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Ho and Gan ﻿Language Testing in Asia
https://doi.org/10.1186/s40468-023-00261-1

Language Testing in Asia

Open Access

RESEARCH

Instructional practices and students’ reading
performance: a comparative study of 10 top
performing regions in PISA 2018
Soi Kei Ho1 and Zhengdong Gan1**Correspondence:zhengdonggan@um.edu.mo
1

Faculty of Education, Universityof Macau, Taipa, Macau, China

Abstract
This comparative study investigated the associations between instructional practices
and students’ reading performance among 10 top performing regions that participated in the Program for International Student Assessment (PISA) 2018. A nationally
representative sample consisting of 80,016 15-year-old students from 5 Asian regions(B-S-J-Z [China], Singapore, Macao, Hong Kong, and Korea) and 5 Western regions (Estonia, Canada, Finland, Ireland, and Poland) were included. A secondary analysis of PISA
survey and assessment data was conducted. T test and ANOVA analyses revealed
systematic differences in instructional practices of the 10 regions. B-S-J-Z (China) had
significantly higher levels of teacher support, teacher-directed instruction, and teacher
stimulation than the other sample regions. Asian regions tended to have higher levelsof teacher support, teacher-directed instruction, teacher feedback, adaptive instruction, and teacher enthusiasm compared with Western regions, although variations
were also found within Asian regions or within Western regions. Hierarchical linear
regression (HLR) analyses indicated that reading performance was positively predictedby teacher support, adaptive instruction, teacher stimulation, and teacher enthusiasm,
but negatively predicted by teacher-directed instruction and teacher feedback. This
study sheds light on the effective instructional practices for optimizing students’ reading performance across different cultural contexts.
Keywords: International assessment, PISA 2018, Instructional practices, Reading
performance, Comparative education

Introduction
As a crucial competency for living and learning, reading is one core subject assessedin the Program for International Student Assessment (PISA). The outstanding performance of the top performing regions has always attracted researchers to study the factors underpinning their academic success (e.g., Lau & Ho, 2015; Qian & Lau, 2022).
Instructional practice is among one of the key factors contributing to high levels of reading performance (Karaman, 2022; Nurmi et al., 2013; OECD, 2019a). While a plethora ofresearch has investigated the associations between instructional practices and reading
performance, relatively little attention is given to the role of cultural context in shaping© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permitsuse, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third
party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or
exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
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this relationship (Haw & King, 2022). This comparative study sought to delve into thisissue further by examining PISA data.
PISA is a triennial international large-scale assessment test conducted by theOrganization for Economic Cooperation and Development (OECD). PISA evaluates
the effectiveness of education systems worldwide by measuring the knowledge andcognitive skills of 15-year-old students from OECD member and partner countries(OECD, 2019a, 2019b). The latest PISA assessment (i.e., PISA 2018) focuses primarily
on reading, supplemented with mathematics and science. Besides assessing students’performance in these subjects, PISA 2018 surveys contextual factors that may impact
students’ reading ability (OECD, 2019a). Of relevance to this study, several variablesin the PISA questionnaire specifically target students’ perceived reading instructional
practices, which can then be related to their performance (Koyuncu & Fırat, 2020;
OECD, 2019b; Rojas-Torres et al., 2021). Drawing on data from PISA 2018, this study
compared 10 top performing regions in terms of their instructional practices, andexamined the associations between instructional practices and reading performancethrough a cross-cultural lens.
Reading literacy: PISA 2018 reading performance assessment framework

According to OECD (2019b), reading literacy is defined as ‘understanding, using, evaluating, reflecting on, and engaging with texts in order to achieve one’s goals, to develop
one’s knowledge and potential, and to participate in society’ (p. 28). The skill of reading is not just important for living in the contemporary society, it is also important forlearning and demonstrating skills in other academic subjects (e.g., Nortvedt et al., 2016;
OECD, 2019a; Yang et al., 2018). Measures of reading literacy are provided by international test assessments such as PISA.
Aligned with the reading assessment framework of previous cycles, PISA 2018 measures students’ reading performance based on three categories of cognitive processes—
locating info (PVRCLI), understanding (PVRCUN), and evaluating and reflecting(PVRCER). PVRCLI is about getting and retrieving information in a piece of writing,as well as finding and choosing the relevant text. PVRCUN involves comprehending theliteral meaning of the text and making inferences. PVRCER refers to judging the quality and reliability of the text, as well as pondering its content and structure (see readingassessment subscales; OECD, 2019a, 2019b).
Recognizing the growing importance of digital literacy, PISA 2018 was expanded
to incorporate digital reading as a crucial component in the assessment (OECD,
2019a). Reflecting this changing nature of reading literacy, PISA 2018 also assesses
students’ ability to read and understand different text structures such as singlesource texts (PVRTSN) and the ability to read multiple-source texts, static anddynamic texts, continuous and non-continuous texts, and mixed texts (PVRTML)
on different reading situations (personal reading; educational reading; occupational
reading). The assessment focuses on the dynamic reading skills which enable learners to cope with the demands of reading in the digital era (Hu & Wang, 2022; Yanget al., 2018).
Understanding the factors that promote success in reading is crucial. According
to OECD (2019a), a number of factors could facilitate the development of reading

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literacy. These include individual factors such as individual reading practices andmotivation, as well as school factors such as teaching practices and classroom support
for reading growth and engagement. Among these factors, high-quality instructional
practice is of great importance (Bozkurt, 2022; Karaman, 2022; Qian & Lau, 2022).
High-quality instructional practice builds foundational reading skills, and promotes
reading motivation and engagement (Haw & King, 2022; Nurmi et al., 2013). With theincreasingly digital world, it is timely for research to inform how instructional practices may be adjusted to align with this broader conceptualization of literacy—digital
literacy (Hu & Wang, 2022).
Indices of reading instructional practice in PISA 2018

Instructional practice refers to the things that happen in the classroom (Depaepe &König, 2018). It is a domain-specific construct studied with a wide range of dimensions and theoretical frameworks (Francisco & Celon, 2020; Guthrie et al., 2012). InPISA assessment, instructional practice refers to a broad range of teaching and learning
activities. Drawing on the Teaching, Monitoring, Questioning Techniques framework(TMQT), PISA 2018 formulated a model of the practices of reading instruction encompassing three dimensions—cognitive activation, classroom management and structure,and classroom supportive climate (Gu & Lau, 2023; OECD, 2019a).
Indices of reading instructional practice were included in PISA 2018 to suggest theirimportance on reading literacy. The index teachers’ stimulation of reading engagement(STIMREAD), derived from the dimension of cognitive activation, refers to the strategies
teachers use to foster students’ motivation and engagement in reading. Teacher-directed
instruction (DIRINS), based on the dimension of classroom management and structure,refers to the teaching approach in which teachers plan, structure, and deliver the lesson. Teacher academic support (TEACHSUP), teacher feedback (PERFEED), adaptive
instruction (ADAPTIVITY), and teacher enthusiasm (TEACHINT) are derived from thedimension of classroom supportive climate. TEACHSUP refers to the support and guidance provided by teachers for facilitating students’ learning in reading. PERFEED refers
to the extent teachers give comments to students regarding their reading performance.
ADAPTIVITY refers to the adjustments teachers make in their instruction for meeting
students’ learning needs. TEACHINT refers to the passion and excitement that teachers
demonstrate in teaching.
Most of the abovementioned indices aligned with that of the previous cycle with reading as the focus (i.e., PISA 2009). For instance, TEACHSUP, DIRINS, PERFEED, andTEACHINT are completely identifiable with those in PISA 2009. However, one index
has been slightly revised (i.e., STIMREAD) and one has been newly added (i.e., ADAPTIVITY). These indices were designed to provide a comprehensive assessment regarding
the quality of reading instruction (Hu & Wang, 2022). It should be noted that these indices of reading instructional practices are applicable to all classes in general; PISA assessment adopts a generic view of literacy, thus the general skills of reading are assumed tobe taught in many different subjects (OECD, 2019a).

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Instructional practices and reading performance: a review of PISA studies

Reading literacy is a main goal of education. It is well-established that effective reading
instruction is essential for students to become proficient readers and to develop strong
literacy skills (Nurmi et al., 2013). Teaching practices encompassing explicit and systematic instruction in the key skills of reading significantly impact on students’ language
learning outcomes such as achievement, motivation, and engagement (e.g., Lau & Ho,
2015; Guthrie et al., 2012).
Such a relationship has also been observed in studies that utilized PISA data (e.g., PISA
2009, 2018). The studies concerned have identified key instructional practices associatedwith reading performance from various theoretical perspectives. For instance, Haw et al.
(2021), through the lens of self-determination theory, found that need supportive teaching was positively correlated with reading achievement. Meng et al. (2017), adopting
Stronge’s teaching dimensions, found that teacher feedback and class discussion wererelated to reading performance. Additionally, Qian and Lau (2022), using PISA reading
instruction dimensions, found that classroom disciplinary climate, adaptive instruction, and stimulation of reading engagement were associated with reading performance.
Although empirical evidence is not lacking, the extant literature has yielded inconsistentfindings. While some studies have shown that teacher feedback is associated with reading performance (Gu & Lau, 2023; Meng et al., 2017), other studies have found either anegative or insignificant contribution (Bozkurt, 2022; Qian & Lau, 2022). Similarly, thelink between teacher-directed instruction and reading literacy is positive in some studies(e.g., Karaman, 2022), but negative in the others (e.g., Hu & Wang, 2022; Ma et al., 2022;
Qian & Lau, 2022). There is little consensus on what constitutes effective instruction forsuccessful reading outcomes.
The inconsistent findings in the literature can be attributed to factors such as differences in participants’ proficiency levels and the contexts in which they are situated in.
Most of these studies involve participants with varying proficiency levels. It is worth
noting that instructional practices can function more or less effectively depending on
students’ abilities and prior knowledge (Lau & Lam, 2017; Medina & McGregor, 2019).
Also, these studies either focused on a single region, or many regions with diverse cultural backgrounds. However, the effectiveness of instructional practice is greatly influenced by the unique characteristics and environments inherent in each educational
system (Yang et al., 2020). Comparative studies involving students with similar levels ofachievement from different cultural regions are, therefore, needed to clarify the associations between instructional practices and reading performance.
Instructional practices and reading performance in PISA studies: potential influencesof cultural context

Contextual factors such as culture play an important role in shaping the effectiveness of
instructional practices (e.g., Gu & Lau, 2023; Ma et al., 2022). Different educational systems emphasize different instructional practices due to varying cultural and pedagogical beliefs (Yang et al., 2020). East-Asian education is often characterized by high levelsof teacher-centered instruction, whereas Western education is typically more studentcentered (Gan et al., 2023). As revealed in PISA studies, Chinese students reported amore disciplinary climate and lower levels of teacher stimulation and scaffolding than

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their Western counterparts (Lau & Ho, 2015; Ho & Lau, 2018). Due to the differences in
the preferences for instructional practices across cultural contexts, the effectiveness of aparticular instructional practice may also differ across cultures. For example, cross-cultural comparative studies have revealed that the association between teachers’ reading
stimulation and performance was significant in the Chinese context but not in the USA(Meng et al., 2017). These findings suggest that an instructional practice which is effective in one cultural context may be ineffective in another due to cultural differences. As
a result, a cross-cultural investigation into the associations between instructional practices and performance is warranted.
Indeed, cross-cultural comparative research on the impact of instructional practiceon reading performance is of growing interest. For example, Ma et al. (2022) examined the relations between perceived teacher feedback and reading achievement acrossindividualistic and collectivistic cultures. They found that the indirect effect of teacher
feedback on reading achievement via reading self-concept was stronger in attentive or
harmonious classroom climate, particularly in Asian regions such as B-S-J-Z (China)
and Singapore. Similarly, Gu and Lau (2023) investigated the associations between reading instruction and reading performance in B-S-J-Z (China), Hong Kong, and Chinese
Taipei. It was found that disciplinary climate and teacher support positively predicted
reading performance for all the three Chinese regions. However, the link between reading instruction (teacher’s stimulation of reading engagement and adaptive instruction)and reading performance was significant in B-S-J-Z (China) and Hong Kong, but not in
Chinese Taipei.
Although these studies have identified notable cross-cultural and intra-cultural similarities and differences in the effectiveness of reading instruction on reading performance, there remains a gap regarding the similarities and differences in the effective
instructional practices that promote high reading performance across different regions(i.e., top performing regions in PISA 2018). Using PISA data as a comprehensive basis
for cross-cultural comparison, we may better understand how cultural contexts shapethe effectiveness of instructional practice for fostering success in reading.
A cross‑cultural comparative study of instructional practices and reading performancein PISA studies: theoretical framework

The influence of instructional practices on students’ learning outcomes can be understood from the sociocultural constructivist perspective. Sociocultural theory (SCT;
Vygotsky, 1986) posits that both the environment and culture play an influencing rolein individual’s learning. Learning occurs within the context of sociocultural interactions
that involve both the learner and teacher (Mascolo, 2009). From this perspective, reading is considered as a sociocultural and cognitive process (Tracey & Morrow, 2006; Liet al., 2022). When the learner acquires cultural knowledge in the text and connect it
to his or her personal interests and experience, meaning in reading is constructed (VanOers, 2009). Effective instruction requires the teacher to direct the learners, help themfacilitate understanding (e.g., through teacher-directed instruction), provide them withscaffolding (e.g., through teacher feedback), and engage them (e.g., through stimulationof reading engagement) as they construct meaning for themselves (Guthrie et al., 2012;
Mascolo, 2009). Previous studies have applied SCT in studying instructional practices

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and students’ learning outcomes. For instance, Van Rijk et al. (2017) adopted SCT toexamine the characteristics of learning environment that promotes students’ reading
comprehension. In Li et al.’s (2022) study, the effectiveness of strategy-based reading
instruction program developed from SCT was investigated in relation to reading performance of language learners from diverse cultures. SCT may shed light on the most effective instructional practices for reading performance in international assessments (e.g.,
PISA) for different cultural contexts.
Summary

Reading is the focus of assessment in PISA 2018. Many studies have utilized PISA data
to examine the factors contributing to students’ reading performance, with instruction
practices being one crucial factor. However, inconclusive findings have been observed
regarding some indices of instructional practice that contribute to reading performance.
It is unclear as to what instructional practices lead to high reading performance. Also,
limited attention has been given to the confounding influence of cultural context on thisrelationship. As such, additional comparative research is needed to ascertain the extent
to which instructional practice foster high reading performance across different regions,particularly the top performing ones. The insights gleamed may inform policies andpractices aimed at enhancing reading performance with the development of more culturally relevant instructional practices.
The present study

Therefore, the purpose of this study was to examine the associations between instructional practices and reading performance in PISA 2018, and whether such associations
vary across different regions. For the analysis, we intentionally chose 10 regions to focus
on, namely Beijing-Shanghai-Jiangsu-Zhejiang (B-S-J-Z [China]), Singapore, Macao,
Hong Kong, Estonia, Canada, Finland, Ireland, Korea, and Poland. These regions were
the top performers in PISA 2018. By studying these explementary examples, we may
provide robust evidence on the specific instructional practices that relate to high reading performance. Given that this selection includes both Asian and Western regions, across-cultural comparison is made possible. Unlike comparative studies that typicallyuse China to represent Asian culture and America to represent Western culture, thisselection includes more diverse regions. In addition to revealing the best practices pertaining to each region, we may also unveil important variations within each culturalgroup (e.g., Asian regions) that have previously been overlooked. There are two researchquestions that this study aims to address: (1) What are the levels of instructional practices and reading performances of the 10 top performing regions in PISA 2018? (2) How
are the instructional practices of these 10 regions related to reading performances in
PISA 2018?

MethodThe data source and sample

This study utilized questionnaire and assessment data from PISA 2018. The dataset
for secondary analysis was downloaded from PISA database, which can be assessed
on OECD website (http://​www.​oecd.​org/​pisa/​data/​2018d​ataba​se). The 2018 data wasPage 6 of 23

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selected for analysis as it is the most recent version with reading being the main assessment domain. Approximately 600,000 students aged 15 from 79 regions participated in
PISA 2018. Student aged 15 who are in the seventh grade or above were recruited randomly using a two-stage stratified sampling method by which schools are sampled first,
and subsequently the classes within the sampled schools are chosen (see PISA technical report for the details; OECD, 2019c). Given the aims of this study, a total of 80,016
students with a mean age of 15 years old from 2963 schools within the 10 top performing regions were selected. Specifically, 12,058 (15.1%) were from B-S-J-Z (China), 6676
(8.3%) were from Singapore, 3775 (4.7%) were from Macao, 6037 (7.6%) were from Hong
Kong, 5316 (6.6%) were from Estonia, 22,653 (28.3%) were from Canada, 5649 (7.1%)
were from Finland, 5577 (7.0%) were from Ireland, 6650 (8.3%) were from Korea, and5625 (7.0%) were from Poland. Among all these students, 40,591 were male (50.7%) and
39,425 were female (49.3%). As of their cultural background, 35,196 were from Asian
regions (43.9%), whereas 44,820 were from Western regions (56.1%). The demographic
information for the sample regions was shown in Table 1.
Measures
Reading performance

PISA 2018 reading test used questionnaire and cognitive items to measure students’reading literacy—the ability to understand, use and reflect on written and digital texts(i.e., the three cognitive processes). In the 1-h computer-based test, students were given
3 sets of reading tasks and responded to multiple choice and open-ended questions. The
test items correspond to students’ abilities due to an adaptive approach to assessment. In
such an approach, the difficulty level of tasks is determined based on students’ performance in prior stages (OECD, 2019a).

Table 1 Demographic information of the 10 top performing regions in PISA 2018
Regions

SampleFemaleMale

School

N (%)

N (%)

N (%)

N (%)

Asian Regions
B-S-J-Z (China)

12,058 (15.1)

5775 (47.9)6283 (52.1)

362

Singapore

6676(8.3)

3277 (49.1)

3399 (50.9)

167

Macao

3775(4.7)

1862 (49.3)

1913 (50.7)

45

Hong Kong6037 (7.6)

2955 (48.9)3082 (51.1)

174

Korea

6650 (8.3)

3191 (48.2)

3459 (51.8)

188

Western Regions
Estonia

5316(6.6)

2651 (49.9)

2665 (50.1)

232

Canada22,653 (28.3)

11,307 (49.9)

11,344 (50.1)

914

Finland5649 (7.1)

2772 (49.1)

2877 (50.9)

214

Ireland5577 (7.0)

2777 (49.7)

2800 (50.3)

157

Poland5625 (7.0)

2857 (50.7)2768 (49.3)

240

Total
Asian Regions

35,196 (43.9)

17,060 (48.4)

18,136 (51.6)

936

Western Regions

44,820 (56.1)

22,364 (49.8)

22,454 (50.2)

1757

All regions

80,016 (100.0)

39,424 (49.2)

40,590 (50.8)

2693

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The overall reading performance (PVREAD) is represented by 10 plausible values(PVs), with the results standardized with a mean of 500 and SD of 100 using the Raschmethod. PISA also provided 10 PVs for each assessment subscale (PVRCLI, PVRCUN,PVRCER, PVRTSN, and PVRTML). The 10 PVs are generated from posteriori distribution by combining Item response theory (IRT) scaling of items and a regression modelwith questionnaire data. Compared with an overall composite score, the 10 PVs depict a
more accurate estimation of students’ possible ability range. PISA recommends researchers to use all these 10 PVs for secondary data analysis (see technical report; OECD,
2019c). As in most PISA studies (e.g., Ma et al., 2021; Meng et al., 2017), students’ mean
reading scores in 10 plausible values (i.e., variables PV1READ to PV10READ) were usedas the outcome variable of this study.

Instructional practices

The PISA 2018 student questionnaire used 6 sets of indices (a total of 22 items) to measure students’ perception of reading instructional practices in their classes in general(OECD, 2019a, 2019c). The indices included TEACHSUP, DIRINS, PERFEED, ADAPTIVITY, STIMREAD, and TEACHINT. TEACHSUP was assessed based on 4 items
measuring the frequency students perceived their teachers provided support in learning. DIRINS was assessed based on 4 items regarding how often students perceived
their teachers provide direct instruction when teaching reading. PERFEED was basedon 5 items that assess how often students perceived their teachers to provide feedback.
ADAPTIVITY was assessed with 3 items on the extent to which students perceived theirteachers to be adaptive in teaching. STIMREAD was assessed with 4 items about how
often students perceived their teachers to provide stimulation. TEACHINT was basedon 4 items measuring the degree to which students perceived their teachers to be enthusiastic with teaching. Students responded to all these 22 items with a 4-point Likert scalefrom 1 (never or hardly ever/never or almost never/strongly agree) to 4 (every lesson/all
lessons/every lesson or almost every lesson/strongly disagree). For the complete items
and response options, see Appendix.
All the above indices were scaled using weighted warm likelihood estimate to have amean of 0 and a standard deviation of 1. Positive values indicate having more positive
attitudes of instructional practices perceived than the average OECD student (OECD,
2019c). In our analyses, the weighted likelihood estimates (WLE) scores were used for all
reading instructional variables (Khorramdel et al., 2020).

Demographic variables

Student-level demographics (gender and socioeconomic status) were included as control variables given their confounding effects on students’ reading performance in PISA
2018. Previous studies have provided evidence of gender differences in reading achievement with girls consistently outperforming boys (e.g., Khorramdel et al., 2020; Ma et al.,2022). Another important variable to consider is socioeconomic status. Socioeconomically advantaged students tend to demonstrate greater reading motivation and successthan their disadvantaged peers (e.g., Koyuncu & Fırat, 2020; OECD, 2019a; Qian & Lau,

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2022). In the current study, students’ socioeconomic status was represented by the economic and sociocultural status (ESCS) index. This index was based on the weighted
mean of three indicators (parental occupation, parental educational level, and homepossessions) scaled with a mean of 0 and a standard deviation of 1. Gender (GENDER)
was coded as a dichotic variable (1 = female, 2 = male).
Data analysis

All data were analyzed using SPSS 20.0. The subjects with missing data were excluded.
Descriptive statistics and zero-order correlations were calculated for all variables ineach of the sample regions. To examine the levels of reading performance of different
regions (RQ1), we calculated their mean reading and subscales scores (PVREAD, PVRCLI, PVRCUN, PVRCER, PVRTSN, and PVRTML). Within each region, the scoresof different reading subscales were then compared to explore its respective strengths
in reading. To examine differences in the levels of instructional practices across the
10 regions (RQ1), one-way ANOVA analyses were performed with the 10 regions as
independent variables and the 6 instructional practices as dependent variables (anexception was for Canada with only data from ADAPTIVITY and STIMREAD). Follow-up post-hoc tests (multiple comparison of means) were conducted using Bonferroni method for revealing cross-regional differences. To further examine whether
the differences are influenced by the cultural context, independent t tests were conducted for all regions within two cultural groupings: Asian and Western regions. The
6 instructional practices were used as the dependent variables, with the two culturalgroupings as the independent variables. To test the associations between instructional
practices and reading performance (RQ2), hierarchical linear regression (HLR) analyses were performed for all region samples, as well as each individual region sample.
The outcome variable was students’ reading performance (PVREAD) and the independent variables were the 6 indices of instructional practices (TEACHSUP, DIRINS,PERFEED, ADAPTIVITY, STIMREAD, and TEACHINT). In step 1, demographics
variables were entered as covariates. In step 2, the variables of instructional practices
were entered. In every step, the fit indices and R2 change of the model were assessed.
The regression analyses were conducted for all the 10 reading PVs, incorporating thesampling weights. For each predictor, the mean of all the 10 regression coefficientsobtained was considered (Rojas-Torres et al., 2021). The level of statistical significancewas set at p < 0.05.

Results
Regional differences in reading performance

Table 2 shows the means and standard deviations of reading performance for all the 10
top performing regions in PISA 2018. The overall reading score (PVREAD) and subscale scores measuring reading performance in different cognitive processes (PVRCLI,PVRCUN, and PVRCER) and text structures (PVRTSN and PVRTML) were presented.
Among the 10 regions, B-S-J-Z (China) achieved the highest scores in all subscales. In
terms of its performance, B-S-J-Z (China) demonstrated higher performance in PVRCER
compared to other subscales. Canada and some Asian regions including Singapore,

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Table 2 Means and standard deviations of the 10 top performing regions on overall reading
performance and scores of reading assessment subscales in PISA 2018
RegionsN

PVREADPVRCLIPVRCUNPVRCER

PVRTSNPVRTML

M(SD)

M(SD)

M(SD)

M(SD)

M(SD)

M(SD)

B-S-J-Z (China) 12,058

560.51(86.14)

588.45(88.87)

565.30(84.57)

569.97a(90.74)

560.03(86.76)

569.09(85.22)

Singapore

6676

548.45(106.92)

551.91(100.34)

547.07(106.57)

559.43a(113.21)

552.52(108.22) 551.33(105.78)

Macao

3775

525.07(88.37)

528.53(82.29)

528.81(87.84)

533.72a(89.77)

528.92(87.10)

530.41(86.33)

Hong Kong

6037

524.28(99.48)

531.25(92.87)

532.33(95.47)

534.15a(93.70)

532.24(92.35)

532.20(96.18)

Estonia

5316

523.27(89.66)

528.18(85.87)

525.94(90.04)

521.80(91.36)

521.94(87.97)

529.11(89.03)

Canada22,653508.64(98.21)

507.48(92.04)

506.74(99.96)

514.58a(102.53)

506.74(99.22)

510.79(98.19)

Finland5649

520.32(96.13)

526.15a(96.23)

518.15(98.26)

516.94(97.25)

518.01(98.89)

520.14(95.43)

Ireland5577

517.70(87.18)

520.26a(86.55)

509.85(88.83)

518.98(92.58)

512.20(90.50)

516.19(89.60)

Korea

6650

515.74(97.91)

522.55(99.29)

523.52(98.27)

523.70(103.43)

519.86(100.52) 527.10a(98.72)

Poland5625

512.63(93.16)

514.82(94.07)

514.55(94.10)

514.82(92.79)

513.14(94.24)

514.98(92.53)

Total

80,016525.25(96.39)

527.01(93.97)

526.14(97.52)

530.78(100.34)

525.28(97.53)

529.40(96.73)

PVRCLI, PVRCUN, and PVRCER are the cognitive process subscales, and PVRTSN, and PVRTML are the text structure subscales
in reading
PVREAD Overall reading performance, PVRCLI Locating information, PVRCUN Understanding, PVRCER Evaluating andreflecting, PVRTSN Reading single text, PVRTML Reading multiple textaIndicates scores significantly higher than other assessment subscales

Table 3 Means and standard deviations of the 10 top performing regions on perceived
instructional practices in PISA 2018 (N = 80,016)
Regions

N

TEACHSUPDIRINSPERFEED

ADAPTIVITYSTIMREADTEACHINT

M(SD)

M(SD)

M(SD)

M(SD)

M(SD)

M(SD)

Asian Regions
B-S-J-Z (China)

12,058.41a(.88).50a(1.01).34(1.03).42(1.03).63a(1.03).37(.97)

Singapore

6676.24(.89)− .02(.97).42a(.93).29(.95).17(.94).27(.96)− .07(1.06).11(.89).00(.93).12(.95).05(.90).16(1.16).41(1.03).33(1.07).42(.93)

Macao

3775

Hong Kong

6037

Korea

6650

− .08(.89)− .02(.96).17(.92)− .01(.92).43(1.06)− .13(.85)− .20(.85)− .06(.88)− .11(.82)

Western Regions
Estonia

5316

Canada22,653

n/a

n/a

n/a

.17(1.08).22(1.06)

Finland5649.20(.90)5577.15(.98)− .16(.92).05(.95)

Ireland− .10(.96)− .20a(.94)

Poland5625− .25 (1.00)− .17 (.93).05 (.88)

35,196.20a(.92).23a(1.05).23a(1.01)

Total
Asian Regions
Western Regions

44,820

All regions

80,016− .12(.93)− .00(.97).12(.95)− .05(.89)− .20(.97)− .13(.94).09(1.02)− .14(.91).30(.96)− .14(.91)− .01(.92)− .11(.93)− .09(.96)

n/a− .14(.93).06(.96).13(.94)− .17 (.99).10 (1.00)− .24a (.98).26a(1.01).32(1.02).25a(.95).01(.94).05(1.02).09(1.02).15(.99).14(1.02).19(1.03)− .08(.96).12(.97)

TEACHSUP Teacher academic support, DIRINS Teacher-directed instruction, PERFEED Teacher feedback, ADAPTIVITY Adaptive
instruction, STIMREAD Teachers’ stimulation of reading engagement, TEACHINT Teacher enthusiasmaIndicates significantly higher/lower levels perceived in comparison to other regions or groups

Macao, and Hong Kong also exhibited better performance in PVRCER. Korea had ahigher score in PVRTML while Finland and Ireland had higher scores in PVRCLI. Estonia and Poland showed consistent performance across all subscales.

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Regional differences in instructional practices

Table 3 presents the means and standard deviations for each instructional practice.
One-way ANOVA revealed significant cross-regional differences for TEACHSUP(F[8, 56690] = 375.295, p < .001), DIRINS (F[8, 56651] = 553.743, p < .001), PERFEED (F[8, 56418] = 321.272, p < .001), STIMREAD (F[9, 77017] = 465.02, p < .001),
and TEACHINT (F[8, 56554] = 447.47, p < .001). B-S-J-Z (China) had significantly
higher levels of TEACHSUP, DIRINS, and STIMREAD compared to the other sample regions. Singapore had a significantly higher level of PERFEED than the otherregions. In contrast, Finland had a lower level of STIMREAD whereas Poland had asignificantly lower level of TEACHINT than the others.
The results of post-hoc testing with Bonferroni method revealed some differences
within Asian or Western regions. For TEACHSUP, Macao and Hong Kong had significantly lower scores compared to other Asian regions, such as B-S-J-Z (China), Singapore, and Korea. Similarly, Finland and Ireland had higher TEACHSUP scores than their
Western counterparts, such as Estonia and Poland. Although DIRINS was found to behigh in Asian regions such as B-S-J-Z (China) and Korea, scores below the OECD average were found for Singapore, Macao, and Hong Kong. As for STIMREAD, Estonia andFinland had lower scores than the other Western regions, such as Canada, Ireland, andPoland.
Further to these, independent t tests showed that cultural context had a significant
effect on TEACHSUP (t[56697] = 26.031, p < .001), DIRINS (t[56658] = 42.675, p < .001),
PERFEED (t[54625] = 25.636, p < .001), ADAPTIVITY (t[76860] = 28.306, p < .001), andTEACHINT (t[56561] = 41.927, p < .001), but not for STIMREAD (p > .05). The Asian
sample regions showed significantly higher levels of TEACHSUP, DIRINS, and TEACHINT compared to both the Western regions and the OECD average. The Western regions
had levels of these variables that were below the OECD average. On the other side, theAsian regions displayed significantly higher levels of PERFEED and ADAPTIVITY thanWestern regions, despite both regions having scores higher than the OECD average.
STIMREAD was above the OECD average for both Asian and Western regions.
Instructional practices and students’ reading performance

Prior to the regression analyses, the correlations among all variables were calculated
for all sample regions. As shown in Table 4, the demographic variables GENDER and
ESCS were found to have a significant negative and positive correlation, respectively,
with overall reading performance. In the subsequent analysis, GENDER and ESCS
were controlled to partial out their influences on the outcome variable. In all sample
regions, TEACHSUP, ADAPTIVITY, STIMREAD, and TEACHINT were significantly
and positively correlated with reading performance in all sample regions. The correlation between PERFEED and reading performance was either negative or insignificant insome Asian and Western regions such as Macao, Korea, Estonia, Finland, and Poland.
On the other hand, DIRINS showed a negative correlation with reading in both Asianand Westerns regions. However, in Macao and Korea, it was positively associated withreading performance.**p < 0.001, *p < 0.05
HLR models shows the contribution of instructional practices to reading performance for all the 10 regions (Table 5). The first model with covariates was significant (FPage 11 of 23

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Page 12 of 23

Table 4 Zero-order correlations among all variables for the 10 top performing regions in PISA 2018
(N = 80,016)
Regions

N

Correlation with overall reading performance
GENDERESCS

TEACHSUPDIRINSPERFEED

ADAPTIVITYSTIMREADTEACHINT

Asian regions
B-S-J-Z(China)12,058− .09**

.39**

.05**

− .04**

.01**

.09**

.19**

.14**

Singapore

6676− .11**

.38**

.00

− .10**

.03*

.08**

.12**

.06**

Macao

3775

.13**

.06**

.03*

.01

.11**

.05*

Hong
Kong

6037

− .17**

.21**

.04*

.01

.01**

.08**

.11**

.11**

Korea

6650

− .10**

.28**

.13**

.08**

− .04*

.10**

.07**

.14**

Western Regions
Estonia

5316

Canada22,653Finland5649

Ireland

5577

Poland5625

Asian
Regions− .12**− .18**− .15**

.09**
n/a

.31**

.09**

.33**

.03*

− .17**

.34**

.00

35,196− .11**.28**

Western
Regions

44,820− .17**

All
Regions

80,016− .14**

Total− .26**

.26**
.26**

− .12**− .05**

n/a− .05**

− .02

− .01

n/a

1.01**

.13**

.08**

n/a

.16**

.12**

.15**

.04*

.09**

.13**

.14**

− .09**

.02

.10**

.11**

.08**

.08**

.01

.02**

.10**

.15**

.11**

.28**

.05**

− .07**

− .01

.09**

.09**

.12**

.24**

.08**

− .01

.02**

.11**

.13**

.13**

− .09**− .04**

.13**
.08**

GENDER Gender, ESCS Economic and sociocultural status index, TEACHSUP Teacher academic support, DIRINS Teacherdirected instruction, PERFEED Teacher feedback, ADAPTIVITY Adaptive instruction, STIMREAD Teachers’ stimulation of reading
engagement, TEACHINT Teacher enthusiasm**p < .001, *p < .05[2, 55762] = 2816.42, p < .001). Both GENDER and ESCS were associated with reading(β =  − .12, p < .001 and β = .07, p < .001). The covariates accounted for 9.2% variance inreading performance. As the instructional practices were entered in step 2, the model
was also significant, F(8, 55756) = 1021.42, p < .001. All the paths toward reading performance were significant. Results indicated a significant positive effect of TEACHSUP, ADAPTIVITY, STIMREAD, and TEACHINT on reading performance. Notably,
STIMREAD had the strongest effect on reading performance (β = .14, p < .001), followed
by ADAPTIVITY (β = .07, p < .001), TEACHINT (β = .06, p < .001), and TEACHSUP(β = .06, p < .001). In contrast, the effect of DIRINS (β =  − .13, p < .001) and PERFEED(β =  − .09, p < .001) on reading performance was negative. All the variables accounted
for 12.8% of the variance explained in reading performance. The second model with
instructional practices included showed significant improvement from the first one(ΔF[6, 6] = 1795, p < .001, ΔR2 = .36), indicating an additional 3.6% of variance explainedin reading performance.

Regional differences in instructional practices and students’ reading performance

The HLR models were significant for both Asian regions (Table 6) and Western regions(Table 7). As illustrated in the tables, the predictive effects of all these instructional
practices were generally consistent with TEACHSUP, ADAPTVITY, STIMREAD, andHo and Gan ﻿Language Testing in Asia

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Page 13 of 23

Table 5 Hierarchical linear regression results for instructional practice variables predicting reading
performance for the 10 top performing regions in PISA 2018 (The Entire Sample; N = 80,016)
Outcome variable

Overall Reading Performance

Independent variables

Step 1

Step 2

Step 1. Demographics
GENDER
ESCS

− .12

.07

− .10

.25

Step 2. Instructional Variables
TEACHSUP.06

DIRINS− .13

PERFEED− .09

ADAPTIVITY.07

STIMREAD.14TEACHINT

.06

R2

.092

ΔR2

.092

.036

F

2816.421021.42

.128

df1

2

a8

df2

55,762

55,765p value< .001< .001

Standardized coefficients (β) were reported. All paths are statistically significant at the p < .001 level
GENDER Gender, ESCS Economic and sociocultural status index, TEACHSUP Teacher academic support, DIRINS Teacherdirected instruction, PERFEED Teacher feedback, ADAPTIVITY Adaptive instruction, STIMREAD Teachers’ stimulation of reading
engagement, TEACHINT Teacher enthusiasm

TEACHINT as positive predictors, and DIRINS and PERFEED as negative predictors.
STIMREAD was the strongest predictor of performance in Asian regions such as B-SJ-Z (China [β = .18, p < .001]), Singapore (β = .09, p < .001), Macao (β = .14, p < .001), andHong Kong (β = .10, p < .001), and in Western regions including Ireland (β = .12, p < .001)
and Poland (β = .09, p < .001). TEACHINT was the strongest predictor in Korea (β = .13,
p < .001). ADAPTIVITY was the strongest predictor in Western regions such as Estonia (β = .10, p < .001), Canada (β = .05, p < .001), and Finland (β = .11, p < .001). Despite
this, a few variations on the impact of these instructional practices across the 10 regions
were found: PERFEED was positively associated with reading performance (β = .01,
p < .001) in Singapore; TEACHSUP was negatively associated with reading performance(β =  − .00, p < .001) in Ireland; ADAPTIVITY was negatively linked with reading performance (β =  − .05, p < .001) in Macao. After controlling for the covariates, all the instructional variables accounted for a substantial amount of variance in reading performancefor all regions: B-S-J-Z (China [4.2%]), Singapore (3.4%), Macao (2.1%), Hong Kong
(1.9%), Estonia (4.2%), Canada (0.5%), Finland (4.3%), Ireland (4.5%), Korea (4.9%), andPoland (2.9%).

Discussion
Reading performance

In terms of reading performance, B-S-J-Z (China) obtained both the highest overall reading score (i.e., PVREAD) and subscale scores measuring reading performance on different reading cognitive processes (i.e., PVRCLI, PVRCUN and PVRCER) as well as text

< .001< .001

p value< .0016584

2621.92

.159

.159

− .08

.38

Step 1< .0016578

6196.55

.034

.193

.02

.09

.08

.01

− .21

.03

− .06

.36

Step 2Singapore (N = 6676)

Standardized coefficients (β) were reported. All paths are statistically significant at the p < .001 level6
11,921

2

11,927

df1

391.981182.92

F

df2

.042

.166

ΔR2

.208

.05

.166

TEACHINT

R2

.05
.18

ADAPTIVITYSTIMREAD

− .12

− .16

PERFEED

.03

− .05

.36

Step 2DIRINS− .08.39

Step 1

B-S-J-Z (China)(N = 12,058)

Overall reading performance

TEACHSUP

Step 2. Instructional Variables

ESCS

GENDER

Step 1. Demographics

Independent variables

Outcome variable< .001

3743

2

58.78

.030

.030

− .11

.12

Step 1< .001

3737

6

25.01

.021

.051

.01

.14

− .05

− .08

− .01

.05

− .10

.12

Step 2

Macao (N = 3775)< .0015773

2199.58

.065

.065

− .14

.19

Step 1< .001

5767

8

65.86

.019

.084

.07

.10

.03

− .08

− .08

.02

− .13

.19

Step 2

Hong Kong (N = 6037)< .0016543

2327.83

.091

.091

− .09
.28

Step 1< .0016537

6132.55

.049

.140

.13

.00

.11

− .21

− .04

.12

− .06
.27

Step 2

Korea (N = 6650)< .001

34,582

2

1782.34

.093

.093

− .09
.28

Step 1< .001

34,5768614.39

.031

.124

.03

.15

.04

− .13
− .09

.08

− .08
.26

Step 2

Total (N = 35,196)

Table 6 Hierarchical linear regression results for instructional practice variables predicting reading performance for the 10 top performing regions in PISA 2018 (The AsianRegions; N = 35,196)

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Page 14 of 23

< .001< .001

p value< .001

20,2552953.54

.086

.086

− .13

.25

Step 1< .001

20,2536505.88

.005

.091

n/a

.02

.05

n/a

n/a

n/a

− .12

.23

Step 2

Canada (N = 22,653)

Standardized coefficients (β) were reported. All paths are statistically significant at the p < .001 level

8
5081

2

5087

df1

111.268305.76

F

df2

.042

.107

ΔR2

.149

.08

.107

TEACHINT

R2

.10
.04

ADAPTIVITYSTIMREAD

− .09

− .16

PERFEED

.10

− .16

.26

Step 2DIRINS− .18.27

Step 1

Estonia (N = 5316)

Overall reading performance

TEACHSUP

Step 2. Instructional variables

ESCS

GENDER

Step 1. Demographics

Independent variables

Outcome variable< .0015285

2500.74

.159

.159

− .25

.30

Step 1< .0015279

8167.36

.043

.202

.08

.06

.11

− .10

− .15

0.07

− .22

.28

Step 2Finland (N = 5649)< .0015381

2369.46

.121

.121

− .11

.32

Step 1< .0015375

8133.43

.045

.166

.10

.12

.06

− .00

− .01

− .20

− .08

.30

Step 2

Ireland (N = 55,77)< .0015415

2474.44

.149

.149

− .16
.35

Step 1< .0015409

6146.30

.029

.178

0.02

0.09

0.08

− 0.01− 0.16

0.02

− .14
.33

Step 2

Poland (N = 5625)< .001

21,1772

1624.59

.133

.133

− .18
.31

Step 1< .001

21,1718531.01

.034

.167

.07

.08

.09

− .16
− .06

.04

− .15
.29

Step 2

Total (N = 44,820)

Table 7 Hierarchical linear regression results for instructional practice variables predicting reading performance for the 10 top performing regions in PISA 2018 (The Western
Regions; N = 44,820)

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structure subscale scores (i.e., PVRTSN and PVRTML) in PISA 2018, suggesting that
B-S-J-Z (China) has been able to maintain a similar level of performance in reading as
seen in previous cycles of PISA (OECD, 2012; Zheng et al., 2022). The three other Asian
regions (i.e., Singapore, Macao, and Hong Kong) also scored higher on both the overall
reading performance and subscale scores compared with Western regions. These results
were consistent with the general perception of the superiority of students from the Confucian-heritage cultures over Western students in academic achievement documented incross-national studies of achievement (e.g., Li, 2002; Cai & Zhu, 2017; Rao et al., 2000; Lau& Lam, 2017). For example, Cai and Zhu (2017) found that Chinese adolescents scoredhigher than their Finnish counterparts on the reading literacy variable in the PISA 2009
reading programme. Lau and Lam (2017) also reported that science performance of students from Asian regions (i.e., Singapore, China, Hong Kong, Chinese Taipei, Macao,
Japan, Korea, and Vietnam) ranked at the top of the 2015 PISA league table. Such superiority in large-scale international academic testing among students from the Confucian-heritage learning cultures in Asian regions can be interpreted with reference to the social andcultural context of education where there is considerable pressure for students to achieve
academically. From a cultural perspective, Confucian ideas emphasize that effort and perseverance are more important to scholastic success than ability (Rao & Chan, 2010; Wang,2013). Students therefore exert effort in academic work because they believe that success isthe result of hard work (Gan, 2009; Gan et al., 2023; Hau & Ho, 2010; Weiner, 1986).
Another explanation that was often advanced to account for ‘Confucian heritage culture’ students’ superiority over Western students in cross-national studies of achievement
is that parents in Asian regions typically have high expectations for their children’s academic success, which in turn may lead to students feeling the pressure to do well in academic studies (Ho et al., 2007). On the other hand, students may feel ashamed when they
are judged negatively by their parents due to inadequate learning (Li, 2002). This emotionof shame-guilt may urge them to work harder to pursue better learning achievement.
Finally, due to a competitive learning environment in some Asian regions, since their early
academic training students are inculcated a belief that high grades on exams largely determine admission into higher education or for getting a job as well as for their social lives ingeneral (Lau et al., 2008). This tends to result in highly efficient adoption of adoption of aperformance-approach goal in learning among the students, which has recently been shown
to both benefit students in achieving high grades in conventional assessments and lead to a
deep cognitive engagement (Liem et al., 2008). Liem et al. (2008) emphasize that in a highly
competitive learning environment where meritocracy system can regulate and determine
one’s advancement in life, attempting to outperform others in academic studies through pursuing a performance-approach goal cannot be regarded as a maladaptive behavior.
Variations in instructional practices

In terms of instructional practices, the results showed significant differences in the
instructional practices investigated (i.e., teacher support, teacher-directed instruction,
perceived feedback, adaptive instruction, teacher stimulation, and teacher enthusiasm)
across the ten regions. The indices of the three of the six instructional practices variables(i.e., teacher support, teacher-directed instruction, and teacher stimulation) reported by
B-S-J-Z (China) were significantly higher compared to the other regions. A higher value

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on the index of each of these teacher behaviors means higher academic support from ateacher, more frequent teacher-directed instruction, and more frequent teacher use ofengagement strategies respectively among students in B-S-J-Z (China). It could be that
due to the influence of a long tradition of Confucian philosophy, Chinese students tend
to expect the teacher to possess knowledge, act as a role model to them, and be responsible for the assessment of their learning (Chuang, 2012), which eventually lead to teachers feeling the pressure to exert much effort in order to do well in teaching.
When the sample regions involved in this study were classified as two broad cultural groups(i.e., Asian and Western regions), the impact of the cultural context on students’ perceived
instructional practices was still considerably visible as the indices of teacher support, teacherdirected instruction, teacher enthusiasm, teacher feedback, and adaptive instruction were significantly higher for the Asian regions than for the Western regions. As the sample regions
could therefore generally be placed on an Asian-Western continuum, the results lend support
for the role of the broad cultural context in shaping instructional practices, providing empirical evidence for the generalizations that are often made about teaching and learning between
Asian and Western cultures (Littlewood, 2001). In other words, cultural context apparently
did affect self-reports of the level of perceived teacher behavior. The generally consistently
higher indices of instructional practices across Asian countries could be due to socioculturalcongruencies that are based on Confucian tradition and emphasis on effort, responsibility, andachievement orientation (Martin et al., 2014). For example, schools and national education
systems in Asian regions tend to be conducted along competitive, iteratively assessed, didactic
instruction lines (Martin et al., 2014). In some Asian regions such as China, exam-oriented
education remains entrenched in the psyche and behavior of educational stakeholders (Tanand Chua, 2015). Koh and Luke (2009) also reported that students’ test scores continue to be
the major indicator of teachers’ job performance and school effectiveness in Singapore. Tan
and Chua (2015) observed that confronted with the demand to produce high exam scores,
didactic teaching usually dominates in many schools in China. Similarly, a competitive andpragmatic learning culture prevails in Singapore where teachers and schools have had great
success in defining and assigning, producing and rewarding factual and basic knowledge, andwhere civic and media, parental and student consciousness is often influenced and mediatedby a competitive focus on examination results and their consequences for students’ lives.
Instructional practices and students’ reading performance

In this study, the results revealed that even when GENDER and ESCS were controlled,
four (i.e., teacher support, adaptive instruction, teacher stimulation, and teacher enthusiasm) of the six instructional practices investigated were significantly and positively
correlated with reading performance in all sample regions. Interestingly, perceived
feedback and teacher-directed instruction were found to be mostly negatively or insignificantly correlated with reading performance in either Asian or Western regions. HLR
modeling analyses further confirmed significant positive predictive effects of teacher
support, adaptive instruction, teacher stimulation, and teacher enthusiasm on reading
performance, with teacher stimulation being the strongest predictor, followed by adaptive instruction, teacher enthusiasm, and teacher support. HLR modeling analyses also
confirmed the generally negative effects of teacher-directed instruction and perceived
feedback on reading performance. These patterns of positive or negative relationships

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between instructional practices and reading performance was still visible when Asian
regions and Western regions were considered separately. These findings therefore enable
us to conclude that students of a particular culture or cultures might benefit or suffer
more from certain instructional practices (Haw & King, 2022; Meng et al., 2017).
While the associations of the instructional practices with reading performance are highly consistent across Asian and Western regions, there were some exceptions where the association of
the instructional practices with reading performance scores did vary within Asian or Westernregions. For example, adaptive instruction showed a negative linkage with reading performancein Macao; teacher feedback was found to be positively correlated with reading performance inSingapore; teacher support was found to be negatively correlated with reading performance in
Ireland. The reason for such exceptions could be due to different ways in which schools andnational education systems implement their processes (Martin et al., 2014). Future studies canadopt qualitative methods such as school observations and interviews to uncover the nuances ofschool or national cultures that might lead to the variances within individual regions.
The prevalence of the negative associations of teacher-directed instruction and teacher feedback with reading performance within either Asian or Western regions is particularly worth noting. Teacher-directed instruction was one of the three instructional practice variables reported tobe significantly more common either in China or in Asian regions that also performed better inreading than other Western regions. The lack of a positive relationship between teacher-directed
instruction and reading performance in this study, however, gains cross-validation from otherevidence (e.g., Lau & Lam, 2017). It could be that over-relying on teacher-directed instruction
strategies caused lower performance, and that academically weaker students tended to receive
more teacher-directed instruction than their academically stronger counterparts (Medina &McGregor, 2019). Researchers and educators also view feedback as one of the powerful influences on learning and achievement (Hattie & Timperley, 2007). However, somewhat similarly tothe negative impact of teacher-directed instruction on performance documented in this study, it
was likely that students who reported they received constant feedback tended to struggle in classthe most (Medina & McGregor, 2019; OECD, 2016). The generally negative impact of teacher
feedback on performance documented in this study might also suggest that it is the quality of
feedback rather than the quantity that matters most.

Implications
This study contributes to the discussion of the influence of social and cultural contexts on
students’ learning and teachers’ instructional practices in the literature. The study provides
empirical support to such socio-cultural theorizing. The significant mean-level differences
in both students’ reading performance and their perceived instructional practices acrossAsian and Western regions suggested that that context did play a powerful role in shaping
students’ learning performance and teachers’ teaching behavior. These differences muse berecognized when attempting to transfer educational policy or theoretical constructs fromone culture to another, designing instructional interventions to enhance students’ learning
motivation, and initiating pre‐service teacher education, in‐service training, and curriculum
reform. For example, pre-service language teacher education courses can have teacher candidates read and analyze empirical studies of the role of different instructional practices instudents’ learning achievement so as to develop an awareness of differential effectivenessof these instructional practices in aiding student learning. Undoubtedly, incorporation ofPage 18 of 23

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such a component into the pre-service teacher education programs empowers teacher candidates to be aware of the utility of optimal instructional practices that will lead to the mostdesirable outcomes of student learning in a particular context.
Furthermore, this study enriched our understanding of the patterns of relations
between instructional practices and students’ reading performance across different cultural contexts. Very interestingly, the study confirmed that both the positive and negative relations of some instructional practices to reading performance were consistentacross Asian and Western regions, which may shed light on universality of instructional
practices for optimizing students’ reading performance despite the differences in cultural contexts. For example, within both Asian and Western regions, teacher-directed
instruction and teacher feedback were found to negatively predict students’ reading performance. This result alerts us to the fact that something is taught or a teacher provides
feedback on error does not mean that students will acquire it or adopt the correct form
immediately or consistently (Lightbown & Spada, 2011). As Lightbown and Spada (2011)
put it, the teacher’s principal role in student learning is to provide a supportive environment in which students are stimulated, engaged in activities that are appropriate to theirlearning needs and style, and where students can experience success in learning.

Conclusion
This study contributes to the field by (1) employing the latest PISA data (PISA 2018 database)to analyze the reading instructional practices of the 10 top-performing regions; (2) examining the associations of reading instructional practices with students’ reading performance;(3) revealing the general existence of a continuum with one end of the Asian regions and theother end of the Western regions in relation to instructional practices and student reading
performance; (4) suggesting that students of a particular culture or other cultures might benefit or suffer from certain instructional practices. Therefore, on the one hand, cultural clusters could be identified in the prevalence of certain instructional practices such as teacher
support, teacher-directed instruction, teacher feedback, adaptive instruction, and teacher
enthusiasm; on the other hand, the prevalence in the positive associations of teacher support,
adaptive instruction, teacher stimulation, and teacher enthusiasm with reading performance,along with the prevalence in the negative associations of DIRINS and PERFEED with reading
performance, was considerably consistent within either Asian or Western regions. Our findings thus shed informative light on socio-cultural theorizing relevant to the role of context inshaping instructional practices and learning achievement, and they assist understanding ofeffects attributable to national context and effects attributable to cultural ethnicity.
Although this study filled the research gap by presenting a holistic picture of the associations of instructional practices with student reading performance based on the latest
PISA data, a few limitations need to be acknowledged. First, as only the 10 top performing
regions in PISA 2018 were included in this study, some Asian regions such as Taiwan andJapan or some Western regions such as America and Britain were not included for analysis. Future studies should include a larger sample of regions to achieve a better generalizability of the findings. Second, while the teacher instructional practice factors included in
the present study performed generally well in predicting student reading performance, theinfluences of student factors such as reading enjoyment, reading self-efficacy, and reading engagement on reading performance were not examined in the present study. Future

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research should thus consider how these student factors may predict reading performance.
Third, since PISA is a cross-sectional study, the associations of instructional practices withreading performance examined in this study were only correlational. It is thus not possible to establish the causal relations among the various variables that were explored inthis study. Future research should continue to examine the specific mechanisms by which
instructional practices impact on students’ reading performance. Fourth, the measures ofteacher instructional practices were largely based on student self-reports, which meansthat the findings might not accurately reflect the reality. Research in the future should consider longitudinal and mixed-methods designs in order to better illustrate the influencesof teacher practices, student and environmental factors on student reading performance.

Appendix
Indices of instructional practice in PISA 2018 (OECD, 2019a, 2019c)
Variables

Items

Response Options

TEACHSUP

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 thestudents understand4-point scale: 1 = never or hardly ever,2 = some lessons, 3 = most lessons,
4 = every lesson

DIRINS

The teacher sets clear goals for our learning;
The teacher asks questions to check whether
we have understood what was taught;
At the beginning of a lesson, the teacher presents a short summary of the previous lesson;
The teacher tells us what we have to learn

4-point scale: 1 = never or hardly ever,2 = some lessons, 3 = most lessons,
4 = every lesson

PERFEED

The teacher gives me feedback on mystrengths in this subject;
The teacher tells me in which areas I can still
improve;
The teacher tells me how I can improve myperformance

4-point scale: 1 = never or hardly ever,2 = some lessons, 3 = most lessons,
4 = all lessons

ADAPTIVITY The teacher adapts the lesson to my class’s
4-point scale: 1 = never or almost never,needs and knowledge;
2 = some lessons, 3 = many lessons,
The teacher provides individual help when a stu- 4 = every lesson or almost every lesson
dent has difficulties understanding a topic or task;
The teacher changes the structure of the lessonon a topic that most students find difficult tounderstand
STIMREAD

The teacher encourages students to express
their opinion about a text;
The teacher helps students relate the stories
they read to their lives;
The teacher shows how the information in
texts builds on what they already know;
The teacher poses questions that motivate
students to participate actively

4-point scale: 1 = never or hardly ever,2 = some lessons, 3 = most lessons,
4 = all lessons

TEACHINT

It was clear to me that the teacher liked teaching us;
The enthusiasm of the teacher inspired me;
It was clear that the teacher likes to deal withthe topic of the lesson;
The teacher showed enjoyment in teaching

4-point scale: 1 = strongly agree, 2 = agree,
3 = disagree,
4 = strongly disagree

TEACHSUP Teacher academic support, DIRINS Teacher-directed instruction, PERFEED Teacher feedback, ADAPTIVITY Adaptive
instruction, STIMREAD Teachers’ stimulation of reading engagement, TEACHINT Teacher enthusiasm

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Abbreviations
PISA	Program for International Student Assessment
B-S-J-Z	China
OECD	Organization for Economic Cooperation and Development
SCT	Sociocultural theory
PVREAD	Overall reading performance
PVRCLI	Locating information
PVRCUN	Understanding
PVRCER	Evaluating and reflecting
PVRTSN	Reading single text
PVRTML	Reading multiple text
ESCS	Economic and sociocultural status index
TEACHSUP	Teacher academic support
DIRINS	Teacher-directed instruction
PERFEED	Teacher feedback
ADAPTIVITY	Adaptive instruction
STIMREAD	Teachers’ stimulation of reading engagement
TEACHINT	Teacher enthusiasm
Acknowledgements
The authors are grateful for use of the data from the PISA database.
Authors’ contributions
Zhengdong Gan and Soi Kei Ho first discussed the conceptualization, methodology, software. Soi Kei Ho, and Zhengdong Gan then prepared the data and wrote the original draft. Soi Kei Ho and Zhengdong Gan reviewed, revised, edited,
and approved the manuscript.
Funding
Not applicable.
Availability of data and materials
The data and materials will be available upon request.

Declarations
Competing interests
The authors declare no competing interests.
Received: 14 July 2023 Accepted: 2 October 2023

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