2. How Science is Assessed
31. PISA is designed to measure what students know, but also the application of science in the real-life situations. Below, we summarise key features of the OECD's framework for measuring science literacy.
The PISA science framework
32. The PISA science framework is set out below in Figure 2.1. Broadly, the cognitive items in the assessment measure "competencies" (skills) and types of knowledge, and then specific scientific content. In addition the background questionnaire measures attitudes towards science expressed by the student.
33. The competencies are the foundation that allows an individual to approach and explain scientific problems in today's world. Each of the science items could be categorised by its main task under one of the competencies, which were as follows:
- Explain phenomena scientifically - recognise, offer and evaluate explanations for a range of natural and technological phenomena.
- Evaluate and design scientific enquiry - describe and appraise scientific investigations and propose ways of addressing questions scientifically.
- Interpret data and evidence scientifically - analyse and evaluate data, claims and arguments in a variety of representations and draw appropriate scientific conclusions
34. Next, exercising these competencies requires different types of knowledge:
- Content knowledge of theories, explanatory ideas, information and facts
- Procedural knowledge is the understanding of how content knowledge is derived, through the mechanisms of collection, analysis and interpretation of data
- Finally epistemic knowledge refers to the nature of knowledge - understanding the difference between facts, observations, hypotheses, models and theories, and why procedures, such as experiments are key to creating knowledge
35. Knowledge is also categorised by major scientific fields, with three content areas:
- Physical systems
- Living systems and
- Earth and space systems
Figure 2.1: Main features of the PISA 2015 science framework
36. Questions are constructed to test each of these categories, and at varying levels of difficulty, in order to identify a student's ability. Their score corresponds to levels of ability, which are summarised in Table 2.1 below.
37. Example questions and how they were adapted for computer-based assessment are provided in Annex C of Volume I of the OECD report.
Table 2.1: Proficiency levels in science, and what they mean
|Level||Lower score limit||Characteristics of tasks|
|6||708||At Level 6, students can draw on a range of interrelated scientific ideas and concepts from the physical, life and earth and space sciences and use content, procedural and epistemic knowledge in order to offer explanatory hypotheses of novel scientific phenomena, events and processes or to make predictions. In interpreting data and evidence, they are able to discriminate between relevant and irrelevant information and can draw on knowledge external to the normal school curriculum. They can distinguish between arguments that are based on scientific evidence and theory and those based on other considerations. Level 6 students can evaluate competing designs of complex experiments, field studies or simulations and justify their choices.|
|5||633||At Level 5, students can use abstract scientific ideas or concepts to explain unfamiliar and more complex phenomena, events and processes involving multiple causal links. They are able to apply more sophisticated epistemic knowledge to evaluate alternative experimental designs and justify their choices and use theoretical knowledge to interpret information or make predictions. Level 5 students can evaluate ways of exploring a given question scientifically and identify limitations in interpretations of data sets including sources and the effects of uncertainty in scientific data.|
|4||559||At Level 4, students can use more complex or more abstract content knowledge, which is either provided or recalled, to construct explanations of more complex or less familiar events and processes. They can conduct experiments involving two or more independent variables in a constrained context. They are able to justify an experimental design, drawing on elements of procedural and epistemic knowledge. Level 4 students can interpret data drawn from a moderately complex data set or less familiar context, draw appropriate conclusions that go beyond the data and provide justifications for their choices.|
|3||484||At Level 3, students can draw upon moderately complex content knowledge to identify or construct explanations of familiar phenomena. In less familiar or more complex situations, they can construct explanations with relevant cueing or support. They can draw on elements of procedural or epistemic knowledge to carry out a simple experiment in a constrained context. Level 3 students are able to distinguish between scientific and non-scientific issues and identify the evidence supporting a scientific claim.|
|2||410||At Level 2, students are able to draw on everyday content knowledge and basic procedural knowledge to identify an appropriate scientific explanation, interpret data, and identify the question being addressed in a simple experimental design. They can use basic or everyday scientific knowledge to identify a valid conclusion from a simple data set. Level 2 students demonstrate basic epistemic knowledge by being able to identify questions that can be investigated scientifically.|
|1a||335||At Level 1a, students are able to use basic or everyday content and procedural knowledge to recognise or identify explanations of simple scientific phenomenon. With support, they can undertake structured scientific enquiries with no more than two variables. They are able to identify simple causal or correlational relationships and interpret graphical and visual data that require a low level of cognitive demand. Level 1a students can select the best scientific explanation for given data in familiar personal, local and global contexts.|
|1b||261||At Level 1b, students can use basic or everyday scientific knowledge to recognise aspects of familiar or simple phenomenon. They are able to identify simple patterns in data, recognise basic scientific terms and follow explicit instructions to carry out a scientific procedure.|