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CCOG for MTH 98 Winter 2023

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Course Number:
MTH 98
Course Title:
Math Literacy II
Credit Hours:
4
Lecture Hours:
30
Lecture/Lab Hours:
20
Lab Hours:
0

Course Description

Covers formulating and clearly communicating arguments supported by quantitative evidence with emphasis on data analysis. Uses data to collaboratively engage with contextual and open-ended mathematical problems. Emphasizes use of mathematical and statistical reasoning through interpreting information, making conjectures, communicating effectively, and verifying results. Emphasizes an understanding of the role of mathematics and how it affects decision making in life. Uses collaborative learning through in-class group interaction. Uses technology throughout the course. Prerequisites: (MTH 58 or MTH 63 or MTH 65 or MTH 70) and (RD 90 or ESOL 260) and (WR 90 and ESOL 262) or IRW 90 or equivalent placement. Audit available.

Intended Outcomes for the course

Upon completion of the course students should be able to

  1. Use and integrate several different types of technology to explore and analyze data to solve problems.
  2. Reflect on and communicate the reasonableness of mathematical approaches and solutions using contextual information.
  3. Articulate individual positions using quantitative reasoning and respectfully consider the approaches and solutions of others.
  4. Critically analyze information for its accuracy, relevance, and credibility to reflect on how mathematics can be used in one’s life and in the larger community.
  5. Engage with and formulate questions to explore mathematical topics, collaborate with others, and persevere through the problem solving process.
  6. Develop self-awareness of the learning process and self-monitor understanding and performance.

Course Activities and Design

This class is designed on a group work and discussion classroom format.  The focus of the activities in the course are reality based problems and situations from which formal definitions and procedures will arise.  A technology component is included as part of the course activities.  

Outcome Assessment Strategies

Different grading structures can be used to determine the course grade, such as mastery grading, percentage-based grading, points-based grading.  Instructors’ grading systems should promote individual student development toward mastery of the skills listed below.

Required assessment shall include:

  1. At least two individual proctored, closed-book, no student-notes exams.  (An instructor-provided conversion chart is allowed an example is attached here) These exams must consist primarily of free response questions.
  2. Group work and class participation
  3. Homework including each of the following:
    1. Technology assignments
    2. At least one group project culminating in a written report and/or oral presentation​​​​​
  4. Must include at least 3 of the following
    1. Online skills assignments
    2. Written Homework
    3. Quizzes
    4. Reflections
    5. Portfolio  
    6. Individual student conference
    7. Community based learning

Course Content (Themes, Concepts, Issues and Skills)

  1.  Models and Curve Fitting
    1. Write a linear equation to model an application.  Graph using technology and make predictions. 
    2. Interpret the slope and intercepts of linear models.
    3. Be able to recognize if given data is linear or not.
    4. Use system of equations to model a situation.  Graph using technology and interpret solution.
    5. Use technology to find equations of best fit for linear models.  Analyze the strength of the model.  Use technology to compare with at least one other type of model such as exponential, quadratic, and/or logistic.
    6. Create and interpret a quadratic equation that models an application.  Use technology to find and interpret intercepts and vertex. 
  2.  Statistical Reasoning
    1. Analyze and interpret different types of average (mean and median) and spread for some applications using technology.  Recognize misuse of measures of spread in statistics. 
    2. Compute and interpret basic probabilities and investigate theoretical vs empirical probability.
    3. Use technology to compute standard deviation (population or sample) and interpret the results.
    4. Look at applications that can be modeled with the normal curve and also use the empirical rule (68-95-99.7 rule) to find probabilities.
    5. Find and interpret margin of error for different applications.  Determine the minimum sample size for a given margin of error and a given confidence level.  Investigate margin of error for various levels of confidence.
    6. Explore applications related to expected value of a discrete variable.
    7. Use histograms and/or stem and leaf to provide a visual interpretation of numerical data from an application.
    8. Interpret unions and intersections of Venn diagram graphs involving applications.