International large-scale assessments of student achievement have played an increasing role in educational research, including Programme for International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS). PISA is a triennial study designed to evaluate education systems worldwide by assessing the skills and competencies of 15-year-old students with core domains in reading, mathematics, and science. TIMSS is a quadrennial international comparative assessment that aims to measure trends in mathematics and science performance of students at the primary (Grade 4) and lower secondary (Grade 8) level. PISA and TIMSS provide rich information and empirical data describing student outcomes as well as contextual variables related to teaching and learning from students, teachers, and school leaders. In the last assessment cycle, 79 countries participated in PISA 2018 and 64 countries participated in TIMSS 2019. 

The number of studies utilizing PISA and TIMSS data to inform research in science education has been on the rise in the past two decades (Hopfenbeck et al., 2018; Liou & Hung, 2015). In a systematic review of studies focusing on  PISA, science was found to be the third most frequently studied discipline, after education and economics (Hopfenbeck et al., 2018). Using PISA and TIMSS studies, researchers could examine a broad range of variables that may contribute to improving student performance and motivation to learn science. For instance, some researchers have focused on examining science teaching and learning particularly inquiry activities such as planning and conducting experiments or investigations (e.g., Aditomo & Klieme, 2020; Cairns, 2019; Teig, 2019). Other studies explored the importance of teacher beliefs in fostering instructional quality (Teig, Scherer, & Nilsen, 2019), teacher collaboration and job satisfaction (Pongsophon & Herman, 2017), and the availability of school resources in supporting science practices (Kang & Keinonen, 2016). These studies provide insights into science teaching and learning within a country—such as England (Jerrim, Oliver, & Sims, 2019), Norway (Teig, 2019), and Portugal (Valente, Fonseca, & Conboy, 2011)—as well as a cross-country comparison (e.g., Aditomo & Klieme, 2020; Forbes, Neumann, & Schiepe-Tiska, 2020; Kang & Keinonen, 2016). 

While a great deal of studies has investigated science teaching and learning using PISA and TIMSS data, there has been little effort to review and synthesize these findings for facilitating further research. Although previous studies have reviewed a number of publications on ILSAs (e.g., Hopfenbeck et al., 2018), none of these studies focused specifically on science research even though this field has frequently been investigated. Hence, the question remains of the extent to which researchers have taken advantage of PISA and TIMSS data to advance research in science education. Consequently, the present study aims to systematically review research on PISA and TIMSS in order to synthesize how data from these international studies were utilized to investigate science teaching and learning. The following research questions (RQs) are used to guide the present study:

1. What are the main characteristics of the studies investigating science teaching and learning using PISA and TIMSS data?
2. How did the findings from these studies contribute to the existing research on science teaching and learning? 

This systematic review intends to provide a synthesis of how research that leveraged TIMSS and PISA data to investigate science teaching and learning has evolved over the last two decades. It serves as an overview of PISA and TIMSS for science education researchers who are unfamiliar with these assessments. It may also encourage others to reflect on the knowledge gained from leveraging data from international large-scale assessments and the research gaps that can be bridged using these data in the field of science education.

Using a systematic review approach, this study summarizes the ways in which TIMSS and PISA have been used to investigate science teaching and learning (RQ1) and to provide an overall view of the findings from the existing research (RQ2). It follows the recommendations from Gough, Oliver, and Thomas (2017) in designing the systematic review process, that is guided by a specific and explicit protocol: (1) developing research questions, conceptual framework, and approach; (2) constructing inclusion criteria and search strategy; (3) selecting studies using the inclusion criteria; (4) coding and describing study characteristics; (5) assessing the quality of studies; (6) synthesis results of the studies; and (7) interpreting and reporting findings. A literature search was conducted using the following databases: ERIC, PsycInfo, Scopus, EBSCO, and Web of Science. These five databases were chosen given their relevance to the field of educational research. The search was undertaken using combinations of the key terms “PISA” or “Programme for International Student Assessment” or “Program for International Student Assessment” or “TIMSS” or “Trends in International Mathematics and Science Study” or “Trends in International Math and Science Study” and “science”. The following first-order criteria for including the studies to the review process were set beforehand : 1. Published in an English peer-reviewed academic journal (i.e., articles). Theoretical or review articles, such as editorial segments or commentaries, can be included if they provide critical discourse on PISA/TIMSS and science teaching and learning. 2. The article was “genuinely” concerned with TIMSS and/or PISA. These studies formed crucial part of the content and were not used only for a reference or citation. 3. The article was concerned with science teaching or learning, that is, must have addressed them explicitly as measures, concepts, or constructs. 4. The article presented either some secondary data analysis of the TIMSS and/or PISA data focusing on science teaching or learning or a critical discourse of the corresponding measures, concepts, or constructs. In addition to the database search, additional studies were also identified by screening relevant journals in the area of science education (e.g., International Journal of Science Education,) and assessment (e.g., Large Scale Assessment). Furthermore, the snowball method was applied to identify additional studies based on several key articles that were already included in the review by scanning the reference section of these articles, tracking references that had cited these articles, and tracing publication lists of scholars from these articles.

After removing duplicate records, the initial search yielded altogether 2342 studies. These studies were screened according to the inclusion criteria by scanning the titles and abstracts. Only 257 studies were included to the next review process. In this step, the full-text articles were assessed following the inclusion criteria. Finally, a total of 101 studies were included for qualitative synthesis. Each study was coded based on the type of research, in which 89 studies were categorized as using PISA and/or TIMSS data for secondary analysis and 12 studies were categorized as critical discourse of the measure, concept, or construct of science teaching and learning. From the secondary analysis category, 84 studies used regular data while only 5 studies used video data. Currently, the coding process for the main characteristics of the 101 included studies is almost complete (RQ1). The process focuses on identifying (1) the aim of the studies, (2) the characteristics of the PISA and TIMSS data utilized in the studies, and (3) how the studies analyzed or criticized the construct of science teaching and learning. The next step focuses on synthesizing the findings from these included studies to identify the relevant factors that explain the implementation of science teaching and learning activities and how these activities may contribute to student outcomes (RQ2). By the time of the conference, complete findings from RQs 1 and 2 will be presented and discussed. The present study will discuss several opportunities and challenges for bridging research gaps in science teaching and learning by harnessing PISA and TIMSS data. This discussion may provide insights into important aspects of science teaching and learning that should be given more attention in PISA and TIMSS. These insights are crucial as international large-scale assessments have become increasingly more relevant for informing educational research, policy, and practice.

Aditomo, A., & Klieme, E. (2020). Forms of inquiry-based science instruction and their relations with learning outcomes: evidence from high and low-performing education systems. International Journal of Science Education, 42(4), 504-525. Cairns, D. (2019). Investigating the relationship between instructional practices and science achievement in an inquiry-based learning environment. International Journal of Science Education, 41(15), 2113-2135. doi:10.1080/09500693.2019.1660927 Forbes, C. T., Neumann, K., & Schiepe-Tiska, A. (2020). Patterns of inquiry-based science instruction and student science achievement in PISA 2015. International Journal of Science Education, 1-24. doi:10.1080/09500693.2020.1730017 Gough, D., Oliver, S., & Thomas, J. (2017). An introduction to systematic reviews. Thousand Oaks, California: Sage. Hopfenbeck, T. N., Lenkeit, J., El Masri, Y., Cantrell, K., Ryan, J., & Baird, J.-A. (2018). Lessons learned from PISA: A systematic review of peer-reviewed articles on the programme for international student assessment. Scandinavian Journal of Educational Research, 62(3), 333-353. Jerrim, J., Oliver, M., & Sims, S. (2019). The relationship between inquiry-based teaching and students’ achievement. New evidence from a longitudinal PISA study in england. Learning and Instruction, 61, 35-44. Kang, J., & Keinonen, T. (2016). Examining factors affecting implementation of inquiry-based learning in Finland and South Korea. Problems of Education in the 21st Century, 74. Liou, P.-Y., & Hung, Y.-C. (2015). Statistical techniques utilized in analyzing PISA and TIMSS data in science education from 1996 to 2013: A methodological review. International Journal of Science and Mathematics Education, 13(6), 1449-1468. Pongsophon, P., & Herman, B. C. (2017). A theory of planned behaviour-based analysis of TIMSS 2011 to determine factors influencing inquiry teaching practices in high-performing countries. International Journal of Science Education, 39(10), 1304-1325. Teig, N. (2019). Scientific inquiry in TIMSS and PISA 2015: Inquiry as an instructional approach and the assessment of inquiry as an instructional outcome in science. (PhD Dissertation). University of Oslo, Norway. Retrieved from https://www.duo.uio.no/handle/10852/71649 Teig, N., Scherer, R., & Nilsen, T. (2019). I know i can, but do i have the time? The role of teachers' self-efficacy and perceived time constraints in implementing cognitive-activation strategies in science. Front Psychol, 10(JULY), 1697. doi:10.3389/fpsyg.2019.01697 Valente, M. O., Fonseca, J., & Conboy, J. (2011). Inquiry science teaching in Portugal and some other countries as measured by PISA 2006. Procedia - Social and Behavioral Sciences, 12, 255-262. doi:10.1016/j.sbspro.2011.02.034