether the Higher Education level alone is capable of achieving a 50 percent GER from its 2018-19 level in the year 2035? Is a moot question that has been examined from different angles including the present status of school education in general and secondary & higher secondary levels in India, in particular. What would be the size of higher education enrolment; if 50 percent GER is to be obtained in 2035 will be another question that would be explored. This has become more so important given the pandemic because of which a significant decline in enrolment in general and school education, in particular, is expected in years that follow, and enrolment at a higher education may also not escape. In addition, the slow down of the economy (GDP contracted by 7.3 percent. in 2020-21) may also adversely affect enrolment at higher education level from the year 2020-21; and thereafter until the economy gets back on to the recovery path which may not happen in the immediate near future

·        Target to achieve 100 percent GER in school education by 2030

·        Education for all children between 3-6 years by 2025

·        Replacing the existing 10+2 with 5+3+3+4: After five years in pre-primary, students to aim at enhanced skills in the new pedagogical system

·        Medium of instruction till Class 5, to be home language or mother tongue

·        Board examination to be broken into two, to test core capabilities

·        Emphasis on socially disadvantaged, girls, socio-cultural identity children for education

·        By 2025, at least 50 percent of students to have exposure to vocational education; etc.

Many of these policy resolutions like the 6 percent expenditure of GDP on education are not new and were also part of the various commissions and committees constituted by the Government of India from time to time including the National Education Commission (1964-1966), popularly known as Kothari Commission. While on the one hand, NEP 2020, clearly specified the year by which the improved GER at higher education level (by 2035) and 100 percent GER at school education level (by 2030) is to be achieved but fail to specify the year by which 6 percent expenditure of the GDP will be attained; rather it has simply said it be achieved at the earliest which has not seen the light of the day even after more than five decades after the same was first recommended. Before we examine the present status of higher education in India and the implication of achieving 50 per GER by 2035, let us first discuss and re-define the Gross Enrolment Ratio.

Redefining Gross Enrolment Ratio

Unlike school education level at which enrolment based indicators such as Gross & Net enrolment ratio as well as Age-specific and Adjusted-NER are frequently computed and use in plan formulation, at the higher education level only Gross Enrolment Ratio is being used to examine the participation of a relevant age-specific population i.e. 18 to 23 years in the higher education programmes. For calculating GER at any level of education, information on total enrolment in a year and the corresponding age-specific population in that year is required. While total enrolment and its male and female bifurcation, as well as enrolment by the social category i.e. Scheduled Castes & Scheduled Tribes, is available from the All-India Survey on Higher Education (AISHS, latest for 2019-20) but the same is not true for the corresponding age-specific population the main source of which is the Census & Registrar General of India, the latest Census figures being available for the year 2011. In the absence of an official projected population based on the 2011 Census, earlier projections based on population up to 2001 are being used by the Ministry of Education to estimate the age-specific population in a year which is adjusted given the total 2011 Census population (details can be seen under the Statistics Section of the Official Website of Department of School Education & Literacy). Because of the limitations in the projected population, GER and other enrolment-based indicators have been seen off the mark in the past decade which is true for all levels of education. Therefore, the latest GER for 2019-20 and also in the past years, the same must be analyzed in light of these limitations. With 50 percent GER at the higher education level, the quantum increase of enrolment in absolute terms cannot be known unless the reliable estimate of the population between the age-group  18 to 23 years is known in the year 2035. The GER for the year 2021 based on the actual Census 2021 population when available may reveal the real situation concerning the participation of 18 to 23 years population in higher education programmes; it is likely to show a declining trend because of the ongoing pandemic across the country.

As per AISHE 2019-10, the GER at the higher education level increased to 27.1 percent from its previous level of 26.3 percent in 2018-19. On the other hand, in absolute terms, the higher education enrolment increased from 37.4 million to 38.5 million during the same period; thus showing an increase of 1.1 million or 2.94 percent over the previous year. A Gross Enrolment Ratio of 27.1 percent roughly indicates that the balance of 72.9 percent population of age group 18 to 23 years is not enrolled in higher education programmes. Can all the remaining 72.9 percent population be treated as out of the education system? Certainly not as a few of them may still either be enrolled in lower levels (not completed higher secondary level), a few of them maybe never enrolled or dropped out from the system, or a few of them may also be enrolled in foreign universities? Compared to overall GER at higher education level, though improved, the GER of Scheduled Castes & Scheduled Tribes population is still low at 23 and 18 percent respectively as against 26.9 percent for male and 27.3 percent for female population. Time has come to explore alternate enrollment-based indicators also for the higher education level (Mehta, 2002). Can higher education grow independently? Can higher education enrolment grow independent to the lower level i.e. higher secondary level which is expected to send graduates to it? Certainly not. Deliberations by Varghese & Mehta (1999) in the case of a World Bank-assisted study on the Upper Primary level of education is worth mentioning.“Universalization of upper primary education in India is normally discussed in terms of enrolling and retaining all children belonging to the age group 11 to 14. This seems more to be a desirable goal to be achieved in the long run than a realizable target at the present levels of development of primary education. Enrollment is a function of the relevant age group at the primary level of education. However, enrolment in upper primary schools is more a function of primary education completion rates than a function of the relevant age group. It is logical to argue that all children in the relevant age group (11 to 14) cannot be enrolled in upper primary classes unless they complete the primary level of education. In other words, all relevant age group children can be provided upper primary education only when all children of the primary school-going age group are enrolled, retained, and complete the primary stage of education. Since primary education is not yet universalized, this implies that the universalization of upper primary education means providing upper primary education for all children who have completed the primary level of education. The present study has adopted this as the operational definition of universalization of upper primary education in India. However, once universalization of primary education is attained, then there cannot be any difference between providing the upper primary level of education to all age group children and those who complete the primary stage of education. The effort at present needs to be to improve the inter-stage transition ratios from primary to upper primary levels of education.”

The above argument is also very much true for the higher education level which will grow in the line of enrolment at the immediate lower level, i.e. higher secondary level which is supposed to supply a continuous flow of higher secondary graduates to the higher education level. This means that the population of age 18 to 23 years, all cannot be admitted to the higher education system simply because of reason that they are not eligible. Higher education level can only accommodate higher secondary graduates. In the light of these observations, there is a need to redefine the Gross Enrolment Ratio at the higher education level to get a better picture of the participation of the relevant age population in higher education programmes. Therefore, instead of a total 18 to 23 years population, the number of higher secondary graduates may be considered in computing ratio which can be termed as Effective Enrolment Ratio at the higher education level.

Another indicator that plays an important role for enrolment at the higher education level to grow is the transition rate from higher secondary to higher education level which can be computed by using the number of higher secondary graduates the system has produced in the previous year to the total number of students admitted in the first year of graduation during the next year is multiplied by 100 to get the transition rate. The future course of enrolment at the higher education level will be guided by these two indicators. Unfortunately, both of these indicators are not readily available from official sources.  Based on few assumptions, NIEPA (December 2020), in its recent study on NEP 2020: Implementation Strategies attempted transition rate from secondary to higher education level (88.1 percent) and used the same in building up different developmental scenarios towards assessing the NPE 2020 goal of 100 percent GER at school level in 2030 and 50 percent GER at the higher education level in 2035. In the light of these observations policy directive of 50 per GER at higher education level should be examined in terms of effective-GER and transition rate from higher secondary to the higher education level rather than simply based on GER. A low transition rate may adversely affect prospects of higher education enrolment to grow in the years that follow.

Effective-Enrolment Ratio = Number of students admitted in the first year of graduation in a year is divided by the number of higher secondary graduates the system has produced in the previous year is multiplied by 100

Transition Rate from Higher Secondary to Higher Education Level = Number of higher secondary graduates the system has produced is divided by the number of students admitted in the first year of graduation level during the next year is multiplied by 100

 Projected Population (18 to 23 years)

To know the size of the higher education enrolment in 2035, we need a projected population of age-group 18 to 23 years in that year. Generally, population projections are made available from sources, like United Nations Population Division (UNPD), the World Bank, and the United Nations Population Fund (UNFPA).  In addition, individual researchers also provide projections for the country as a whole or even at the sub-national level. It was also a practice of the Registrar General & Census Commissioner, India to undertake projections immediately after the latest census figures are out on behalf of the Planning Commission (now renamed as NITI Aayog) who in turn used to set up a committee of experts which was first constituted in the year 1958 which continued up to the Census 2001. Unlike the previous constitution of the Expert Committees, the Technical Group on Population Projections based on the 2011 Census was constituted by the National Commission on Population, Ministry of Health & Family Welfare, Government of India on July 2014 report of which was made available in the public domain as late as on July 2020. In the absence of the Official Projections enrollment-based indicators, such as GER were computed based on the adjusted projections made earlier based on up to the 2001 Census. Since the Official Projections were released in July 2020 and AISHE 2018-19 in August 2019, AISHE couldn’t use the Official Projections made available by the Technical Group on Population Projections. But AISHE 2019-20 GER which was released in June 2021 is also not based on the official projections for the unknown reasons.

GER 2019-20: Based on Expert Committee & United Nations Projections

By using the adjusted population (18 to 23 years), the Gross Enrolment Ratio at higher education level obtained through the AISHE 2019-20 comes out to be 27.10 percent: Total enrolment, 38.53 million & age-specific 18 to 23 years population, 142.20 million. In the light of the Expert Committee projections, the same can now be re-calculated for the year 2019-20 (Table 1). The age-specific population (18 to 23 years) based on the Expert Committee projections in 2019-20 comes out to be 150.79 million which gives a GER of 25.55 percent; a difference of 1.55 percentage points. It is hoped that AISHE while presenting GER 2020-21 will not only use the Expert Committees projections for 2020-21 but may also like to revise its previously computed GER during the period 2011-12 to 2019-20. An attempt has also been made in the present article to re-calculate GER in the light of the Expert Committee projections during the period 2011-12 to 2019-20 and the same separately for the male as well as for female population is presented in Table 2 along with the GER published by the AISHE which reveals a slight variation in both the estimates in the initial years but the same in the later years, especially in the years 2018-19 & 2019-20, as reported above, is found significant which is true for both the GER of the male and female population.

In addition to the Expert Committee, projections are also made available by the United Nations in 2019 (Department of Economic and Social Affairs, Population Division: World Population Prospects 2019, accessed on June 3, 2021, from https://population.un.org/wpp/DataQuery/e). As per these projections, India would have had a total population of 1366.42 million in 2019 to which, if a percentage of 11.33 is applied will give a total of 154.82 million population in the age group 18 to 23 years. Using the United Nations projections and a total enrolment of 38.54 million, gives a GER of 24.89 percent in 2019-20 which is quite similar to one estimated based on the Expert Committee projections. Thus both the estimates of GER computed based on the Expert Committee (25.55 percent) and United Nations projections (24.89 percent) suggest that GER 2019-20 is a bit lower than one provided by the AISHE (27.1 percent).

Size of Enrolment, if a GER of 50 Percent is achieved in 2035

The details of the Expert Committee projections are presented in Table 1 which reveals that India’s total population is expected to be 1513.6 million in 2035 as against 1553.7 million projected by the United Nations (2019); actual Census 2011 population being 1210.9 million. In this note, Official Projections (July 2020) made available by the Technical Group on Population Projections set up by the National Commission on Population (Government of India) has been used to estimate the quantum of the enrolment in 2035 in the absolute and percentage form. The total population of India is expected to grow at an annual rate of 0.9196 percent per annum or 311.43 million in absolute terms between the years 2011 to 2036. The share of the 18 to 23 years old population to total population in the 2011 Census was 11.38 which as per the Expert Committee projections is likely to be declined to 9.18 percent in 2035; the year by which India desires to attain a GER of 50 percent. The size of the 18 to 23 years population in 2035 is likely to be 138.99 million. To attain 50 percent GER, the size of higher education enrolment in 2035 would be required to be 67.12 million which is 74.17 percent higher than the actual AISHE enrolment in the year 2019-20. Thus to attain a GER of 50 percent in 2035, higher education enrolment will be required to grow at an average annual growth rate of 3.53 percent per annum. Will it be possible? To know it below we examine the growth of higher education enrolment in India, we analyzed both the absolute and average annual growth rate between different periods. We would also explore whether it be attained at the current status of school education in India.

Challenges Ahead to Meet 50 Percent GER

Given the growth rate achieved in enrolment at the higher education level in the past, with a little push achieving a growth of 3.53 percent per annum to attain a GER of 50 percent in 2035 looks achievable. A look at the Tables 2 & 3 reveals that enrolment at the higher education level increased at an annual compounded rate of 3.58 percent during the period 2011-12 to 2019-20 the same in case of female enrolment (4.77 percent) was significantly higher than their male (2.46 percent) counterpart during the same period. In the initial period (1950-51 to 1960-61) just after the independence, female enrolment increased at an annual rate of 14.05 percent compared to 8.51 percent in the case of male enrolment. However, thereafter the same could able to increase with every passing year but couldn’t maintain the pace. However, all during the years, the rate of increase was never below1.98 percent except the period 1980-81 to 1990-91. Further, it has been observed that the higher education enrolment experienced a high growth rate of 11.83 percent during the decade 2000-01 to 2011-12; again growth rate in case of female enrolment (13.26 percent) was significantly higher than their counterpart males (10.81 percent) which may be attributed to more number of higher education institutions including the independent institutions included in the data collection as well as the inclusion of any course, like hotel management, nursing, etc having a duration of three years after the higher secondary level when the AISHE was launched in the year 2011-12. Thereafter, higher education enrolment couldn’t look back and maintain its increasing trend during the remaining period of AISHE (up to the year 2019-20). As has already been mentioned above the same has increased at an average annual rate of 3.58 percent per annum during the period 2011-12 to 2019-20. During recent years, the growth rate of women’s enrolment has been higher than the men’s enrolment which looks to reach the saturation point. Further, it has been observed that the average annual growth rate during the period 2012-13 to 2019-20 each year further reveals that the same has consistently increased and was above 3.53 percent each year, 7.31 percent being the highest during the period 2012-13 to 2013-14 all which suggest that the same remained higher than the required growth rate of 3.53 percent per annum to achieve the targeted 50 percent GER/3.53 percent annual growth rate in 2035.

Separately, a glance at the undergraduate level (Table 3) reveals that the same has also been consistently increased from 23.9 million in 2012-13 to 29.8 million in the year 2018-19; but declined slightly to 29.6 million in 2019-20, the year for which the AISHE is the latest available. Further, it has been observed that the share of undergraduate enrolment to total higher education enrolment is about 80 percent which is also true separately for men and women enrolment however women enrolment at 80.26 percent is slightly higher than their male counterparts. During the same period, the GER at the higher education level has also shown a consistent improvement which in the latest AISHE year is 27.1 percent for total enrolment as against as GER of 26.9 percent in case of men and 27.3 percent for women enrolment. Further, a look at the percentage share of women’s enrolment to total enrolment at the higher education level also shown a consistent increase which was as low as 11.30 percent in 1950-51 but further increase to 39.30 percent in 2008-9 and 49.03 percent in 2019-20. As it seems that with a little push, the share of women’s enrolment may further move towards 50 percent of the total higher education enrolment.

The Way Forward

The growth in higher education enrolment which has been attained since the independence and more specifically in the recent past is termed impressive and has been achieved during the normal course of time when there was no epidemic like the COVID-19 and the ongoing pandemic which has changed the lives of many. The COVID-19 pandemic has affected educational systems across the World, leading to the closures of schools, universities & colleges, and other such institutions. According to UNESCO, around 1.4 billion learners across the world were not able to attend school or university. The impact was more severe for the disadvantaged and socially deprived sections of the society and their families, causing economic hardships on families. It is not that the lives of only the students have been affected but their parents are also badly affected, many of who have lost their jobs and those who could manage their jobs, the salaries have gone down, many of the parents remained without salaries and few of them are still without the salary. The Centre for Monitoring Indian Economy (CMIE) based on the 30-day moving average estimated that as of 5th June 2021, the unemployment rate which remained high at 12.57 percent has badly affected both the rural (11.5 percent) and urban (15.5 percent) areas of the country. The average unemployment rate for May 2021 stood at 11.90, 14.71, and 10.63 percent respectively in all, urban, and rural areas which were as high as 45.6 percent in the case of Delhi. CMIE further estimated that because of lockdown due to pandemic in different states there was a loss of as many as 7.46 million jobs in April 2021 which affected both the salaried and non-salaried jobs. As per CMIE, a total of 10 million people lost their jobs during the second pandemic and the income of about 97 percent of the households’ has declined since the beginning of the pandemic last year. A good number of workers in the fear of long lockdown migrated to their home town mostly to the rural areas which were as high as 800 thousand alone in Delhi; this affecting education of their wards. Many of them may remain in villages and a few of them may not return to work; thus significantly affecting the education of their wards. Those who manage to continue their jobs but with a reduced salary, many of them have migrated their wards from low-fee private schools to government schools. In addition, children of government schools and their parents who migrated to villages may see a steep decline in enrolment in government schools in 2020-21 data collection of which is currently undergoing across the country.

GER of 100 percent at School Education by 2030

Needless to say that higher education level cannot grow independent to school education because of which as mentioned above NEP 2020 envisages attaining a GER of 100 percent in case of school education in India by the year 2030. To view the current status of school education in India and whether the same is in a position to help India in attaining a GER of 100 percent in 2030, various enrolment and efficiency-related indicators including the retention and transition rates have been critically analyzed during the period 2017-18 & 2018-19. Higher education enrolment is not expected to increase unless the efficiency of the school education system is improved to a significant effect which at present is found to be a highly inefficient one and also the quality of enrolment statistics has deteriorated recently because of the erratic enrolment at the elementary level of education. Unlike AISHE, UDISE+ is not yet available for the year 2019-20 as the time-lag in school education statistics has recently increased significantly.

Enrolment-based Indicators

Gross & Net Enrolment Ratio as well as Age-specific Enrolment Ratio and Adjusted-NER at different levels of schools education and corresponding age-group, such as 6 to 10+, 11 to 13+, 6 to 13+, etc. have been analyzed (Table 4). In addition, efficiency indicators, such as, average annual drop-out rate, retention, and transition rates both at the state as well as at all-India level have also been analyzed all of which have implications for India achieving the goal of universal school education & high GER at the higher education level.

Table 4 presents a variety of enrolment-based indicators at different levels of education at the all-India level which reveals that despite significant improvement in all spheres of school education in India, the goal of universal school education is still a far distant dream which is not likely to be realized shortly. Enrolment decline during 2018-19 over the previous 2017-18 is in the tune of 2.63 million will further deteriorate efforts being made towards achieving the goal of school education in general and universal primary education in particular which is reflected in enrolment ratio at the primary level of education. During 2017-18 to 2018-19, enrolment in Grade I declined to 24.75 million from 25.09 million in 2017-18; thus showing a decline of 0.34 million in absolute terms or 1.3 percent in percentage form. It is also worth mentioning that a huge decline of about 59 million enrolment was noticed in 2017-18 when Student Data Management Information System (SDMIS) in-sync with U-DISE was launched in 2016-17.

Primary Level

Irrespective of a type of enrolment ratio, a steep decline has been observed across enrolment types amongst which GER at the primary level is the steepest one which has decline to 92.56 in 2018-19 from its previous 102.79 level in 2017-18 because of which enrolment ratio at upper primary, secondary and higher secondary levels may see a steep decline in years that follow (Table 4).

A Gross Enrolment Ratio of 92.56 percent in 2018-19 indicates that roughly about 7 percent of children including the overage and underage ones are yet to be enrolled against which 89.14 percent children of age 6+ to 10+ years are enrolled in Grades I to V; thus indicting that remaining 11 percent children are not enrolled in Grades I to V but all of them may not be treated as out of school as a few of them may be enrolled in higher grades for which the Adjusted-NER is computed. A 93.60 percent Adjusted-NER indicated that about 94 percent of the total 6+ to 10+-year-old children are enrolled but not necessarily in the corresponding Grades I to V. This otherwise also indicate that the remaining 6 percent of the total 6+ to 10+ years-old children are not enrolled either in corresponding Grades I to V and or higher grades. On the other hand, 94.26 percent Age-specific Enrolment Ratio indicates that more than 94 percent of the total 6+ to 10+-year-old are enrolled and the remaining 6 percent are yet to be enrolled which is huge if the size of the population is somewhat 118 million. It may also be observed that 2018-19 figures are that of before COVID-19 which was first noticed in January 2020 in India because of which schools may experience a large number of dropouts which is not confined only to government schools but has also affected small private unaided schools. The year 2020-21 & 2021-22 may see a further decline in enrolment; thus affecting severely efforts being made in India towards universal school education. It is also interesting to observe that except Lakshadweep, all other States & UTs have shown a significant decline in GER in 2018-19 over the previous year 2017-18; all of which indicate far-reaching implications for other levels of education to grow (Table 5).

Upper Primary Level

Net Enrolment Ratio at Upper Primary level indicate that the same has declined to 68.99 percent in 2018-19 from its previous level, 70.52 percent thus indicating that about 31 percent of the total 11+ to 13+ year children are not enrolled in the corresponding Grades VI to VIII which is considered as huge towards achieving the goal of universal elementary enrolment (Table 5). On the other hand, a 76.97 percent Adjusted-NER indicates that about 33 percent of the total 11+ to 13+ children are yet to be enrolled in corresponding Grades VI to VIII or higher grades. On the other hand, 88.55 percent Age-specific enrolment ratio at the upper primary level indicates that about 11 percent of children of age-group 11+ to 13+ are yet to be enrolled. State-specific NER at the upper primary level further indicates that the same has declined in 2018-19 in most of the states with Bihar (71.01 percent), Gujarat (72.80 percent), Jharkhand (71.18 percent), Madhya Pradesh (69.57 percent), Uttar Pradesh (58.26 percent) and West Bengal (71.65 percent) having low to very low NER all which indicate task ahead is challenging one and need meticulous planning at all levels of school education to attain 100 percent GER in 2030.

Elementary Level

In addition to primary & upper primary levels of education, enrolment ratios have also been analyzed at the elementary level of education. A NER of 81.46 percent at the elementary level indicates that a significant 19 percent of children of 6+ to 13+ years are not enrolled in the corresponding Grades I to VIII. The remaining children of this age group may either be out of school or a few of them may either be enrolled in higher grades (Table 6). Adjusted-NER further suggests that a little over 87 percent are enrolled either in Grades I to VIII or also in the higher grades. On the other hand, a 92.08 percent Age-specific enrolment ratio suggests that only 8 percent of children of 6+ to 13+ years are yet to be enrolled (15 million) which in absolute terms is huge as the size of the total population of this age group is 188 million. If India wants to achieve the goal of universal school enrolment in 2030, it has to give attention to large states, such as Andhra Pradesh, Bihar, Jharkhand, Gujarat, Madhya Pradesh, Odisha, Rajasthan, Uttar Pradesh, West Bengal, and other such states having low enrolment ratio at the elementary level of education.

Secondary & Higher Secondary Level

Quite disappointed to observe the status of secondary and higher secondary levels of education as the net enrolment ratio is reported to be as low as 48.60 and 30.78 percent respectively which indicate that more than 50 and 70 percent of children of the corresponding age groups in 2018-19 were yet to be enrolled (Tables 7 & 8). Can secondary and higher secondary levels grow independently to lower levels? Certainly not. Enrolment in secondary level i.e. Grades IX & X is not a function of the corresponding age-specific population i.e. 14-15 years but is a function of elementary graduates i.e. those who successfully pass Grade VIII. Thus unless the efficiency of the elementary level of education is not improved to a significant effect, neither the goal of universal secondary nor higher secondary education is expected to be achieved. Therefore, in the next section, a few of the efficiency-related indicators are critically analyzed which also plays an important role in ensuring 100 percent GER at school education in 2030.

Dropout Rate

On the one hand, there is a steep decline in enrolment and on the other hand, those who stay do not complete an educational level and leave the system before the completion. Table 8 presents the dropout rate at primary, upper primary and secondary levels of education for both Cohorts 2016-17 and 2017-18 in the case of General, Scheduled Castes, and Scheduled Tribes population. The drop-out rate at the primary level, irrespective of the social category has shown an increase for the 2017-18 cohort from its previous level i.e. Cohort 2016-17 which is obvious because of the steep decline in enrolment during 2017-18 and 2018-19. Of the total enrolment (123.81 million) in Grades I to V in 2016-17, 3.51 percent dropped out from the system before the completion of a grade as against 4.45 percent during the year 2017-18. It may be recalled that the size of enrolment in primary grades in 2017-18 was in the tune of 122.38 million in 2017-18. A 4.45 percent drop out at all-India level is termed as average annual drop out rate which over the primary cycle of five years come to around 17.8 percent which means that of the total enrolment in Grades I to V, roughly about 18 percent dropped out from the system before the completion of the primary level. Both the SC (5.16 percent) & ST (5,48 percent) categories also reported a high drop-out rate compared to 4.37 percent in the case of the OBC category. Barring the General category, contrary to general belief dropout rate in the case of boys is a bit higher than their counterpart girl which is true for the SC, ST, and OBC children.

A glance at the state-specific drop-out rate at the primary level of education (Table 9) further reveals that about 11 states have drop-out rates higher than the national average of 4.45 as against 10 states having lower rates at the upper primary level (national average 4.68 percent). At the secondary level, irrespective of boys and girls, the dropout rate is very high barring a few states such as Chandigarh, Himachal Pradesh, Lakshadweep, and Kerala. A few states from the north-eastern region, such as Arunachal Pradesh (13.78 percent), Meghalaya (16.88 percent), and Nagaland (11.41 percent) are having the dropout rate above 10 percent in case of primary level. On the other hand, Bihar (7.76 percent) and Uttar Pradesh (9.71 percent), the most populous states of the country having high dropout rate is unless checked, the goal of universal primary education may not be cherished shortly. At the upper primary level, Bihar (12.43 percent), Chhattisgarh (7.02 percent), Gujarat (7.39 percent), Jharkhand (10.21 percent), Madhya Pradesh (5.93 percent) and Uttar Pradesh (5.74 percent) have high dropout rate all of which suggests that the top most priority must be given to theses states to check high incident of drop out. Further, it has been observed that compared to drop out at the primary and upper primary level, the same at the secondary level is alarmingly high at 17.87 percent in addition to which states like Bihar also having a high drop out (28.46 percent) rate at this level of education. On the one hand, there is a high incidence of dropout at the primary level and those who complete the primary level do not stay and drop out before they complete the upper primary level of education. Besides, all those who complete the upper primary level of education, do not necessarily have been transited to the next level of education because of which the next indicator we discuss below is the transition rate (Table 10).

Transition Rate

The Transition Rate for the year 2017-18 presented in the Table 10 reveals that about 91 percent of children transit from the primary to upper primary level of education and no significant deviation is observed between boys and girls transition rate. However, more children from the general category (94 percent) transit from primary to upper primary level compared to 87, 88, and 89 percent of children respectively transit from the SC, ST, and OBC category. The transition rate remains almost stagnant for the past so many years. On the one hand, a good number of children left the system before the completion of primary level, and on the other hand, about ten percent of children drop out from the system in transition; thus severely affecting the efforts being made towards universal school education by 2030. There is no option but to improve the efficiency of the primary education system which must send an adequate number of primary graduates to the upper primary system. Thereafter children must continue in Grades VI to VIII and complete Grade VIII and transit to the first grade of the next higher level, i.e. Grade IX of secondary level. The gains that we achieved towards universal enrolment are slowly but surely being losing fast and we are back to square one. If not improved, India may not achieve a target GER of 100 percent by 2030 and a GER of 50 percent in 2035 at the higher education level as envisaged by NEP 2020.

The state-specific transition rate presented in Table 11 indicates that only three states, namely Bihar, Jharkhand, and Uttar Pradesh have a lower transition rate than at the national average of 91 percent from primary to upper primary level against 9 and 12 states from the elementary to secondary and from secondary to higher secondary level of education. Further, it is observed that the lowest 77 percent transition rate has been observed in the state of Bihar and the highest, 100 in a couple of states. Further, no significant difference is observed in boys and girls transition from the primary to upper primary levels of education and from elementary to secondary level of education; however, in a few states, more girls used to transit than their boy’s counterpart in case of secondary to higher secondary level of education. The transition rate in the most populous states of Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh is unless improved, India may not move towards achieving the goal of school education in the real sense. It is sorry to observe that in most of the efficiency and enrolment-based indicators no visible improvement has been noticed which is despite the nationwide programmes currently under implementation.

Retention Rate

The last indicator which we discuss below also falls under the category of efficiency indicators is retention rate at the primary and elementary level which gives us information about the retaining capacity of the system (Table 12). Grade V enrolment is linked to enrolment in Grade I five years back as against Grade VIII enrolment is linked to Grade I enrolment 8 years back and so at the secondary and higher secondary levels of education. A look at Table 12 reveals that that prima-facie it looks that both boys and girls almost equally retain which is true for all levels of education. A retention rate of 86.30 percent at the primary level of education indicate that only 86 of the total 100 children who entered the system five years back could able to reach Grade V; the balance of 14 children couldn’t reach Grade V in 2018-19 and dropped out from the system however a few of them still be in the system because of the repetition. It may be recalled that the average annual drop out rate in 2018-19 at the primary level presented above is 4.45 percent which also indicate that about 17.80 percent of the total enrolment in Grades I to V couldn’t remain in the system which is huge if the size of total primary enrolment is in the tune of 122.38 million. The retention rate of girls at the primary level of education (86.90 percent) is a bit higher than their counterpart boys (85.70 percent); which is also true for many states. On the other hand, 67 percent children of those who enrolled in Grade I eight years back could only reach Grade VIII in 2018-19 which otherwise indicate that 33 percent of the total enrolled couldn’t remain in the system; however, a few of them may still be in the system because of the repetition. On the other hand, only 56.90 and 38.00 percent could remain in the secondary and higher secondary levels of education indicating about 43 and 52 percent of the total enrolment couldn’t remain and dropped out from the system. Not only the universal secondary and higher secondary levels of education but even universal primary and elementary education is not in the sight which is quite similar to the situation a decade back.

Despite the high incidence of drop out a few could manage to complete primary, elementary and other levels of education. The GER, NER, Age-specific ER, Adjusted-NER, Transition Rate, and Retention rates analyzed suggest that even in quantitative terms India is still far away from attaining the status of universal primary education in a real sense less we achieve the universal elementary and secondary level of education. The low level of participation at the primary level and high incidence of dropout suggest that the system is inefficient and not supplying an adequate number of primary graduates to the upper primary level of education in the absence of which upper primary level of education is not adequately growing. Needless to mention that upper primary and other higher levels of education cannot grow on their own as upper primary is not a function of the corresponding age-specific population i.e. 11+ to 13+ years but it is the function of primary graduates. Therefore, there is no option but to improve the efficiency of the primary level of education and further improve the transition from primary to the upper primary level of education. None of the levels of education can grow independent to the immediate lower level which is also true for higher education level which as envisaged in NPE 2020 cannot attain a 50 percent GER unless the higher secondary level supplies an adequate number of graduates on regular basis.

Concluding Observations: Pandemic Likely Impact on Higher Education

Because of the facts presented above both the higher education as well as the school education sector will not remain the same like it was before the outbreak of the COVID-19. For about last more than 15 months no face-to-face learning is taking place which is true for all the levels of education in India. Even the Central Board of Secondary Education (CBSE) & Indian Certificate of Secondary Education (ICSE) has canceled the Board Examinations so as the host of the State Secondary Boards and National Institution of Open Schooling (NIOS); thus affecting everyone, rich or poor. Further, as per the Right to Education Forum, about ten million girls in India could drop out of secondary schools due to the pandemic; thus severely affecting enrolment at the higher education level in the years that follows. In addition, the recent steep decline in school education enrolment would also severely influence higher education enrolment to grow.

The post-pandemic impact on higher education is likely to be affected by both the face-to-face as well as distance learning modes. Both the students at home, as well as those who desire to join international universities and international students who desire to join the Indian higher education system, are expected to be severely affected by the COVID-19. Jain & Ruby (2020) discusses different dimensions which would have far-reaching implications for the enrolment at the higher education level to grow. The face-to-face programmes are affected by the high incidence of dropouts across the school levels of education. Part of the growth in higher education enrolment is because of private institutions that have almost trapped all the potential students who can pay the fee under its ambit but are not likely to maintain the momentum as they have almost reached the saturation point. Growth in higher education enrolment in the recent past is also attributed to increased distance learning through the online platform but the same, especially in the rural areas is marred by the high cost of contents as well cost and speed & stability of the internet connectivity. All these limitations are further compounded by the COVID-19 which would affect demand for higher education and parents’ choices for the same to which falling economy has added fuel in the fire; thus may likely to dent India’s efforts towards attaining a GER of 50 percent in 2035. The impact of COVID-19 on prospects of higher education enrolment is like to be more severe than the famous 2009 recession which the World had experienced. It may impact India more than the other countries as its economy in terms of GDP during 2020-21 is contracted by a historical 7.3 percent which may result in less funding for the educational programmes in general and higher education in particular which has already started reflecting into. Economy revival is not expected before the financial year 2022-23. Because of the fear of the spread of pandemic, schools, colleges, offices, institutions and universities are closed for almost 15 months and students both in schools and colleges, parents, teachers & faculty, administrators all stayed at home many of who as described above have lost jobs or their salaries have been cut significantly all which force parents to rethink about the education of their wards once the lockdown is over especially the higher education sector. Their capacity to fund higher education will now be re-evaluated once the pandemic is over. They may even decide to withdraw or stop funding higher education of their wards or may defer admission for a year or two all of which in addition to the availability of lower funds from the government sources; higher education GER may not escape the impact of all these factors. As has been presented above that because of the pandemic, the unemployment rate remained very high at 12.57 and loss of 7.46 million jobs all of which would affect demand for higher education in years to come. Parents may have no option but to withdraw their ward or postpone their admissions if the fee is not reduced or waived or financial assistance is provided to all those who lost their jobs or drawing low salaries and the impact of the pandemic is more on this segment of the population. As of now, no financial assistance has been promised by the UGC or the Central Government to the families affected the most all which would influence higher education enrolment to grow in years that follow.

Because of the pandemic, to reduce chances of infection and cost-cutting, students avoid traveling far places and look for opportunities nearby which resulted in a lack of adequate places in the local higher education institutions while at other locations the same may be found in surplus. As has been specified above those who were planning to go abroad for higher studies may now opt to skip because of the fear of infection and travel cost and look for opportunities locally near home or they may defer their decision for a year or two. Because of the comparatively low cost of higher education, India may attract a few additional international students both of which may help in increasing enrolment but may not affect GER to a great extent. If this is found true, India may expect to have students from diverse backgrounds in the year that follows. Alternatively, to avoids face to face learning, students may also look for opportunities in open and distance learning mode which may also result in low cost of education for which India may need to provide stable internet connectivity, especially in the rural areas which is need of the hour in addition to which we need to relook into the cost of distance and open learning programmes in India. NEP 2020 while envisaging a 50 percent GER by 2035 also based on the assumption that distance and open learning degree courses will play a pivotal role in achieving it. If we could provide affordable distance learning programmes, the same may also attract students towards higher education those who because of one or the other reasons couldn’t yet join the system. Available data further indicate that share of distance and open learning enrolment to total higher education enrolment is almost stagnant for the last so many years. We must now act to popularize distance learning programmes and ensure that they are treated at par with face-to-face programmes. Because of the COVID-19 and hardships, parents may like to move towards distance education programmes for which the government should also provide additional incentives to ensure that we do not miss the opportunity. To ensure this to happen we must create a conducive environment by creating necessary infrastructure with emphasis on internet connectivity and bandwidth in both the rural and urban areas which must be made affordable. Despite which people in rural areas will still be facing a shortage of computers and devices to access the online programme. As per the NSSO data, only 10.7 and 23.8 percent of households have had access to computers and internet connectivity respectively in 2017-18. Additionally, well-established institutions especially in the rural areas and also in the universities must strengthen their online platforms to ensure that learners get access to these facilities to use the same for the distance and open learning programmes/degrees on their premises or there be an option of guided online degree programmes. All these efforts, still may not able to significantly contribute to the desire for GER of 50 percent in 2035. The initiatives as indicated above may still be found insufficient because of which tie-up with the big business houses to establish learning centers may also be explored which may be funded through the CSR funds. Like AISHE, a separate MIS may be initiated to develop a sound database exclusively for open and distance learning programmes which must also include all state and private open universities. To see all these to happen, let us wait for 2020-21 & 2021-22 enrolment statistics which is most like to experience a declining trend which is not good to attain a high GER of 50 percent in 2035.

References

All India Survey on Higher Education 2019-20 & Other Years, Department of Higher Education, Ministry of Education, Government of India, New Delhi, released in June 2020.

Can There be Alternative Indicators of Enrolment: A Critical Review of Frequently Used Indicators, Journal of Educational Planning & Administration, July 2002, NIEPA, New Delhi.

Can India Achieve its Enrolment Target Post-pandemic?, Eklovya Jain and Alan Ruby, University World News: The Global Window on Higher Education, 25 July 2020.

Investment Priorities & Cost Analysis: A Study of Upper Primary Education in India (with Dr. N. V. Varghese), Vikas Publishing & NIEPA, New Delhi, 2001.

National Education Policy 2020, Ministry of Human Resource Development, Government of India, New Delhi, July 2020.

NEP 2020: Implementation Strategies, National Institute of Educational Planning and Administration, New Delhi, December 2020.

NSSO 75th Round on Key Indicators of Household, Social Consumption on Education in India, Ministry of Statistics and Programme Implementation, National Statistics office, Government of India, July 2017 to June 2018.

United Nations (2019), Department of Economic and Social Affairs, Population Division: World Population Prospects 2019, accessed on June 3, 2021, from https://population.un.org/wpp/DataQuery/e.

Rashtriya Uchchatar Shiksha Abhiyan: National Higher Education Mission (PDF). Ministry of Human Resource Development. National Informatics Centre. Retrieved 2 February 2014

Technical Group on Population Projections National Commission on Population, Ministry of Health & Family Welfare, Government of India, July 2014. Family Welfare, Government of India, July 2014.

Monday, June 07, 2021

Performance Grading Index (PGI): A Few Observations

                                 Arun C Mehta

Formerly Professor & Head of EMIS Department
NIEPA, New Delhi (India)
E-mail: acmehta100@gmail.com

 Background

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

 The first PGI based on 2017-18 data was undertaken by DSE&L in consultation with the NITI Aayog was based on a set of 70 indicators of which 16 indicators were based on the National Achievement Survey conducted by the NCERT in 2017 which have again been used in computing PGI 2018-19. The remaining set of data was updated by the internal mechanism of Samagra Shiksha through online portals of Shagun, UDISE+, and Mid Day Meal all of which are being maintained by the Department of School Education & Literacy of the Ministry of Education, Government of India. 

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

 Domain III: Infrastructure & Facility Indicators

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

 Despite the significant improvement, a good number of schools are yet to be provided with functional toilets which are evident, if a glance at the UDISE 2017-18 data is made which reveals that only 92.70 percent of schools have had the same in 2018-19, In absolute number, as many as 55,321 schools were yet to be provided toilet facility in schools in 2018-19 as against 1,13,278 schools are still without functional toilets. Further, it has been observed that by and large schools under most of the government managements have had an even lower percentage of such schools in 2017-18. Therefore, in addition to boys and girls functional toilets in schools used in PGI computation, the percentage of schools (all schools including private unaided schools together for both boys & girls) with functional toilets should have been used in PGI computation. In the case of a few indicators, such as transition rate overall as well as separately of boys and girls have been utilized in PGI 2018-19 computation.

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.

 Schools having Electricity & Computer Facility

 Schools having electricity connection, computer, functional computer, and internet connection presented at the all-India level for the year 2017-18 and in a few selected states reveal that our schools are not equipped to meet challenges paused by the pandemic. Even the basic requirement such as, the electricity connection is yet to be provided to the majority of schools which is true for both the rural and urban areas. A glance at the table reveals that of the total 1.5 million schools engaged in school education in the country, only 63.14 percent of schools have got the electricity connection compared to a little more than 50 percent of such primary schools. It is also true that just schools having electricity connections don’t necessarily mean that schools get an uninterrupted power supply. It has also been observed in the past that schools generally do not have separate funds to pay electricity bills because of which is generally observed that even schools have a connection but they do not have the power in school in the real sense.

 

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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