Distinguishing between observational, experimental, and research questions (e.g., Observational-How does a cricket chirp? Experimental--Does the amount of light affect how a cricket chirps? Research-Do all crickets chirp? Why do crickets chirp?).
Identifying multiple variables that affect a system and using the variables to generate experimental questions that include cause and effect relationships.
Using logical inferences derived from evidence to predict what may happen or be observed in the future.
Providing an explanation (hypothesis) that is reasonable in terms of available evidence.
A list of materials needed that specifies quantities (e.g., 250 ml water).
A procedure that lists significant steps sequentially and describes which variable will be manipulated or changed and which variables will remain the same ("Fair Test").
An appropriate format for recording data.
A strategy for conducting multiple trials ("Fair Test").
Choosing appropriate measurements for the task and measuring accurately.
Collecting data and recording accurate and complete data from multiple trials.
Selecting an appropriate perspective (e.g., cross section, top view, side view) and recording precise proportions.
Determining an appropriate representation (line graph in addition to prior examples) to represent their findings accurately.
Selecting a scale that is appropriate for range of data to be plotted, labeling units, and presenting data in an objective way.
Including clearly labeled keys and symbols, when necessary.
Using correct scientific terminology to label representations.
Identifying relationships of variables based upon evidence.
Questioning data that might not seem accurate or does not fit into the pattern of other findings.
Explaining data using correct scientific terminology
Using experimental results to support or refute original hypothesis.
Considering all data when developing an explanation/conclusion.
Identifying problems/flaws with the experimental design.
Using additional resources (e.g., books, journals, databases, interview, etc.) to strengthen an explanation.
Preparing a conclusion statement/summary.
Explaining how experimental findings can be generalized to other situations.
Developing questions that reflect prior knowledge.
Refining and focusing broad ill-defined questions.
Predicting results (evidence) that support the hypothesis.
Proposing a hypothesis based upon a scientific concept or principle, observation, or experience that identifies the relationship among variables.
A diagram labeled using scientific terminology that supports procedures and illustrates the setup.
A procedure that lists significant steps that identify manipulated (independent) and responding (dependent) variables.
A control for comparing data when appropriate.
Identification of tools and procedures for collecting data and reducing error.
Accurately quantifying observations using appropriate measurement tools.
Using technology to collect, quantify, organize, and store observations (e.g., use of probe).
Recording multiple perspectives to scale (e.g., magnification, cross section, top view, side view, etc.).
Representing independent variable on the "X" axis and dependent variable on the "Y" axis.
Determining a scale for a diagram that is appropriate to the task.
Using technology to enhance a representation.
Using color, texture, symbols and other graphic strategies to clarify trends/patterns within a representation.
Identifying, considering and addressing experimental errors (e.g., errors in experimental design, errors in data collection procedures).
Identifying limitations and/or sources of error within the experimental design.
Using scientific concepts, models, and terminology to report results, discuss relationships, and propose new explanations.
Generating alternative explanations.
Documenting and explaining changes in experimental design.
Sharing conclusion/summary with appropriate audience beyond the research group.
Using mathematical analysis as an integral component of the conclusion.
Identifying additional data that would strengthen an investigation.
Explaining limitations for generalizing findings.
Explaining relevance of findings (e.g., So what?) to the local environment (community, school, classroom).
Devising recommendations for further investigation and making decisions based on evidence for experimental results.
Framing testable questions showing evidence of observations and prior knowledge to illustrate cause and effect.
Developing a testable question appropriate to the scientific domain being investigated.
Developing a testable/guiding hypothesis and predictions based upon evidence of scientific principles.
Predicting results (evidence) that support the hypothesis.
Clearly distinguishing cause and effect within a testable/guiding hypothesis.
Procedures that incorporate appropriate protection (e.g., no food in lab area).
Appropriate tools, units of measurement and degree of accuracy.
Components that reflect current scientific knowledge and available technology.
Use of scientific terminology that supports the identified procedures
Collecting significant data by completing multiple trials;
Evaluating and revising procedures as investigation progresses.
Representing data quantitatively to the appropriate level of precision through the use of mathematical calculations.
Developing the skill of drawing a "best fit" curve from data.
Recording accurate data, free of bias.
Explaining importance of avoiding plagiarism/fabrication of other recorded research data.
Accounting for identified experimental errors.
Analyzing significance of experimental data.
Critically examining and explaining the relationship of evidence to the findings of others (e.g., classmates or scientists in the field).
Proposing, synthesizing, and evaluating alternative explanations for experimental results.
Citing experimental evidence within an explanation.
Including logically consistent position to explain observed phenomena.
Comparing an experimental conclusion to other proposed explanations by peer review (e.g., students, scientists or local interest groups).
Conducting objective scientific analysis and evaluating potential bias in the interpretation of evidence.
Identifying and evaluating uncontrolled variables inherent in experimental model.
Using technology to communicate results effectively and appropriately to others (e.g., power point, web site, posters, etc.).
Predicting/recommending how scientific conclusions can be applied to civic, economic or social issues.
Proposing and evaluating new questions, predictions, procedures and technology for further investigations.