Scientific literacy

1 Foundations of Scientific Literacy

1.1 Definitions and Key Components

Scientific literacy refers to the ability to understand major scientific ideas, interpret and assess evidence, and use scientific reasoning to guide decisions. It is not limited to memorizing facts; it also includes understanding how knowledge is produced and revised, how data are interpreted, and what kinds of questions are and are not well suited to scientific investigation. As a learning goal, scientific literacy combines conceptual understanding with practical skills, such as reading scientific representations, recognizing uncertainty, and distinguishing evidence-based claims from unsupported assertions.

Key components typically include (1) content knowledge across disciplines, (2) epistemic knowledge about how science works, (3) reasoning with evidence and models, and (4) communication and media literacy for interpreting scientific information in public contexts and classroom materials.

1.2 Scientific Reasoning Skills

1.2.1 Evidence Evaluation

Evidence evaluation is the process of judging the strength of a claim by examining the quality of supporting data, the appropriateness of methods, and the match between conclusions and observations. It includes identifying what was measured, whether comparisons were fair, and how results were analyzed. It also involves recognizing that evidence comes in different forms—quantitative measurements, qualitative observations, and lines of reasoning from multiple studies.

In educational settings, evidence evaluation often emphasizes questions such as: What is the claim? What evidence is provided? How were data collected or derived? Are there alternative explanations consistent with the observations? Strong scientific literacy treats evidence as gradational rather than absolute, acknowledging that some findings are more directly supported than others.

1.2.2 Uncertainty and Variability

Uncertainty and variability are central to scientific reasoning because measurements and natural processes vary across time, conditions, and samples. Scientific literacy therefore includes understanding that results can include random error, systematic error, and statistical fluctuation. It also includes knowing that uncertainty can be reduced through improved measurement, larger samples, careful controls, and transparent reporting.

Learners develop this component by interpreting error bars and ranges, distinguishing between uncertainty in a single measurement and variability across observations, and understanding that uncertainty does not equal ignorance. Instead, it indicates the limits of what can be concluded based on the available data.

1.3 Understanding How Science Works

1.3.1 The Scientific Method and Inquiry

The idea of a single universal “scientific method” is often oversimplified; real investigations typically involve iterative cycles of questioning, investigating, analyzing, and revising. Scientific inquiry can be experimental, observational, computational, or engineering-design based. Scientific literacy involves recognizing these forms and understanding that good inquiry includes clear questions, appropriate methods, controlled comparisons when possible, and careful interpretation.

Inquiry also includes the expectation that results should be reproducible or at least testable by others. Even when replication is not immediate, scientific reasoning relies on coherence among findings, methodological transparency, and cumulative knowledge across studies.

1.3.2 Models, Theories, and Laws

Science uses models to represent, explain, and predict phenomena. Models may be physical, mathematical, conceptual, or computational, and they are judged by usefulness, scope, and explanatory power. The term “theory” is used in science to denote well-supported explanatory frameworks, while “law” typically refers to consistent relationships observed in nature. Scientific literacy distinguishes these meanings from everyday uses of the words.

Learners benefit from seeing that models and theories are refined over time as new evidence appears, and that the credibility of scientific explanations depends on evidence, not on authority alone. Understanding the hierarchy of explanatory roles—models as tools, theories as explanatory structures, and laws as observed regularities—helps students interpret scientific statements accurately.

1.4 Communication and Media Literacy

1.4.1 Interpreting Graphs and Diagrams

Scientific literacy requires competence in interpreting visual information such as graphs, charts, diagrams, and tables. This includes understanding axes, scales, units, legends, and data presentation choices. Students also need to read trends without overextending interpretations—for example, distinguishing between correlation shown in a graph and a causal mechanism that is not directly demonstrated.

Instruction often addresses common skills: reading experimental layouts, understanding how variables are represented, recognizing outliers and missing data, and interpreting uncertainty information such as confidence intervals. When students can interpret visuals, they can better evaluate claims that rely on quantitative evidence.

1.4.2 Identifying Misleading Scientific Claims

Media and popular presentations sometimes simplify or distort scientific results. Scientific literacy therefore includes recognizing misleading patterns, such as cherry-picking selective data, confusing coincidence with causation, using overly broad generalizations, or presenting preliminary findings as established fact. It also involves detecting when claims lack relevant methodology information, when sample sizes are inadequate, or when uncertainty is ignored.

A key skill is to ask what would count as supporting evidence and whether the presented information meets that standard. Rather than dismissing all unfamiliar claims, scientifically literate readers evaluate them using criteria of evidence quality, methodological transparency, and consistency with existing knowledge.

2 Curriculum Design and Learning Progressions

2.1 Learning Goals and Benchmarks

Curriculum design for scientific literacy specifies what learners should know and do at different stages. Goals typically include conceptual targets (core ideas across disciplines), reasoning targets (evidence evaluation, model use, uncertainty interpretation), and communication targets (reading representations and articulating evidence-based explanations). Benchmarks help teachers align classroom activities with broader outcomes and reduce variability in instructional expectations.

Effective benchmarks describe both performance and understanding. For example, rather than only stating that students “learn about uncertainty,” a benchmark can specify that students interpret uncertainty ranges and explain what those ranges mean for conclusions.

2.2 Age-Appropriate Scaffolding

2.2.1 Elementary Foundations

At the elementary level, scientific literacy often begins with observational competence and language for describing phenomena. Learners practice asking questions, making careful observations, and building simple explanations. Instruction emphasizes concrete examples and guided inquiry, using age-appropriate representations and hands-on experiences.

Scaffolding commonly includes sentence frames for describing observations, structured opportunities to compare outcomes, and emphasis on measurement basics such as units and consistent procedures. The goal is to form habits of careful observation and evidence-focused reasoning before introducing formal terminology.

2.2.2 Middle School Development

In middle school, students typically expand reasoning skills by comparing explanations, using simple models, and interpreting more structured data representations. They begin to distinguish between different types of variables, consider alternative explanations, and recognize that uncertainty and variability influence interpretation.

Curriculum at this stage often supports students in moving from “what happened” to “what it suggests,” connecting results to claims through reasoning. Students also strengthen argumentation practices by evaluating evidence quality and considering whether conclusions match the data.

2.2.3 Secondary Refinement

Secondary education refines scientific literacy through more demanding inquiry, deeper engagement with models and evidence chains, and stronger attention to scientific practices. Students may work with experimental design concepts, statistical reasoning at an accessible level, and more complex representations.

Instruction also focuses on disciplinary coherence and transfer: applying evidence evaluation and uncertainty reasoning to new contexts, interpreting scientific articles or reports, and developing the ability to justify decisions using evidence-based arguments rather than intuition.

2.3 Cross-Curricular Connections

2.3.1 Science and Mathematics Integration

Mathematics supports scientific literacy by providing tools for representing relationships, interpreting data, and understanding measurement. Curriculum integration can include working with graphs, proportional reasoning, scales, and basic statistical concepts such as averages and variability. When integrated intentionally, math becomes an enabler rather than a separate hurdle.

Effective integration also addresses interpretation: students learn not only how to calculate but also how to explain what a mathematical result means in a scientific claim. This strengthens the link between quantitative evidence and reasoning.

2.3.2 Science and Language Arts Integration

Language arts contributes to scientific literacy by supporting reading comprehension, argumentation structure, and evidence-based writing. Students learn how scientific claims are phrased, how evidence is summarized, and how uncertainty is communicated in understandable language.

Cross-curricular approaches can include analyzing scientific texts for structure, identifying claims and supporting reasons, and practicing writing explanations that connect observations to conclusions. This integration helps students communicate scientific ideas accurately and persuasively.

2.4 Inclusive Teaching Approaches

2.4.1 Supporting Diverse Learners

Inclusive scientific literacy instruction supports learners with different backgrounds, language proficiencies, and learning needs. Strategies may include multiple representation formats (visual, verbal, hands-on), explicit modeling of scientific reasoning steps, and opportunities for guided participation during inquiry.

Curriculum design can also ensure that students encounter scientific ideas through varied examples and that assignments consider accessibility of materials and language load. By reducing barriers to entry, teachers can better evaluate students’ scientific understanding rather than their ability to decode complex text or unfamiliar contexts.

2.4.2 Encouraging Student Agency and Curiosity

Agency supports engagement and persistence in inquiry. Students can be encouraged to propose questions, choose investigation approaches within constraints, and reflect on how evidence affects their thinking. Curiosity is cultivated by connecting classroom phenomena to personally meaningful experiences, while maintaining evidence-focused reasoning.

Agency is also supported through feedback that emphasizes process—such as refining methods or reinterpreting data—rather than only correctness. Over time, students learn that scientific literacy includes the willingness to revise explanations when evidence warrants change.

3 Instructional Strategies

3.1 Inquiry-Based Learning

3.1.1 Question Formulation

Question formulation helps students practice turning curiosity into testable or investigable prompts. Teachers can guide students to convert broad interests into specific questions that identify variables, measurement targets, or observable outcomes. This stage often includes discussing what evidence would be needed to answer a question.

In practice, students may work from provided scenarios—such as everyday observations or classroom phenomena—to generate questions that can be supported through inquiry. Well-designed questions emphasize clarity, feasibility, and alignment with available methods.

3.1.2 Planning and Conducting Investigations

Planning investigations involves selecting appropriate procedures, deciding how to measure or observe, identifying controls or comparisons, and determining how data will be recorded. Conducting the investigation includes following procedures reliably while remaining attentive to data quality and documentation.

For scientific literacy, the emphasis is not only on completing a task but also on making the logic of the investigation visible. Students learn to justify choices, track variables, document results, and interpret outcomes in light of method constraints.

3.2 Phenomena-Based Instruction

3.2.1 Using Real-World Events

Phenomena-based instruction uses observable events—ranging from classroom demonstrations to everyday occurrences—to anchor learning. The instructional challenge is to select phenomena that are accessible, safe to investigate, and rich enough to support multiple scientific ideas and questions.

Real-world events can also motivate relevance. However, instruction should avoid relying on assumptions about causes; instead, learners gather evidence to construct explanations and test competing ideas where possible.

3.2.2 Building Explanations from Observations

Students build explanations by linking evidence from observations to claims about underlying mechanisms. Teachers support this process by prompting students to describe what they saw, identify patterns, and propose causal or explanatory mechanisms consistent with the data.

Over time, explanations become more structured: learners use scientific vocabulary appropriately, reference evidence explicitly, and revise their ideas when new evidence does not align with prior assumptions.

3.3 Argumentation and Evidence-Based Discussion

3.3.1 Claim–Evidence–Reasoning Frameworks

Claim–Evidence–Reasoning frameworks provide a structured approach for argumentation in science. A claim states an explanation or conclusion; evidence includes observations or measurements; reasoning connects evidence to the claim using scientific principles or model-based logic.

These frameworks support scientific literacy by making the relationships among ideas explicit. Students practice not only stating conclusions but also justifying them and acknowledging what evidence supports the reasoning. When used effectively, the framework helps students avoid “evidence-free explanations.”

3.3.2 Peer Review and Constructive Feedback

Peer review and feedback encourage students to evaluate the clarity and validity of reasoning in others’ work. Constructive feedback focuses on whether evidence is adequate, whether reasoning matches evidence, and whether alternative explanations were considered.

In classroom contexts, peer review can be scaffolded through checklists, discussion protocols, and roles that reduce social friction. The process helps learners internalize criteria for strong evidence and more accurately interpret scientific communication.

3.4 Practical Science and Simulations

3.4.1 Laboratory Experiences

Laboratory work offers firsthand experience with measurement, controlled conditions, and data recording. Scientific literacy develops when students treat lab work as an evidence-generating process rather than a “cookbook” activity. Emphasis is placed on designing comparisons, tracking sources of error, and interpreting results in context.

Students also benefit from learning how to document procedures and data clearly, enabling them to evaluate whether conclusions are justified. Reflection on what the lab could and could not determine supports epistemic understanding.

3.4.2 Modeling and Virtual Labs

Simulations and virtual labs are useful when real experiments are impractical, slow, or unsafe. They allow learners to explore relationships by manipulating variables and observing simulated outcomes. For scientific literacy, the instructional task is to connect model behavior to real-world phenomena and to discuss model limitations.

Students should learn that simulations can be calibrated, but they are not identical to reality. This creates an opportunity to discuss assumptions built into models, the difference between predicted and observed outcomes, and how to interpret results under those constraints.

3.5 Differentiated Support

3.5.1 Vocabulary and Concept Development

Vocabulary support helps learners interpret scientific language and use it precisely. Differentiation may include pre-teaching key terms, providing visual glossaries, using word roots strategically, or offering multiple examples that clarify meaning in context.

Concept development also requires concrete anchoring. Teachers can connect new terms to observable phenomena and to prior models. When students connect vocabulary to evidence, language becomes a tool for reasoning rather than a barrier to understanding.

3.5.2 Reading Strategies for Scientific Texts

Scientific texts often contain dense information and specialized structure. Reading strategies include identifying main claims, locating supporting evidence, interpreting figures, and tracking uncertainty statements. Teachers can guide students through annotation practices and question prompts such as “What is the evidence?” and “What would weaken the claim?”

Differentiation may involve selecting texts at appropriate reading levels, providing summaries without removing critical reasoning components, and using guided reading to model how to interpret experimental context and results.

4 Assessment of Scientific Literacy

4.1 Formative Assessment

4.1.1 Diagnostic Checks and Misconceptions

Diagnostic checks identify prior knowledge, misunderstandings, and gaps in reasoning. These assessments can include short questions, quick interviews, or guided tasks that reveal how students interpret evidence and scientific explanations.

Because scientific literacy includes how learners reason, diagnostic tools often focus on reasoning patterns. For example, students might be asked to interpret a graph or choose between explanations based on evidence. Responses help teachers target instruction to the specific cognitive obstacles students face.

4.1.2 Exit Tickets and Concept Checks

Exit tickets provide brief evidence of learning at the end of a lesson. Concept checks ask targeted questions about key ideas or reasoning skills, such as interpreting variables in a scenario or explaining uncertainty in a measurement.

When designed well, exit tickets can reveal whether students can connect claims to evidence. Teachers can then adjust upcoming instruction, reteach specific steps of reasoning, or provide additional practice with representations.

4.2 Summative Assessment

4.2.1 Performance Tasks

Performance tasks assess scientific literacy through authentic, multi-step work. Students may design an explanation, interpret a dataset, conduct a simulated investigation, or write a short evidence-based argument. These tasks allow assessment of reasoning processes rather than only recall.

Scoring focuses on criteria such as evidence selection, reasoning coherence, correct use of uncertainty language, and clarity of communication. Performance tasks can also include opportunities for revision, reflecting the iterative nature of scientific understanding.

4.2.2 Investigations and Practical Exams

Investigations and practical exams assess competence in procedures, measurement practices, and data interpretation. Students may be evaluated on their ability to follow a plan, record results accurately, and interpret outcomes with appropriate caveats.

In scientifically literate performance, success depends on how well conclusions align with data and method. Practical assessments thus reward careful documentation, transparent handling of uncertainty, and reasoning grounded in observations.

4.3 Assessing Evidence Use and Reasoning

4.3.1 Rubrics for Scientific Writing

Rubrics translate expectations into observable indicators for writing. Common criteria include clarity of claims, relevance and sufficiency of evidence, quality of reasoning links, and accurate interpretation of graphs or tables.

Well-crafted rubrics also attend to uncertainty: students should be able to express limitations, avoid overstating conclusions, and differentiate between observed data and theoretical interpretation. Rubrics can be used both for grading and for instructional feedback.

4.3.2 Interpreting Data Under Uncertainty

Assessments that target uncertainty ask students to interpret ranges, confidence intervals, or variability without converting uncertainty into doubt. Students demonstrate scientific literacy when they can explain what uncertainty means for the strength of a conclusion and when they can identify what additional data would improve confidence.

Tasks may include comparing two evidence sets, deciding whether differences are meaningful given variability, or identifying where measurement limitations constrain interpretation. Such assessments directly test reasoning aligned with scientific practice.

4.4 Validity, Reliability, and Fairness

4.4.1 Designing Objective and Authentic Items

Validity ensures that assessments measure scientific literacy rather than unrelated skills. Reliability ensures consistent scoring and results across time or evaluators. Fairness ensures that students are not disadvantaged by irrelevant factors, such as unfamiliar language complexity or access to materials.

Objective items can be used for foundational skills—like interpreting a graph—while authentic tasks evaluate reasoning and communication. Combining item types often provides a more balanced assessment profile.

5 Content Domains and Literacy Links

5.1 Physical Sciences and Literacy

5.1.1 Energy, Matter, and Change

Energy and matter concepts provide opportunities for evidence-based explanations about conservation, transfer, and transformation. Scientific literacy develops when students connect observable changes—such as temperature shifts or phase changes—to models and measured quantities.

Instruction can emphasize how to interpret data showing changes over time, how to reason about energy pathways using evidence, and how to distinguish between descriptive observations and mechanistic explanations.

5.1.2 Forces, Motion, and Systems

Forces and motion support scientific reasoning through the interpretation of motion data and the use of models. Students practice identifying relevant variables, distinguishing between force and motion, and reasoning about system behavior using evidence.

A systems perspective encourages students to consider interacting components and constraints. Literacy links emerge when students explain outcomes in terms of model-based expectations and when they evaluate whether observed patterns align with predicted behavior.

5.2 Life Sciences and Literacy

5.2.1 Cells, Genetics, and Evolution Concepts

Life science topics support scientific literacy by inviting learners to interpret evidence from diagrams, experimental descriptions, and comparative reasoning. Students build understanding of inheritance and variation by interpreting patterns and relating them to mechanisms.

In evolution-related contexts, literacy emphasizes how evidence supports explanations, how uncertainty is handled when evidence is incomplete, and how claims are evaluated through lines of evidence rather than single observations.

5.2.2 Ecology and Interdependence

Ecology provides a strong setting for systems thinking and evidence evaluation. Students learn how organisms interact with resources and each other, and how changes in one part of a system can affect others.

Scientific literacy is strengthened when students interpret population data, reason about feedback and limitations, and communicate explanations using evidence. Attention to uncertainty helps students avoid overclaiming from limited or localized data.

5.3 Earth and Space Sciences and Literacy

5.3.1 Climate, Weather, and Cycles

Earth science literacy can be developed through interpreting observational and modeled information about atmospheric and environmental processes. Students learn to distinguish between weather variability and longer-term climate patterns by interpreting data over time.

Instruction can focus on reading graphs, understanding cycles, and explaining mechanisms while acknowledging limitations of measurement. Students also practice evaluating claims based on whether the presented data align with the timescale of the question.

5.3.2 Earth History and Timescales

Earth history concepts encourage understanding of evidence from indirect sources such as rock records and stratification. Students learn that scientific claims about deep time rely on converging evidence and measurement interpretation.

Literacy grows when students compare timescales, interpret geological dating concepts at a conceptual level, and discuss how uncertainty affects conclusions. Emphasis is placed on reasoning from evidence rather than treating large timescales as purely memorized facts.

5.4 Engineering Design as Literacy Practice

5.4.1 Constraints, Tradeoffs, and Testing

Engineering design offers an evidence-based pathway to literacy because students must define criteria, explore constraints, and test solutions. Learners practice using data to revise designs, which reinforces the iterative nature of scientific reasoning.

Students strengthen literacy by evaluating performance measures, comparing alternatives, and explaining why design choices affect outcomes. This approach makes the relationship between evidence and decision-making tangible.

5.4.2 Criteria for Success and Iteration

Criteria for success define what “works” and how progress is judged. In iterative design, students treat failures as informative evidence that guides improvement. This supports scientific literacy by demonstrating that conclusions about effectiveness depend on measurable performance.

Instruction often includes reflection on which changes improved results and which variables were not controlled. Over time, students learn to plan systematic improvement using evidence rather than relying on trial-and-error without analysis.

6 Everyday Applications and Decision-Making

6.1 Reading the Science in Everyday Life

6.1.1 Nutrition, Health Claims, and Evidence

Everyday science literacy includes interpreting health and nutrition claims that appear in advertisements, social media, or product labels. Learners practice identifying what evidence is cited, whether claims reflect study types, and how results generalize beyond a specific sample.

Instruction can focus on recognizing differences among correlational findings, controlled comparisons, and mechanistic plausibility. Students also learn to separate marketing language from evidence-based statements by checking what was measured and how outcomes were defined.

6.1.2 Technology and Product Information

Technology and product information often includes performance specifications, test results, and risk or safety statements. Scientific literacy enables learners to interpret what those specifications mean, what conditions they apply to, and what limitations exist.

Students can practice reading measurements, understanding claims about materials or efficiency, and evaluating whether the offered evidence is relevant to the user’s context. This supports decision-making grounded in data rather than impressions.

6.2 Evaluating Risk and Risk Communication

6.2.1 Comparing Claims and Data Sources

Risk communication presents information using probabilities, comparisons, and sometimes uncertainty ranges. Scientific literacy involves comparing claims across sources, checking whether outcomes and definitions match, and identifying the timeframe of risk.

Learners benefit from tasks where they compare different presentations of the same concept and determine which one provides clearer assumptions and evidence. Emphasis is placed on consistent metrics and transparent reporting of uncertainty.

6.2.2 Understanding Correlation vs. Causation

A common literacy hurdle is confusing correlation with causation. Students learn that correlation indicates a relationship but does not by itself identify mechanisms or responsible causes. Causal claims require additional evidence such as experimental manipulation, temporal order, or converging reasoning.

Instruction can use accessible examples from daily life, emphasizing how to ask what alternative explanations could produce the observed association. Students also practice interpreting statements that acknowledge limitations, strengthening their ability to handle scientific claims responsibly.

6.3 Citizen and Community Roles (Non-controversial Contexts)

6.3.1 Participating in Local Science Programs

Community science programs—such as environmental monitoring, astronomy events, or school-based science fairs—provide settings where students use evidence and observation skills outside the classroom. Scientific literacy develops through participation in data collection, record keeping, and interpretation.

These programs also build confidence in making sense of scientific outputs shared publicly, such as summary reports or observational charts. Learners practice communicating findings in clear, evidence-based formats.

6.3.2 Using Evidence for Personal and Community Choices

Scientific literacy supports everyday decisions about services, products, and personal planning by encouraging evidence review and thoughtful comparison. In non-controversial contexts, students learn to prioritize sources that provide transparent methods and relevant metrics.

Decision-making tasks may involve evaluating study summaries, interpreting measurement claims, or comparing alternatives using performance data. This reinforces the practical value of scientific reasoning: choosing actions consistent with evidence and clearly acknowledging limitations.

7 Misconceptions, Challenges, and Teacher Support

7.1 Common Misconceptions

Common misconceptions include treating scientific explanations as fixed facts rather than evidence-based models, assuming that uncertainty means a claim is meaningless, or interpreting graphs without understanding scale and variability. Learners may also conflate “scientific” with “certain,” or equate the number of times a claim is repeated with its quality.

Misconceptions can also involve misunderstanding how variables work, such as believing that correlations always imply causal direction. Addressing these issues requires targeted instruction that connects reasoning steps to evidence.

7.2 Challenges in Scientific Reading and Interpretation

7.2.1 Confusing Vocabulary and Everyday Meanings

Scientific terms sometimes overlap with everyday language but carry different meanings in scientific contexts. When learners use familiar meanings, they may misinterpret claims, especially in topics involving measurement, change, force, or probability.

Teachers can mitigate this by explicitly contrasting meanings, using multiple contexts, and ensuring that terminology is tied to observations and examples rather than memorized definitions.

7.2.2 Overreliance on Intuition

Students may default to intuition when data are abstract or when explanations are counterintuitive. This can lead to interpreting patterns incorrectly, ignoring uncertainty, or accepting claims that match prior beliefs.

Instruction can counter this by structuring evidence-based reasoning routines, providing practice interpreting data, and encouraging students to explain their thinking in terms of evidence and reasoning. Reflection activities help students recognize when intuition is insufficient and when additional evidence is needed.

7.3 Teacher Professional Development

7.3.1 Building Assessment Literacy

Assessment literacy includes understanding how to design and interpret measures of scientific literacy. Teachers need skills in creating valid tasks, scoring with rubrics consistently, and using results to plan instruction. Professional development also supports teachers in distinguishing between performance on recall questions and competence in evidence reasoning.

Training can involve calibrating scoring across examples, analyzing student work to identify common reasoning errors, and learning how to align assessments with learning goals and benchmarks.

7.3.2 Planning Coherent Learning Sequences

Coherent sequences ensure that skills and concepts build progressively rather than appearing as isolated topics. Teachers benefit from understanding how earlier experiences prepare students for later reasoning tasks, and how representations and argumentation practices should develop over time.

Professional development can support planning by emphasizing learning progressions, revisiting core ideas with increasing complexity, and using evidence from formative assessment to adjust instructional pacing. Coherence also means consistently reinforcing scientific literacy routines—such as reading uncertainty information and connecting claims to evidence—across topics and grade levels.