III: Moving Beyond Policies for Indian Teacher Quality

The Effects of Traditional and Non-Traditional Teacher Quality on Student Achievement in India

There is a global consensus that quality education depends upon quality teaching (Azam & Kingdon, 2014; Darling-Hammond, 2000; Hanushek & Rivkin, 2003), and ample evidence broadly confirms that teacher quality impacts student performance (Abe, 2014; Fong-Yee & Normore, 2006). In fact, according to some studies, teachers may be the single most important school-related factor in student achievement (Goldhaber, 2016). Teachers play both symbolic and practical roles in schools and are the focus of education polity' in national education systems worldwide. The Organization for Economic Co-operation and Development (OECD) asserts that of those school factors that policymakers can and often do influence, “teacher quality is the single most influential factor in determining student achievement” (2005, p. 2). Not surprisingly, there is an intense focus among policymakers and researchers on teachers and their impact on student learning, achievement, and transition to productive citizenship, often to the relative exclusion of other factors found to comprise a “quality” education (Hanushek & Rivkin, 2006, p.1053).

Surprisingly, there is no common standard for either defining or measuring teacher quality, and both researchers and policymakers around the world use different approaches (Churchward & Willis, 2019). For example, in the United States, the importance of “highly qualified” teachers is reflected in national educational agendas and U.S. legislation such as the No Child Left Behind (NCLB) Act of 2001 and the Every Student Succeeds Act (ESSA) of 2015 as subject matter certified, university graduates (Darling-Hammond, 2000, 2016). Whereas, in China, teachers are expected to be continuously engaged in professional development activities, presumably leading them to become higher quality teachers who can better contribute to their students’ achievement (Robinson, 2008).

In India, the importance of high-quality teachers is embedded in national education policies and decision-making. For example, in 2004, the National Council of Educational Research and Training (NCERT) in India revised the existing National Curriculum Framework (NCF) to improve teacher quality by revamping teacher education. This was part of a vision to prepare every child in the country to grow in India’s fast-changing labor market and participate in the global economy (NCERT, 2005). Accordingly, the Indian Minister of

State for Human Resource Development extended the duration of teacher education nationwide in an attempt to improve teacher quality (Nanda, 2017). Unfortunately, duration of training does not necessarily equate to improved quality, and NCERT does not provide a well-defined concept of a “highly qualified” teacher to evaluate education policies aimed at improving teacher quality in India.

Although there is some research on teacher quality in India (e.g., Azam & Kingdon, 2014; Muralidharan & Sundararaman, 2011; Nanda, 2017), research provides limited clarity on the definition of teacher quality in Indian education and no standard measure of teacher quality in Indian education, broadly speaking. For example, some research on teacher quality in India has proposed using the VAM (value-added model) approach (Azam & Kingdon, 2014), but VAM has not been adopted more widely either in policy, practice, or research in India. Teacher qualifications, teacher certification, and professional development, however, are more widely used as indicators of teacher quality in both national and local education policy and practice worldwide (Goe & Stickler, 2008; Kumar, 2019; Wiseman & Al-bakr, 2013). The focus on these traditional measures of teacher quality ignores that there are other non-traditional teacher quality variables, which may have a much larger impact on Indian student outcomes.

As one of the largest economies and educational systems in the world (Raut,

2017), it is surprising that there is little data on teacher quality in India. However, for the first time in 2009, two Indian states, Tamil Nadu and Himachal Pradesh, participated in the Programme for International Student Assessment (PISA). The “embarrassing” results of PISA 2009 (Chhapia, 2013, p. 1; OECD, 2010) ranked India second to last amongst 73 countries, and resulted in the country opting out of the 2012 and 2015 rounds of the international assessment (Lockheed et al., 2015). Figures 9.1-9.3 show India’s ranking among the 73 participating countries, with only Kyrgyzstan scoring lower that India in mathematics, science, and reading. Figures 9.1-9.3 also show the rankings of the two Indian states of Tamil Nadu and Himachal Pradesh alongside India.

India’s PISA 2009 results revealed that not more than 15% of the children (15 years of age) who participated in the testing performed at the basic level (OECD, 2010). Furthermore, publicizing India’s poor performance in PISA 2009 in the media created a public “shock” over India’s education system (Lahiri, 2012). For example, an article in a leading Indian newspaper, The Times of India, stated that an eighth-grade Indian student is at a similar level to a third-grade South Korean student in mathematics, and an eighth-grade Indian student in reading is, on average, equivalent to a Shanghai second- grader. Following these publicized results, questions immediately arose related to the quality of Indian teachers, and teachers were blamed for the poor performance of students (Lahiri, 2012; Rao, 2013).

The policy cycle of poor student achievement leading to teacher blaming and shaming, which then leads to teacher-focused policies and standards

implementation, is a frequent phenomenon in systems that regularly participate in international educational assessments (LeTendre & Wiseman, 201Б). But, for India this was a new experience following PISA 2009. Even if teacher quality in India is a contributing factor to Indian students’ low performance, Indian policymakers and teacher preparation professionals need more information on what teacher quality in India is, how to empirically measure it, and both valid and reliable estimates of the impact of teacher quality on student achievement. In Indian national education policy since the early 2000s, there has been a decided focus on building traditional teacher quality, through increased qualifications and the certification of teachers, but no empirical studies have investigated the differences in teacher quality in India.

This research, therefore, investigates how traditional and non-traditional measures of teacher quality are differently related to student performance in India. This question addresses Indian policymakers’ assumptions - as demonstrated in Indian national education policies - about the impact of traditional (rather than non-traditional) teacher quality on student achievement. Addressing these assumptions is necessary for Indian policymakers, educators, and the public to make informed decisions regarding the nature of reforms needed to prepare and support high quality teachers in India who can consistently and measurably enhance student performance.

Traditional vs. Non-traditional Measures of Teacher Quality

Teacher quality is traditionally measured using simple descriptions of teachers’ qualifications, certification, educational credentials, and most frequently by their students’ average achievement scores (Wiseman & Al-bakr, 2013). But, these measures fall short by confounding the characteristics of teacher quality with its effects. For example, qualifications, certification, and credentialing may be better identified as characteristics of teacher quality, while student achievement is more likely to be an effect or outcome of teacher quality. There are other indicators of teacher quality that may be more reflective of a teachers’ quality in doing their jobs rather than static documentations of quality, which comprise all of the traditional measures.

Several international studies have found an association between student achievement and teacher qualification, if the qualification is content related, but research in India varies from the international results. For example, a crossnational study on teacher quality found that students whose teachers are certified in mathematics, have three or more years of experience teaching mathematics, and are also mathematics-major holders have a measurably positive effect on student outcomes compared to teachers qualified in subjects other than mathematics (Akiba et al., 2007). International evidence consistently supports the assertion that subject-matter knowledge positively associates with student outcomes. Goldhaber and Brewer (2000) measured subject-matter knowledge of mathematics with a master’s or a bachelor’s degree in mathematics. Similarly, Rowan, Chiang, and Miller (1997) measured subject knowledge in terms of whether the teacher had a mathematics degree at the bachelor’s level and whether the teacher could answer a mathematics quiz in a teacher survey. A study conducted in five countries (South Africa, Botswana, Mozambique, Tanzania, and Namibia) using die Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) II data revealed that teacher content knowledge plays a big role in impacting student outcomes (Spreen & Fancsali, 2005).

Studies in India, however, have found that students’ background and school type may mediate the effects of teacher qualification. For example, a study in the state of Andhra Pradesh in India showed that mathematics teachers in government schools have on average three years or more experience than mathematics teachers in private schools (Singh & Sarkar, 2012), and that there is a far larger percentage of teachers in government schools who hold a diploma, bachelor’s, or master’s degree compared to private schools. However, the number of bachelor’s and master’s degrees held by teachers in areas other than teaching is higher in private schools. Surprisingly, Indian students’ mathematics scores are higher in private schools than in government schools despite the fact that government teachers demonstrate more of the traditional measures of teacher quality (Singh & Sarkar, 2012). The question then arises as to why learning outcomes are higher in some Indian schools when there are “higher quality” teachers in other schools.

There is contested evidence that teacher certification impacts student achievement. Darling-Hammond (2000) conducted a state level analysis using data from the United States to examine the relationship between certification status and student achievement on the National Assessment of Educational Progress (NAEP). Although this study revealed that certification status is an important determinant of student outcomes, other research finds that there is no association between teacher certification and student achievement (Ballou & Podgursky, 2000). In fact, Ballou & Podgursky (2000) commented that certification prevents intelligent and capable people from entering the teaching profession. Likewise, there is contradictory evidence that teacher professional development influences student achievement in India compared to international evidence.

For example, a report by Wenglinsky (2002) stated that students whose teachers receive professional development perform better than students whose teachers do not receive any professional development, especially in mathematics, where there was a difference of one frill grade. Other international studies, have also shown that professional development impacts teachers’ content knowledge and teaching practices, thus significantly impacting student achievement (Polly et al., 2015). In India, however, Azam and Kingdon (2014) found that there is no association between teacher in-service training and Indian student achievement. They conclude that there may be unnecessary emphasis on teacher in-service training by Indian policymakers.

Other measures of teacher quality are less static and more active than teacher qualifications, certification, and professional development. These non-tradi- tional measures suggest that the quality of teachers is not a product of their credentialing, qualifications, or otlier static individual accomplishments, but are instead a product of the ways they understand their students individually and as part of a community, which are often indicated by teachers’ behavior in relation to school climate. These behaviors, such as absenteeism and teacher attitude, also may indicate the ways that teachers are committed to their students and the responsibilities of teaching.

Studies have shown absenteeism lowers student performance and is also an economic concern for schools (Finlayson, 2009; Porres, 2016). In fact, evdience shows that the more days a teacher is absent or out of the classroom, the more negatively it affects student performance (Miller, 2012). Teachers’ behaviors can reflect their attitudes and influence their school’s climate for learning and performance as well. For example, teachers who project a feeling of trust in their students’ ability to do mathematics communicate confidence in the pupils’ future performance, leading to a positive growth in mathematics learning (Lee & Loeb, 2000). During instruction, a positive reaction by a teacher rather than a neutral response facilitates student learning because it encourages students to participate in class discussions (Centra & Potter, 1980). In respect to student-teacher relationships, evidence suggests that a positive teacher’s attitude also helps develop a strong rapport between a teacher and student, impacting student outcomes (Downey, 2008; Gallagher et al., 2013).

Rationale, Design, and Data

Human capital theory and neoinstitutional theory are heuristic tools that can each be used to frame ways that education policies and teacher quality interact across levels and cultures. These theoretical frameworks also suggest broader political, cultural, and economic contexts for teacher quality in national educational systems, like India’s. For example, while human capital theory provides a framework for understanding economic investment in India as a tool for building teacher quality (Johnes et al., 2017), neoinstitutional theory provides explanations for the development and impact of globally shared norms, bounded rationality, and loose coupling (Wiseman et al., 2014), which may be used to understand why India follows a globally legitimized model of educational improvement by focusing on teacher quality. For example, the Indian education policy focus on teacher quality may be an example of global isomorphism as more countries worldwide focus on teacher quality as a way to improve student outcomes (Akiba & LeTendre, 2017). Or, from a human capital perspective, it could be a solutions-oriented approach to improving student performance (e.g., Bold et al., 2019).

While a neoinstitutional framework might suggest that policies on teacher quality in India may isomorphically develop to align with global norms, regardless of their practical outcomes, human capital theory explains why economic returns on investment in teacher quality explains policymakers’ de facto education-economy link in India. From a human capital perspective, investment in teachers’ human capital results in improved student scores, a globally competitive and skilled workforce, and future economic returns for both the individual and the nation (Mincer, 1981). This provides an explanation for why Indian educational policymakers focus on teacher quality improvement as a mechanism for increasing students’ skills, abilities, attitudes and ultimately their demonstrated achievement. A human capital rationale, which is often implicitly adopted by education policymakers, suggests that in addition to individual returns through competitive jobs based both locally and internationally, increased teacher quality results in a skilled workforce that brings both economic enhancement to countries like India as well as global recognition (Johnes et al., 2017).

It is difficult to conclusively know which framework has more explanatory power in the case of Indian education policy and whether traditional or non- traditional teacher quality relates to student performance in the Indian context if there is no meaningful measure of teacher quality in India. Yet, this study still asks how traditional and non-traditional measures of teacher quality differently predict student performance in India. Since there is little empirical research addressing this broad question in India, this study asks three further questions related to teacher quality in India.

Based on a synthesis of the literature on traditional measures of teacher quality, the first research question (RQ1) suggests that student performance is significantly dependent on either the qualifications or certification status of teachers. According to the literature, completing an ISCED 5 (i.e., university or college) degree and being certified by a government or government- approved training organization provides teachers with updated key strategies to teach their subjects, which significantly influences student achievement (Wenglinsky, 2002). For example, like the education systems in the west, and in particular the United States, where teacher subject-matter competency is required to teach a particular subject, subject-matter competency in mathematics is a key priority in the NCF 2005 in India (NCERT, 2005). As a result, RQ1 asks: Which traditional teacher quality variables are significantly related to student performance in India?

Moving to the second research question (RQ2), evidence suggests that the regular presence or attendance of a teacher is among the most significant non- traditional measures of teacher quality. Research shows that teacher absenteeism communicates to students that school attendance is not important, and therefore has a negative impact on student outcomes (Uehara, 1999). Likewise, teacher attitude is consistently identified as a key non-traditional measure of teacher quality because a teacher’s attitude effects the school climate and, as a result, student learning (Rowan et al., 1997; Wenglinsky, 2002). Therefore, RQ2 asks: Which non-traditional teacher quality variables arc significantly related to student performance in India P

Using evidence from investigations of RQ1 and RQ2, it should be clear whether the performance of students is significantly related to non-traditional teacher quality variables more than traditional teacher quality in India. Despite its strong economic influence, India is still classified as a “developing” country and human capital theory suggest that investment in teacher quality acts as a future investment for the country and also for individuals (Mincer, 1981). This linear rationale suggests that basic structural and infrastructure characteristics, such as teacher qualification and certification should lead directly to changes in student achievement. Evidence also suggests, however, that if India provides incentives to teachers to reduce teacher absenteeism and improve teacher attitudes, student outcomes will also increase (Porres, 2016). Therefore, RQ3 asks the original research question: How arc traditional and non-traditional measures of teacher quality differently related to student mathematics performance in Indial

The study uses correlation analysis, multiple regression, and hierarchical linear modeling (HLM) to answer these questions using PISA 2009 data. PISA 2009 provides school-level, teacher-related data for Tamil Nadu and Himachal Pradesh. The data from the PISA 2009 school and student questionnaires for the two states was filtered and merged to produce a dataset representative of these two Indian states. While this data is not representative of India as a whole, it is the most complete representative dataset available on Indian education for this kind of analysis.

There were originally 65 participating countries or economies in PISA 2009. Thirty-four were member countries, and ten additional economies or countries could not participate within the PISA 2009 timeframe, participating instead in the PISA 2009 Plus project administered in 2010. Two Indian states, Himachal Pradesh and Tamil Nadu, participated in the 2009 Plus testing (Walker, 2011). The data and results of PISA 2009 Plus were merged with the 2009 data and results (Walker, 2011). The Indian states of Himachal Pradesh (QHP) and Tamil Nadu (QTN) were the only two states of India that participated in the 2009 plus testing. The country code for India is a three-character code (356) representing Himachal Pradesh (HP) and Tamil Nadu (TN). A total of 213 schools from both Indian states participated in PISA 2009, of which 66 schools were from Himachal Pradesh and 147 schools were from Tamil Nadu (Areepattamannil, 2014).

PISA 2009 testing was administered to 15-year-olds from seventh grade and above, and PISA sought to be as inclusive as possible in respect to both participating students and schools. There were 1,616 students from seventh grade onwards that participated in PISA 2009 in Himachal Pradesh and 3,210 students from Tamil Nadu. Students from diverse socio-economic backgrounds and abilities participated in PISA, but schools could exclude students from participating in the tests if they were intellectually disabled, had functional disabilities, or if they had low English language experience (OECD, 2010). Furthermore, private and public schools participated in the tests, although special needs or small schools in remote regions were excluded.

The OECD used the survey method to collect data for PISA 2009 (OECD, 2010). The operational definitions of variables included in these analyses is summarized below and in Table 9.1.

Student Achievement in India 203

Table 9.1 Descriptive Statistics for Key Teacher Quality Variables (Himachal Pradesh and Tamil Nadu, PISA 2009)







Student Level Achievement Outcomes

Student Math Score (PV1MATH)






Student Science Score (PV1SCIE)






Student Reading Score (PV1READ)






Student Level Background Characteristics

Female Student (FEMALE)






Student Speaks Language of Test at Home (LANGUAGE)






Student SES (ESGS)






School Level Teacher Quality Predictors

% Qualified Teachers (PERCQUAL)






% Certified Teachers (PERCCERT)






Teacher Absenteeism Hinders Student Learning (ABSENTEEISM)






Teacher Behavior Effect on School Climate (TEACBEHA)






Dependent Variables

Student achievement scores (PV1MATH, Mean=339.87, SD=65.73; PV1SCIE, Mean=334.77, SD=63.54; PV1READ, Mean=323.01, SD=72.76). The student achievement scores in mathematics, science, and reading are available for all of the Indian students that participated in PISA 2009. Although 4,826 Indian students participated, those with one or more missing data items were removed for a final sample size of 3,692. First plausible values were used in these analyses rather than a composite of all five plausible values. Although there are problems with the use of plausible values from large-scale assessments like PISA (Monseur & Adams, 2009), evidence suggests that use of only one plausible value in statistical analyses may lead to an underestimation of standard errors (Laukaityte & Wiberg, 2017); however, additional analyses using all plausible values and a composite found similar results throughout.

Independent Variables (Student Level)

Female Student (FEMALE, Mean=0.50, SD=0.50) is an indicator of whether a student is female (1) or not (0). Student gender has been shown in previous research to be a strong predictor of student achievement in India and elsewhere (White et al., 2016). Including this variable holds gender constant when considering the effects of teacher quality on student achievement in India. It also provides a measure of the effect a student’s gender has on their achievement scores when other independent variables are held constant.

Student Speaks Language of Test at Home (LANGUAGE, Mean =0.67, SD=0.47) is a measure of whether an Indian student speaks die language the PISA assessment is delivered in at home. The test language for students in India was either Tamil, Hindi, or English (Walker, 2011), and die languages students said diey speak at home were the same choices (Tamil, Hindi, or English), but sdll 33% of India students indicated they did not speak die language of the test at home.

Student SES (ESCS, Mean=-1.71, SD=1.10) is an index on economic, social, and cultural status (ESCS), which is an indicator for students’ socioeconomic status (SES). The ESCS index was created by the PISA technical team from indicators based on student background questionnaire responses related to reported parental occupation, parental education level, and home possessions (OECD, 2012, pp. 312-315).

Independent Variables (School Level)

Traditional teacher quality variables include teacher qualification and teacher certification. Percent Qualified Teachers (PERCQUAL, Mean=52.21, SD=23.13) is the percentage of qualified teachers at each school in the Indian PISA 2009 Plus data. A qualified teacher is one with an ISCED 5A (college or university) qualification. Percent Certified Teachers (PERCCERT, Mean=87.83, SD=23.87) is the percent of Hilly certified teachers out the total number of both full- and part-time teachers in a school. Certification is handled by the Centre for Teacher Accreditation (CENTA), but CENTA allows private and non-governmental providers to deliver training and certification to teachers in India (Kumar, 2019).

Non-traditional teacher quality variables include teacher absenteeism as well as an index of the degree to which teacher behavior effects school climate. Teacher Absenteeism Hinders Student Learning (ABSENTEEISM, Mean=0.37, SD=0.55) indicates the degree to which teacher absenteeism hinders student learning. The responses range from 0 (Not at All) to 3 (A Lot). Teacher Behavior Effect on School Climate (TEACBEHA, Mean=0.60, SD=1.03) is an index created by the PISA technical team, which is coded for positive teacher behavior and includes parameters from other PISA items related to teachers’ expectations of students, student-teacher relations, teachers meeting individual student needs, teacher absenteeism, staff resisting change, teachers being too strict with students, and students not being encouraged to achieve their full potential (OECD, 2012, p.16). Although teacher absenteeism is one of the parameters used to create Teacher Behavior Effect on School Climate and there is a correlation at the school-level between this variable and Teacher Absenteeism (r = -0.515, p<.01), multicollinearity does not pose a problem when including both variables as predictors at the school level in the HLM analyses based on the persistence of independent effects and a weak association of the two variables (Yu et al., 2015).

The study findings are restricted by its limited scope and the nature of the variables. First, the teacher quality' variables obtained from the PISA 2009 database are perception based because they are measured based on the views or opinions of the person filling out the survey. As a result, the data collected may be biased or not a true representation of other states in India. Thus, the results from PISA 2009 cannot be generalized beyond the states of Tamil Nadu and Himachal Pradesh to the whole country. Even though results are described here as “Indian” it should be noted that this describes only the Indian states Tamil Nadu and Himachal Pradesh. A representative generalization for all of India, however, is not possible with the PISA 2009 Plus data.

Analysis and Findings

Correlation analyses were conducted to assess the relationship between the key independent variables and student performance. The results of these correlation analyses are shown in Table 9.2. There was no statistically significant correlation between the percent of qualified teachers (PERCQUAL) and student performance in math, science, or reading. Similarly, there was no significant association between teacher absenteeism (ABSENTEEISM) and student performance. There was a small association between teacher behavior (TEACBEHA) and student math scores (r = 0.122, pc.10), but not with science or reading scores. The strongest associations were between the percent of qualified teachers (PERCCERT) and student performance in math (r = -0.187, pc.05), science (r = -0.142, p c.10), and reading (r = -0.182, p c.05), but these associations were also negative and quite small. The correlations suggest that there is little direct association between either traditional or non-traditional measures of teacher quality in India and Indian students’ academic performance, and that when there was an association, it was small and negative (e.g., PERCCERT).

The research literature remains, however, quite clear that teacher quality' matters. To further investigate the issue, linear regression analyses were conducted regressing student background characteristics and both traditional and non-traditional measures of teacher quality on students’ math, science, and reading achievement. The results of these regression analyses are shown in Table 9.3.

Although not the focus of the analyses here, it is notable that there is significant variation in the effects of students’ gender, language spoken in the home, and SES, and often not in the way's that are typically predicted. For example, it is not unusual to find that student math achievement scores decrease on average when an Indian student is female (b = -7.09, p <.001) or that Indian female students are significantly advantaged over boy's in reading achievement (e.g., b = 20.84, p <.001) in the baseline model (Areepatta- mannil, 2014). But, the fact that any gendered effect on math and science achievement disappears when teacher quality variables are included in the linear regression is remarkable. Perhaps this is a unique contextual effect for India,

Table 9.2 Pearson Correlations Between Student Achievement Scores, Student Level Characteristics, and Key Teacher Quality Predictors (Himachal Pradesh and Tamil Nadu, PISA 2009)


Student Math Score


Student Science Score


Student Reading Score (PV1READ)

Student Level Background Characteristics

Female Student (FEMALE)


-0.056 **


0.151 **

Student Speaks Language of Test at Home (LANGUAGE)


-0.129 **

-0.075 **


Student SES



0.189 **

0.175 **

0.109 **

School Level Teacher Quality Predictors

% Qualified







% Gertified




-0.187 *

-0.142 *

-0.182 *

Teacher Absenteeism Hinders Student Learning (ABSENTEEISM)





Teacher Behavior Effect on School Climate (TEACBEHA)


0.122 *



* p<.10, * p<.05, ** p<.01

but this would require further focused study to confirm. It is also worth noting that when a students’ gender does have the most effect (i.e., positive effects of female student status on reading achievement) those positive effects are enhanced when traditional teacher quality indicators are held constant (b = 32.97, p <.01) and somewhat diminished when non-traditional teacher quality indicators are held constant (b = 19.20, p <.10). This suggests that in reading, some of the effects attributed to student’s gender are more likely due to non- traditional teacher quality.

Another unusual result is that whether a student speaks the language of the test at home has a negative effect on math achievement, which is increased when both traditional (b = -24.60, p<.05) and non-traditional (b = -25.14, p<.05) teacher quality indicators are held constant. And, finally, student SES has the expected positive impact on math, science, and reading achievement,

Tabic 9.3 Linear Regression of Student Achievement on Student Background Characteristics and Key Teacher Quality Predictors (Himachal Pradesh and Tamil Nadu, PISA 2009)

Dependent Variables

Math Achievement (FV1MATH)

Science Achievement (PV1SCIE)

Reading Achievement (PV1READ)

Model Ml

Model М2

Model М3

Model M4

Model SI

Model S2

Model S3

Model S4

Model R1

Model R 2

Model R3

Model R4

Female Student (FEMALE)

  • -7.09***
  • (1.97)*
  • -0.87
  • (10.27)
  • -8.91
  • (9.97)
  • 0.52
  • (10.43)
  • -1.98
  • (1.96)
  • 9.23
  • (9.90)
  • 1.69
  • (9.66)
  • 9.05
  • (10.12)
  • 20.84***
  • (2 222)
  • 32.97**
  • (10.80)
  • 19.20+
  • (10.94)
  • 33.61**
  • (11.12)

Student Speaks Language of Test at Home (LANGUAGE)

  • -9.35***
  • (2.19)
  • -24.60*
  • (10.75)
  • -25.14*
  • (10.72)
  • -23.64*
  • (10.93)
  • -2.13
  • (2.18)
  • -3.91
  • (10.35)
  • -6.41
  • (10.40)
  • -4.76
  • (10.60)
  • 2.27
  • (2.48)
  • 3.18
  • (11.30)
  • -0.12
  • (11.77)
  • 3.41
  • (11.65)

Student SES


  • 10.03***
  • (0.94)
  • -4.09
  • (4.79)
  • 0.23
  • (4.73)
  • -4.40
  • (4.85)
  • 10.09***
  • (0.94)
  • -1.85
  • (4.62)
  • 2.64
  • (4.59)
  • -2.26
  • (4.70)
  • 7.91***
  • (1.07)
  • -2.83
  • (5.04)
  • 3.31
  • (5.19)
  • -3.06
  • (5.16)

% Qualified



  • -0.28
  • (0.22)
  • -0.23
  • (0.22)
  • -0.07
  • (0.21)
  • -0.06
  • (0.21)
  • 0.10
  • (0.23)
  • 0.11
  • (0.23)

% Certified Teachers


  • -0.43*
  • (0.22)
  • -0.48*
  • (0.22)
  • -0.36+
  • (0.21)
  • -0.39+
  • (0.21)
  • -0.66**
  • (0.23)
  • -0.68**
  • (0.23)

Teacher Absenteeism Hinders Student Learning (ABSENTEEISM)

  • -3.32
  • (10.23)
  • -3.33
  • (10.56)
  • -1.47
  • (9.92)
  • 1.20
  • (10.24)
  • -4.01
  • (11.23)
  • -5.09
  • (11.25)

Teacher Behavior Effect on School Climate (TEACBEHA)

  • 6.99
  • (5-77)
  • 7.48
  • (5.82)
  • 4.03
  • (5.59)
  • 6.67
  • (5.64)



  • 4.57
  • (6.20)


  • 367.53***
  • (2.14)
  • 405.96***
  • (21.76)
  • 364.61***
  • (12.91)
  • 402.66***
  • (22.69)
  • 355.56***
  • (2.14)
  • 366.26***
  • (20.96)
  • 344.27***
  • (12.52)
  • 362.60***
  • (22.00)
  • 324.46***
  • (2.42)
  • 355.19***
  • (22.88)
  • 323.71***
  • (14.17)
  • 355.373***
  • (24.17)














'Standard errors are in parentheses.

but only when teacher quality variables are not part of the model. This suggests that both traditional and non-traditional teacher quality mediates the effects of socioeconomic status on student achievement in India.

In these linear regression estimates, the only teacher quality indicator posting a statistically significant effect on any of the student achievement scores is the percent of certified teachers, which has a small, negative effect on math achievement (b = -0.48, p<.05), science achievement (b = 0.39, p<.10), and reading achievement (b = -0.68, p<.01). Since being certified is traditionally considered a measure of positive teacher quality and has been shown to have a positive effect on student achievement in other studies (Goldhaber & Brewer, 2000), the negative results were initially surprising. However, in India, the NCTE determines the qualifications required by teachers, while the certification, which is a relatively new concept in India, is handled by the Centre for Teacher Accreditation (CENTA).

From the limited information available, existing teachers are certified by CENTA based on their years of experience. However, the NCTE has failed to prevent entry of private and unrecognized institutions that provide training and certifications to teachers, and private schools readily employ such teachers (Nirav, 2012). There are no evidence-based studies available showing that teacher certification positively impacts student outcomes in India, specifically. This additional information suggests reasons why certified Indian teachers may yield low student outcomes. The certification is awarded often by unrecognized, private institutions that have relatively little or limited expertise in training teachers, so certified Indian teachers are of lower quality in some instances than non-certified Indian teachers.

While these linear regression results are somewhat unexpected compared to analyses of PISA data in other educational systems and among cross-national samples, the results suggest that there may be contextual effects that are unique to India (e.g., gender and language effects running counter to findings in other countries) and that some of these unique contextual effects may also be due to differences in traditional and non-traditional teacher quality. Given the evidence from the bivariate correlational and linear regression analyses, it is appropriate to move on to an estimate of the effects of teacher quality in India using multilevel, multivariate statistical modeling; specifically, two-level hierarchical linear modeling (HLM). HLM analyses are an appropriate next step due to the need to estimate effects using nested national survey data, such as PISA, that provides both school-level (level two) variables on teacher quality as estimates of individual-level (level one) models of student achievement (Raudenbush & Bryk, 2002).

The first level equation (eq. 1) estimates the influence of a student’s gender (Piy), language (p2;), and socioeconomic status (p3y) on that student’s achievement scores, as indicated below:Yjj = p0j + Pij(FEMALE)jj + P2j(LANGUACE)ii + p3j(ESCS);j + e^ (eq. )Y,, is the dependent variable (either math, science, or reading achievement score, depending on the model) for the fh student within school j. p(l, is an estimate of the adjusted mean dependent variable for school /; and is a student level residual. By assumption, E(e,j) = 0 and Var (r,y) = a2.

For the school-level equation (eq. 2), the mean dependent variable (p0;) is modeled as a function of traditional teacher quality (yo i and y02) and non- traditional teacher quality (у()з and y(M) as follows^- = yoo + Yoi(PERCQUAL)0y + у()2(PERCCERT)qj+ yo3(ABSENTEEISM)oj + y„4(TEACBEHA)0j + u()j (eq. ) Here, y()o is an estimate of the adjusted school mean dependent variable and uOj is a school level residual. By assumption, E(u{)j) - 0 and Var(woy) = too. Each model and equation was run at the student level only (Model 1), with traditional teacher quality indicators only at the school level (Model 2), with non-traditional teacher quality indicators only at the school level (Model 3), and as a dill model with all indicators at both levels (Model 4).

The HLM analyses confirm much of what the univariate, bivariate, and multivariate linear regression analyses suggested, but also provided more clarity around which traditional and non-traditional teacher quality measures have the most effect on student achievement in India. The results of the HLM analyses are shown in Table 9.4.

The first research question (RQ1) asked: Which traditional teacher quality variables are significantly related to student performance in India ? The results of the HLM analyses show that the percent of certified teachers (PERCCERT) is the only traditional teacher quality indicator with a statistically significant effect on Indian (i.e., Tamil Nadu and Himachal Pradesh) students’ achievement. It also shows that this effect is small and consistent across all achievement outcomes. An increase in the percent of certified Indian teachers per school is likely to reduce students’ mathematics achievement scores by -0.40 points (p<.05), reduce students’ science achievement by -0.37 points (p<.05), and reduce students’ reading achievement by -0.39 (p<.05). As discussed earlier, the small, negative effects of Indian teachers being certified may be explained by the questionable quality of certification providers and content in India. The percent of qualified teachers (PERCQUAL) has no statistically significant effect, which suggests that student achievement is not affected at all by whether more teachers at a school have achieved an ISCED БА (college or university) qualification.

Unlike in the linear regression analyses, the HLM results do not indicate any variation in the effects of student gender on achievement based on whether traditional teacher quality is included in the model or not. The effects of student gender on achievement also align with previous studies in the HLM models, where female has a negative effect on math and science achievement, but a positive effect on reading achievement regardless of the type of teacher quality held constant in the model. Likewise, the impact of whether a student speaks the language of the test at home or not did not vary by type of teacher quality included in the HLM models, but instead varied between science achievement (positive, significant effect) and both math and reading achievement (negative, non- statistically significant effects).

Table 9.4 HLM Estimates of the Impact of Traditional and Non-Traditional Teacher Quality on Student Achievement in India (Tamil Nadu and Himachal Pradesh, PISA 2009)

Dependent Variables

Math Achievement

Science Achievement

Reading Achievement

Model Ml

Model М2

Model М3

Model M4

Model SI

Model S2

Model S3

Model S4

Model Rl

Model R2

Model R3

Model R4

Student Level Background Characteristics

Female Student (FEMALE)

  • -10.79***
  • (2.31)
  • -10.56***
  • (2.32)
  • -10.79***
  • (2.3)
  • -10.54***
  • (2.32)
  • -5.10**
  • (1.92)
  • -4.85*
  • (1.92)
  • -5.10**
  • (1.91)
  • -4.84*
  • (1.92)
  • 15.41***
  • (2.09)
  • 15.67***
  • (2.09)
  • 15.42***
  • (2.08)
  • 15.71***
  • (2.09)

Student Speaks Language of Test at Home (LANGUAGE)

  • -4.15
  • (4.38)
  • -3.62
  • (4.34)
  • -4.17
  • (4.37)
  • -3.63
  • (4.33)
  • 7.16*
  • (3.63)
  • 7.62*
  • (3.61)
  • 7.12*
  • (3.61)
  • 7.58*
  • (3.59)
  • -1.03
  • (5.08)
  • -0.39
  • (5.06)
  • -1.02
  • (5.06)
  • -0.35
  • (5.04)

Student SES


1.49 (1.16b)

  • 1.41
  • (1.15)

1.43 (1.15)

  • 1.35
  • (1.14)
  • 1.79
  • (1.13)
  • 1.71
  • (1.12)
  • 1.73
  • (1.12)
  • 1.64
  • (1.12)
  • -0.32
  • (1.22)
  • -0.41
  • (121)
  • -0.40
  • (1.20)
  • -0.50
  • (1.20)

School Level Teacher Quality Predictors

% Qualified



  • 0.07
  • (0.14)
  • 0.07
  • (0.13)
  • 0.18
  • (0.14)
  • 0.18
  • (0.13)
  • 0.15
  • (0.16)
  • 0.16
  • (0.15)

% Certified



  • -0.38*
  • (0.17)
  • -0.40*
  • (0.16)
  • -0.36*
  • (0.16)
  • -0.37*
  • (0.15)
  • -0.37*
  • (0.17)
  • -0.39*
  • (0.16)

Teacher Absenteeism Hinders Student Learning (ABSENTEEISM)

  • 5.94
  • (6.71)
  • 4.11
  • (6.33)
  • 7.08
  • (7.38)
  • 5.43
  • (6.93)
  • 8.42
  • (8.55)
  • 6.68
  • (8.11)

Teacher Behavior Effect on School Climate (TEACBEHA)

  • 6.96*
  • (3.5)
  • 7.23*
  • (3.34)
  • 6.96**
  • (3.51)
  • 7.25
  • (3.33)
  • 9.75*
  • (3.87)
  • 10.05**
  • (3.72)


  • 351.73***
  • (5.37)
  • 381.07***
  • (16.13)
  • 345.31***
  • (5.89)
  • 376.14***
  • (15.53)
  • 336.24***
  • (5.08)
  • 357.67***
  • (15.66)
  • 329.41***
  • (5.48)
  • 351.95***
  • (15.1)
  • 314.98***
  • (5.89)
  • 338.83***
  • (16.46)
  • 305.92***
  • (6.1)
  • 331.39***
  • (15.96)

Random Effects

Level 2 Variance, u0













Level 1 Variance, r













'’Standard errors are in parentheses.

The second research question (RQ2) asked: Which non-traditional teacher quality variables are significantly related to student performance in India P The HLM results show that the indicator of teachers’ behavior having an effect on school climate (TEACBEHA) is the only non-traditional teacher quality indicator with a statistically significant effect on Indian student achievement. Teachers’ behavior having an effect on school climate has a positive effect on students’ math achievement (b=7.23, p<.05), science achievement (b=7.25, p<.05), and reading achievement (b=10.05, pc.01). There is also a marked increase in the effect of teachers’ behavior on reading achievement beyond math and science achievement, which suggests that this non-traditional measure of student achievement impacts Indian students’ reading learning and performance more than in math and science. Teacher absenteeism (ABSENTEEISM) also posts similar strength positive effects on Indian student achievement. As recognized earlier, the teacher behavior index measure used here is calculated partially with the teacher absenteeism variable, which may explain why the absenteeism indicator has an effect but is not statistically significant.

The third and main research question (RQ3) asked: How are traditional and non-traditional measures of teacher quality differently related to student performance in India? The HLM results clearly show that traditional measures of Indian teacher quality have either no statistically significant effect or a small, negative effect on students’ achievement in math, science, and reading. In contrast, the chief non-traditional measure of Indian teacher quality (TEACBEHA) is a positive and statistically significant indicator of student achievement across all three subject areas. In other words, evidence from the analyses shown here suggest that a non-linear, non-traditional measure of teacher quality has more positive and significant effects on student learning and, therefore, student achievement than traditional teacher quality indicators.


India, a developing economy, participated in PISA 2009 Plus international assessments and fared poorly by comparison with other countries. The news of India’s performance created a “shock wave” in India and teachers were blamed for the low quality of education and poor student performance (Lahiri, 2012). This study analyzed the influence of teacher quality variables on students’ mathematics performance using data from PISA 2009 Plus. The study’s purpose was to provide a greater understanding of what constitutes teacher quality in one of the world’s fastest growing economies and largest populations as well as to provide empirical evidence of Indian teachers’ quality and its effects on student performance so that Indian policymakers may make informed, data- driven decisions about how to improve teacher quality and education more broadly in India.

The empirical results show that traditional teacher quality (i.e., teacher qualification and certification), while perhaps necessary for the development of a strong educational system overall and legitimacy of India’s educational system in the global economy, does not affect student achievement, based on PISA 2009 Plus data. It follows then from the empirical evidence that non-traditional measures of teacher quality, namely teacher absenteeism and teacher attitude (measured as the effect of teachers’ behavior on school climate) are likely to be more significant and impactful indicators of student achievement. These non-tradi- tional teacher quality characteristics, however, are not as easily developed as traditional teacher quality because they are not explicit components of either university or teacher preparation programs in most countries, including India (Bawane, 2019; Subidia, 2019).

To improve teacher quality in India, specifically, the NCF (National Curriculum Framework) 2005 advocated for the strengthening of teacher preparation programs in the country and asserted that teacher qualification and certification as key teacher quality characteristics that influence student achievement gains (NCERT, 2005). As a country, India recognizes its untapped reservoir of human capital, and the Indian government is, therefore, investing in teacher education and training as a means for the country’s growth. It has been assumed by Indian education policymakers that high quality teachers with credentials and training will raise the educational outcomes of students, thus increasing the country’s human capital (Kumar, 2019). The evidence present here suggests that instead of traditional teacher quality leading to gains in student learning, quite the opposite is true. While India invests in teacher training institutions (i.e., DIETs), Colleges of Teacher Education (CTEs), and teacher curriculum development as shown in NCTE 2009, teachers attitudes and behaviors, which are influenced by teachers’ expectations of students, student-teacher relations, teachers meeting individual student needs, staff resisting change, teachers being too strict with students, and students not being encouraged to achieve their full potential, are the real change-makers.

Finally, the results of this research suggest that future research should include quantifiable (i.e., not perception-based), non-traditional teacher quality indicators to estimate the influence of teacher quality on student learning and performance. Second, the research questions of this study may be applied to analyses of other available data (e.g., the Indian Central Board of Secondary Education or the Council for Indian School Certificate Examination data) to see if the results from PISA 2009 Plus persist. This may then lead to some clarity on the reasons for Indian students’ performance on PISA 2009 Plus and provide data-based evidence for teachers, educational administrators, and policymakers to introduce changes.


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10 Evidence-Based Policy

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