Quality of teaching and learning in science.
Date: 2018-02-27
Institutions: Joint Research Centre
Subjects: education policy, learning, report, scientific education, teaching, teaching quality

Quality of Teaching and 
Learning in Science  

 Patrícia Dinis da Costa 

 Luísa Araújo 

2018 

1 

EUR 28865 EN

This publication is  a Science for Policy report by the Joint Research Centre (JRC), the European Commission’s 
science  and  knowledge  service.  It  aims  to  provide  evidence-based  scientific  support  to  the  European 
policymaking  process.  The  scientific  output  expressed  does  not  imply  a  policy  position  of  the  European 
Commission. Neither the European Commission nor any person acting on behalf of the Commission is  
responsible for the use that might be made of this publication. 

Contact information  
Name: Patricia Dinis Mota da Costa 
Address: TP 582 – Via E .Fermi 2749 – I-21027 – Ispra (Va) - Italy 
Email: patricia.costa@ec.europa.eu  
Tel.: +39 0332 78 9173 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC109064 

EUR 28865 EN 

PDF 

ISBN 978-92-79-76294-9 

ISSN 1831-9424 

doi:10.2760/860512

Luxembourg: Publications Office of the European Union, 2018 

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How  to  cite  this  report:  Costa,  P.  &  Araújo,  L., Quality  of  Teaching  and  Learning  in Science, EUR 28865 
EN,  Publications  Office  of 
ISBN  978-92-79-76294-9, 
doi:10.2760/860512, JRC109064. 

the  European  Union,  Luxembourg,  2018, 

All images © European Union 2018, except: [page 1], 2018. Source: [© Thinkstock / iStock] 

Title: Quality of Teaching and Learning in Science 

Abstract 
This  report  describes  different  teaching  practices  and  the  learning  environment  in  the  science  classroom  and 
their relation with students’ achievement in European countries. Using PISA 2015 data, the results strengthen 
the evidence base and can inform policy initiatives that focus on high-quality teaching. 

Contents 

Executive summary ................................................................................... 5 

1. Introduction and background .................................................................. 7

2. The PISA 2015 assessment framework ..................................................... 9

3. Literature review .................................................................................. 10

4. Purpose of the report ............................................................................ 15

5. Data source ......................................................................................... 16

6.Variables measuring teaching practices and learning environment in science 18 

7. Teaching practices and achievement: variations across EU MS  .................. 22

7.1. Teaching practices in science ............................................................... 22 

7.2. Disciplinary climate in science classes .................................................... 28 

7.3. Teacher support in science classes ........................................................ 29 

7.4. Ability grouping within classes  ............................................................. 31 

8. The relationship between teaching practices, learning environment and

students’ achievement ........................................................................... 34 

8.1. Research question ............................................................................. 34 

8.2. Methodology .................................................................................... 34 

8.3. Results ........................................................................................... 35 

Conclusions and discussion ........................................................................ 42 

References .............................................................................................. 45 

List of abbreviations and definitions............................................................50 

List of boxes............................................................................................51 

List of figures...........................................................................................52 

List of tables............................................................................................53 

Annexes .................................................................................................. 54 

3 

Note 

This  technical  brief  is  part  of  the  CRELL  IX  administrative  agreement  between  DG 
Education and Culture (EAC) and DG Joint Research Centre (JRC). In particular, it refers 
to point 2.1 – Technical Report No 1 of the Technical Annex that accompanies the CRELL 
IX agreement.  

4 

Executive summary 

Quality teaching and learning is linked to the structural and process 
characteristics  of  educational  systems.  Importantly,  the  role  of  education 
policies, of schools and of teachers in promoting high student performance 
is  increasingly  recognised  (IEA,  2016;  Hanushek  &  Woessmann,  2014). 
International  large-scale  surveys  (ILSA)  such  as  the  Programme  for 
International  Student  Assessment  (PISA)  allow  us  to  envisage  what  is 
amenable to change beyond what is determined by culture and to consider 
reforms  that  improve  learning  conditions  (Hanushek  &  Woessmann, 
2014). 

This report focuses on identifying the variation in different teaching 
practices  in  the  science  classroom  and  their  relation  with  students’ 
achievement.  Using  PISA  data  collected  in  the  student  and  school 
questionnaires, the report offers an overview of the variations in teaching 
practices across European Union (EU) Member States (MS) and how they 
relate  to  students’  achievement  in  science.  For  this  purpose,  we  present 
univariate  statistics  for  28  EU  MS1.  More  specifically,  this  report  answers 
the following research question: What is the relationship between teaching 
practices, the learning environment and students’ achievement in EU MS? 
Multilevel analyses are used for the PISA 2015 data available (considering 
both  20  EU  MS  and  16  EU  MS)  including  the  following  different  levels  of 
analysis: country, school and students/classroom characteristics. 

Teaching  practices  and  the  learning  environment 

in  science 
encompass inquiry-based teaching, teacher-directed instruction, perceived 
feedback,  adaptive  instruction,  teacher  support,  disciplinary  climate  and 
ability  grouping.  With  respect  to  inquiry-based  science  teaching,  the 
results  in  this  report  show  that  this  pedagogical  approach  can  make  a 
positive  difference  to  students’  achievement  when  a  more  basic  level  of 
scientific inquiry is considered. This is in contrast to previous findings that 
considered  a  composite  measure  of  inquiry  in  the  science  classroom 
(OECD,  2016a).  Thus,  our  analyses  capture  inquiry-based  teaching 
instruction and its relation with students’ achievement in a novel way and 
suggest  that  explaining  how  a  science  idea  can  be  applied  to  different 
phenomena is an effective teaching practice. 

The analyses provided in this report contribute to our understanding 
of  the  differences  and  similarities  among  countries  and  provide  evidence 
regarding teaching effectiveness, giving an overview of what works well in 
the  science  classroom  in  EU  MS.  This  information  strengthens  the 
evidence base and can be used at the EU level to share knowledge about 
good  practices  and  to  inform  policy  initiatives  that  focus  on  high-quality 
teaching  (European  Commission,  2016).  Specific  actions  in  this  area  are 
intended  to  help  raise  the  skills  levels  of  pupils  and  the  workforce  by 

1 For some analyses, data are not available for all 28 EU participating countries in PISA 2015, and 
results are presented for fewer MSs. 

5 

 
 
                                           
improving  the  effectiveness  of  education  and  training  systems  (European 
Commission, 2015).  

Main findings 

The  main  findings  with  clear  policy  relevance  from  this  report  are 

the following: 

1) Univariate analysis 

•  Teacher-directed  instruction  is  more  frequently  associated  with 

higher achievement in science. 

•  In the majority of EU MS, there is a positive relationship between a 
higher  frequency  of  inquiry,  as  measured  by  the  teacher’s 
explanation  of  how  science  ideas  can  be  applied,  and  students’ 
achievement in science. 

•  In  all  28  EU  MS,  a  better  disciplinary  climate  is  associated  with 

higher achievement in science. 

2) Multivariate analysis 

•  Teacher-directed  instruction  is  positively  associated  with  students’ 

performance in science in 16 EU MS.   

•  Inquiry  instruction,  as  measured  by  the  variable  “the  teacher 
explains how an idea can be applied”, favours students’ achievement 
in science in 14 EU MS. The exceptions are Bulgaria and Estonia.  
•  Teachers’ flexibility in adapting the lessons to students with different 
is  positively 

knowledge  and  abilities 
associated with students’ achievement in science in 16 EU MS. 

instruction) 

(adaptive 

•  A  better  disciplinary  climate  is  associated  with  better  student 

performance in science in all EU MS, except in France.  

•  In  general,  ability  grouping  within  classes  does  not  influence 

students’ achievement in science.  

•  Accountability,  namely 

the  existence  of  external  exams 
(assessments used to monitor the quality of education) in EU MS, is 
positively associated with students’ performance in science. 

6 

 
 
 
 
 
1. Introduction and background 

The  Programme 

for  International  Student  Assessment  (PISA) 
captures  the  learning  environment  in  science  classrooms  by  asking 
students  and  school  principals  questions  about  the  frequency  of  specific 
school  science  activities  and  related  conditions  for  learning.  In  terms  of 
teaching practices and the learning environment in science, the questions 
focus  on  teacher-directed  instruction,  inquiry-based  instruction,  teacher 
support and feedback, and disciplinary climate. 

informative 

lessons  that 

Teacher-directed  and  inquiry-based  instruction  refer  to  instructional 
practices  that  are  specific  to  the  way  teachers  teach  science.  The  first 
encompasses  well-structured  and 
include 
teachers’  explanations  of  concepts,  classroom  debates  and  students’ 
questions (OECD, 2016c). The students have a predominantly passive role 
in  the  acquisition  of  this  knowledge.  In  contrast,  inquiry-based  teaching 
refers  to  science  activities  that  lead  students  to  study  the  natural  world 
and to explain scientific ideas by engaging in experimentation and hands-
on activities (OECD, 2016c). Although the students have a predominantly 
active role during these activities, they are guided by their teachers, who 
ask  leading  questions  and  model  the  thought  processes  involved  in 
science inquiry (Hanauer et al., 2009; OECD, 2016c). Thus, as defined in 
PISA, during inquiry-based teaching students are actively doing laboratory 
work  and  carrying  out  experiments.  Nonetheless,  inquiry-based  teaching 
can  also  include  teacher-directed  aspects,  such  as  asking  students  to 
make  specific  observations  and  reach  predefined  conclusions  and/or 
requesting  predictions  and  explanations  of  phenomena  (OECD,  2016c; 
Wenning,  2007).  As  Hanauer  et  al.  (2009,  p.  21)  put  it,  “The  term 
scientific  inquiry  as  manifest  in  different  educational  settings  covers  a 
wide  range  of  diverse  activities.  The  differences  in  types  of  scientific 
inquiry  can  be  organized  along  a  continuum  according  to  the  degree  of 
teacher  control  and  intellectual  sophistication  involved  in  each  type  of 
inquiry.” 

The  more  general  conditions  for  learning  are  not  specific  to  a  given 
school  subject  or  discipline  and  are  regarded,  in  the  school  effectiveness 
literature, as general teaching effectiveness-enhancing factors (Kyriakides 
et  al.,  2014).  These  general  effectiveness  learning  conditions  should  be 
considered  in  conjunction  with  specific  teaching  practices  when  trying  to 
understand  educational  outcomes  and  related  predictive  factors  (OECD, 
2015).  These  include  teacher  support  and  feedback,  which  reflect  the 
notions  of  differentiation  and  reinforcement  in  teaching,  and  disciplinary 
climate  and  its  importance  in  understanding  learning  outcomes.  For 
example, 
that  undisciplined,  disruptive  student 
behaviours prevent learning from taking place (OECD, 2014). 

teachers  believe 

Previous  studies  that  use  data 

large-scale 
assessments (ILSA), namely CRELL’s teaching practices report (Isac et al., 
2015),  pinpoint  the  positive  relation  between  teachers’  use  of  cognitive 

international 

from 

7 

 
activation teaching strategies and students’ achievement. Similarly, but in 
relation to innovative practices such as using digital supports for learning, 
research with TIMSS data suggests that using computers to look up ideas 
improves students’ achievement (Falck et al., 2015). These are examples 
of  different  dimensions  of  teaching  quality  that  have  recently  been 
investigated. 

This  report,  on  the  quality  of  teaching  and  learning,  builds  on  this 
knowledge  base  by  considering  teaching  practices  that  are  specific  to 
science teaching, namely teacher-directed teaching and inquiry practices. 
Specifically,  we  used  PISA  2015  data  to  examine  the  reported  use  of 
different  science  teaching  practices  and  their  relationship  with  students’ 
achievement  in  science  in  participating  European  Union  (EU)  Member 
States (MS). To understand how they relate to students’ achievement, we 
include  other  teaching  effectiveness  dimensions,  such  as  teacher  support 
and  disciplinary  climate.  Moreover,  we  take  into  account  student, 
classroom  and  school  input  factors  (e.g.  gender,  socio-economic  status, 
class  size  and  ability  grouping)  to  control  for  student  background  and 
structural system-level educational factors that may be related to science 
learning outcomes. 

Given  that  in  PISA  2015  the  main  domain  assessed  was  science,  these 
analyses provide insights about what works well in the science classroom 
in EU MS. This information strengthens the evidence base and can be used 
at  the  EU  level  to  share  knowledge  about  good  practices  and  to  inform 
teaching  (European 
policy 
Commission, 2016). Specific actions in this area are intended to help raise 
the skills levels of pupils and the workforce by improving the effectiveness 
of education and training systems (European Commission, 2015). 

focus  on  high-quality 

initiatives 

that 

8 

 
 
2. The PISA 2015 assessment framework 

Science literacy in PISA is defined as: 

the  ability  to  engage  with  science-related  issues,  and  with  the 
ideas  of  science,  as  a  reflective  citizen.  A  scientifically  literate 
person  is  willing  to  engage  in  reasoned  discourse  about  science 
and  technology,  which  requires  the  competencies  to  explain 
phenomena  scientifically,  evaluate  and  design  scientific  inquiry, 
and  interpret  data  and  evidence  scientifically  (OECD,  2016a,  p. 
13). 

The  PISA  2015  assessment  framework  is  conceptualised  to  capture 
how  science  teachers  teach  science  and  how  they  use  both  teacher-
directed  instruction  and  inquiry-based  practices  (OECD,  2016a).  The  first 
relates  to  activities  that  are  teacher  centred  or  teacher  initiated.  For 
example,  the  teacher  takes  the  lead  and  is  the  one  who  conducts 
experiments  and  asks  questions.  Accordingly,  teacher-directed  science 
instruction  refers  to  the  delivery  of  “clear  and  informative  lessons  on  a 
topic,  which  usually  includes  teacher’s  explanations,  classroom  debates 
and students’ questions” (OECD, 2016c, p. 63). Inquiry-based instruction 
is  defined  in  the  PISA  assessment  framework  as  the  type  of  instruction 
that calls for real-life applications. The OECD (2016c) defines it as follows: 
“In  science  education,  inquiry-based  instruction  is  about  engaging 
students  in  experimentation  and  hands-on  activities,  and  also  about 
challenging  students  and  encouraging  them  to  develop  a  conceptual 
understanding of scientific ideas” (OECD, 2016c, p. 69). 

Nonetheless,  the  PISA  definition  of  inquiry  also  refers  to  having 
students  develop  a  conceptual  understanding  of  scientific 
ideas. 
Accordingly,  those  activities  that  relate  to  real-life  applications  are  also 
considered  inquiry-based  instruction,  even  if  they  are  teacher  directed or 
teacher  initiated.  Specifically,  whereas  in  PISA  “the  teacher  explains 
scientific  ideas”  is  considered  teacher-directed  science  instruction  (OECD, 
2016c, p. 63), “the teacher explains how a science idea can be applied to 
a  number  of  different  phenomena”  is  an  inquiry-based  practice  (OECD, 
2016c, p. 71). 

9 

 
3. Literature review 

Quality  in  teaching  and  learning  are  linked  to  structural  and  process 
features  of  educational  systems.  The  role  of  education  policies,  schools 
and  teachers  in  promoting  high  student  performance  is  increasingly 
recognised  (Hanushek  &  Woessmann,  2014;  IEA,  2016).  ILSA  such  as 
PISA call attention to similarities and differences across different countries 
and highlight  trends over time: “While some of those differences may be 
attributable  to  culture,  the  fact  that  the  relationship  has  changed 
significantly in some countries suggests that policy and practice can make 
a  difference”  (OECD,  2013,  p.  2).  Furthermore,  in  terms  of  comparisons 
across  countries,  ILSA  allow  us  to  envisage  what  is  amenable  to  change 
beyond  what  is  determined  by  culture  and  to  consider  reforms  that 
improve learning conditions (Hanushek & Woessmann, 2014). In terms of 
science teaching in PISA, evidence about what constitutes quality teaching 
is  limited  to  the  finding  that,  on  average  across  OECD  countries,  using 
teacher-direct  instruction  more  frequently  is  associated  with  higher 
science  achievement,  after  controlling  for  the  socio-economic  status  of 
students  and  schools  (OECD,  2016c,  p.  65).  Conversely,  “greater 
exposure  to  inquiry-based  teaching  is  negatively  associated  with  science 
performance in 56 countries and economies” (OECD, 2016, p. 71). 

The  PISA  definition  of  inquiry  in  science  teaching  is  about  the 
students  initiating  science  activities,  such  as  designing  their  own 
experiments and raising their own questions for investigation. This reflects 
the  notion  that  “students  should  engage  in  science  using  the  same 
methods  and  approaches  similar  to  those  that  scientists  use  to  carry  out 
scientific  investigations”  (Gee  &  Wong,  2012,  p.  303).  However, 
researchers  have  also  found  that  student-led  investigations  may  lack  the 
kind of teacher-led  instructional guidance that  promotes learning  (Flick & 
Lederman,  2004;  Jiang  &  McComas,  2015).  As  Kirshner  et  al.  (2006) 
suggest,  if  students  are  left  on  their  own  to  solve  problems  and  to 
independently  select  and  carry  out  investigations,  their  efforts  may  be 
counterproductive. That is, their learning of scientific facts and ideas may 
be  inhibited  rather  than  enhanced  because  such  inquiry-based  processes 
place cognitive demands on students that they cannot handle. 

Science inquiry in the literature about the teaching of science appears 
as “an umbrella term for a range of educational and professional activities 
within  the  sciences”  (Hanauer  et  al.,  2009,  p.  15).  Hodson  (1996)  notes 
that  it  is  difficult  to  define  science  inquiry,  because  scientific  inquiry  is 
governed by a specific knowledge structure that involves the appropriation 
of  specific  concepts.  On  the  one  hand,  there  is  consensus  that  inquiry-
based  science  teaching  is  characterised  by  activities  that  are  student 
initiated, for example: 

•  authentic  and  problem-based  learning  activities  where  there  may 

not be a correct answer; 

10 

 
 
•  a  certain  amount  of  experimental  procedures,  experiments  and 

hands-on activities, including searching for information; 

•  self-regulated  learning  sequences  in  which  student  autonomy  is 

emphasised; 

•  discursive  argumentation  and  communication  with  peers  (“talking 

science”) (Jorde et al., 2012).  

On  the  other  hand,  inquiry-based  teaching  may  involve  some 
teacher-led  activities.  Furthermore,  meta-analyses  that  have  measured 
the impact of both teacher-initiated and student-initiated science activities 
have found larger mean effect sizes for teacher-initiated activities than for 
those with student-led conditions (Furtak et al., 2012). 

A  clear  dichotomy  in  instructional  modes  that  are  either  teacher  led 
or student initiated may nonetheless offer too narrow a view of what really 
happens  in  the  science  classroom  and  its  relation  with  students’ 
achievement.  Indeed,  research  highlights  this  and  offers  empirical 
evidence  that  different  levels  of  inquiry  need  to  be  considered  to 
understand  what  quality  of  science  teaching  and  learning  looks  like 
(Furtak  et  al.,  2012;  Minner  et  al.,  2010).  One  approach  is  to  consider  a 
continuum  in  which  at  one  end  the  teacher  does  all  the  activities  and  at 
the  other  the  students  do  all  the  activities.  For  instance,  Jiang  and 
McComas  (2015)  examined  the  science  achievement  of  15-year-olds  in 
PISA 2006 for all participating countries and found that the highest score 
was  achieved  at  level  2  of  inquiry-based  teaching.  This  level  reflects  a 
balance  between 
inquiry-based 
instruction  in  that  students  conduct  activities  and  draw  conclusions  from 
data,  but  teachers  design  investigations  and  ask  questions  (Jiang  & 
McComas, 2015). 

instruction  and 

teacher-directed 

A similar study with data for eight countries that participated in PISA 
2006  reported  that  a  higher  frequency  of  investigations  in  science 
teaching  and  learning  tend,  on  average,  to  be  associated  with  lower 
achievement. However, in the same countries, students who reported high 
levels of participation in application models in  science  had higher  science 
scores.  As  the  authors  conclude,  hands-on  activities  related  to  the  latter 
can  be  positively  related  to  science  achievement,  but  student-initiated 
investigations whereby students design their own experiments, choose an 
experimental  design  and  test  their  own  hypotheses  are  negatively 
associated with achievement in science (Valente et al., 2011). 

As  previously  discussed,  results  from  PISA  2015  (OECD,  2016c) 
indicate  that  an  emphasis  on  inquiry-based  teaching,  as  measured  by  an 
index  composed  of  several  variables,  is  negatively  associated  with 
students’  achievement.  In  contrast,  the  index  capturing  teacher-directed 
instruction has a positive relation with achievement, and this is in line with 
research  findings.  Direct  instruction  models  that  are  teacher  centred 
encompass  well-structured  lessons  and  have  been  shown  to  have  a 
positive  impact  on  students’  achievement  (Mayer,  2004).  Nonetheless, 

11 

 
when  considering  only  one  of  the  variables  in  the  index  of  inquiry-based 
teaching,  “the  teacher  explains  how  a  science  idea  can  be  applied  to  a 
number  of  different  phenomena”,  the  OECD  (2016c)  finds  that  it  is 
associated  with  a  12-point  increase  in  students’  scores,  after  controlling 
for socio-economic status. 

The conceptual framework of the Teaching and Learning International 
Survey  (TALIS)  also  considers  contrasting  teaching  beliefs  and  related 
instructional  approaches,  namely  direct  instruction  and  a  constructivist 
view  of  teaching  and  learning.  The  assessment  framework,  which  guided 
data  collection  in  the  first  TALIS  cycle  in  2008  and  the  following  one  in 
2013,  addresses  the  differences  between  these  two  approaches  as 
follows: 

The  direct  transmission  view  of  student  learning  implies  that  a 
teacher’s  role  is  to  communicate  knowledge  in  a  clear  and 
structured  way,  to  explain  correct  solutions,  to  give  students 
clear  and  resolvable  problems,  and  to  ensure  calm  and 
concentration in the classroom. In contrast, a constructivist view 
focuses  on  students  not  as  passive  recipients  but  as  active 
participants  in  the  process  of  acquiring  knowledge.  Teachers 
holding this view emphasize facilitating student inquiry, prefer to 
give  students  the  chance  to  develop  solutions  to  problems  on 
their  own,  and  allow  students  to  play  an  active  role  in 
instructional activities (OECD, 2009, p. 92). 

This  description  clearly  shows  that  a  direct  transmission  view  of 
teaching  equates  to  teacher-directed  instruction  and  that  a  constructivist 
view  equates  to  inquiry-based  teaching,  as  defined  in  the  PISA  2015 
assessment  framework  (OECD,  2016a).  Findings  from  TALIS  show  that 
teachers  of  reading,  writing  and  literature  at  the  lower  secondary  level 
(International  Standard  Classification  of  Education  (ISCED)  2)  reported 
using  more  structured  teaching  practices  than  student-oriented  teaching 
practices  or  enhanced  teaching  activities.  That  is,  they  mainly  structured 
their practices around stating learning goals, summarising former lessons, 
reviewing  homework,  checking  exercise  books  and  checking  for  student 
understanding.  Enhanced  teaching  practices,  such  as  assigning  student 
debates,  essays  and  projects,  took  place  to  a  greater  extent  in  reading 
lessons  than  in  the  science  and  mathematics  lessons.  With  TALIS  one 
cannot link  this information with students’ achievement, as only teachers 
and school principals are sampled, but research suggests that in language 
education teaching practices that combine student-initiated activities with 
teacher-directed  practices  (such  as  delivering  content  in  a  lecture  style) 
seem  to  be  the  most  effective  for  learning  (Creemers  et  al.,  2013). 
Evidence  even  suggests  that  in  some  cases  lecture-style  teaching 
produces  better 
learning  outcomes.  For  example,  Schwerdt  & 
Wuppermann (2011) found a positive relation between more lecture-style 
presentation  in  the  language  classroom,  as  opposed  to  students’  working 
on projects, and students’ achievement. 

12 

To  sum  up,  research  based  on  data  from  international  large-scale 
assessments does  not offer a clear picture of which teaching  practices or 
combination  of  practices  are  positively  associated  with  student 
performance. Moreover, there may be differences in frequency of use and 
in  the  type  of  associations  depending  on  the  school  subject.  In  fact,  the 
effectiveness  of  different  pedagogical  practices  may  vary  depending  on 
the activities and content studied. In addition, one must bear in mind that, 
although teaching practices are process variables designed to capture the 
teaching  and  learning  process,  the  PISA  survey  does  not  include 
classroom  observations.  These  would  be  the  means  to  capture  the  type 
and frequency of both generic and domain-specific teaching practices. 

As captured in the last PISA 2015 assessment, the only inquiry-based 
science  teaching  practice  that  is  positively  associated  with  achievement 
pertains to a basic level of inquiry that is teacher initiated: “The teacher 
explains  how  a  science  idea  can  be  applied  to  a  number  of 
different  phenomena.”  This  variable  captures  the  delivery  of  science 
instruction in a particular way; the teacher delivers content knowledge by 
explaining  how  a  science  idea  can  be  applied  to  different  phenomena. 
Because  inquiry-based  science  teaching  presupposes  real-life  applications 
of  knowledge  (OECD,  2016a)  this  variable  reflects  inquiry,  even  if  such 
inquiry  is  teacher  initiated.  In  contrast,  the  variable  “the  teacher 
explains  scientific  ideas”,  which  refers  in  the  PISA  reports  to  teacher-
directed  instruction,  says  nothing  about  the  way  the  teacher  explains 
scientific  ideas.  As  previously  discussed,  the  index  that  includes  this  and 
other  inquiry-based  teaching  practices  shows  a  negative  association  with 
students’  achievement  (OECD,  2016c).  Other  analyses  of  PISA  data, 
namely  that  of  the  2012  round,  indicates  that  in  the  mathematics 
classroom asking students how they solved a problem, to apply what they 
have learned to different contexts, and to reflect on a problem and giving 
them  problems  that  require  thinking  for  an  extended  time  is  related  to 
higher  scores  (Costa  &  Araújo,  2015).  The  OECD  (2016c)  reports  similar 
results  for  this  PISA  2012  round  focusing  on  mathematics:  higher 
achievement  for  students  who  reported  greater  exposure  to  this  kind  of 
cognitive  activation  in  PISA  2012.  In  contrast,  students  who  perform  at 
lower proficiency levels tend to report that their teachers engage more in 
teacher-directed instruction. 

the 

In  exploring 

teaching  quality  and 
relationship  between 
educational outcomes (OECD, 2014), as previously mentioned, we need to 
consider  specific  teaching  practices  in  conjunction  with  general  teaching 
effectiveness-enhancing  factors  (Kyriakides  et  al.,  2014;  OECD,  2014). 
Even  if  establishing  cause  and  effect  relationships  between  teaching 
effectiveness  factors  and  students’  achievement  remains  difficult,  it  is 
clear  that  approaches  to  teaching  and  learning  go  hand  in  hand  with 
specific  pedagogical  practices  that  can  be  influenced  by  the  surrounding 
context.  In  particular,  the  pedagogical  stance  of  the  teacher  is  important 
for  students’  achievement.  As  Hattie  (2009,  p.  2)  states,  content-specific 
knowledge  is  not  enough  to  be  a  successful  teacher.  It  is  equally 

13 

important  to  establish  “clear  learning  intentions  and  success  criteria,  a 
classroom  environment  that  tolerates  errors,  and  tasks  for  students  that 
are challenging but commensurate with their abilities”. 

Thus,  regarding  other  factors  related  to  learning  environment 
conditions,  the  literature  shows  that  attending  schools  with  a  better 
disciplinary  climate  positively  affects  students’  scores  (Costa  &  Araújo, 
2015;  Martin  &  Mullis,  2013).  The  same  is  true  for  adaptive  instruction 
(Gomendio, 2017). Teacher support is relevant for both teaching practices 
and students’ achievement (Isac et al. , 2015). Several studies also reveal 
that  student  performance  is  higher  when  students  receive  feedback  from 
teachers (Hattie & Timperley, 2007; Lipko-Speed et al., 2014). 

With  respect  to  other  factors  that  may  be  related  to  educational 
outcomes,  the  literature  suggests  that  in  addition  to  student  and  family 
background  characteristics,  we  should  consider  country  and  educational 
system-level  characteristics.  For  instance,  a  country’s  economic  wealth, 
educational  tracking  or  ability  grouping,  school  autonomy,  accountability 
and  teachers’  qualifications  have  been  shown  to  relate  to  student 
performance. 

increases 

them  are  not  well  understood 

First,  economic  wealth  can  contribute 

to  explaining  country 
disparities.  The  literature  shows  that  more  wealth  is  associated  with 
higher educational  achievement (Barber, 2006; Hanushek & Woessmann, 
2014; OECD, 2010). However, similar wealth across different countries is 
also  associated  with  educational  disparities,  and  the  driving  mechanisms 
(Vettenranta  & 
that  underlie 
Harju-Luukkainen,  2013).  Second,  a  recurrent  finding  is  that  educational 
tracking 
from 
from  more  advantaged 
disadvantaged  backgrounds  and 
backgrounds  (Hanushek,  2011;  Hanushek  &  Woessmann,  2014).  Third, 
accountability coupled with school autonomy, on the other hand, seems to 
promote  more  equitable  education  outcomes  (Hanushek  &  Woessman, 
2014).  That  is,  systems  in  which  students  take  national  exams  tend  to 
perform  better  and  when  this  accountability  feature  is  present  alongside 
school  autonomy  (e.g.  where  schools  make  decisions  regarding  choice  of 
textbooks  or  the  hiring  of  teachers)  performance  is  also  better  (Hippe  et 
al., 2016). 

the  achievement  gap  between  students 

those 

Findings  are  less  clear  regarding  teachers’  qualifications,  mainly  in 
terms  of  whether  they  are  fully  certified  to  teach  a  given  subject,  but 
studies  suggest  that  qualification  makes  more  of  a  difference  for  student 
performance  than  experience.  This  is  most  probably  the  case  because 
teachers become more effective in their initial years, but beyond that their 
experience  does  not  have  a  significant  impact  on  students’  achievement 
(Schwerdt  &  Wuppermann,  2011).  A  recent  report  with  cross-country 
comparisons  corroborates  this  evidence  and  documents  that  in  recent 
years  many  countries  have  put  an  emphasis  on  upgrading  teachers’ 
qualifications  (IEA,  2015),  but  that  “there  is  no  simple,  universal 

14 

relationship  between  teacher  experience  and  student  achievement”  (IEA, 
2015, p. 3).  

15 

 
4. Purpose of the report

This  report  uses  data  from  PISA  2015  to  identify  the  variation  in 
different  teaching  practices  in  the  science  classroom  and  their  relation 
with  students’  achievement.  Specifically,  using  information  from  the 
student  and  school  questionnaires,  the  report  gives  an  overview  of  the 
variations  in  teaching  practices  across  EU  MS  and  how  they  relate  to 
students’  achievement  in  science.  For  this  purpose,  we  use  univariate 
statistics.  In  addition,  it  explores  the  proportion  of  variance  in  students’ 
achievement  that  can  be  explained  by  the  use  of  different  teaching 
practices.  Finally,  the  report  also  aims  to  answer  the  following  research 
question:  What  is  the  relationship  between  teaching  practices,  learning 
environment and students’ achievement in EU MS? A multilevel analysis is 
used  for  the  available  PISA  2015  data,  which  includes  different  levels  of 
analysis.  These  analyses  contribute  to  our  understanding  of  the 
differences  and  similarities  among  countries  and  provide  evidence 
regarding teaching effectiveness. 

16 

5. Data source 

The  PISA  was  launched  in  2000  by  the  Organisation  for  Economic 
Co-operation  and  Development  (OECD).  Since  then,  the  OECD  has  been 
running  this  ILSA  of  15-year-old  students’  skills  in  mathematics,  science 
and  reading  every  three  years.  Each  PISA  assessment  cycle  has  a  main 
domain,  which  was  mathematics  in  2012  and  science  in  2015  and  which 
will  be  reading  in  2018.  In  fact,  PISA  is  a  cognitive  test  that  measures 
knowledge  of  a  given  subject  matter  studied  in  school  and  is  in  many 
ways  different  from  content-based  tests.  Content-based  tests  are  those 
that  focus  on  testing  the  curriculum  learned  at  school  (Araújo  et  al., 
2017).  PISA  is  designed  to  measure  students’  ability  to  use  or  apply  the 
knowledge  acquired  in  school  to  solve  problems  they  might  encounter  in 
everyday  life.  As  such,  it  captures  the  ability  of  15-year-olds  to  either 
enter the work force or pursue further studies (OECD, 2009). 

In PISA, students’ test scores in mathematics, science and reading, 
are  computed  according  to  item  response  theory  (IRT)  and  standardised 
with  an  OECD  mean  of  500,  set  in  the  first  cycle  of  the  survey,  and  a 
standard deviation of 100. 

As  is  widely  recognised  by  educational  stakeholders  –  decision-
makers, teachers, students,  parents and the general public  – PISA offers 
comparative  indicators  of  students’  achievement  and  has  been  used  to 
monitor  educational  systems  worldwide.  One  of  the  central  purposes  of 
PISA 
information  about  students’ 
performance in reading, mathematics and science, thus enabling countries 
to monitor their progress in meeting key learning objectives. 

is  to  collect  and  report  trend 

Importantly,  PISA  has  been  used  to  assess  student  learning 
outcomes  since  2000  and  is  used  every  three  years  in  many  OECD  and 
non-OECD  countries  and  economies.  Forty-three  countries  participated  in 
the first assessment cycle, 41 in the second cycle in 2003, 57 in the third 
cycle  in  2006,  75  in  the  fourth  cycle  in  2009  and  65  in  the  fifth  cycle  in 
2012.  In  the  latest  PISA  assessment,  which  took  place  in  2015,  72 
countries  and  economies  participated  (OECD,  2016b).  All  28  EU  MS 
participated  in  this  last  assessment  (Austria  (AT),  Belgium  (BE),  Bulgaria 
(BG),  Croatia  (HR),  Cyprus2  (CY),  the  Czech  Republic  (CZ),  Denmark 
(DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (EL), 

2 Notes regarding Cyprus: 

“Note  by  Turkey:  The  information  in  this  document  with  reference  to  ‘Cyprus’  relates  to  the 
southern  part  of  the  Island.  There  is  no  single  authority  representing  both  Turkish  and  Greek 
Cypriot  people  on  the  Island.  Turkey  recognises  the  Turkish  Republic  of  Northern  Cyprus  (TRNC). 
Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall 
preserve its position concerning the ‘Cyprus issue’. 

Note by all the European Union Member States of the OECD and the European Union: The Republic 
of  Cyprus  is  recognised  by  all  members  of  the  United  Nations  with  the  exception  of  Turkey.  The 
information in this document relates to the area under the effective control of the Government of 
the Republic of Cyprus.” (OECD, 2016, V.1, p. 27). 

17 

 
                                           
Hungary  (HU),  Ireland  (IE),  Italy  (IT),  Latvia  (LV),  Lithuania  (LT), 
Luxembourg (LU), Malta (MT), the Netherlands (NL), Poland (PL), Portugal 
(PT),  Romania  (RO),  Slovakia  (SK),  Slovenia  (SI),  Spain  (ES),  Sweden 
(SE) and the United Kingdom (UK)). 

In  addition  to  the  results  of  students’  performance  in  science,  PISA 
also  gathers  contextual 
the  application  of 
questionnaires3 to the students and to school principals (OECD, 2016a, p. 
15).  More  specifically,  as  defined  by  the  OECD,  the  questionnaires  cover 
the following aspects: 

information 

through 

•  “Aspects  of  students’  lives,  such  as  their  attitudes  towards  learning, 
their  habits  and  life  in  and  outside  of  school,  and  their  family 
environment. 

•  Aspects  of  schools,  such  as  the  quality  of  the  schools’  human  and 
material  resources,  public  and  private  management  and  funding, 
decision-making processes, staffing practices and the school’s curricular 
emphasis and extracurricular activities offered. 

•  Context  of  instruction,  including  institutional  structures  and  types, 
class size, classroom and school climate, and reading activities in class. 

•  Aspects  of  learning,  including  students’  interest,  motivation  and 
engagement.” (OECD, 2016a). 

3  Four  additional  optional  questionnaires  were  offered  in  PISA:  a  computer  familiarity 
questionnaire,  an  educational  career  questionnaire,  a  parent  questionnaire,  and  a  teacher 
questionnaire. 

18 

 
 
                                           
6.  Variables  measuring 
environment in science 

teaching  practices  and 

learning 

PISA  2015  captures  the  learning  environment  in  science  classrooms 
by  asking  students  questions  about  the  frequency  of  specific  school 
science  activities  and  related  conditions  for  learning.  We  followed  a 
theoretical  model  based  on  the  PISA  2012  taxonomy  of  educational 
outcomes  and  predictive  factors4  (OECD,  2016a)  and  on  the  PISA  2015 
teacher  questionnaire  framework5.  Accordingly,  our  variables  focus  on 
teaching  practices/strategies,  teacher  support,  disciplinary  climate  and 
ability  grouping.  While  teaching  strategies  are  specific  types  of 
instructions  that  capture  how  teachers  teach  science,  the  others  are 
general  effectiveness-enhancing  factors  (Figure  6.1).  The  OECD  (2016c) 
groups  teaching  practices/strategies  for  science  into  four  approaches: 
teacher-directed instruction, inquiry-based instruction, perceived feedback 
and adaptive instruction. These approaches are based on questions, from 
the PISA 2015 students’ questionnaire, asking how often specific activities 
happened  in  the  science  classroom  (hence,  the  OECD  uses  information 
from the students’ questionnaire in relation to students’ perception of their 
teachers’ instructional practices across EU MS). 

Figure 6.1. Teaching practices and learning environment in science 

Teaching practices and learning environment in science 

Teaching  
practices/ 

strategies 

Teacher-directed instruction 

Inquiry - explanation of ideas that can 
be applied 

Perceived feedback 

Adaptive instruction 

Teacher support in science lessons 

Disciplinary climate 

Ability grouping within classes 

4 See https://www.oecd.org/pisa/pisaproducts/PISA-2012-technical-report-final.pdf, p. 49. 
5  See  https://www.oecd.org/pisa/pisaproducts/PISA-2015-draft-questionnaire-framework.pdf,  p. 
22. 

19 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
The  OECD  created  the  following  indices  for  teaching  practices 

(aggregated measures):  

•  teacher-directed instruction; 
•  perceived feedback; 
•  adaptive instruction; 
•  teacher support to students; and 
•  disciplinary climate. 

Higher  values  for  these  indices  indicate  that  the  activities/practices 
happened  more  frequently  in  science  lessons.  The  OECD  also  created  an 
index  named  “Enquiry-based  science  instruction”,  which  combines  nine 
statements  to  measure  the  extent  to  which  science  teachers  encourage 
students to be deep learners and the extent to which they are encouraged 
to  enquire  about  a  science  problem  using  scientific  methods,  including 
experiments. Regarding the latter, i.e. the inquiry-based instruction index, 
we  used  the  variable  measuring  the  frequency  with  which  “the  teacher 
explains  how  <school  science>  idea  can  be  applied  to  different 
phenomena”,  because  research  indicates  that  this  variable  indicates  a 
more  basic  level  of  scientific  enquiry  (Jiang  &  McComas,  2015)  and 
illustrates  real-life  applications  in  science  lessons.  Indeed,  findings  from 
the  OECD  (2016c)  reveal  that  this  variable  is  the  only  one  in  the  index 
that is positively associated with students’ achievement. Regarding ability 
grouping,  we  used  information  reported  by  the  principal  on  “Ability 
grouping within classes”. 

Detailed  information  regarding  the  variables  used  to  measure 
teaching  practices  and  the  learning  environment  in  science  is  given  in 
Tables 6.1 and 6.2. 

responses 

Our  analyses 

rely  on  student  and  principal 

to 
questionnaires.  This  approach  was  followed  because  the  link  between 
classroom teaching  practices and students’ achievement  is indirect in the 
sense  that  students  and  principals,  not  teachers,  respond  to  questions 
about  teaching  practices  in  their  schools.  Moreover,  the  science  teachers 
that responded to the questionnaire are not linked to the specific students 
they taught. The information available is relative to the students attending 
schools  where  the  principals  report  that  teachers  expose  the  students  to 
certain  learning  experiences  and  have  certain  expectations  for  their 
learning and achievement. 

20 

 
 
 
 
Table 6.1. Information on the variables used to measure teaching practices and the 
learning environment in science 

Name of the variable  Questions 

Teaching practices/strategies 

No of 
categories 

Questionnaire 

Teacher-directed 
science instruction 
(index based on items 
from question ST103) 

Inquiry instruction 
(Item ST098Q06TA) 

Perceived feedback 
(index based on items 
from question ST104) 

Adaptive instruction 
(index based on items 
from question ST107) 

How often do these things happen 
in your lessons for this <school 
science> course? 
– the teacher explains scientific ideas; 
– a whole class discussion takes place 
with the teacher; 
– the teacher discusses our questions; 
– the teacher demonstrates an idea. 
The teacher explains how a 
science idea can be applied to a 
number of different phenomena 
How often do these things happen 
in your lessons for this <school 
science> course? 
– the teacher tells me how I am 
performing in this course; 
– the teacher gives me feedback on 
my strengths in this <school science> 
subject; 
– the teacher tells me in which areas I 
can still improve; 
– the teacher tells me how I can 
improve my performance; 
– the teacher advises me on how to 
reach my learning goals. 
How often do these things happen 
in your lessons for this <school 
science> course? 
– the teacher adapts the lesson to my 
class’ needs and knowledge; 
– the teacher provides individual help 
when a student has difficulties 
understanding a topic or task; 
– the teacher changes the structure of 
the lesson on a topic that most 
students find difficult to understand. 

4 

4 

4 
4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

Student 

Student 

Student 
Student 

Student 

Student 

Student 

Student 

Student 

Student 

Student 

Student 

Student 

21 

 
 
 
 
 
 
 
Table 6.2. Information on the variables used to measure teaching practices and 

the learning environment in science (cont.) 

Name of the variable  Questions 

No of 
categories 

Questionnaire 

Teacher support 
(index based on items 
from question ST100) 

Disciplinary climate 
(index based on items 
from question ST097) 

Ability grouping  
within classes 
(Item SC042Q02TA) 

How often do these things happen 
in your <school science> lessons? 
– 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 the material; 
– the teacher gives students an 
opportunity to express their opinions. 
How often do these things happen 
in your <school science> lessons?  
– students do not listen to the 
teacher; 
– there is noise and disorder; 
– the teacher has to wait  for the 
students for a long time; 
– students cannot work well; 
– students do not start working for a 
long time after the lesson begins. 
What is your school’s policy about 
this for students in <national 
modal grade for 15-year-olds>? 
– students are grouped by ability 
within their classes. 

4 

4 

4 

4 

4 

4 

4 
4 

4 
4 

3 

Student 

Student 

Student 

Student 

Student 

Student 

Student 
Student 

Student 
Student 

School 

22 

 
 
 
 
 
 
 
7. Teaching practices and achievement: variations across EU MS  

This section offers an overview of the variations in teaching practices 
in  science  lessons  across  EU  MS,  including  the  frequency  of  different 
teaching  practices  and  their  relationship  with  students’  achievement. 
Specifically,  univariate  statistics6  are  presented,  showing  the  association 
with each teaching practice and students’ science achievement. Its aim is 
to complement the information available in the OECD PISA reports and to 
describe differences and similarities within and across EU MS7. 

7.1. Teaching practices in science 

Teacher-directed science instruction 

The  goal  of  teacher-directed  science  instruction  is  to  provide  a  well-
structured, clear and informative lesson on a topic, which usually includes 
explanations  from  the  teacher,  classroom  debates  and  questions  from 
students.  To  assess  the  extent  to  which  science  teachers’  direct  student 
learning  in  science  lessons,  a  continuous  index8  was  constructed  by  the 
OECD  based  on  the  frequency  of  four  events  that  happen  in  students’ 
science  lessons.  The  frequency  ranges  from  “never  or  almost  never”  to 
“every  lesson  or  almost  every  lesson”.  Higher  values  on  this  index  on 
science  instruction  indicate  more  frequent  use  of  these  strategies, 
according to students’ reports. 

6 Please note that this section presents only univariate/correlational analyses. 
7 The total number of EU MS may vary according to the index/variable used, as in some EU MS not 
all the variables were part of the questionnaires.  

8 More information can be found at http://www.keepeek.com/Digital-Asset-
Management/oecd/education/pisa-2015-results-volume-ii_9789264267510-en#.WR7lVuuGOos  

23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Figure 7.1. Teacher-directed instruction and science performance9 

Results based on students’ reports 

NL 
FI 
MT 
RO 
IT 
EL 
DE 
ES 
CY 
PL 
LU 
UK 
EU 27 
FR 
AT 
PT 
HR 
SE 
HU 
CZ 
IE 
DK 
LV 
BG 
BE 
EE 
LT 
SK 

0 

5 

10 

15 

20 

25 

Score-point difference associated with one-unit increase in 
the index of teacher-directed science instruction 

Source: Own analysis based on PISA 2015 data. See also Table 7.1, Annex A.  

Figure 7.1 shows the score-point difference in science associated with 

a one-unit increase in the index of teacher-directed science instruction. 

The graph reveals that in all EU MS using teacher-directed instruction 
more  frequently  is  associated  with  higher  science  achievement.  The 
performance differences can amount to as much as 20 score-points, as in 
the  Netherlands,  or  between  10  and  20  score-points,  as  in  Croatia, 
Portugal,  Austria,  France,  the  United  Kingdom,  Luxembourg,  Poland, 
Cyprus,  Spain,  Germany,  Greece,  Italy,  Romania,  Malta  and  Finland,  and 
in  the  other  12  EU  MS  the  performance  differences  are  smaller  than  10 
score-points. 

9  All  the  score-point  differences  are  statistically  significant.  Countries  are  ranked  in  descending 
order of the score-point difference associated with the index of teacher-directed science instruction. 
For Slovenia no data are available for this index. 

24 

 
 
 
 
                                           
Inquiry instruction 

Inquiry instruction refers to students’ engagement in experimentation 
and hands-on activities. We use the variable reported by the students on 
the frequency with which the “teacher explains how a science idea can be 
applied  to  a  number  of  different  phenomena”.  The  variable  has  originally 
four  answer  categories  (“in  all  lessons”;  “in  most  lessons”;  “in  some 
lessons”;  “never  or  hardly  ever”).  Following  the  OECD  approach  we 
dichotomise  the  variable  grouping  “in  all  lessons”  with  “in  some  lessons” 
and “in some lessons” with “never or hardly ever”.  

Figure 7.2. Inquiry instruction and science performance 10 
Results based on students’ reports 
Measured by “teacher explains how a science idea can be applied” –  
This happen in “most” or “all” science lessons 

FR 
NL 
MT 
BE 
LU 
CY 
LV 
RO 
FI 
ES 
DK 
PT 
LT 
UK 
AT 
DE 
EU 28 
HR 
HU 
SE 
IT 
EL 
CZ 
EE 
IE 
SK 
BG 
PL 
SI 

-10 

0 

10 

20 

30 

40 

50 

Score-point difference in science  

Source: Own analysis based on PISA 2015 data. See also Table 7.2, Annex A.  

Figure  7.2  reveals  that  in  almost  all  the  EU  MS  there  is  a  positive 
relationship  between  a  higher  frequency  of  use  on  inquiry,  as  measured 
by  the  teacher’s  explanation  of  how  science  ideas  can  be  applied,  and 

10 Countries are ranked in descending order of the score-point difference in science. 

25 

 
 
 
                                           
students’ science achievement. The differences reach more than 30 points 
in France, the Netherlands, Malta and Belgium. The exception is Slovenia 
where  a  higher  frequency  of  inquiry  instruction  in  the  classroom  has  a 
negative association with students’ achievement in science.  

Perceived feedback  

Perceived  feedback  refers  to  information  that  students,  after  being 
assessed, get from their peers, teachers and parents. This information can 
modify  or  reinforce  student  behaviours  and  can  take  several  forms,  such 
as  praise,  surprise,  approval  or  punishment.  However,  not  all  types  of 
feedback are equally effective. 
The  index  of  perceived  feedback11  was  constructed  by  the  OECD  from 
students’  reports  of  their  perception  of  how  often  their  science  teachers 
provide them with regular feedback (categories: “never or almost never”; 
“some  lessons”;  “many  lessons”;  “every  lesson  or  almost  every  lesson”) 
and inform them or give individual information in their science lessons. 

Higher  values  of  this  index  indicate  that  the  activities  happened  more 

frequently in science lessons. 

Figure  7.3  shows  that  all  EU  MS  present  a  negative  relationship  of 
perceived feedback with students’ performance in science. The MS with a 
higher  performance  difference  are  Hungary,  Italy,  Luxembourg,  the 
Netherlands  and  Belgium.  In  Malta  and  Romania  the  difference  is  not 
statistically significant. 

These results are in line with those from the OECD (2016c), where a 
similar  analysis  was  made.  In  addition,  other  authors  also  found  that 
giving  feedback  can  either  positively  or  negatively  influence  learning  and 
achievement (e.g. Hattie & Timperley, 2007). The negative results may be 
related to the notion that teachers adjust their feedback to learning goals 
(Hattie, 2009) and that learners who need more feedback to improve their 
learning may be lower achieving students.  

More 

11 
found 
Management/oecd/education/pisa-2015-results-volume-ii_9789264267510-en#.WR7lVuuGOos 

http://www.keepeek.com/Digital-Asset-

information 

can 

be 

at 

26 

 
 
 
 
 
 
 
 
 
 
                                           
 
Figure 7.3. Perceived feedback and science performance12 

Results based on students’ reports 

HU 
IT 
LU 
NL 
BE 
HR 
AT 
SK 
EL 
DE 
PT 
ES 
EE 
FI 
EU 27 
FR 
LT 
BG 
SE 
LV 
DK 
IE 
PL 
CY 
CZ 
UK 
MT 
RO 

-25 

-20 

-15 

-10 

-5 

0 

Score-point difference associated with one-unit increase in 
the index of perceived feedback  

Source: Own analysis based on PISA 2015 data. See also Table7.3, Annex A.  

Adaptive instruction 

Adaptive  instruction  refers  to  teachers’  flexibility  in  adapting  the 
lessons  for  students  with  different  knowledge  and  abilities,  including  for 
individual  students  who  are  struggling  with  a  topic  or  task.  A  continuous 
index  on  adaptive  instruction13  that  indicates  how  often  science  teachers 
in  the  school  tailor  lessons  to  the  students  in  their  classes  (including  to 
individual students who are struggling with a task) was constructed by the 
OECD.  It  is  based  on  the  frequency  (“never  or  almost  never”;  “some 
lessons”;  “many  lessons”;  “every  lesson  or  almost  every  lesson”)  with 
which some events happen in students’ science lessons.  

12  The  score-point  differences  that  are  statistically  significant  are  presented  in  a  darker  blue. 
Countries are ranked in ascending order of the score-point difference associated with the index 
of perceived feedback. For Slovenia no data are available for this index. 

More 

13 
found 
Management/oecd/education/pisa-2015-results-volume-ii_9789264267510-en#.WR7lVuuGOos. 

http://www.keepeek.com/Digital-Asset-

information 

can 

be 

at 

27 

 
 
                                           
Higher values in this index indicate that these practices happened more 

frequently in science lessons. 

Figure 7.4. Adaptive instruction and science performance 14 

Results based on students’ reports 

NL 
DK 
FI 
ES 
DE 
UK 
CY 
BG 
CZ 
PT 
LV 
EU 25 
PL 
IE 
EE 
LT 
EL 
HU 
SK 
HR 
SI 
IT 
LU 
FR 
AT 
BE 

-5 

0 

5 

10 

15 

20 

25 

Score-point difference associated with one-unit increase in 
the index of adaptative instruction 

Source: Own analysis based on PISA 2015 data. See also Table 7.4, Annex A.  

Figure 7.4 shows that in 18 EU MS a more frequent use of adaptive 
instruction  is  positively  and  significantly  associated  with  students’ 
performance in science. The performance differences ranged from 3 to 23 
score-points,  with  the  highest  values  in  the  Netherlands,  Denmark  and 
Finland.  In  Croatia,  Slovenia,  Italy,  Luxembourg,  France,  Austria  and 
Belgium the score-point differences are of a very small magnitude and are 
not  statistically  significant.  To  sum  up,  the  results  show  that  European 
students do best when teachers provide feedback. 

14  The  score-point  differences  that  are  statistically  significant  are  presented  in  a  darker  blue. 
Countries are ranked in descending order of the score-point difference associated with the index of 
perceived feedback. For Malta, Romania and Sweden no data are available for this index. 

28 

 
 
 
                                           
These  results  are  in  line  with  OECD  findings,  which  show  that 
impact  on  student  outcomes 

instruction  has  a  positive 

adaptive 
(Gomendio, 2017). 

7.2. Disciplinary climate in science classes 

Disciplinary  climate  refers  to  the  students’  behaviour  and  attitudes 
within the classroom in terms of interruptions that occur in science lessons 
(e.g. noise, disorder, students not working well, students taking too much 
time to quieten down). 

Figure 7.5. Disciplinary climate and science performance 15 
Results based on students’ reports 

MT 
CY 
HR 
EL 
SK 
LU 
RO 
UK 
DE 
CZ 
HU 
FR 
EU 28 
LT 
BG 
IT 
NL 
SI 
PT 
AT 
SE 
BE 
EE 
DK 
FI 
ES 
LV 
PL 
IE 

0 

5 

10 

15 

20 

25 

30 

35 

Score-point difference associated with one-unit increase in 
the index of disciplinary climate 

Source: Own analysis based on PISA 2015 data. See also Table 7.5, Annex A.  

15  The  score-point  differences  that  are  statistically  significant  are  presented  in  a  darker  blue. 
Countries are ranked in descending order of the score-point difference associated with the index of 
disciplinary climate. 

29 

 
 
 
 
 
                                           
In  PISA  2015,  the  index  of  disciplinary  climate  was  constructed  by 
the  OECD  from  students’  reports  on  how  often  (“every  lesson”,  “most 
lessons”,  “some  lessons”,  “never  or  hardly  ever”)  some  interruptions 
happened  in  science  lessons  (“every  lesson”;  “most  lessons”;  “some 
lessons”;  “never  or  hardly  ever”).  Higher  values  on  the  index  indicate  a 
more positive disciplinary climate in science lessons. 

Figure  7.5  reveals  that  in  all  EU  MS  a  better  disciplinary  climate  is 
associated with higher  science achievement. The performance differences 
can  amount  to  as  much  as  30  score-points,  as  in  Malta,  or  between  20 
and  30  score-points,  as  in  the  United  Kingdom,  Romania,  Luxembourg, 
Slovakia,  Greece,  Croatia  and  Cyprus.  In  17  EU  MS,  the  performance 
difference  varies  between  10  and  20  score-points.  The  EU  MS  with  the 
weakest 
relationship  between  disciplinary  climate  and  students’ 
achievement are Ireland, Poland and Latvia. 

Disciplinary  climate  has  been  shown  to  have  a  positive  association 
with  achievement  (Costa  &  Araújo,  2015;  Gomendio,  2017).  This 
classroom  climate  characteristic  is  a  strong  driver  of  students’  scores  in 
science. In this sense, our results are in line with the literature. 

7.3. Teacher support in science classes16  

Teacher  support  in  PISA  refers  to  the  students’  answers  to  items 
related to teachers’ interest and perseverance in making concepts clear to 
students. The index of teacher support was constructed by the OECD from 
students’  reports  on  the  frequency  of  how  often  the  teacher  helps 
students with their learning in their science lessons. The frequency ranges 
from “every lesson” to “never or hardly ever”. Higher values on this index 
indicate more frequent use of these strategies. 

Figure  7.6  shows  that  in  seven  EU  MS  higher  teacher  support  in 
science  lessons  is  associated  with  higher  science  achievement.  On  the 
other  hand,  in  10  EU  MS  the  association  between  teacher  support  and 
students’ achievement is negative. The performance differences vary from 
–12  score-points,  in  Slovakia,  to  20  score-points,  in  Malta.  In  11  EU  MS 
the  relationship  between  teacher  support  and  science  skills  was  not 
statistically  significant.  These  results  are  in  line  with  the  OECD  (2016c) 
findings,  which  highlight  that  teacher  support  is  not  always  associated 
with  student  performance 
the 
socio-economic status of students and schools is accounted for. However, 
the  OECD  (2016c)  also  found  that  the  association  between  teacher 
support  and  student  performance  becomes  positive  after  accounting  for 
the  socio-economic  status  of  students  and  schools.  The  literature  shows 
that “disadvantaged students are in greater need of teacher support, and 

in  science.  This 

true  before 

is 

More 

16 
found 
Management/oecd/education/pisa-2015-results-volume-ii_9789264267510-en#.WR7lVuuGOos 

http://www.keepeek.com/Digital-Asset-

information 

can 

be 

at 

30 

 
 
                                           
they  also  tend  to  score  lower  in  the  PISA  assessments,  so  once  the 
analysis  accounts  for  socio-economic  status,  the  association  between 
teacher  support  and  science  performance  becomes  positive  in  many 
education systems” (OECD, 2016, V2, p. 96). 

Figure 7.6. Teacher support and science performance17 

Results based on students’ reports 

MT 
FI 
DK 
SE 
CY 
UK 
LT 
EE 
IE 
LV 
EU 28 
HU 
DE 
PT 
HR 
SI 
FR 
ES 
PL 
EL 
LU 
NL 
AT 
CZ 
IT 
BE 
BG 
RO 
SK 

-15 

-10 

-5 

0 

5 

10 

15 

20 

25 

Score-point difference associated with one-unit increase in 
the index of teacher support  

Source: Own analysis based on PISA 2015 data. See also Table 7.6, Annex A.  

17  The  score-point  differences  that  are  statistically  significant  are  presented  in  a  darker  blue. 
Countries are ranked in descending order of the score-point difference associated with the index of 
teacher support. 

31 

 
 
 
 
 
 
 
                                           
7.4. Ability grouping within classes  

Ability  grouping  within  classes  was  reported  by  the  principals.  The 
Likert  scale  used  distinguishes  the  following  response  options:  a)  for  all 
classes, b) for some classes, c) not for any classes. 

Figure 7.7 shows that in 15 EU MS there are statistically significant 
differences  in  students’  achievement  in  science  for  students  in  schools 
whose principal reported that students are grouped by ability within their 
classes  for  some  or  all  subjects.  However,  these  differences  can  be 
associated with either higher or lower achievement. In Hungary, Slovakia, 
Latvia,  the  Czech  Republic  and  Slovenia,  students  in  schools  whose 
principals  reported  that  students  are  grouped  by  ability  within  their 
classes for some or all subjects perform better. On the other hand, in the 
Netherlands,  Austria,  Malta,  Luxembourg,  Germany,  Bulgaria,  Belgium, 
Estonia  and  Cyprus,  students  in  schools  whose  principals  reported  that 
students  are  grouped  by  ability  within  their  classes  for  some  or  all 
subjects have lower scores. These results show that in the majority of EU 
MS  there  is  no  association  between  ability  grouping  within  classes  and 
students’  performance  in  science.  In  15  EU  MS  ability  grouping  within 
classes  can  be  either  positively  or  negatively  associated  with  students’ 
performance.  Indeed,  ability  grouping  within  classrooms  may  not  always 
have a negative effect because it may reflect adaptive classroom teaching 
practices  that  benefit  students.  In  fact,  as  acknowledged  by  the  OECD 
PISA  Governing  Board  and  as  research  by  Slavin  et  al.  (1990)  suggests, 
ability  grouping  may  better  address  the  needs  of  individual  students  and 
be more beneficial academically if implemented for some subjects.  

32 

 
 
 
 
Figure 7.7. Ability grouping within classes and science performance18 

Results based on school principals’ reports 

HU 
SK 
LV 
CZ 
FR 
SI 
LT 
EL 
FI 
PL 
HR 
RO 
SE 
IE 
CY 
EU 28 
ES 
IT 
EE 
PT 
DK 
BE 
BG 
UK 
LU 
DE 
MT 
AT 
NL 

-70 

-50 

-30 

-10 

10 

30 

50 

70 

Score difference of students in schools whose principal 
reported that students are grouped by ability within their 
classes for some or all subjects 

Source: Own analysis based on PISA 2015 data. See also Table 7.7, Annex A.  

18  The  score-point  differences  that  are  statistically  significant  are  presented  in  a  darker  blue. 
Countries  are  ranked  in  descending  order  of  the  score  difference  of  students  in  schools  whose 
principal reported that students are grouped by ability within their classes for some or all subjects.  

33 

 
                                           
 
Highlights – Univariate analysis 

•  Using teacher-directed instruction more frequently is associated 

with higher achievement in science. 

•  All EU MS present a negative relationship of perceived feedback 

with students’ performance in science. 

•  In  18  EU  MS,  more  frequent  use  of  adaptive  instruction  is 
students’ 

associated  with 

significantly 

positively 
performance in science. 

and 

•  In  all  EU  MS,  a  better  disciplinary  climate  is  associated  with 

higher achievement in science. 

•  In  the  majority  of  EU  MS  there  is  a  positive  relationship 
between a higher frequency of use of enquiry, as  measured by 
the  teacher’s  explanation  of  how  science  ideas  can  be  applied, 
and students’ science achievement. 

•  In  seven  EU  MS,  higher  teacher  support  in  the  science 
classroom is associated with higher achievement in science. On 
the  other  hand,  in  10  EU  MS,  the  association  between  teacher 
support and students’ achievement is negative. 
In  five  EU  MS,  students  in  schools  whose  principals  reported 
that  students  are  grouped  by  ability  within  their  classes  for 
some  or  all  subjects  perform  better.  In  contrast,  in  10  EU  MS, 
students in schools whose principals reported that students are 
grouped  by  ability  within  their  classes  for  some  or  for  all 
subjects perform worse. 

34 

 
 
 
8.  The  relationship  between  teaching  practices,  the  learning 
environment and students’ achievement 

This section details the multivariate analyses considering variables at 
the student level and at the school level, with the outcome measure being 
the  students’  PISA  score  in  science.  Variables  measuring  students’ 
socio-economic  background  and  teaching  practices  in  science  classrooms 
are  included  in  the  model.  As  for  the  school-level  variables,  the  model 
includes variables related to school characteristics. The analysis is carried 
out  for  the  EU  MS  for  which  PISA  2015  data  are  available  for  all 
variables19,  namely  Bulgaria,  Croatia,  the  Czech  Republic,  Estonia, 
Finland,  France,  Germany,  Greece,  Hungary,  Ireland,  Italy,  Latvia, 
Lithuania, Luxembourg, the Netherlands, Poland, Portugal, Slovakia, Spain 
and the United Kingdom. 

8.1. Research question 

This section aims to answer the following research question: What is 
the relationship between teaching practices, the learning environment and 
students’  achievement  in  EU  MS20?  To  answer  this  question  we  use  the 
data available for the different levels of analysis. 

8.2. Methodology 

Due to the hierarchical structure of the PISA data in which students 
are  nested  in  schools  we  use  multilevel  modelling  (Goldestein,  2003)  in 
order to identify the variation in different teaching practices in the science 
classroom  and  their  relation  with  students’  achievement.  The  analysis  is 
presented  for  the  EU  participating  MS  in  PISA  2015.    The  variance 
components  model  was  applied  to  the  data  and  the  model  was  then 
estimated  using  iterative  generalised  least  squares  (IGLS)  (Goldestein, 
1986).  The  computational  component  was  generated  using  MLWin  2.24 
software  (Rabash,  2009).  The  bottom-up  procedure  and  the  deviance 
were  used  to  decide  which  variables  to  include  in  the  model  and 
multicollinearity was checked. 

To provide an answer to our research question, multilevel regression 
models were used. The variables21 are included at the student level and at 
the  school  level.  At  the  student  level,  variables  measuring  teaching 
practices  and  learning  environment  in  science  were  considered,  namely 

19  Belgium  was  excluded  from  the  analysis  because of  non-existence  of one  of the  variables  used 
for the country level analysis. 
20 For which the data for all variables are available.  
21 More information regarding the variables can be found in Annex B – Table 7.1. 

35 

 
 
 
 
 
                                           
the  index  of  teacher-directed  instruction,  the  inquiry  instruction  variable, 
the  adaptive  instruction  index  and  the  perceived  feedback  index.  In 
addition, we control for a set of socio-economic characteristics to account 
for the role of other student key background factors (gender, immigration 
background,  economic,  social  and  cultural  status  (ESCS),  class  size  and 
students’  motivation  for  achievement).  At  the  school  level,  the  following 
variables  are  included  in  the  model:  school  average  of  students’ 
socio-economic  status,  school  management  (public  vs  private),  school 
size,  proportion  of  science  teachers  that  are  fully  certified,  and  school 
autonomy (index). The outcome variable considered in our analysis is the 
students’ scores in science. 

In PISA, information about teaching and learning is collected mainly 
through  the  student  and  school  questionnaires22,  with  school  principals 
responding to the latter. 

8.3. Results 

First, we ran a three-level model considering students at  level one, 
schools at level two and countries at level three. The null model was run 
considering  the  following  20  EU  MS:  Bulgaria  (BG),  Croatia  (HR),  the 
Czech  Republic  (CZ),  Estonia  (EE),  Finland  (FI),  France  (FR),  Germany 
(DE),  Greece  (EL),  Hungary  (HU),  Ireland  (IE),  Italy  (IT),  Latvia  (LV), 
Lithuania  (LT),  Luxembourg  (LU),  the  Netherlands  (NL),  Poland  (PL), 
Portugal  (PT),  Slovakia  (SK),  Spain  (ES),  and  the  United  Kingdom  (UK). 
This  model  contains  only  the  dependent  variable,  i.e.  students’  scores  in 
science  and  does  not  include  covariates  other  than  a  constant.  The  null 
model  allows  the  proportion  of  variability,  calculated  using  the  variances 
estimated  for  the  errors,  between  students  within  schools,  between 
schools within countries and between countries to be obtained. The results 
(Figure  8.1)  show  that  the  proportion  of  the  country-level  variance 
explained  by  students’  achievement  in  science  is  5.2%  and  that  the 
proportion  of  the  variance  as  a  result  of  differences  between  schools  is 
21%. 

After  estimating  the  null  model  we  ran  a  second  model  (2),  which 
includes variables measuring teaching practices and learning environment 
and the control variables at the student and school level. Finally, together 
with  the  variables  of  the  second  model  we  included  country-level 
covariates  (model  3),  namely  GDP  per  capita  (euros)  by  country,  as  a 
measure  of  economic  wealth23,  a  dichotomous  variable  indicating  the 
existence  or  not  of  external  evaluation24,  as  a  proxy  of  accountability. 

22 There are also data available on teaching that were collected through the teacher questionnaire, 
but  only  five  EU  MS  participated  in  this  optional  questionnaire.  However,  students’  achievement 
and characteristics are not directly linked to the teachers. 
23  Following  the  approach  of  Hanushek  &  Woessmann  (2014)  we  divided  the  GDP  per  capita  by 

1000.  

24 Based on Education at a Glance (2015).  

36 

 
 
                                           
These  predictors,  together  with  the  proportion  of  fully  certified  teachers, 
are used as proxies for teaching quality. These covariates have been used 
by Hanushek and Woessmann (2014) and have been found to be reliable 
indicators  of  cross-country  variations,  in  some  cases  contributing  to 
boosting  students’  achievement  (Hanushek  &  Raymond,  2006;  Hanushek 
& Woessmann, 2014; OECD, 2011). 

Figure 8.1. Proportion of between-school and between-country variances – 20 EU MS 

0.50 

0.45 

0.40 

0.35 

0.30 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

s
e
r
o
c
s

e
c
n
e
i
c
s

’
s
t
n
e
d
u
t
s

n

i

e
c
n
a
i
r
a
v

f
o
n
o
i
t
r
o
p
o
r
P

U
E

0
2

Between schools 

Between countries  

Source: Own analysis based on PISA 2015 data. See also Table 8.1, Annex B.  

that  could  be  attributed 

Having explored the teaching practices and learning environment and the 
control  variables  at  the  student  and  school  levels  there  was  still  some 
unexplained  variance 
to  country-level 
characteristics.  As  expected,  controlling  for  the  country-level  predictors 
led to a large decrease in the country-level unexplained variance from 462 
to  279.  This  corresponds  to  a  decrease  of  39.6%  of  the  unexplained 
variance25.  This  suggests  that  around  40%  of  the  differences  between 
countries in science achievement is explained by the measures pertaining 
to a country’s economic wealth and school accountability. 

More specifically, across the 20 EU MS considered in the analysis, students 
in  countries  that  have  accountability  policies,  as  measured  by  the 
existence  of  external  evaluation,  score  27  score-points  higher  in  science. 
The  variable  measuring  a  country’s  economic  wealth  is  not  significantly 
associated  with  students’  performance  in  science.  This  means  that, 

25  The  value  of  39.6%  is  obtained  by  comparing  the  random  part  (constant)  for  the  country  level                         

in models 2 and 3, which are presented in Annex B – Table 8.2.  

37 

 
 
 
 
                                           
 
 
 
 
 
 
 
 
 
 
although  some  literature  suggests  that  differences  in  cognitive  skills  are 
associated  with  differences  in  economic  growth,  this  country-level 
characteristic  may  not  be  so  important  when  considering  other  students 
and school variables across these 20 EU MS. 

Table 8.1. Three -level model for the 20 EU – Association between teaching 
practices and school learning environment 26 and students’ performance in science 

Results based on students and school principals’ reports 

Statistically significant 
coefficient 

Yes 

No 

d
n
a

i

s
e
c
i
t
c
a
r
p
g
n
h
c
a
e
t
g
n
i
r
u
s
a
e
m
s
e
l
b
a
i
r
a
V

t
n
e
m
n
o
r
i
v
n
e
g
n
n
r
a
e
l

i

l

o
o
h
c
s

-
y
r
t
n
u
o
C

l
e
v
e
l

s
e
l
b
a
i
r
a
v

Teacher-directed science instruction 

Inquiry  –  the  teacher  explains  how 
<school science> idea can be applied 

Perceived feedback 

Adaptive instruction 

Teachers’ support in science classes 

Disciplinary climate 

Ability grouping 

Economic wealth 

Accountability 

X 

X 

X 

X 

X 

X 

X 

X 

X 

Source: Own analysis based on PISA 2015 data. See also Table 8.3, Annex B.  

In  addition,  we  found  that  all  the  variables  measuring  teaching 
practices  and  learning  environment,  except  for  ability  grouping  within 
classes, are statistically significant (Table 8.1). Darker blue cells indicate a 
positive  significant  coefficient  and  lighter  blue  cells  indicate  a  negative 
statistically significant coefficient. 

26  Three-level  model  with  the  following  control  variables  considered:  1)  at  the  student  level, 
gender,  immigration  background,  economic,  social  and  cultural  status  (ESCS),  class  size  and 
students’  motivation  to  achieve;  and  2)  at  the  school  level,  school  average  of  students´  socio-
economic  status,  school  management  (public  vs  private),  school  size,  proportion  of  science 
teachers fully certified and school autonomy.  

38 

 
 
                                           
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
To explore the research question more in depth according to specific 
contexts, we ran a multilevel model for each country. The same two-level 
model  –  students  and  schools  –  was  applied  to  16  EU  MS27  (Bulgaria, 
Croatia, Czech Republic, Estonia, Finland, France, Germany, Greece, Italy, 
Lithuania,  Luxembourg,  the  Netherlands,  Portugal,  Slovakia,  Spain  and 
United  Kingdom).  Some  countries  were  excluded  from  the  analysis  on 
account  of  the  non-existence  of  variance  between  schools,  which 
precludes the use a multilevel model, namely Latvia, Ireland and Poland. 

Table 8.2 presents a summary of the findings on teaching practices 
and  the  learning  environment  that  explain  variation  in  students’ 
achievement  across  the  different  EU  MS.  As  previously  indicated,  darker 
blue  cells  indicate  a  positive  significant  coefficient  and  lighter  blue  cells 
indicate a negative statistically significant coefficient.   

The results show that: 

•  Teacher-directed  instruction  is  positively  associated  with  students’ 

performance in science in 16 EU MS. 

•  Enquiry  instruction,  as  measured  by  the  variable  "the  teacher 
explaining  how  an 
favours  students’ 
achievement  in  science  in  14  EU  MS.  The  exceptions  are  Bulgaria 
and Estonia. 

idea  can  be  applied", 

•  Perceived  feedback  has  a  negative  (and  statistically  significant) 

relationship with students’ performance in science in all EU MS. 

•  Adaptive 

instruction 

is  positively  associated  with  students’ 

achievement in science in all 16 EU MS. 

•  Teacher support is negatively associated with students’ performance 
in  science  in  11  EU  MS.  The  EU  MS  with  no  association  between 
teacher  support  and  students’  achievement  are  Estonia,  Finland, 
France, Germany and Lithuania. 

•  A  better  disciplinary  climate  is  associated  with  better  students’ 

performance in science in all EU MS, except in France. 

•  Only  in  the  Netherlands  is  ability  grouping  in  a  few  lessons 
(compared  with  its  use  in  all  lessons)  associated  with  higher 
achievement in science. 

27 Hungary was excluded from the analysis because of the very high percentage of missing values. 

39 

 
 
 
 
 
 
                                           
Table 8.2. Teaching practices and learning environment and science performance28 

Results based on students’ and school principals’ reports 

Teaching practices and learning environment in science 

Teaching practices/strategies 

l

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–

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I

Bulgaria 

Croatia 

Czech Republic 

Estonia 

Finland 

France 

Germany 

Greece 

Italy 

Lithuania 

Luxembourg 

Netherlands 

Portugal 

Slovakia 

Spain 

United Kingdom 

EU MS with positive  
association  

16 

14 

EU MS with no association 

EU MS with negative  
association 

0 

0 

2 

0 

0 

0 

16 

16 

0 

0 

0 

5 

11 

16 

0 

0 

0 

15 

1 

Source: Own analysis based on PISA 2015 data. See also Tables 8.4 and 8.5, Annex B.  

28  Control  variables  considered:  1)  at  the  student  level,  gender,  immigration  background, 
economic,  social  and  cultural  status  (ESCS),  class  size  and  students’  motivation  to  achievement; 
and 2) at the school level, school average of students’ socio-economic status, school management 
(public vs private), school size, proportion of science teachers fully certified and school autonomy. 

40 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
In  general,  the  results29  also  indicate  that  higher  students’  motivation 
to  achieve  favours  students’  achievement  in  science  in  all  16  EU  MS.  In 
terms of other background variables (fixed part), the model shows that in 
14 EU MS boys perform better in science than girls. The exceptions are in 
Finland,  where  girls  tend  to  perform  better  than  boys  in  science,  and  in 
Lithuania,  where  no  association  was  found.  In  addition,  the  higher  the 
socio-economic status of the students attending a school, the higher their 
science achievement and,  in  general, immigrant status is  associated with 
lower achievement. 

In  terms  of  school  factors,  the  results  reveal  that,  the  higher  the 
average  socio-economic  status  of  the  students  attending  a  school,  the 
higher their science achievement, and in some EU MS students attending 
private schools perform better than students attending public schools. The 
exception is Finland. 

29  See  Table  8.4  in  Annex  B  for  the  model  summary  on  the  control  variables  at  the 
student/classroom  level  and  at  the  school  level  and  Table  8.5  in  Annex  B  for  more  detailed 
information by country. 

41 

 
 
 
 
 
 
 
 
 
                                           
Highlights – Multivariate analysis 

•  Teacher-directed  instruction  is  positively  associated  with 

students’ performance in science in the 16 EU MS. 

•  Inquiry instruction, as measured by the variable “the teacher 
idea  can  be  applied  to  different 
students’ 

explaining  how  an 
phenomena”, 
achievement in science in 14 out of 16 EU MS. 

is  positively  associated  with 

•  Perceived feedback has a negative association with students’ 

performance in science in the 16 EU MS. 

•  Adaptive  instruction  is  positively  associated  with  students’ 

achievement in science in all 16 EU MS. 

•  Teacher  support  is  negatively  associated  with  students’ 

performance in science in 11 of 16 EU MS. 

•  A  better  disciplinary  climate  is  associated  with  better 

students’ performance in science in 15 out of 16 EU MS. 

•  Only  in  one  EU  MS  is  using  ability  grouping  in  a  few  lessons 
(compared  with  its  use  in  most  or  all  lessons)  is  associated 
with higher achievement in science. 

42 

 
 
 
 
Conclusions and discussion 

This  report  on  the  quality  of  teaching  and  learning  provides  insights 
into  teaching  practices  that  are  specific  to  science  teaching,  namely 
teacher-directed teaching and inquiry practices. In particular, we focus on 
the  reported  use  of  different  science  teaching  practices  and  their 
relationship  with  student  science  achievement.  To  understand  this 
association,  we  include  other  teaching  effectiveness  dimensions,  such  as 
teacher  support  and  disciplinary  climate,  and  we  account  for  student, 
classroom  and  school  input  factors  (e.g.  gender,  socio-economic  status, 
class  size  and  ability  grouping)  to  control  for  the  background  and 
contextual factors that may be related to science learning outcomes. Our 
analysis uses cross-sectional data from PISA 2015 for the EU participating 
MS  and  is  based  on  students’  and  school  principals’  answers  about  the 
frequency  of  specific  school  science  activities  and  related  conditions  for 
learning. 

The  results  show  that  teacher-directed  instruction  is  positively 
associated  with  students’  performance  in  science.  This  is  true  even  when 
other teaching, learning and individual factors are considered and is in line 
with the patterns found by the OECD (2016c) and Mayer (2004). 

Importantly,  we  also  found  a  positive  relationship  between  a  higher 
frequency  of  enquiry-based  teaching,  as  measured  by  the  teacher’s 
explanation  of  how  science  ideas  can  be  applied  to  different  phenomena 
and  students’  science  achievement.  This  is  in  contrast  with  findings  from 
the  OECD  (2016c).  Our  measure  for  enquiry-based  teaching  illustrates 
real-life applications and indicates a more basic level of scientific enquiry. 
On  the  other  hand,  OECD  findings  are  based  on  an  index  composed  of 
several  variables.  In  this  sense,  our  study  adds  to  existing  evidence  by 
showing  that  a  more  basic  level  of  enquiry  is  positively  associated  with 
students’ achievement. 

Our  results  also  suggest  that  a  more  frequent  use  of  adaptive 
instruction,  defined  as  teachers’  flexibility  in  adapting  lessons  to  the 
students’  needs,  is  positively  and  significantly  associated  with  students’ 
performance  in  science.  This  shows  that  students  from  the  EU  MS 
considered in our analysis perform better when teachers have the capacity 
to  respond  and  adapt  their  practices  for  individual  student  needs.  Again, 
this is in accordance with OECD findings. 

Our  multivariate  analysis  reveals  that  a  better  disciplinary  climate  is 
associated with better student performance in science. This finding shows 
that  disciplinary  climate  is  a  strong  driver  of  students’  performance  in 
science.  Other  studies  also  reveal  a  positive  association  between  a  good 
disciplinary  climate  and  students’  achievement  (Costa  &  Araújo,  2015; 
Gomendio, 2017). 

Considering the results for the model with all EU MS and the models 
for  individual  countries,  one  pattern  seems  clear  in  relation  to  teaching 

43 

 
teacher-directed 

instruction,  adaptive 

instruction  and 
practices: 
disciplinary  climate  are  always  positively  associated  with  science 
achievement.  Inquiry-based  instruction  is  overwhelmingly  positive  and  it 
is  not  associated  with  achievement  in  only  two  countries.  In  this  sense, 
these  findings  suggest  that  these  are  the  teaching  practices  that  matter 
most and that these should be considered by stakeholders to improve the 
quality of teaching and learning in the science classroom. 

In  what  refers  to  perceived  feedback,  as  measured  by  the 
assessment  feedback  students  receive  from  their  teachers,  it  has  a 
negative association with students’ performance in  science. This negative 
association  is  in  line  with  the  findings  from  the  OECD  (2016c).  Hattie 
(2009)  indicates  that  these  results  may  be  related  to  the  notion  that 
teachers adjust their feedback to students’ learning needs. This being the 
case, low achievers may be the ones who need more feedback to improve 
their learning. In addition, school climate can also influence the findings in 
this regard. 

In  terms  of  teacher  support,  our  results  show  that  its  relationship 
with  student  performance  in  science  can  be  either  positive  or  negative. 
This  depends  on  the  type  of  association  with  achievement  that  we 
capture,  namely  univariate  association  or  multivariate  association  (which 
includes  different  teaching  practices  in  the  classroom,  individual  and 
learning  environment  characteristics).  A  negative  association  was  found 
when  other  teaching  practices,  individual  and  school  characteristics  are 
considered. This might be because disadvantaged students are in greater 
need of teacher support and our analysis considers students with different 
levels of skills. 

To  sum  up,  teacher  feedback  is  always  negatively  associated  with 
achievement and the relation between teacher support and achievement is 
also  negative  in  the  majority  of  countries.  As  mentioned  in  the  previous 
section,  this  may  be  a  result  of  teachers  giving  more  feedback  to  low 
achievers. 

Ability grouping within classes can be positively or negatively related 
to  students’  achievement.  This  is  true  in  our  univariate  analysis.  On  the 
other  hand,  this  kind  of  ability  grouping  does  not  seem  to  influence 
students’  scores  in  science  when  other  factors  are  considered.  The 
exception  is  the  Netherlands,  where  a  negative  association  with  science 
performance  was  found  in  the  multivariate  analysis.  In  this  sense,  the 
results  show  that,  when  other  variables  are  considered,  ability  grouping 
does  not  have  a  statistically  significant  association  with  students’ 
achievement in every country, with the exception of the Netherlands. 

It  is  also  a  relevant  finding  that  the  variance  among  the  20  EU  MS 
considered in the analysis is explained by accountability, as measured by 
the  existence  of  external  evaluation.  That  is,  students  in  countries  that 
have  external  evaluation  score  higher  in  science.  These  findings  extend 
previous  findings  by  the  OECD  (2016c)  regarding  teaching  practices  and 

44 

their relation with achievement and by Hanushek and Woessmann (2014) 
in  terms  of  the  positive  association  between  the  existence  of  external 
exams  and  achievement.  Importantly,  it  extends  their  results  to  a  larger 
number of countries. 

In  conclusion,  this  report  provides  insights  about  what  works  well  in 
the  science  classroom  in  EU  MS,  but  additional  insights  within  national 
borders can perhaps offer explanations for students’ science achievement 
that  our  estimates  do  not  capture.  Nevertheless,  this  information 
strengthens  the  evidence  base  and  can  be  used  at  the  EU  level  to  share 
knowledge about good practices and to inform policy initiatives that focus 
on high-quality teaching (European Commission, 2016). 

45 

 
 
 
 
 
 
 
 
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50 

 
 
 
List of abbreviations and definitions 

•  EU country abbreviations 

Code 
AT 
BE 
BG 
CY 
CZ 
DE 
DK 
EE 
EL 
ES 
FI 
FR 
HR 
HU 
IE 
IT 
LT 
LU 
LV 
MT 
NL 
PL 
PT 
RO 
SE 
SI 
SK 
UK 

Country name 
Austria 
Belgium 
Bulgaria 
Cyprus 
The Czech Republic 
Germany 
Denmark 
Estonia 
Greece 
Spain 
Finland 
France 
Croatia 
Hungary 
Ireland 
Italy 
Lithuania 
Luxembourg 
Latvia 
Malta 
The Netherlands 
Poland 
Portugal 
Romania 
Sweden 
Slovenia 
Slovakia 
United Kingdom 

51 

 
 
List of boxes 

Highlights – Univariate statistics 

Highlights – Multivariate statistics 

52 

 
List of figures  

Figure 6.1. Teaching practices and learning environment in science 

Figure 7.1. Teacher-directed instruction and science performance 

Figure 7.2. Inquiry instruction and science performance 

Figure 7.3. Perceived feedback and science performance 

Figure 7.4. Adaptive instruction and science performance 

Figure 7.5. Disciplinary Climate and science performance 

Figure 7.6. Teacher support and science performance 

Figure 7.7. Ability grouping within classes and science performance 

Figure 8.1. Proportion of between-school and between-country variances – 20 EU 

53 

 
 
List of tables 

Table 6.1. Information on the variables used to measure Teaching practices and learning 
environment in science 

Table 6.1. Information on the variables used to measure Teaching practices and learning 
environment in science (cont.) 

Table 8.1. Three level model for the 20 EU – Association between teaching practices and 
school learning environment and students’ performance in science 

54 

 
 
 
Annexes 

55 

Annex A 

Table.7.1 - Score-point difference in science associated with one-unit increase in the 
index of teacher-directed science instruction30 

Results based on students’ reports 

Country 

b 

b.t 

SK 
LT 
EE 
BE 
BG 
LV 
DK 
IE 
CZ 
HU 
SE 
HR 
PT 
AT 
FR 
EU 27 
UK 
LU 
PL 
CY 
ES 
DE 
EL 
IT 
RO 
MT 
FI 
NL 

7.47 
3.03 
4.11 
4.08 
4.66 
4.55 
7.01 
6.13 
13.12 
6.49 
5.56 
7.31 
5.43 
8.25 
2.69 
8.26 
13.26 
8.75 
7.40 
9.08 
9.11 
9.54 
10.18  11.43 
5.26 
10.33 
5.53 
10.96 
11.17 
9.20 
11.49  35.96 
4.29 
11.75 
3.45 
13.20 
9.44 
13.24 
8.59 
13.78 
7.92 
14.26 
8.85 
15.28 
6.98 
15.42 
7.88 
17.78 
1.95 
18.25 
7.78 
19.35 
5.15 
19.77 
7.16 
20.36 

30  All  the  score-point  differences  are  statistically  significant.  Countries  are  ranked  in  ascending 
order  of  the  score-point  difference  associated  with  the  index  of  teacher-directed  science 
instruction. For SL there is no data available for this index. 

 
 
 
 
 
                                           
Table.7.2 - Score-point difference in science for inquiry instruction - measured by teacher 
explains how a science idea can be applied.31  

Results based on students’ reports 
This happen in “most” or “all” science lessons 

Country 

SI 
PL 
BG 
SK 
IE 
EE 
CZ 
EL 
IT 
SE 
HU 
HR 
EU 28 
DE 
AT 
UK 
LT 
PT 
DK 
ES 
FI 
RO 
LV 
CY 
LU 
BE 
MT 
NL 

Score 
difference 
-4.74 
0.45 
3.69 
10.97 
11.12 
11.38 
13.26 
13.75 
17.95 
19.64 
19.69 
20.36 
20.63 
22.01 
22.09 
23.01 
23.78 
23.80 
23.88 
24.04 
24.21 
24.51 
25.55 
26.00 
29.65 
32.17 
36.84 
37.06 

31 Score point difference between students reporting that teacher explains how a science idea can 
be  applied  in  “most”  or  “all”  science  lessons  and  students  reporting  that  teacher  explains  how  a 
science  idea  can  be  applied  never  or  almost  never  in  science  lessons.  Countries  are  ranked  in 
ascending order of the score-point difference in science. 

 
 
 
 
 
 
 
                                           
 
Table 7.3. Score-point difference in science associated with one-unit increase in the index of 
perceived feedback32 

Results based on students’ reports 

Country 

b 

b.t 

RO 
MT 
UK 
CZ 
CY 
PL 
IE 
DK 
LV 
SE 
BG 
LT 
FR 
EU 27 
FI 
EE 
ES 
PT 
DE 
EL 
SK 
AT 
HR 
BE 
NL 
LU 
IT 
HU 

-0.25 
-0.56 
-0.78 
-1.76 
-3.06 
-5.43 
-5.83 
-8.59 
-6.07 
-8.79 
-6.18 
-10.38 
-6.36 
-10.40 
-5.71 
-10.55 
-7.03 
-11.12 
-6.28 
-11.69 
-6.36 
-11.73 
-7.62 
-12.18 
-12.62 
-7.68 
-13.26  -39.73 
-9.35 
-13.65 
-8.55 
-14.40 
-9.15 
-14.58 
-9.06 
-14.64 
-15.13 
-7.47 
-15.13  -10.61 
-16.46 
-9.33 
-17.00  -10.25 
-18.35  -10.80 
-19.24  -11.94 
-19.88 
-8.71 
-20.14  -15.14 
-21.14  -12.76 
-22.57  -13.11 

32 Countries are ranked in descending order of the score-point difference associated with the index 

of perceived feedback. For SL there is no data available for this index. 

 
 
 
 
 
                                           
Table 7.4. Score-point difference in science associated with one-unit increase in the index of 
adaptive instruction33 

Results based on students’ reports 

Country 

b 

BE 
AT 
FR 
LU 
IT 
SI 
HR 
SK 
HU 
EL 
LT 
EE 
IE 
PL 
EU 25 
LV 
PT 
CZ 
BG 
CY 
UK 
DE 
ES 
FI 
DK 
NL 

-2.28 
-1.40 
-0.75 
0.46 
0.66 
0.79 
1.31 
3.40 
3.93 
4.46 
6.24 
6.35 
6.96 
7.16 
7.22 
7.30 
7.98 
8.82 
10.16 
11.26 
12.40 
13.39 
15.20 
16.39 
17.34 
22.98 

b.t 

-1.58 
-0.93 
-0.51 
0.30 
0.39 
0.50 
0.94 
2.21 
2.22 
2.92 
3.91 
4.15 
5.46 
5.12 
22.47 
3.92 
5.72 
5.36 
5.70 
7.25 
6.86 
8.43 
11.00 
10.19 
10.69 
9.91 

33. Countries are ranked in ascending order of the score-point difference associated with the index 
of perceived feedback. For MT, RO, SE there is no data available for this index. 

 
 
 
 
                                           
Table 7.5. Score-point difference associated with one-unit increase in the index of disciplinary 
climate34 

Results based on students’ reports 

Country 

b 

IE 
PL 
LV 
ES 
FI 
DK 
EE 
BE 
SE 
AT 
PT 
SI 
NL 
IT 
BG 
LT 
EU 28 
FR 
HU 
CZ 
DE 
UK 
RO 
LU 
SK 
EL 
HR 
CY 
MT 

7.43 
9.40 
9.79 
10.74 
11.36 
11.69 
11.96 
12.75 
13.57 
13.93 
14.57 
14.98 
15.12 
15.25 
15.66 
15.94 
16.58 
17.68 
17.82 
19.48 
19.74 
20.21 
20.22 
21.02 
22.46 
23.65 
23.89 
23.97 
29.90 

b.t 

4.48 
5.12 
5.27 
5.86 
5.99 
7.22 
6.29 
9.58 
7.27 
6.50 
7.27 
5.82 
6.44 
9.02 
8.18 
10.94 
46.69 
9.20 
8.46 
11.84 
10.01 
12.31 
8.71 
16.88 
12.37 
10.03 
14.38 
15.65 
17.00 

34 Countries are ranked in ascending order of the score-point difference associated with the index 
of disciplinary climate. 

 
 
 
 
                                           
Table.7.6. - Score-point difference in science associated with one-unit increase in the index of 
teacher support35 

Results based on students’ reports 

Country 

b 

b.t 

SK 
RO 
BG 
BE 
IT 
CZ 
AT 
NL 
LU 
EL 
PL 
ES 
FR 
SI 
HR 
PT 
DE 
HU 
EU 28 
LV 
IE 
EE 
LT 
UK 
CY 
SE 
DK 
FI 
MT 

-11.84 
-11.36 
-8.45 
-7.43 
-7.26 
-7.17 
-6.08 
-5.92 
-4.90 
-4.57 
-3.41 
-3.25 
-3.16 
-3.13 
-2.77 
-1.65 
-1.16 
-1.05 
-0.73 
-0.17 
1.79 
3.17 
4.65 
4.90 
7.03 
8.71 
12.00 
12.27 
19.88 

-6.83 
-5.51 
-4.92 
-4.20 
-4.40 
-4.18 
-3.16 
-2.21 
-3.10 
-3.01 
-1.96 
-1.99 
-1.84 
-1.11 
-1.61 
-1.01 
-0.67 
-0.60 
-2.13 
-0.09 
1.35 
1.95 
3.82 
2.88 
4.31 
5.33 
6.61 
8.10 
9.33 

35 Countries are ranked in ascending order of the score-point difference associated with the index of 

teacher support 

 
 
 
 
                                           
Figure 7.7. Score difference in science of students in schools whose principal reported that students 
are grouped by ability within their classes for some or all subjects36 

Results based on school principals’ reports 

Country 

NL 
AT 
MT 
DE 
LU 
UK 
BG 
BE 
DK 
PT 
EE 
IT 
ES 
EU 28 
CY 
IE 
SE 
RO 
HR 
PL 
FI 
EL 
LT 
SI 
FR 
CZ 
LV 
SK 
HU 

Score 
difference 
-62.72 
-57.12 
-47.03 
-43.40 
-42.14 
-30.95 
-22.45 
-18.24 
-14.23 
-11.04 
-8.77 
-7.32 
-7.14 
-7.38 
-5.57 
-5.34 
-3.16 
-1.77 
0.16 
1.30 
4.55 
6.20 
7.75 
8.32 
11.74 
14.92 
17.64 
41.30 
67.91 

36  Countries  are  ranked  in  ascending  order  of  the  score  difference  of  students  in  schools  whose 
principal  reported  that  students  are  grouped  by  ability  within  their  classes  for  some  or  all 
subjects. 

 
 
                                           
Annex B 

Table. 8.1 – Variables included in the multilevel models 

          Code  Questionnaire 

Student 
Student 
Student 
Student 
School 

ST004D01T 
ESCS 
IMMIG 
MOTIVAT 
CLSIZE 

Variables 
STUDENT  
Gender 
SES –  Economic, social and cultural status 
Immigration background 
Motivation for achievement (index) 
Class size 
TEACHING PRACTICES AND TEACHING PRACTICES AND LEARNING ENVIRONMENT 
Instructional practices in science 
Teacher-directed science instruction (index) 
Inquiry  -  The  teacher  explains  <school  science>  idea  can 
be applied 
Perceived feedback (index) 
Adaptive instruction (index) 
Teacher quality 
Disciplinary climate in science classes 
Teacher support in a science classes of students choice 
Ability grouping within classes 
SCHOOL 
School average PISA index of economic, social and cultural 
status 
School private or public 
School size 
Index proportion of science teachers fully certified (index) 
Index of school autonomy (index) 

SC013Q01TA 
SCHSIZE 
PROSTCE 
SCHAUT 

School 
School 
School 
      School  

DISCLISCI 
TEACHSUP 
SC042Q02TA 

Student 
Student 
Student 
Student 

TDTEACH 
ST098Q06TA 

Student 
Student 
School 

PERFEED 
ADINST 

Student 

Table 8.2. Proportion of between-school and between-country variances – 20 EU 

20 EU 

Null Model 

Fixed Part (constant)                                                        
Random Part (constant) - Student level                      

Random Part (constant) - School level                         
Random Part (constant) - Country level                          
School variance expl. % 
Country variance expl. % 

489 
7484 
2192 
536 
21.5 
5.2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8.3.  Multilevel coefficients for the relationship between Teaching practices and learning 
environment in science and students’ science achievement37. 

Null 
Model 

Model 2 

Model 3 

Coef. 

Std. Error 

Coef. 

Std. Error 

9.3 

9.3 

8.6 

8.6 

495 

508 

489 

0.74 

1.02 

1.00 

0.94 
1.05 

0.94 
1.04 

-5.2 
-16.9 

-5.2 
-16.8 

1.96 
1.39 
2.71 
0.89 
0.21 

1.95 
1.39 
2.7 
0.89 
0.21 

12.3 
16.8 
-21.4 
11.1 
0.4 

Fixed Part (constant)                                                        
STUDENT 
12.3 
Gender 
16.7 
SES 
-21.4 
Immigration background 
11.1 
Motivation for achievement 
0.4 
Class size 
TEACHING PRACTICES AND TEACHING PRACTICES AND LEARNING ENVIRONMENT 
Instructional practices in science 
Teacher-directed science 
instruction (index) 
Inquiry - The teacher 
explains <school science> 
idea can be applied 
Perceived feedback (index) 
Adaptive instruction 
(index) 
Teacher quality 
Disciplinary climate in 
science classes 
Teacher support in a 
science classes of students 
choice 
Ability grouping within 
classes 
SCHOOL 
School average PISA index 
of economic, social and 
cultural status 
School private or public 
School size 
Index proportion of science 
teachers fully certified 
(index) 
Index of school autonomy 
(index) 
COUNTRY 
Economic wealth 
Accountability 
Random Part (constant)       
(School level)                          
Random Part (constant)       
(Country level)                          
Deviance 

5.44 
3.7032 
0.00 

5.46 
3.7161 
0.00 

51.6 
-5.0 
0.0 

51.6 
-4.9 
0.0 

0.0 
27.1 

0.19 
7.88 

1595946 

998399 

998313 

2192 

11.8 

11.9 

0.74 

0.73 

0.72 

1.44 

3.04 

5.89 

0.73 

0.71 

1.44 

3.07 

5.98 

-4.5 

-4.5 

-1.8 

-1.7 

488 

486 

462 

279 

536 

9.1 

2.0 

9.1 

1.9 

37  Multilevel  regression  of  students’  performance  in  science.  The  first  model  is  the  null  model; 
model (2) includes including variables measuring teaching practices and learning environment 
and  the  control  variables  at  the  student  and  school  level;  model  (3)  adds  country  level 
covariates.  Standard errors in parenthesis . p-values: bold for p < = 0.05. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Table 8.4. Students/classroom and schools characteristics and science performance 38 

Results based on students and school principals’ reports 

Control variables at the student/classroom and school 
levels 

S
C
S
E

)
e
v
i
t
a
n
=
1
(
G
I

M
M

I

r
o
f

n
o
i
t
a
v
i
t
o
M

t
n
e
m
e
v
e
h
c
a

i

e
z
i
S

s
s
a
C

l

e
t
a
v
i
r
p
/
c
i
l

b
u
p

l

o
o
h
c
S

)
c
i
l

b
u
p
=
1
(

S
C
S
E

l

o
o
h
c
S

e
z
i
s

l

o
o
h
c
S

e
c
n
e
i
c
s

f
o

n
o
i
t
r
o
p
o
r
P

d
e
i
f
i
t
r
e
c

y

l
l

u
f

s
r
e
h
c
a
e
t

y
m
o
n
o
t
u
a

l

o
o
h
c
S

l

)
e
a
m
e
F

1
(

r
e
d
n
e
g

Bulgaria 

Croatia 

Czech Republic 

Estonia 

Finland 

France 

Germany 

Greece 

Italy 

Lithuania 

Luxembourg 

Netherlands 

Portugal 

Slovakia 

Spain 

United Kingdom 

38  Darker  blue  cells  indicate  a  positive  significant  coefficient  and  lighter  blue  cells  indicate  a 
negative statistically significant coefficient.   

 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
 
Table 8.5. Multilevel coefficients for the relationship between teaching practices and learning 
environment in science and students’ science achievement by country39 

Bulgaria 

Croatia 

Czech Republic 

Fixed Part (constant)                                                        

  Gender 
T
N
E
D
U
T
S

ESCS 
Immigration background 
Motivation for achievement 
Class size 

Coef. 

547.1 
5.9 
7.4 
-33.9 
4.4 
-0.3 

Std. 
Error 
40.21 
2.41 
1.54 
12.02 
1.25 
0.60 

Coef. 

417.4 
23.0 
12.4 
-10.7 
7.4 
0.8 

Std. 
Error 
35.94 
2.89 
1.62 
3.66 
1.23 
1.04 

Coef. 

463.3 
17.6 
17.1 
-20.0 
10.2 
1.8 

Std. 
Error 
38.33 
2.25 
1.59 
5.83 
1.22 
0.58 

Instructional practices in science 

T
N
E
M
N
O
R
I
V
N
E
G
N
I
N
R
A
E
L

D
N
A
S
E
C
I
T
C
A
R
P
G
N
I
H
C
A
E
T

L
O
O
H
C
S

Teacher-directed science 
instruction (index) 
Inquiry - The teacher explains 
<school science> idea can be 
applied 
Perceived feedback (index) 
Adaptive instruction (index) 
Teacher quality 
Disciplinary climate in science 
classes 
Teacher support in a science 
classes of students choice 
Ability grouping within classes 
School average PISA index of 
economic. social and cultural 
status 
School private or public 
School size 
Index proportion of science 
teachers fully certified  
Index of school autonomy  

3.8 

1.18 

7.8 

1.08 

5.3 

1.29 

-0.7 

1.29 

-3.5 

1.32 

-3.9 

1.19 

-10.1 
10.8 

1.43 
1.48 

-14.4 
5.3 

1.48 
1.08 

-9.4 
10.5 

1.48 
1.41 

5.2 

1.24 

11.6 

1.31 

8.7 

1.14 

-4.0 

-3.2 

1.33 

4.23 

-3.1 

-2.6 

1.32 

4.10 

-9.4 

1.51 

0.2 

4.00 

84.9 

8.00 

70.4 

7.56 

82.9 

5.56 

-41.3 
0.0 

17.47 
0.01 

22.4 
0.0 

15.68 
0.01 

-23.8 
0.0 

9.25 
0.01 

2.1 

19.08 

15.1 

11.77 

13.4 

13.07 

-14.5 

19.01 

38.2 

25.46 

40.4 

29.26 

39 Statistically significant coefficients are presented in bold.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Table 8.5. Multilevel coefficients for the relationship between teaching practices and learning 
environment in science and students’ science achievement by country (cont.) 

Estonia 

Finland 

France 

Coef. 

Std. 
Error 
34.73 
2.23 
1.58 
3.75 
1.46 
0.24 

4.7 

-1.1 

1.52 

1.59 

549.2 
15.7 
18.7 
-18.7 
13.0 
-0.1 

Fixed Part (constant)                                                        
Gender 
ESCS 
Immigration background 
Motivation for achievement 
Class size 
Instructional practices in science  
Teacher-directed science 
instruction (index) 
Inquiry - The teacher explains 
<school science> idea can be 
applied 
Perceived feedback (index) 
Adaptive instruction (index) 
Teacher quality  
Disciplinary climate in science 
classes 
Teacher support in a science 
classes of students choice 
Ability grouping within classes 
School average PISA index of 
economic. social and cultural 
status 
School private or public 
School size 
Index proportion of science 
teachers fully certified  
Index of school autonomy  

-16.2 
0.0 

-19.2 
11.7 

11.49 
0.01 

1.59 
1.59 

-13.7 

11.88 

22.33 

44.3 

1.69 

1.63 

9.50 

4.10 

-1.5 

9.6 

9.6 

5.3 

Coef. 

600.9 
-6.5 
24.7 
-68.6 
17.1 
0.6 

11.4 

-6.4 

-20.8 
12.6 

-0.5 

-5.4 

14.9 

16.0 
0.0 

6.9 

9.0 

T
N
E
M
N
O
R
I
V
N
E
G
N
I
N
R
A
E
L

T
N
E
D
U
T
S

D
N
A
S
E
C
I
T
C
A
R
P
G
N
I
H
C
A
E
T

L
O
O
H
C
S

Std. 
Error 
27.28 
2.36 
1.78 
7.85 
1.25 
0.69 

Coef. 

464.8 
20.0 
16.6 
-22.9 
7.2 
3.0 

Std. 
Error 
26.21 
2.43 
1.59 
3.92 
1.29 
0.55 

1.59 

7.2 

1.35 

1.56 

-6.5 

1.13 

1.36 
1.84 

-8.3 
3.6 

1.32 
1.43 

1.78 

-1.0 

1.47 

4.07 

-7.2 

4.32 

6.98 

70.5 

6.91 

10.07 
0.01 

11.45 

2.2 
0.0 

6.7 

6.93 
0.01 

9.14 

14.30 

-24.1 

16.91 

5.9 

1.61 

3.2 

1.18 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8.5. Multilevel coefficients for the relationship between teaching practices and learning 
environment in science and students’ science achievement by country (cont.) 

Germany 

Greece 

Italy 

Coef. 

Std. 
Error 
27.69 
3.26 
1.76 
4.99 
1.72 
0.69 

7.6 

-3.0 

1.45 

1.60 

551.5 
21.6 
10.2 
-41.5 
8.6 
-0.4 

Fixed Part (constant)                                                        
Gender 
ESCS 
Immigration background 
Motivation for achievement 
Class size 
Instructional practices in science  
Teacher-directed science 
instruction (index) 
Inquiry - The teacher explains 
<school science> idea can be 
applied 
Perceived feedback (index) 
Adaptive instruction (index) 
Teacher quality  
Disciplinary climate in science 
classes 
Teacher support in a science 
classes of students choice 
Ability grouping within classes 
School average PISA index of 
economic. social and cultural 
status 
School private or public 
School size 
Index proportion of science 
teachers fully certified  
Index of school autonomy  

-15.2 
10.5 

14.16 
0.01 

19.7 
0.0 

2.03 
1.78 

-79.3 

26.92 

82.3 

1.48 

2.09 

8.44 

7.97 

5.07 

-3.2 

8.2 

9.5 

3.8 

Coef. 

528.0 
12.7 
12.9 
-18.1 
11.3 
0.0 

11.8 

-4.2 

-16.4 
6.9 

10.1 

-7.6 

60.1 

-43.2 
0.0 

-4.2 

29.9 

T
N
E
M
N
O
R
I
V
N
E
G
N
I
N
R
A
E
L

T
N
E
D
U
T
S

D
N
A
S
E
C
I
T
C
A
R
P
G
N
I
H
C
A
E
T

L
O
O
H
C
S

Std. 
Error 
28.49 
2.39 
1.52 
5.18 
1.49 
0.39 

Coef. 

553.3 
23.3 
6.5 
-28.0 
3.5 
-0.1 

Std. 
Error 
29.11 
2.47 
1.34 
5.15 
1.52 
0.43 

1.42 

14.4 

1.72 

1.39 

-3.5 

1.35 

1.54 
1.39 

-18.1 
5.7 

1.42 
1.71 

1.60 

5.5 

1.51 

1.36 

-3.5 

1.54 

6.82 

71.9 

6.77 

18.03 
0.02 

-25.7 
0.0 

14.83 
0.01 

11.51 

-24.0 

13.45 

29.45 

-22.0 

29.60 

0.3 

5.29 

2.2 

4.03 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8.5.  Multilevel coefficients for the relationship between teaching practices and learning 
environment in science and students’ science achievement by country (cont.) 

Lithuania 

Luxembourg 

Netherlands 

Fixed Part (constant)                                                        

  Gender 
T
N
E
D
U
T
S

ESCS 
Immigration background 
Motivation for achievement 
Class size 

Instructional practices in science 

T
N
E
M
N
O
R
I
V
N
E
G
N
I
N
R
A
E
L

D
N
A
S
E
C
I
T
C
A
R
P
G
N
I
H
C
A
E
T

L
O
O
H
C
S

Teacher-directed science 
instruction (index) 
Inquiry - The teacher explains 
<school science> idea can be 
applied 
Perceived feedback (index) 
Adaptive instruction (index) 

Teacher quality 

Disciplinary climate in science 
classes 
Teacher support in a science 
classes of students choice 
Ability grouping within classes 
School average PISA index of 
economic. social and cultural 
status 
School private or public 
School size 
Index proportion of science 
teachers fully certified  
Index of school autonomy  

Coef. 

405.2 
6.1 
13.1 
-6.1 
13.3 
1.0 

Std. 
Error 
35.93 
2.52 
1.58 
6.52 
1.05 
0.43 

Coef. 

458.0 
17.8 
16.2 
-15.8 
5.9 
1.0 

Std. 
Error 
33.12 
1.95 
2.35 
3.03 
1.89 
1.01 

Coef. 

345.3 
10.6 
7.6 
-23.4 
12.0 
-0.8 

Std. 
Error 
107.05 
3.88 
2.47 
9.63 
2.56 
1.57 

2.6 

1.27 

11.0 

1.40 

8.8 

2.89 

-5.9 

1.27 

-10.0 

1.51 

-7.2 

2.25 

-13.6 
9.0 

1.64 
1.55 

-21.6 
6.0 

2.66 
1.97 

-18.1 
17.2 

2.10 
2.44 

7.8 

1.27 

11.9 

1.29 

7.2 

2.17 

0.2 

0.0 

1.40 

4.22 

-4.7 

1.60 

-6.8 

2.39 

10.2 

7.56 

26.1 

11.26 

51.3 

7.13 

59.4 

5.77 

124.6 

21.02 

24.9 
0.0 

16.74 
0.01 

-9.3 
0.0 

9.17 
0.01 

10.0 
0.0 

10.12 
0.01 

34.3 

17.03 

1.7 

9.83 

-5.5 

31.12 

16.2 

15.94 

-3.1 

16.97 

128.1 

97.87 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8.5. Multilevel coefficients for the relationship between teaching practices and learning 
environment in science and students’ science achievement by country (cont.) 

Fixed Part (constant)     

  Gender 
T
N
E
D
U
T
S

ESCS 
Immigration background 
Motivation for achievement 
Class size 

Instructional practices in science 

T
N
E
M
N
O
R
I
V
N
E
G
N
I
N
R
A
E
L

D
N
A
S
E
C
I
T
C
A
R
P
G
N
I
H
C
A
E
T

L
O
O
H
C
S

Teacher-directed science 
instruction (index) 
Inquiry - The teacher explains 
<school science> idea can be 
applied 
Perceived feedback (index) 
Adaptive instruction (index) 

Teacher quality 

Disciplinary climate in science 
classes 
Teacher support in a science 
classes of students choice 
Ability grouping within classes 
School average PISA index of 
economic. social and cultural 
status 
School private or public 
School size 
Index proportion of science 
teachers fully certified  
Index of school autonomy 

Portugal 

Slovakia 

Spain 

Coef. 

553.4 
19.3 
20.6 
-12.3 
16.0 
0.4 

Std. 
Error 
34.73 
2.92 
1.74 
6.45 
2.04 
0.85 

Coef. 

429.3 
16.9 
11.6 
-43.8 
11.6 
2.3 

Std. 
Error 
31.39 
2.25 
1.77 
12.00 
1.37 
0.61 

Coef. 

580.6 
11.7 
17.4 
-30.7 
15.9 
0.0 

Std. 
Error 
18.61 
2.30 
1.11 
4.21 
1.44 
0.20 

8.0 

1.61 

4.1 

1.20 

14.6 

1.58 

-6.3 

2.03 

-4.0 

1.34 

-9.6 

1.61 

-20.6 
10.0 

1.83 
2.05 

-12.8 
8.1 

1.47 
1.36 

-19.5 
4.8 

1.61 
1.71 

10.8 

1.96 

13.6 

1.91 

9.3 

1.47 

-4.7 

-4.6 

1.96 

6.71 

-7.9 

-3.3 

1.08 

-6.7 

1.67 

3.36 

0.5 

3.33 

33.8 

4.27 

58.7 

6.88 

14.9 

3.54 

-1.4 
0.0 

-0.8 

11.00 
0.00 

2.7 
0.0 

5.37 
0.01 

-2.9 
0.0 

5.29 
0.00 

14.28 

28.8 

10.81 

-1.7 

6.82 

-19.5 

17.08 

16.0 

18.30 

-15.9 

11.51 

 
 
 
 
 
 
Table 8.5. Multilevel coefficients for the relationship between teaching practices and learning 
environment in science and students’ science achievement by country (cont.) 

T
N
E
M
N
O
R
I
V
N
E
G
N
I
N
R
A
E
L

T
N
E
D
U
T
S

D
N
A
S
E
C
I
T
C
A
R
P
G
N
I
H
C
A
E
T

L
O
O
H
C
S

Fixed Part (constant)     
Gender 
ESCS 
Immigration background 
Motivation for achievement 
Class size 
Instructional practices in science 
Teacher-directed science 
instruction (index) 
Inquiry - The teacher explains 
<school science> idea can be 
applied 
Perceived feedback (index) 
Adaptive instruction (index) 
Teacher quality 

Disciplinary climate in science 
classes 
Teacher support in a science 
classes of students choice 
Ability grouping within classes 
School average PISA index of 
economic. social and cultural 
status 
School private or public 
School size 
Index proportion of science 
teachers fully certified  
Index of school autonomy 

United Kingdom 

Coef. 

493.7 
7.9 
17.6 
-18.1 
8.2 
0.3 

Std. 
Error 
24.53 
2.46 
1.79 
4.39 
1.40 
0.54 

8.9 

1.99 

-5.5 

1.68 

-15.6 
13.1 

1.89 
2.25 

12.6 

1.48 

-5.7 

-0.1 

2.07 

3.47 

62.1 

6.60 

-5.8 
0.0 

8.91 
0.01 

11.3 

10.64 

28.8 

11.55 

 
 
 
 
 
 
 
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doi:10.2760/860512 

ISBN 978-92-79-76294-9