This resource contains activity handouts, a rubric, a facilitation guide, and tex files. The material is meant to be used for those teaching a college algebra course. The activities are meant to provide a deeper understanding (than a traditional course offers) of some of the topics covered in a college algebra course. The activities are intended for group activities and options exist for use in a single class or multiple classes.

This resource contains activity handouts, a rubric, a facilitation guide, and tex files. The material is meant to be used for those teaching a college algebra course. The activities are meant to provide a deeper understanding (than a traditional course offers) of some of the topics covered in a college algebra course. The activities are intended for group activities and options exist for use in a single class or multiple classes.

Thank you for choosing the Dana Center Math Pathways (DCMP) Curriculum resource. The DCMP course programs are research-based and developed from the DCMP Curriculum Design Standards. To obtain the complete course, which includes instructional resources, rubrics, PowerPoints, and answer keys for the preview and practice assignments, you can visit the Dana Center Curriculum Resource Portal to request access. For a low-cost digital version that integrates seamlessly with most Learning Management Systems (LMS), you will need to fill out a Lumen Learning Online Homework Manager (OHM) request form. For any other questions, concerns, or support, please contact Charles A Dana Center danacenter@austin.utexas.edu.Licensing These materials are copyrighted © 2020 by the Charles A. Dana Center at The University of Texas at Austin and are licensed under CC BY-NC-ND 4.0. Under this license, these materials are available to copy and redistribute in any medium of format for non-commercial use only. Appropriate attribution is required. Alterations to formatting are acceptable (e.g., font change, removal of a nonmathematical image, addition of accessibility tools), but derivatives of these materials are prohibited. These materials constitute a comprehensive course curriculum, which was carefully designed to promote scaffolded conceptual understanding and increase levels of student persistence. Because of the intentional design of the course, we recommend using the lessons in the order they are given. Some lessons refer to previous lessons or anticipate future lessons. The following conditions are examples of acceptable use of these materials.a)     Educators and administrators may reproduce and use one printed copy of the material for their personal use without obtaining further permission from the University, so long as all original credits, including copyright information, are retained.b)    Educators may reproduce multiple copies of pages for student use in the classroom, so long as all original credits, including copyright information, are retained.c)     Educators may reproduce and use parts of the materials in presentations, so long as all original credits, including copyright information, are retained.d)    Educators and administrators may reproduce these materials for use for professional development within their departments so long as all original credits, including copyright information, are retained. e)     Educators and administrators may upload the materials to a learning management system so long as all original credits, including copyright information, are retained.f)     Educators and administrators may provide these materials to a local print shop to create copies of the student materials. All original credits, including copyright information, must be retained. These materials may be sold through your department or in college bookstores in order to recover printing costs. g)    Educators and administrators agree that they will not post instructor answer documents in any student-accessible locations.   

This unit begins with the collection of student data that will be used throughout the course. Several early lessons have a student success focus of creating a learning community, forming effective study groups, and crafting written arguments. Mathematically, topics include voting schemes, descriptive statistics and graphical displays, theoretical probability, conditional probability, conversions, indices, weighted averages, expected value, simple and weighted moving averages, part-to-part and part-to-whole ratios, absolute and relative change.

This unit begins with the collection of student data that will be used throughout the course. Several early lessons have a student success focus of creating a learning community, forming effective study groups, and crafting written arguments. Mathematically, topics include voting schemes, descriptive statistics and graphical displays, theoretical probability, conditional probability, conversions, indices, weighted averages, expected value, simple and weighted moving averages, part-to-part and part-to-whole ratios, absolute and relative change.

This unit begins with the collection of student data that will be used throughout the course. Several early lessons have a student success focus of creating a learning community, forming effective study groups, and crafting written arguments. Mathematically, topics include voting schemes, descriptive statistics and graphical displays, theoretical probability, conditional probability, conversions, indices, weighted averages, expected value, simple and weighted moving averages, part-to-part and part-to-whole ratios, absolute and relative change.

This section builds on previous sections and provides engagement with more complex graphical displays. Topics include analyzing stacked and comparative stacked column graphs, motion bubble charts, and heat maps.  Some of the displays you encounter will be misleading or erroneous. You will also start from data and choose a way to model the data. You will be asked to draw conclusions and make decisions based on multiple pieces of quantitative information, and to write about those conclusions and decisions.

This section builds on previous sections and provides engagement with more complex graphical displays. Topics include analyzing stacked and comparative stacked column graphs, motion bubble charts, and heat maps.  Some of the displays you encounter will be misleading or erroneous. You will also start from data and choose a way to model the data. You will be asked to draw conclusions and make decisions based on multiple pieces of quantitative information, and to write about those conclusions and decisions.

This section builds on previous sections and provides engagement with more complex graphical displays. Topics include analyzing stacked and comparative stacked column graphs, motion bubble charts, and heat maps.  Some of the displays you encounter will be misleading or erroneous. You will also start from data and choose a way to model the data. You will be asked to draw conclusions and make decisions based on multiple pieces of quantitative information, and to write about those conclusions and decisions.

This section on modeling includes distinguishing proportionality and linearity, using proportionality to estimate, interpolation and extrapolation, an informal introduction to piecewise linear functions, regression, correlation vs. causation, and relationship strength. Non-linear modeling topics include exponential growth and decay, logistic, predator-prey, cyclical/periodic, and the effect of parameter changes.

This section on modeling includes distinguishing proportionality and linearity, using proportionality to estimate, interpolation and extrapolation, an informal introduction to piecewise linear functions, regression, correlation vs. causation, and relationship strength. Non-linear modeling topics include exponential growth and decay, logistic, predator-prey, cyclical/periodic, and the effect of parameter changes.

This section on modeling includes distinguishing proportionality and linearity, using proportionality to estimate, interpolation and extrapolation, an informal introduction to piecewise linear functions, regression, correlation vs. causation, and relationship strength. Non-linear modeling topics include exponential growth and decay, logistic, predator-prey, cyclical/periodic, and the effect of parameter changes.

This section on modeling includes distinguishing proportionality and linearity, using proportionality to estimate, interpolation and extrapolation, an informal introduction to piecewise linear functions, regression, correlation vs. causation, and relationship strength. Non-linear modeling topics include exponential growth and decay, logistic, predator-prey, cyclical/periodic, and the effect of parameter changes.

This section on modeling includes distinguishing proportionality and linearity, using proportionality to estimate, interpolation and extrapolation, an informal introduction to piecewise linear functions, regression, correlation vs. causation, and relationship strength. Non-linear modeling topics include exponential growth and decay, logistic, predator-prey, cyclical/periodic, and the effect of parameter changes.

This section is designed to support you in becoming an educated consumer of statistical information. Topics include observational and experimental studies and their conclusions, sampling processes, sampling and non-sampling errors, types of bias and how to minimize them, and appropriate conclusions. Additional topics include designing experimental studies, cause and effect, confounding variables, placebos and the placebo effect, blinding and double-blinding, and blocking.

This section is designed to support you in becoming an educated consumer of statistical information. Topics include observational and experimental studies and their conclusions, sampling processes, sampling and non-sampling errors, types of bias and how to minimize them, and appropriate conclusions. Additional topics include designing experimental studies, cause and effect, confounding variables, placebos and the placebo effect, blinding and double-blinding, and blocking.

This section is designed to support you in becoming an educated consumer of statistical information. Topics include observational and experimental studies and their conclusions, sampling processes, sampling and non-sampling errors, types of bias and how to minimize them, and appropriate conclusions. Additional topics include designing experimental studies, cause and effect, confounding variables, placebos and the placebo effect, blinding and double-blinding, and blocking.