School Improvement in Maryland
Algebra and Data Analysis - Instructional Strategies

Mathematics High School Assessment Field Tests: What We Are Learning from the Algebra/Data Analysis HSA?

Areas of Growth:

  • Students are responding more to the constructed response items and the quality of their responses is improving.
  • Students' responses addressing the cue for an explanation are more clearly presented and fully developed.
  • Students' understanding of Goal 3 content is improving.

Informing Instruction:

Students will continue to benefit from:

  • Justifying their answers. An appropriate justification may include:
    • a statement of a mathematical principle and/or a definition
    • a demonstration that the solution satisfies the conditions of the problem
  • Writing equations to model a situation or to represent a pattern
  • Using a table of a linear function to find the y–intercept of the equation
  • Justifying that a pattern is a function
  • Finding the domain and range of functions from a graph
  • Recognizing the scales of the x– and y–axes are important, specifically:
    • the scales of the axes may be different
    • the unit of the scales will not always be one
  • Using two points from a graph to determine the slope of a line
  • Interpreting the slope and y–intercept of lines in the context of the problem
  • Interpreting the solution of a system of equations in the context of the problem
  • Graphing inequalities for which the coefficient of the variable is negative
  • Interpreting frequency tables to find measures of central tendency
  • Understanding how measures of central tendency are effected by:
    • extreme data points
    • changes in the data set
  • Reading and understanding box–and–whisker plots, including:
    • using measures of variability to interpret data and compare data sets
    • recognizing that the mean cannot be determined from a box and whisker plot
  • Interpreting frequency tables to find probabilities from the results of a simulation or a survey
  • Understanding probability simulations, including:
    • distinguishing between a simulation and an experiment
    • justifying that a simulation model represents a given probability
    • defining a trial
    • describing how to interpret the data from a simulation to calculate the experimental probability
    • reading random number tables
  • Understanding sampling, including:
    • distinguishing between a representative and simple random sample
    • designing a simple random sample
    • recognizing how a simple random sample insures independent selection of the members of the sample as well as each member of the population having an equally likely chance to be chosen for the sample
Instructional Strategies