This study investigates how various teaching practices affect science performance. In our society, science skills are essential: science is everywhere and is necessary for handling most of the challenges of our modern societies. In this way, it is important that citizens, and even more young people, develop their competences in science. According to PISA 2015, students from the French speaking community of Belgium show a science literacy level below the OECD average and it is true in each field assessed (explain phenomena scientifically, evaluate and design scientific inquiry, interpret data and evidence scientifically).

Teaching practices are central and it is well established that they influence students’ motivation and achievement (Hattie, 2009). But what makes good teaching is not easy to define and even more difficult to measure. In science, there is no clear consensus among researchers regarding the impact of the inquiry-based teaching and learning on achievement (Furtak, 2012; Strijbos, Kirschner and Martens, 2004). PISA has developed indicators aiming at measuring the teaching practices in classroom but again the link with students’ science performance is not obvious (OECD, 2018). Two main reasons could explain the weakness of the observed relationship. Firstly, student skills are built up throughout all the school career while in PISA, the achievement is measured and related to the current teacher only. Secondly, researchers tend to contrast teacher-guided instruction in science and inquiry-based teaching (Mayer, 2004; OECD, 2018). However, researchers in education agree that teaching efficacy lies in the combination of different practices (Baumert and al, 2010; Creemers and Kyriakides, 2008; Klieme and al, 2009). Klieme and colleagues (2009) defined a three dimensional model of instructional quality: cognitive activation, supportive climate and classroom management. Cognitive activation refers to teaching practices that “encourage students to discover and understand the meaning underlying procedures, to discuss the relationships between concepts, to compare different solution strategies and to solve non-routine problems.” (Lipowsky et al., 2009). We can reasonably state that in science, most of the inquiry-based activities are part of the cognitive activation dimension. Supportive climate “(…) covers features of teacher-learner interaction such as teacher-student relationships, positive and constructive teacher feedback, a positive approach to student errors and misconceptions, individual learner support and caring teacher behavior”. Finally, the last dimension “classroom management” is about creating and maintaining an orderly classroom atmosphere: “to this end, teachers must be able to establish clear rules and procedures, manage transitions between lesson segments smoothly, keep track of student’s work, plan and organize their lessons well, manage minor disciplinary problems and disruptions, stop inappropriate behavior, and keep whole-group focus”.

Using PISA 2015 data, the aim of this study is to relate the three dimensions of the model to science performance. Do students who perceive a high cognitive activation, a high supportive climate and a high classroom management perform better than those who don’t perceive a high level of cognitive activation, supportive climate and/or classroom management?

The 2015 PISA databases for the French speaking community of Belgium has been used. In addition to the cognitive test, 15 years old students filled out a context questionnaire. Questions about students’ perception of teaching practices in science classroom were used. A bunch of items using Likert-scales are available, concerning teacher support, feedback, discipline, adaptive instruction in science lessons, teacher-directed science instruction and inquiry-based science teaching. For each of the three dimensions of instructional quality, items were selected from these scales. Cognitive activation is measured by three items from the inquiry-based science teaching scale (“Students are required to argue about science questions”, “Students are asked to draw conclusions from an experiment they have conducted”, “The teacher explains how a school science idea can be applied to a number of different phenomena”). Supportive climate is measured by two items from the teacher support scale (“The teacher continues teaching until the students understand”, “The teacher gives students an opportunity to express opinions”), one item from the feedback scale (“The teacher tells me in which area I can still improve”) and one item from the adaptive instruction scale (“The teacher provides individual help when a student has difficulties understanding a topic or task”). Classroom management is measured by three items: one item related to discipline (“There is noise and disorder”) plus two items about teacher-directed science instruction (“The teacher explains scientific ideas”, “The teacher demonstrates an idea”). The four points Likert-scale items were turned into dichotomous variables: “in all lessons” or “in most lessons” versus “in some lessons” or “never or hardly never”. We investigated our study question through a Latent Class Analysis (LCA) with Mplus7. “LCA is a multivariate method designed to identify unobserved subpopulations of individuals on the basis of multiple measures” (Lin & Tai, 2015). The first step of our analysis was to identify the best number of latent classes that can be observed in our population. The second step was to compare the average achievement for each subgroup. Besides the Latent Class Analysis, quadratic regressions were also conducted to examine the configuration of the relation between type of teaching practices and performance. This last element of investigation is again in agreement with the model of Klieme at al. (2009), effective teaching should combine the right amount of practices of each dimensions.

The results of the LCA indicate that the three-class model fitted better the data. The class probabilities indicate that 31.4% of students were classified as class 1, 35.8% as class 2 and 32.8% as class 3. Class 1 captures students with a high probability to respond positively to the items on cognitive activation, supportive climate and classroom management (it occurs “in all lessons” or “in most lessons”). At the opposite, class 3 is composed with students who responded more negatively (“in some lessons” or “never or hardly never”) to the items of the three dimensions. Finally in class 2, students tend to indicate a low cognitive activation, a high level of supportive climate and a mixed classroom management. In a second step, we linked the type of instruction to the mean science scores and observed significant differences between class 2 and the other one (class 1: mean=515 (4.62), class 2: mean=524 (4.55), class 3: mean=508 (4.39)). Students which have a high probability to be in class 2 have higher achievement. In other words, it means that a low cognitive activation, a high level of supportive climate and a mixed classroom management are associated with better achievement. The more surprising result concerns the low cognitive activation. Competence measured in PISA is the result of skills built up in interaction with teachers throughout the whole school career while it is only connected to the current teacher. Moreover, science is a broad domain that may request different pedagogical approaches and that is often taught by different teachers depending on the discipline (chemistry, biology and physics). In PISA, students were asked to answer the teaching attributes keeping in mind one of their science courses, meaning that depending on the course they chosen, different students responded in reference to different disciplines.

Baumert, J., Kunter, M., Blum, W., Brunner, M ? Voss T., Jordan, A., Klusmann, U., Krauss, S., Neubrand, M., & Tsai, Y.-M. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress, American Educational Research Journal, Vol. 47/1, pp. 133-180 Creemers, B. and Kyriakides, L. (2008). The Dynamics of Educational Effectiveness: A Contribution to Policy, Practice and Theory in Contemporary Schools, Routledge, AbingdonFurtak, E.M., Seidel, T., Iverson, H., & Briggs, D. (2012). Experimental and quasi-experimental studies of inquiry-based science teaching: a meta-analysis. Review of Educational Research, 82(3), 300-329 Hattie, J. (2009). Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement, Routledge, London. Klieme, E., C. Pauli and K. Reusser (2009). The Pythagoras study: Investigating effects of teaching and learning in Swiss and German mathematics classrooms, in Janík, T. and T. Seidel (eds.), The Power of Video Studies in Investigating Teaching and Learning in the Classroom, Waxmann, Münster.Lin & Tai, 2015 Lin, S.-W., & Tai, W.-C. (2015). Latent Class Analysis of Students’ Mathematics Learning Strategies and the Relationship between Learning Strategy and Mathematical Literacy. Universal Journal Of Educational Research, 3 (6), 390-395. Lipowsky, F., Rakoczy, K., Pauli, C., Drollinger-Vette, B., Klieme, E., & Reusser, K. (2009). Quality of geometry instruction and its short-term impact on students’ understanding of Pythagorean Theorem, Learning and Instruction, 19. Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, Vol. 59/1, pp. 14–19. Strijbos, J.-W., Kirschner, P. A., and Martens, R. L. (eds.) (2004). What We Know About CSCL, Springer, Dordrecht, Netherlands. OECD (2018). The science of teaching science. PISA, OECD Publishing, Paris.