Teacher support for student learning

Annex A. Construction of indices

Explanation of the indices

This policy paper uses indices derived from the PISA 2022 student, teacher and school questionnaires. PISA indices summarise responses from students, teachers and school principals to a series of related questions. The questions were selected from a larger pool on the basis of theoretical considerations and previous research. The PISA 2022 Assessment and Analytical Framework (OECD, 2023[8]) provides an in-depth description of this conceptual foundation.

To ensure the validity and comparability of certain indices across countries and economies, item response theory (IRT) modelling and classical test theory were applied where appropriate. For a detailed description of the methods, see the PISA 2022 Technical Report (OECD, 2024[19]).

The following indices are used in this report.

Indices Based on Student Responses

The PISA index of economic, social and cultural status (ESCS)

The PISA index of economic, social and cultural status (ESCS) is a composite score derived from three key variables related to family background: parents’ highest level of education in years (PAREDINT), parents’ highest occupational status (HISEI), and home possessions (HOMEPOS).

• Parents’ highest level of education in years: Students’ responses regarding their mother’s and father’s education were classified using ISCED-11 (UNESCO-UIS, 2012[20]). The index of highest education level of parents (HISCED) corresponds to the higher ISCED level of either parent. Additionally, this index was recoded into estimated number of years of schooling (PAREDINT). The conversion from ISCED levels to year of education follows an internationally standardized approach, consistent across all countries. This conversion was established based on the cumulative years of education values assigned to each ISCED level in PISA 2018. Further details on this correspondence can be found in the PISA 2022 Technical Report (OECD, 2024[19]).

• Parents’ highest occupational status: Occupational data for both the student’s father and mother were obtained from responses to open-ended questions. The responses were coded using four-digit ISCO codes (ILO, 2007) and then mapped to the international socio-economic index of occupational status (ISEI) (Ganzeboom and Treiman, 2003[21]). In PISA 2022, the 2008 version of ISCO and ISEI were used. Three indices were calculated based on this information: father’s occupational status (BFMJ2); mother’s occupational status (BMMJ1); and the highest occupational status of parents (HISEI), which corresponds to the higher ISEI score of either parent or to the only available parent’s ISEI score. For all three indices, higher ISEI scores indicate higher occupational status.

• Home possessions (HOMEPOS) is a proxy measure for family wealth. In PISA 2022, students reported the availability of household items at home, including books at home and country-specific household possessions identified as relevant indicators of family wealth within their national context. HOMEPOS summarizes all reported household and possession items into a single index.

For the computation the PISA index of economic, social and cultural status (ESCS), missing values for PAREDINT, HISEI or HOMEPOS were imputed using predicted values supplemented by a random component, based on a regression involving the other two variables. If data were missing for more than one of the three components, ESCS was not computed, and a missing value was assigned.

In PISA 2022, ESCS was computed by attributing equal weight to its three standardised components. These three components were standardised across the OECD countries, with each OECD country contributing equally to the calculation. The final ESCS variable was transformed so that a score of 0 represents the average OECD student, and 1 corresponds to the standard deviation across equally weighted OECD countries.

Index of teacher support in mathematics

Students were asked how often the following occurred in their mathematics classes (“never or hardly ever”, “some lessons”, “most lessons”, “every lesson”):

• The teacher shows an interest in every student's learning.

• The teacher gives extra help when students need it.

• The teacher helps students with their learning.

• The teacher continues teaching until students understand.

These four statements were combined to create an index of teacher support (TEACHSUP), standardized with an average of zero and a standard deviation of one across OECD countries. Positive values on the indices mean that the student reported more frequent teacher support in mathematics lessons than did students on average across OECD countries.

Index of quality of student-teacher relationships

Students were asked to rate their agreement with the following statements using a four-point scale: “strongly disagree”, “disagree”, “agree”, “strongly agree”:

• The teachers at my school are respectful towards me.

• If I walked into my classes upset, my teachers would be concerned about me.

• If I came back to visit my school three years from now, my teachers would be excited to see me.

• I feel intimidated by the teachers at my school.

• When my teachers ask how I am doing, they are really interested in my answer.

• The teachers at my school are friendly towards me.

• The teachers at my school are interested in students' well-being.

• The teachers at my school are mean towards me.

Responses to these eight statements were combined to create the index of “Quality of student-teacher relationships” (RELATST), standardized with an average value of zero and a standard deviation of one across OECD countries. Positive values in the index indicate that a student reported higher-quality student-teacher relationships than did students on average across OECD countries.

Index of disciplinary climate in mathematics lessons

Students were asked how often the following occurred in their mathematics classes (“never or hardly ever”, “some lessons”, “most lessons”, “every lesson”):

• Students do not listen to what the teacher says.

• There is noise and disorder.

• The teacher has to wait a long time for students to quiet down.

• Students cannot work well.

• Students do not start working for a long time after the lesson begins.

• Students get distracted by using <digital resources> (e.g. smartphones, websites, apps).

• Students get distracted by other students who are using <digital resources> (e.g. smartphones, websites, apps).

Responses to these seven statements were combined to create the index of disciplinary climate (DISCLIM), standardized with an average of zero and a standard deviation of one across OECD countries. Positive values on the index indicate that a student reported a better disciplinary climate in mathematics lessons than did students on average across OECD countries.

Index of student persistence

Students were asked to rate their agreement with statements measuring their persistence using a five-point scale: “strongly disagree”, “disagree”, “neither agree nor disagree, “agree”, “strongly agree”. The statements included:

• I keep working on a task until it is finished

• I apply additional effort when work becomes challenging.

• I finish tasks that I started even when they become boring.

• I stop when work becomes too difficult.

• I am more persistent than most people I know.

• I give up after making mistakes.

• I quit doing homework if it is too long.

• I complete tasks even when they become more difficult than I thought.

• I finish what I start.

• I give up easily.

Responses to these ten statements were combined to form the index of student persistence (PERSEVAGR). Some of these items were reverse-coded before scaling to ensure higher index values consistently reflect greater persistence. A positive value on the index indicates that a student reported greater persistence than did students on average across OECD countries.

Index of mathematics anxiety

Students were asked to rate their agreement with statements measuring their attitudes towards mathematics using a using a four-point scale: “strongly disagree”, “disagree”, “agree”, “strongly agree”. The statements included:

• I often worry that it will be difficult for me in mathematics classes.

• I get very tense when I have to do mathematics homework.

• I get very nervous doing mathematics problems.

• I feel helpless when doing a mathematics problem.

• I worry that I will get poor marks in mathematics.

• I feel anxious about failing in mathematics.

Responses to these six statements were combined to form the index of mathematics anxiety (ANXMAT). A positive value on the index indicates that a student reported higher levels of mathematics anxiety than did students on average across OECD countries.

Indices Based on Teachers’ Responses

The Teacher Questionnaire is an international option that countries/economies could choose to implement. Routing within the questionnaire was used to deliver specific questions to mathematics teachers and specific questions to all other, non-mathematics teachers. Other questions in the questionnaire were administered to all teachers.

Teacher’s self-efficacy in classroom management

Teachers were asked to rate their ability to manage classroom behaviour using a four-point scale: “Not at all”, “To some extent”, “Quite a bit”, “A lot”. Specifically, they assessed their capacity to:

• Control disruptive behaviour in the classroom

• Make my expectations about student behaviour clear

• Get students to follow classroom rules.

• Calm a student who is disruptive or noisy.

Responses to these four statements were combined to create the index of teacher’s self-efficacy in classroom management (SEFFCM). This scale was not administered to teachers who reported teaching mathematics to students in the national modal grade for 15-year-olds in the current school year.

Teacher’s self-efficacy in maintaining positive relations with students

Teachers were asked to rate their ability to maintain positive relations with students using a four-point scale: “Not at all”, “To some extent”, “Quite a bit”, “A lot”. Specifically, they assessed their capacity to:

• Get students to believe they can do well in school work.

• Help my students value learning.

• Motivate students who show low interest in school work.

• Help students think critically.

Responses to these four statements were combined to create the index of teacher’s self-efficacy in maintaining positive relations with students (SEFFREL). This scale was not administered to teachers who reported teaching mathematics to students in the national modal grade for 15-year-olds in the current school year.

Indices Based on School Principal’s Responses

Shortage of educational staff

School principals were asked to rate the extent to which shortages of education staff hinder instruction in their school, using a four-point scale: “Not at all”, “Very little”, “To some extent”, and “A lot”. Specifically, they assessed whether instruction was affected by:

• A lack of teaching staff.

• Inadequate or poorly qualified teaching staff.

• A lack of assisting staff.

• Inadequate or poorly qualified assisting staff.

Responses to these four statements were combined to create the index of “Shortage of educational staff” (STAFFSHORT). Positive values on this index indicate that a principal perceived the availability and/or quality of the human resources in her or his school as an obstacle to providing instruction to a greater extent than did principals on average across OECD countries.

School size

The index of school size (SCHSIZE) represents the total enrolment at a school. It is based on the enrolment data provided by the school principal, summing the number of girls and boys at a school.

Student-teacher ratio

The student-teacher ratio (STRATIO) was calculated by dividing the number of enrolled students in the school by the total number of teachers, as reported by the school principal.

Class size

School principals were asked about the average size of the test language classes in their school. The nine response categories were “15 students or fewer”, “16-20 students”, “21-25 students”, “26-30 students”, “31-35 students”, “36-40 students”, “41-45 students”, “46-50 students”, and “More than 50 students”. The average class size (CLSIZE) was derived from the midpoint of each response category, with the lowest category assigned a value of 13 and the highest category a value of 53.

Mathematics class size

School principals were asked about the average class size of mathematics classes in their school. The nine response categories were “15 students or fewer”, “16-20 students”, “21-25 students”, “26-30 students”, “31-35 students”, “36-40 students”, “41-45 students”, “46-50 students”, and “More than 50 students”. The average math class size (MCLSIZE) was derived from the midpoint of each response category, with the lowest category assigned a value of 13 and the highest category a value of 53.