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e-Learning for Educators
Course Information

Using Digital Portfolios to Foster Student Learning**
Course Description

In this course participants will learn how to utilize Google Sites to develop student-based or classroom-based e-Portfolio’s as an alternate form of assessment as well as to showcase student work. Participants will learn what e-Portfolio’s are and discuss the benefits and uses of them as a teaching and learning tool. Participants will have hands-on practice using Google Sites to create an ePortfolio with best practice elements. Finally, participants will evaluate the classes they teach  to ensure that their students will have artifacts aligned to course objectives/standards that are ePortfolio-ready!

**This course will require that participants hold WV certification and are employed as a classroom teacher or school administrator. 

Course Syllabus

Real-world data are a glimpse into a complex story that involves much more detail than the numbers would suggest. We want students to look "behind the scenes" by organizing, representing and analyzing these data. Technology is central to this task, both as a source of data and as a tool for data analysis. Technology tools and web-based materials provide important ways for math educators to meet key NCTM and ISTE standards that emphasize problem solving and making connections between mathematics, other disciplines and the real world. These standards include a significant emphasis on representing and analyzing data, including a focus on being able to evaluate the sources of data and the effectiveness of different representations that students will encounter both inside and out of school. This course will explore a range of web-based resources and exemplary projects which utilize technology to support these goals. Participants will learn how to find sources of real data on the Web and explore how technology tools such as spreadsheets can help students analyze, visualize and make sense of these data. Participants will complete the course with a collection of resources and beginning project ideas that serve their curricular goals.

Goals and Objectives

This course will enable participants to:

Learn how Common Core State Standards (and the NCTM Math and ISTE standards) can be met by using real world data in math classrooms supported by appropriate technology

Learn how to find mathematical data on the Web and download it into a spreadsheet

Learn how to find and evaluate projects that include analysis of real data

Learn to evaluate the appropriateness of varied data sets for specific pedagogical goals

Learn about a variety of data analysis tools appropriate for classroom use

Develop a personal collection of web-based resources for curricular use

Develop preliminary plans for a technology-enhanced classroom activity that uses real data


This is an introductory course for teachers, technology specialists, curriculum specialists, professional development specialists, or other school personnel. Participants are expected to have regular access to computers, and proficiency with email and current web-browsers.

Course Details

This course is divided into six one-week sessions which each include readings, an activity, and an online discussion among course participants. The time for completing each session is estimated to be two to four hours.

The outline for the course is as follows:

Session One: What Can We Learn from Data? How Can Technology Support Telling the Story of Data?

Session Two: Understanding Patterns and Making Predictions

Session Three: Developing a Statistical Analysis

Session Four: Telling the Story of Data using Graphs and Technology

Session Five: Exploring Sources of Real Data on the Web

Session Six: Developing Preliminary Plans for a Technology-Enhanced Classroom Project that Uses Real Data

In the first two sessions, participants will learn how technology can support the use of real world data in the math classroom to teach important mathematical content and will consider how technology can help students make predictions. In Session 3, participants will analyze two sets of data and will think about the challenges and opportunities of implementing technology-enhanced data analysis activities in the classroom. Sessions 4 and 5 will present participants with a number of online tools and web-based resources that can be used in a classroom unit on data analysis. They will also consider how technology-enhanced data analysis in the math classroom can help meet ISTE technology standards. Finally, in Session 6, participants will make preliminary classroom plans for a technology-enhanced lesson that uses real data.

Course participants are expected to complete weekly assignments, including active participation in the online discussion board.



These are suggested criteria to be used for evaluating successful participation in and completion of this course.

Discussion Postings

Participants are expected to respond to the online discussion prompt in each of the course sessions with an original posting. Participants are also expected to respond to the postings of other course participants in each course session.
Guidelines for original discussion postings can be found here.

Course Activities

Participants are expected to complete the required course readings and activities as posted in each of the session assignment pages. Participants are expected to post reflections about the assigned readings and the completed activities in the online course discussion.

Final Product

Participants are expected to complete and submit the final product during throughout the course sessions.

Final Course Survey

Participants are expected to complete the final course survey within one week of the end of the last course session.


Orientation Quiz & Workshop Surveys: Participants must complete an Orientation Quiz with a score of 90% or higher.  In addition, participants are expected to complete both a pre-course and a post-course surveys. The Orientation Survey is to be completed by Sunday during the Orientation Session and the Final Survey is to be completed by Sunday during Session Six.

Copyright and Plagiarism

All resources referenced during the course will be properly documented. Copyright guidelines are to be observed throughout the course project and all course activities.  All work associated with course projects, course assignments and course discussions will be original to each course participant. Fair use does not apply to the course project. 

Plagiarism, the reproduction of all or any part of another individual’s or organization’s work, by a course participant of work associated with the course project or other course assignments at any point during the course will result in no credit being awarded for the course.


All grades in the course gradebook must be a "C" for successful course completion.  A grade of "C" indicates that all work has been completed and the work meets the expectations for that assignment.

The grade scheme for this course will be:
C = all work meets the guidelines provided
I = the work submitted is incomplete and/or does not meet the guidelines provided
N = no work has been submitted 

Certificate of Completion

Upon successful completion of this course, Using Digital Portfolios to Foster Student Learning, participants will receive a Certificate of Completion documenting successful completion of the course requirements. Certificates are distributed to each qualifying participant by attachment to the final project dropbox after the completion of the course.

Non-Degree Graduate Credit Information

Participants in this course are eligible to receive non-degree graduate credits from either West Virginia University, West Virginia State University Marshall University or Concord University. Credits will be awarded at the end of the semester. Additional information is available on the course News/Welcome Page.


This course will help participants learn and apply the following Common Core Mathematics Standards to their teaching:

Common Core Standards

Grade Level

Standards for Mathematical Practice

3. Construct viable arguments and critique the reasoning of others

Mathematically proficient students understand and use stated assumptions, definitions, and previously established results in constructing arguments. They make conjectures and build a logical progression of statements to explore the truth of their conjectures. They are able to analyze situations by breaking them into cases, and can recognize and use counterexamples. They justify their conclusions, communicate them to others, and respond to the arguments of others. They reason inductively about data, making plausible arguments that take into account the context from which the data arose. Mathematically proficient students are also able to compare the effectiveness of two plausible arguments, distinguish correct logic or reasoning from that which is flawed, and "if there is a flaw in an argument "explain what it is. Elementary students can construct arguments using concrete referents such as objects, drawings, diagrams, and actions. Such arguments can make sense and be correct, even though they are not generalized or made formal until later grades. Later, students learn to determine domains to which an argument applies. Students at all grades can listen or read the arguments of others, decide whether they make sense, and ask useful questions to clarify or improve the arguments.

4. Model With Mathematics

Mathematically proficient students can apply the mathematics they know to solve problems arising in everyday life, society, and the workplace. In early grades, this might be as simple as writing an addition equation to describe a situation. In middle grades, a student might apply proportional reasoning to plan a school event or analyze a problem in the community. By high school, a student might use geometry to solve a design problem or use a function to describe how one quantity of interest depends on another. Mathematically proficient students who can apply what they know are comfortable making assumptions and approximations to simplify a complicated situation, realizing that these may need revision later. They are able to identify important quantities in a practical situation and map their relationships using such tools as diagrams, two-way tables, graphs, flowcharts and formulas. They can analyze those relationships mathematically to draw conclusions. They routinely interpret their mathematical results in the context of the situation and reflect on whether the results make sense, possibly improving the model if it has not served its purpose.

5. Use appropriate tools strategically

Mathematically proficient students consider the available tools when solving a mathematical problem. These tools might include pencil and paper, concrete models, a ruler, a protractor, a calculator, a spreadsheet, a computer algebra system, a statistical package, or dynamic geometry software. Proficient students are sufficiently familiar with tools appropriate for their grade or course to make sound decisions about when each of these tools might be helpful, recognizing both the insight to be gained and their limitations. For example, mathematically proficient high school students analyze graphs of functions and solutions generated using a graphing calculator. They detect possible errors by strategically using estimation and other mathematical knowledge. When making mathematical models, they know that technology can enable them to visualize the results of varying assumptions, explore consequences, and compare predictions with data. Mathematically proficient students at various grade levels are able to identify relevant external mathematical resources, such as digital content located on a website, and use them to pose or solve problems. They are able to use technological tools to explore and deepen their understanding of concepts.


Measurement & Data

Represent and interpret data


Draw a picture graph and a bar graph (with single-unit scale) to represent a data set with up to four categories. Solve simple put-together, take-apart, and compare problems1 using information presented in a bar graph.


Draw a scaled picture graph and a scaled bar graph to represent a data set with several categories. Solve one- and two-step "how many more"  and "how many less" problems using information presented in scaled bar graphs. For example, draw a bar graph in which each square in the bar graph might represent 5 pets.


Generate measurement data by measuring lengths using rulers marked with halves and fourths of an inch. Show the data by making a line plot, where the horizontal scale is marked off in appropriate units— whole numbers, halves, or quarters.


Make a line plot to display a data set of measurements in fractions of a unit (1/2, 1/4, 1/8). Solve problems involving addition and subtraction of fractions by using information presented in line plots. For example, from a line plot find and interpret the difference in length between the longest and shortest specimens in an insect collection.


Make a line plot to display a data set of measurements in fractions of a unit (1/2, 1/4, 1/8). Use operations on fractions for this grade to solve problems involving information presented in line plots. For example, given different measurements of liquid in identical beakers, find the amount of liquid each beaker would contain if the total amount in all the beakers were redistributed equally.



Graph points on the coordinate plain to solve real world and mathematical problems


Use a pair of perpendicular number lines, called axes, to define a coordinate system, with the intersection of the lines (the origin) arranged to coincide with the 0 on each line and a given point in the plane located by using an ordered pair of numbers, called its coordinates. Understand that the first number indicates how far to travel from the origin in the direction of one axis, and the second number indicates how far to travel in the direction of the second axis, with the convention that the names of the two axes and the coordinates correspond (e.g., x-axis and x-coordinate, y-axis and y-coordinate).


Represent real world and mathematical problems by graphing points in the first quadrant of the coordinate plane, and interpret coordinate values of points in the context of the situation.


Statistics & Probability

Develop an understanding of statistical variability


Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers. For example, "How old am I?" is not a statistical question, but "How old are the students in my school?" is a statistical question because one anticipates variability in students' ages.


Understand that a set of data collected to answer a statistical question has a distribution which can be described by its center, spread, and overall shape.


Recognize that a measure of center for a numerical data set summarizes all of its values with a single number, while a measure of variation describes how its values vary with a single number.


Statistics & Probability

Summarize and describe distributions


Display numerical data in plots on a number line, including dot plots, histograms, and box plots.


Summarize numerical data sets in relation to their context, such as by:

Reporting the number of observations.

Describing the nature of the attribute under investigation, including how it was measured and its units of measurement.

Giving quantitative measures of center (median and/or mean) and variability (interquartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered.

Relating the choice of measures of center and variability to the shape of the data distribution and the context in which the data were gathered.


Statistics & Probability

Use random sampling to draw inferences about a population


Understand that statistics can be used to gain information about a population by examining a sample of the population; generalizations about a population from a sample are valid only if the sample is representative of that population. Understand that random sampling tends to produce representative samples and support valid inferences.


Use data from a random sample to draw inferences about a population with an unknown characteristic of interest. Generate multiple samples (or simulated samples) of the same size to gauge the variation in estimates or predictions. For example, estimate the mean word length in a book by randomly sampling words from the book; predict the winner of a school election based on randomly sampled survey data. Gauge how far off the estimate or prediction might be.


Statistics and Probability

Draw informal comparative inferences about two populations


Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. For example, the mean height of players on the basketball team is 10 cm greater than the mean height of players on the soccer team, about twice the variability (mean absolute deviation) on either team; on a dot plot, the separation between the two distributions of heights is noticeable.


Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations. For example, decide whether the words in a chapter of a seventh-grade science book are generally longer than the words in a chapter of a fourth-grade science book.


Statistics & Probability

Investigate patterns of association in bivariate data


Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.


Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.


Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.


Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequencies calculated for rows or columns to describe possible association between the two variables. For example, collect data from students in your class on whether or not they have a curfew on school nights and whether or not they have assigned chores at home. Is there evidence that those who have a curfew also tend to have chores?


Interpreting Categorical & Quantitative Data

Summarize, represent, and interpret data on a single count or measurement variable


Represent data with plots on the real number line (dot plots, histograms, and box plots).


Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.


Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).


Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.

Summarize, represent, and interpret data on two categorical and quantitative variables


Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.


Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.

Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.

Informally assess the fit of a function by plotting and analyzing residuals.

Fit a linear function for a scatter plot that suggests a linear association.


© 2010. National Governors Association Center for Best Practices and Council of Chief State School Officers. All rights reserved.

This course, Using Real Data in the Math Classroom, will help participants meet the ISTE Educational Technology Standards and Performance Indicators for All Teachers (http://edtechleaders.org/documents/NETSAdminTeachers.pdf), especially Standards II, III, IV.

For more information about Technology Integration visit: http://www.iste.org

About this Course

This course was developed by EdTech Leaders Online at Education Development Center. EdTech Leaders Online provides capacity building, training, and online courses for school districts, state departments of education, and colleges and universities.

Last update:  October , 2014

© 2012 Education Development Center, Inc., through its project, EdTech Leaders® Online, http://www.edtechleaders.org. All rights reserved.