PISA 2015 Results

Achievement scales
Scale Creation
  • A generalized partial credit IRT model was used to create the achievement scales.
    • New scales were standardized with a mean score of 500 and standard deviation of 100 among OECD countries.
    • Existing scales were scaled on a calibration sample including responses from past PISA waves, and equated to previous scales using linear transformations.
  • PISA uses the imputation methodology usually referred to as plausible values (PVs).
    • For each scale and subscale, ten plausible values per student were included in the international database.
    • Plausible values were imputed using a multi-dimensional model.

 

List of Achievement Scales

Science

 

Science knowledge subscales

  • Content
  • Procedural
  • Epistemic

 

Science competency subscales

  • Explain phenomena scientifically
  • Evaluate and design scientific enquiry
  • Interpret data and evidence scientifically

 

Science system subscales

  • Physical
  • Living
  • Earth and space

Reading

Mathematics

Collaborative problem solving

Financial literacy

Background scales
Scale Creation

Simple indices are the variables that were constructed through the arithmetic transformation or recoding of one or more items in exactly the same way across assessments.

New and trend scale indices are the variables constructed through the scaling of multiple items. Unless otherwise indicated, the index was scaled using a two-parameter item response model (a generalized partial credit model was used in the case of items with more than two categories) and values of the index correspond to Warm likelihood estimates (WLE).

Scale indices were constructed through the scaling of items. Typically, scale scores for these indices were estimates of latent traits derived through item response theory (IRT) scaling of dichotomous or Likert-type items.

Only scale indices are listed below.

 

List of Background Scales

Student-level scale indices

  • Broad interest in science topics
  • Epistemic beliefs about science
  • Sense of belonging
  • Teacher-directed instruction
  • Perceived feedback
  • Adaptive instruction
  • Enquiry-based instruction
  • Teacher support
  • Disciplinary climate in science classes
  • Achievement motivation
  • Enjoyment of science
  • Science self-efficacy
  • Science activities
  • Instrumental motivation to learn science
  • Index of economic, social and cultural status
  • Home possessions
  • Adaption of instruction
  • Inquiry-based science teaching and learning practices
  • Teacher support in a science classes
  • Sense of Belonging to School
  • Test Anxiety
  • Being Bullied
  • Enjoy cooperation
  • Value cooperation
  • Cultural possessions at home
  • Home educational resources
  • ICT Resources
  • Teacher Fairness
  • Family wealth

 

School-level scale indices

  • School resources
    • Staff shortage
    • Shortage of educational material
  • Educational leadership
    • Educational leadership
    • Curricular development
    • Instructional leadership
    • Professional development
    • Teachers participation
    • Shortage of educational material
    • Shortage of educational staff
    • Student-related factors affecting school climate
    • Teacher-related factors affecting school climate
  • School climate
    • Student behavior
    • Teacher behavior

 

Optional ICT familiarity questionnaire scale indices   

  • ICT use outside of school leisure
  • ICT use outside of school for schoolwork
  • Use of ICT at school in general
  • Students’ ICT Interest
  • Students’ Perceived ICT Competence
  • Students’ Perceived Autonomy related to ICT Use
  • Students’ ICT as a topic in Social Interaction

 

Optional parent questionnaire scale indices

  • Child’s past science activities
  • Parental current support for learning at home
  • Parental emotional support
  • School policies for parental involvement
  • Parents perceived school quality
  • Parents’ view on science
  • Parents concerns regarding environmental topics
  • Parents’ view on future environmental topics
Overview of key study results

Science (primary focus)

  • Singapore outperformed all other participating countries and economies in science.
  • On average across OECD countries, 79% of students performed at or above Level 2 in science, the baseline level of proficiency.
  • On average across OECD countries, boys scored slightly higher than girls in science.
  • Mean performance in science improved significantly between 2006 and 2015 in Colombia, Israel, Macao (China), Portugal, Qatar, and Romania.
  • A quarter of students envisioned themselves working later on in a science-related career.
  • Girls and boys were almost equally likely to expect to work in a science-related career, but they had different interests and different ideas about what those careers might be.
  • In general, boys participated more frequently in science-related activities and had more confidence in their abilities in science than girls.

 

Reading

  • Singapore, Hong Kong (China), Canada and Finland were the highest-performing countries and economies in reading.
  • Nearly one in ten students in OECD countries was a top performer in reading, but two in ten students had not attained the baseline level of proficiency in the subject.
  • Few countries have seen consistent improvements in reading performance since PISA 2000.
  • Albania, Estonia, Georgia, Ireland, Macao (China), Moldova, Montenegro, Russia, Slovenia, and Spain were able to simultaneously increase the share of top performers and reduce the share of low achievers in reading.
  • The gender gap in reading narrowed somewhat between 2009 and 2015.

 

Mathematics

  • Asian countries and economies outperformed all other countries in mathematics.
  • Around one in ten students in OECD countries was a top performer in mathematics, on average; but in Singapore, more than one in three students were top performers in the subject.
  • Boys tended to score higher than girls in mathematics, but in nine countries and economies, girls outperformed boys.

 

Others

  • Canada, Denmark, Estonia, Hong Kong (China) and Macao (China) achieved high performance and high equity in education opportunities.
  • Access to schooling was nearly universal in most OECD countries
  • Socio-economic status was associated with significant differences in performance in most countries and economies that participated in PISA.
  • Many students are very anxious about school work and tests and the analysis reveals this is not related to the number of school hours or the frequency of tests but with how supportive they feel their teachers and schools to be.
  • Students in schools where life satisfaction is above the national average reported a higher level of support from their teacher than students in schools where life satisfaction is below average.
  • While students who do well in financial literacy are likely to perform well in the PISA reading and mathematics assessment too, on average around 38% of the financial literacy score reflects factors that are not captured by the PISA reading and mathematics assessments, and are thus unique to financial skills.
  • Gender differences in financial literacy are mixed, unlike in mathematics and reading.