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Ideas for Progress: Science, Range 16–19

To enhance their skills in each science-related strand, students who score in the 16–19 score range on the ACT® college readiness assessment may benefit from activities that encourage them to do the following:

Score Range 16–19
Interpretation of Data
  • locate and select data in complex data presentations
  • locate similar data points in different data presentations related to the same experiment
  • combine data from separate but related data presentations to create a summary of the data
  • display data in a variety of formats (e.g., line graphs, pie charts, bar graphs)
  • develop a set of guidelines to help a younger audience select and use data from a complex data presentation
  • review data tables in research reports, and determine the best ways to analyze and interpret the data (e.g., observe the sizes of intervals between data points)
  • create a visual display that summarizes a set of raw data
  • use given data to estimate unknown values in a table or graph
Scientific Investigations
  • perform experiments that require multiple steps
  • review multiple alternative experimental procedures for answering the same question, and identify similarities and differences
  • read experiments, and identify the tools and measurements used
  • conduct a simple experiment that makes use of a control group
  • summarize the design of experiments, including the questions asked, the variables manipulated, and the methods used
  • discuss how the effectiveness of the experiment is related to the methods used
  • select experiments, from a variety of sources, that answer a similar question
Evaluation of Models
  • read descriptions of experiments (e.g., science fair projects, science education journals), and discuss whether the stated conclusions support or contradict the hypotheses
  • formulate hypotheses, predictions, or conclusions based on the results of an experiment
  • determine those conditions of a model that must be assumed for the model to be accurate
  • review a model to gauge its ability to explain past observations about that model
  • compare models that explain different phenomena, including how they support their claims
  • critique the claims and evidence presented by peers by citing examples from data sets that support or refute their claims
  • present competing models, and evaluate their strengths and weaknesses