Marc Freed portfolio

About me

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I am a mathematical economist. I received a B.A. in mathematics and economics from Brown University in 1975 and an M.S. in management from MIT in 1982. In 1994, I passed oral and written doctoral qualifying exams in economics at the Stern School of Business at NYU. I did not complete a dissertation.

From 1982-2017 I worked as a bond trader, an investment banker, and a fund manager in the financial services industry. I retired from that career in December 2017.

My actual teaching experience includes only one year (1975-76) as a high school math and physics teacher in Africa as a Peace Corps volunteer, and one year (2001-02) as a Visiting Assistant Professor of Economics at Union College in Schenectady.

I enrolled in the CDIT program at the University at Albany in May 2019 in order to learn how to use online education to share my expertise in quantitative analysis with students and adult learners. I plan to use the knowledge and skills I acquire in the program to help others to develop their own quantitative capabilities.

Project Proposal

Teaching students to manage data

Human beings have always used stories to make sense of their world. Only in the past few centuries have we begun to validate our stories with statistical evidence. Data is the critical ingredient in this process. This project uses a Knowledge Building framework to teach instructors how to teach their students to develop ideas that they can explore with data, and how to identify, gather, and analyze relevant data.

Learning Outcomes

Target audience

This course has value for teachers at any academic level and in any professional environment. Teachers themselves must determine appropriate levels of complexity for their students. For example, an appropriate topic for elementary students might be as simple as having them analyze the colors of cars in their school parking lot. Older students with experience using the internet to conduct more serious research can undertake projects that require large datasets available online. Learners in non-academic environments may have different performance and learning contexts from academic learners.

Learning outcomes

The following table lists the learning outcomes for this course. Not all are relevant for all levels. They are largely sequential with the exception of "abstraction" that teachers may omit for younger students. I have indicated suggested levels for each outcome, but teachers know their students best so they must make the final determinations. Finally, I have included a column listing the related Bloom skills based on their revision by Anderson et al. (2001). Teachers should note that this exercise essentially inverts the normal order of the taxonomy because it requires students to develop an idea before they even begin to think about data. Ideation is a creative process in which students of all ages can participate with no knowledge beyond what they bring to class. The teacher's second task is to guide the students through an evaluation of their idea to determine if it is susceptible to data-driven analysis. In contrast to topics affected by opinions and values, data-driven projects tend to migrate from speculation to application. As a result, the latter steps in the process tend to involve skills nearer the bottom of Bloom's revised taxonomy.

Learning activity	Learning outcome	Minimum grade	Bloom skill
Ideation	define ideas that students can analyze with data	all	Creativity
Idea refinement	convert ideas to workable problems with measurable solutions	all	Application, Analysis
Idea decomposition	decompose ideas into sequential steps to explore and analyze	all	Application
Abstraction	develop theories, i.e. testable hypotheses, to validate ideas	Middle school	Analysis
Data identification	identify data required for analysis	all	Knowing
Data storage	decide how to store data	all	Application
Data collection	identify data sources and collect data	all	Knowing
Data preparation	develop system to clean and verify data	all	Knowing
Data representation	create visual representations of data, e.g. charts, graphs, tables	all	Analysis
Data analysis	perform elementary statistical analysis of data	Middle school	Analysis
Software selection	select software required for advanced analysis and modeling	High school	Application
Algorithms & procedures	build models to analyze data and produce results	High school	Evaluation
Automation	write code to run model	High school	Application
Simulation	test model	High school	Analysis
Evaluation	analyze results of model test - repeat until good	High school	Evaluation
Production	move finished product from laboratory to production	High school	Application

Reference

Anderson, L.W. (Ed.), Krathwohl, D.R. (Ed.), Airasian, P.W., Cruikshank, K.A., Mayer, R.E., Pintrich, P.R., Raths, J., & Wittrock, M.C. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's Taxonomy of Educational Objectives (Complete edition). New York: Longman.

Needs Assessment

Stakeholders

This class may be organized by a school administrator, an academic department head, a teacher, or even students themselves. Whoever organizes it must include the following stakeholders in the project before beginning the actual instructional design.

Stakeholders	Status	Role	Responsibilities
Teacher	Mandatory	Owner, manager or co-manager	Coordinate stakeholder activities Align design with personal skills and style Evaluate students' external access to technology Evaluate parental capacity to support students (K-12 only)
Students	Mandatory	Contributing consumers	Provide formative and summative assessments of learning experience
Managers/supervisors	Mandatory	Co-manager or advisor	Verify compliance with relevant standards and requirements
School/Organizational administrators	Mandatory	Senior manager, sponsor	Evaluate, approve required financial and technological resources
Parents	Mandatory (K-12 only)	Monitor and advisor	Identify and report resources available to students at their homes Monitor homework and progress
Professional organizations	Optional	Recognized experts and educational scholars	A resource for all other stakeholders that provides information on best practices and current standards for teaching practice, administrative guidance, and course design
Instructional designers	Optional	CMS administrator & designer	Execute ADDIE process to meet teaching objectives and administrative guidelines Optional only if teacher has adequate IDT skills
IT support	Optional	In-house resource or 3rd party vendor	Install and support technology required by design for F2F activities Support software installation and use for remote access by personal devices
IT vendors	Optional	Commercial vendors	Suppliers of commercial and open source software can provide tools to make project more sophisticated but are not absolutely necessary to convey most important ideas

While all mandatory stakeholders must be involved in a project of this type irrespective of its scale, a single teacher can create a modest class on this topic with little more than approval from supervisors, administrators, and parents. It is critical to scale the project to fit the available resources. Only after doing that, can the project leader define an attainable list of objectives.

If instructional design support is available, it may make sense for the teacher and ID professional to co-manage the project. If ID support is unavailable or minimal, the teacher managing the project must limit IT use to tools that she or he can deploy successfully with the students.

Students can explore this topic to a significant degree with little or no software; but having software support enables students to experiment with larger datasets and a broader array of tools. Open source software exists to do almost anything a teacher would want to do on this topic, so teachers or ID professionals interested in using commercial software should attempt to obtain it from vendors who provide their products for free to educational institutions. Paying for software should be a last option because it is not necessary to accomplish the objectives of this program.

Learner needs and characteristics

Before creating a syllabus for a course on this topic, a teacher must identify a level of complexity appropriate for the students taking the course. This is a function of both background and resources. Fitting the course to the educational backgrounds of the students is critical. For example, a class of college-bound upper class high school science students can take on more challenging projects than a class containing students with a mix of backgrounds. Students with programming experience can take on more complex problems that students with no programming experience. Humans have collected and analyzed data in some form for centuries without the aid of any computational technology. Students learning about that process can do the same thing, if necessary. A lack of technological skill is not a barrier to understanding the most important concepts taught in a course on this topic.

Students with extensive personal access to information technology offer teachers more choices in designing the course. Students who lack such access will be more reliant on resources provided by and possibly at their schools. The teacher and designer, if involved, must design a course that accounts for the resources available to the students. Classes containing both poorly and well-endowed students should refrain from designing a program that give an advantage to students with more resources.

Learner Analysis and Context

Because of its importance in our lives, learners at every level need to understand data at some level. What one needs to understand depends, obviously, on one's age and capacity to understand, apply and evaluate it. The following table presents a brief summary of the different learning contexts in which instructors may teach students how to manage, understand, and use data.

Learner context	Elementary	Middle school	High school	Higher Ed/Professional
Pedagogical approach	Constructivist	Constructivist	Constructivist	Constructivist
Audience	Small group	Small group	Small group	Small group/individual
Learning processes	Learn & practice skills	Learn & practice skills	Learn & practice skills	Learn & practice skills
Projects	simple, stylized	stylized, real world	real world	real world
Delivery	F2F	F2F/blend	F2F/blend/virtual	F2F/blend/virtual
Instructional technology	Elementary math software	Spreadsheets/databases	Spreadsheets/databases/ computational software	Spreadsheets/databases/ computational software
Learner assessment	Teamwork/projects	Teamwork/projects/quizzes	Teamwork/projects/quizzes	Teamwork/projects
Instructor assessment	Peer evaluation/student survey	Peer evaluation/student survey	Peer evaluation/Student survey	Student survey

Performance Objectives

After completing this mini-course, teachers will:

1. know and understand the Data Management Cycle
2. know how to apply each step of the cycle to problems involving data
3. know how to write lesson plans for each of the three parts of the the data management process

Curriculum Map

Data management cycle

Idea generation process

Data organization process

Static data analysis

Dynamic data analysis

Task Analysis

Idea generation process

I. Learning activity: Ideation

Learning outcome

Students learn to define ideas that they can analyze with data

Tasks

A. Choose a topic to investigate

Group chooses a topic with teacher input appropriate to the age of the group

B. Identify measurable ideas associated with chosen topic(s)

1. For each idea, students answer the following questions with teacher guidance appropriate to the age and prior knowledge of the students.

a. Does analysis of idea require data?

b. Is required data available to class conducting research?

c. Does available time permit adequate analysis?

d. Are student skills sufficient to conduct analysis?

e. Does course have sufficient financial and technological resources to conduct analysis?

2. Repeat process for each idea until finding at least one that meets all criteria (a-e). If more than one passes all screens, class may select one or assign different topics to groups within class.

II. Learning activity: Idea refinement

Learning outcome

Students convert ideas to workable problems with measurable solutions

Tasks

A. List problems to solve

Groups discusses ideas to identify problems they need to solve to evaluate each one.

III. Learning activity: Idea definition

Learning outcome

Groups decompose problems into sequential steps

Tasks

A. Identify steps required to solve each problem.

B. Organize steps in the order in which they need to take place.

C. Construct a Problem Process Table (sample below) for each problem to use to organize the data required for each problem.

Problem Process Table
Step	Data description	Data format	Source	Storage	Preparation

IV. Learning activity: Abstraction

Learning outcome

Groups develop theories, i.e. testable hypotheses, to analyze the problems they must solve to validate ideas.

Tasks

A. Students formulate possible explanations for the phenomena they observe that motivate their ideas. Teachers may need to direct this process very incrementally for elementary students. Students in middle school and above should be able to develop theories in group discussions.

Data organization process

I. Learning activity: Data identification

Learning outcome

Groups identify data required for analysis

Tasks

A. Identify all the data types required for each problem

1. Quantitative data

a. collected by polls, surveys, or interviews

b. downloaded from online sources

c. computed from other data

d. live feed (advanced students only)

2. Textual data

a. paper documents

b. electronic files

3. Image data

a. physical

b. electronic files

B. Describe each item of data required for each step of a problem in its Problem Process Table.

C. List the format of each item of data to be collected, from the choices in (A) above, in the Problem Process Table.

II. Learning activity: Data storage

Learning outcome

Groups decide how they will store their data

A. How to store data depends on several variables

1. Quantity of data

2. Form in which data will be stored

a. paper

b. electronic file

c. database

3. Expertise of students with different storage options

III. Learning activity: Data collection

Learning outcome

Groups identify sources of data and collect it.

Tasks

A. Identify data sources by type:

1. collected by students

2. non-electronic public sources

3. electronic public sources

4. private sources

B. Collect and store data

IV. Learning activity: Data preparation

Learning outcome

Students clean and verify data

Tasks

A. Review data for errors

B. Correct errors

Static data analysis

I. Learning activity: Data representation

Learning outcome

Students learn how to represent basis statistical concepts in tables, charts and graphs.

Tasks

A. Describe ways of presenting data in tables, charts, and graphs

B. Show appropriate uses of each type of presentation

II. Learning activity: Elementary statistics

Learning outcome

Students learn basic statistical concepts

Tasks

A. Name elementary statistics and understand their meanings

1. Mean, variance, standard deviation, skewness, kurtosis, correlation coefficient

B. Ordinary least squares regression

1. Correlation coefficients, t-statistics, F-statistics, R-squared

II. Learning activity: Computational tools and methods

Learning outcome

Students learn how to compute statistics and interpret results

Tasks

A. Compare computational choices and software

1. Computations by hand

2. Calculators

3. Spreadsheets

B. Drawing conclusions from statistical observations

1. "Most people use statistics like a drunk man uses a lamppost; more for support than illumination." - Mark Twain

2. Correlation is not causation.

Dynamic data analysis

I. Learning activity: Modeling and simulation of data

Learning outcome

Advanced students (high school and above) learn basic modeling principles and simulation techniques

Tasks

A. Software selection

B. Develop models and algorithms

C. Programming and automation

D. Simulation

E. Evaluation

References and Resources

The following articles provide background information on the knowledge building framework, Skim or read them as necessary.

• Bereiter, C., & Scardamalia, M. (2010). Can Children Really Create Knowledge? Canadian Journal of Learning and Technology, 36(1) [1]
• Hong, H., Scardamalia, M., & Zhang, J. (2010). Knowledge Society Network: Toward a dynamic, sustained network for building knowledge. Canadian Journal of Learning and Technology, 36(1). [2]
• Scardamalia, M. & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (pp. 97-118). New York: Cambridge University Press. [3]
• Scardamalia, M., & Bereiter, C. (2010). A Brief History of Knowledge Building. Canadian Journal of Learning and Technology, 36(1) [4]
• Zhang, J., Scardamalia, M., Lamon, M., Messina, R., & Reeve, R. (2007). Socio-cognitive dynamics of knowledge building in the work of 9- and 10-year-olds. Educational Technology Research and Development, 55(2), 117–145. [5]

The following articles provide information on data literacy in education an in general. In some, the focus is on how teachers can use data available to them from assessments to improve their own practices. That is not a topic of this course, but the material in this course will aid you in understanding those articles.

• Coburn, C., & Turner, E. (n.d.). The Practice of Data Use: An Introduction. American Journal of Education, 118(2), 99–111. [6]
• Erwin, R. (2015). Data Literacy: Real-World Learning through Problem-Solving with Data Sets. American Secondary Education, 43(2), 18–26.
• Gould, R. (2017). Data Literacy is Statistical Literacy. Statistics Education Research Journal, 16(1), 22–25.
• Mandinach, E., & Gummer, E. (2013). A Systemic View of Implementing Data Literacy in Educator Preparation. Educational Researcher, 42(1), 30–37. [7]
• Martin, E.R.. (2014). What is Data Literacy? Journal of eScience Librarianship, 3(1), 1–2. [8]
• Reeves, T., & Honig, S. (2015). A classroom data literacy intervention for pre-service teachers. Teaching and Teacher Education, 50, 90–101. [9]
• Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S., Wuetherick, B. (2014). Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report. Halifax, NS: Dalhousie University [10]

The mini-course includes the following links to YouTube videos.

• 365 Data Science. (2018). Can you become a data scientist? [11]
• Anywhere Math. (2016). Introduction to statistics. [12]
• Evans, Bethany. (2018). Data literacy in education. [13]
• NTSAToday. (2009). Modeling and simulation 101. [14]