Scientific literacy and contexts in PISA 2006 science
Bibliographic record
Abstract
International assessments contribute to a greater understanding of science education around the world by helping participating countries understand potential changes in policies, programs, and practices in science teaching.In 2006, science was the primary domain for the Programme for International Student Assessment (PISA), supported by the Organization for Economic Cooperation and Development (OECD) and conducted by the Australian Council for Educational Research (ACER).Compared to the school program orientation of Trends in International Math and Science Study (TIMSS), PISA provides a unique and complementary perspective by focusing on the application of knowledge to science and technology-related life situations.The orientation of PISA is one of scientific literacy, a theme of great importance to the science education community.One question formed the basis for the PISA 2006 Science survey of 15-year-olds-What is important for citizens to know, value, and be able to do in situations involving science and technology?The question is deceptively simple.Answering the question in the form of a framework for an international assessment and a survey that included a test and questionnaires for students and administrators differentiates PISA 2006 Science from other assessments, both international and national.The question and answer also broadens our understanding of the purposes of science education.Several features of the question clarify the uniqueness of PISA 2006 Science.First, the question centers on citizens.As citizens, what scientific knowledge is important?Certainly, basic concepts of the disciplines, but that knowledge must be understood and applied in contexts that individuals encounter in life.At this point we also note that citizens often encounter situations that require some knowledge and understanding of science itself.That is, they need to know something about the processes of scientific inquiry and scientific explanations.Now, and in the foreseeable future, citizens will have to address numerous challenges that are clearly related to science and technology.Contemporary challenges include: Health maintenance and appropriate responses to infectious diseases; for example, applying biomedical advances and responding to H1N1 virus and Swine flu.Natural resources use and appropriate conservation and efficiency use of renewable and nonrenewable energy resources in an increasingly carbon-constrained world; for example, increased use of wind and solar energy.Environmental quality and an appropriate response to global climate change; for example, reduction of carbon emissions through cap and trade agreements.Citizens confront these and other challenges from perspectives that are personal, social, and global.The PISA 2006 Science survey used the contexts displayed in Table 1.Scientific literacy involves more than scientific knowledge.The PISA 2006 Science survey uses contexts to present science in a variety of situations that citizens confront.Assessment tasks had to be part of a 15-yearold's experience.Assessment items were framed with contexts that included the details needed to formulate specific questions.To be clear, the PISA 2006 Science survey was not an assessment of contexts.The primary
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".