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A Community Based Approach to Engaging Students and Teachers in Effective STEM Education

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MULTI STEM

MULTI STEM is a Community Based Approach to Engaging Students and Teachers in Effective STEM Education

Multi STEM (NSF 1615193) has been advancing efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students' motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM) by implementing a strategy that purposefully combines educational methodologies based on the Global Learning and Observation to Benefit the Environment (GLOBE) program. Pre-service and in-service teachers participated in sessions that developed skills to attract and retain students in education pathways to STEM careers through a community-based model that comprehensively addresses teacher preparedness and student participation in STEM fields. Over the four years of Multi STEM to date, we have grown into a community of co-learners...scientists and teacher educators, undergraduate research assistants, and pre- and in-service teachers.
A Community-based Approach to Engaging Students and Teachers
This video documents three years of work on an ITEST grant that represents a collaboration between Metropolitan State University of Denver, Community College of Denver, GLOBE, and University of New Hampshire. Our work has focused on broadening access to STEM pathways for students and families who are underrepresented in STEM opportunities. The video shares an overview of our project components--workshops for preservice and inservice elementary and secondary teachers, near peer mentorship of K-12 students by college students, and STEM career expos.
http://stemforall2020.videohall.com/p/1876
Learn about local STEM events in Metro Denver...

Discover STEM an online resource of STEM happenings for parents, educators and community members

STEM Teaching Tools

STEM Teaching Tools: http://stemteachingtools.org/

Multi STEM Newsletters
MULTI Oct 2019 Newsletter MULTI January 2020 Newsletter MULTI May 2020 Newsletter

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stem workshop or instruction development

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MULTI STEM, Additional resources for educators.... Request an onsite STEM workshop, a STEM Speaker, or instructional development assistance

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Multi STEM (NSF 1615193) has been advancing efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students' motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM) by implementing a strategy that purposefully combines educational methodologies based on the Global Learning and Observation to Benefit the Environment (GLOBE) program.

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Data Literacy for the Data-phobic: 7 Things Beginners Need to Know

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Data Literacy for the Data-phobic: 7 Things Beginners Need to Know

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Data is a part of all of our lives these days no matter what industry we work in, how much technology we use, and how we get information about what’s happening in our world.

But do we fully understand this data? Furthermore, do we know how to best use it and share it with others?

Thank goodness for data visualizations. Data visualizations help translate numbers into meaningful insights you can use to make decisions, even if you’re still a tad data-phobic. And if you are, you aren’t alone. Even though having a good grasp of data is a vital skill in the workplace, most organizations are lagging behind, according to Gartner.

To be able to create and sometimes to fully understand data visualizations, we need to overcome any fears we may have and become more data savvy. I am not a statistician by any means, but I’ve found my knowledge and skills related to data to be invaluable as an information designer and writer at Venngage Infographics. I’m excited to share some basics with you so you can help your team increase their data-driven decision-making and thereby their performance.

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Data is a part of all of our lives these days no matter what industry we work in, how much technology we use, and how we get information about what's happening in our world.

But do we fully understand this data? Furthermore, do we know how to best use it and share it with others?

Thank goodness for data visualizations. Data visualizations help translate numbers into meaningful insights you can use to make decisions, even if you're still a tad data-phobic. And if you are, you aren't alone. Even though having a good grasp of data is a vital skill in the workplace, most organizations are lagging behind, according to Gartner.

To be able to create and sometimes to fully understand data visualizations, we need to overcome any fears we may have and become more data-savvy.

I am not a statistician by any means, but I've found my knowledge and skills related to data to be invaluable as an information designer and writer at Venngage Infographics. I'm excited to share some basics with you so you can help your team increase their data-driven decision-making and thereby their performance.

Table of contents:

What is data literacy? A definition

Data literacy is the ability to read data, work with data, and communicate about data by putting it in proper context.

It's about more than just understanding and working with numbers, or numeracy. Data literacy includes understanding:

How to assess data sources and collection methods

What various applications for a given data set can be

The value of working with understanding data

Related: What Is Data Visualization? (Definition, Examples, Best Practices)

Why is data literacy important?

Data literacy is important because when we make decisions based on data we can improve our outcomes, including in a business context.

"Data literacy has become important, for almost everyone. Companies need more people with the ability to interpret data, to draw insights, and to ask the right questions in the first place. These are skills that anyone can develop, and there are now many ways for individuals to upskill themselves and for companies to support them, lift capabilities, and drive change. Indeed, the data itself is clear on this: Data-driven decision-making markedly improves business performance."

- Josh Bersin and Marc Zao-Sanders in Harvard Business Review

Even though data literacy is becoming increasingly important in the workplace, most organizations lack these skills. Employees that are data literate are more likely to say they are performing very well. If you're wondering how your own skills stack up, check out this assessment.

"Until recently people could easily ignore data in their daily work. The company's 'gearheads' and 'quants' were isolated in specialist departments, tech handled the mundane stuff, and managers could brush off the benefits of improved data quality with the attitude, 'We're doing just fine. Why bother?' But now that's changing. The headline result of my most recent "scan" of the data space is that fear has replaced apathy as the number one enemy of data ... You can become a credible leader and dispel the fear of data in your team as well as your own fear by increasing your abilities and inspiring the entire department to embrace data."

- Thomas C. Redman in Harvard Business Review

Many people are also seeking to visualize and communicate about data (including in infographics), and data literacy is critical to do this well. For example, this financial projection report includes raw numbers, tables, and graphs. When you understand well what the data is saying, you are better prepared to highlight key takeaways for your audience.

Perhaps you started visualizing data you collected through surveys and now you want to know what other data is available to support survey findings. Or maybe you've only been looking at data related to current customers and now you want to look at data that might help you target new customers.

In both of these cases and many more, you will need to collect and analyze data but you will likely also want to share it with others. Venngage's Chart Maker tool is easier to use than most data analytics and BI platforms, so you can easily get started visualizing tabular data (data that you find in tables).

Are data literacy skills hard to build?

If it's so important, then why is it lacking in the workplace?

A major reason is that leaders may not have these skills themselves. The fact that data-literate employees are typically isolated on IT or BI teams also doesn't help colleagues share and spread this know-how.

Sure, some people haven't yet recovered from that awful math class in high school. They may think that they are simply bad at math.

Others may be ok or even good at math, but don't want any mistakes they make to lead to potentially hugely detrimental consequences, including a loss of reputation.

All these challenges are understandable, and they can be overcome. Many data-savvy folks struggle to keep it basic for beginners, so my goal is to help you slowly grasp some basics to gain some skills and more confidence.

7 things beginners need to know about data literacy

1. Uncertainty is inevitable

What? You might be thinking, "I thought data was about hard facts and figures." Any statistician will tell you that any given data set is incomplete, that statistics is actually often about explaining levels of uncertainty. For example, we might be 95% confident that our lunch today is 87% to 91% likely to be super delicious.

This is important to keep in mind especially if we are communicating data. If we are using visuals, such as infographics, to do so, they will likely mask the messiness of reality and make our statements seem more factual. We can be responsible communicators by understanding the data well and, when possible, explaining what we still don't know.

Related: How to Use Data Visualization in Your Infographics

2. There are various types of data

Because of all the uncertainties to weigh, data collection can be a laborious, detailed process. If you are not collecting it yourself, then what's most important to know is that there are several types of data that can be collected, and each is appropriate for different situations.

Chances are when you think of data, you think of quantitative data, which is based on precise measurements and often analyzed with statistical methods.

Quantitative data, also known as numerical data, can come in different forms.

Discrete data can be counted and divided into smaller groups, such as the number of people in a crowd. Alternatively, continuous data exists on a continuum, for example length. There are two types of continuous data: Interval data has no true "zero", such as temperature, whereas ratio data does, such as weight. Continuous data is often visualized in line graphs, such as profit growth reports.

Qualitative data, on the other hand, is descriptive, based on observations that cannot be measured, such as gender or language spoken. Analysis of this type of data often involves categorization into themes or patterns based on characteristics. Qualitative data may be referred to as categorical data.

There are also two types of categorical data. Nominal data can be used to measure frequencies and percentages and can be displayed in a pie chart or bar chart. If the data is put into some sort of logical order (such as breakfast, lunch, and dinner), the data is considered to be ordinal data.

Sometimes both qualitative and quantitative data is collected and analyzed - this is known as mixed methods. They may be mixed and interpreted in different ways, so this may be worth looking into if you are using this type of dataset.

All this to say: understanding the type of data you're working with will help you determine how to best communicate it, whether it's to choose the order the data should be presented or what type of charts to visualize it.

3. Data sources matter

Most if not all of the main data collection methods you have likely already heard of:

If data is collected firsthand by a researcher, it is considered primary data. Secondary data is data that has already been collected and has been made available for others to analyze and share.

Chances are you will be using secondary data, so here are some quick questions you can ask yourself to make sure the data is high quality:

1. Is the data from a reputable source? Does that source share information about how the data was collected (this may be included in documents referred to as metadata)? In other words, are they transparent?

2. Is it open, restricted, or unavailable? Open or public data may be made available by government entities or intergovernmental organizations, but some data may be restricted. Privacy is a big deal, and there are many laws that prevent sharing data that may violate people's rights or dignity.

3. Is the data set the right one for the job at hand? Assessing this requires some logical, cautious thinking. For example, if I want to say something about high schoolers living in America, I'd better make sure I can find data that represents the many young people living across the states. If I can't, then I should specify that I'm referring to high schoolers in New York, or in Buffalo specifically.

4. Is it aggregated and/or normalized? Aggregated data has been summarized so you can't identify characteristics at the individual level. A common example is data from Google Analytics: You know how many people visited a specific web page, and you may even know what cities they live in, but you don't know their names. Normalized data helps us compare numbers by using a common scale. A common example is when we see rates per capita: It's less meaningful to know the number of car accidents in towns in your state, and more meaningful to know if some towns have a surprisingly high number of accidents given the size of the population.

Once you have data in hand then you are ready to start using it.

4. It's important to keep data organized

By now you might be thinking, wow this is a lot to keep track of. Managing data is not for the faint of heart. It requires a lot of attention to detail and follow-through.

If you are working with a relatively small data set, such as survey responses, then you can probably just use one or a series of spreadsheets.

If you are downloading public data sets, like Census figures, you may have more on your hands than you need. Some data sets may even need to be cleaned, which means incorrect or duplicate data has to be deleted. Sometimes the data even needs to be reformatted-for example, if numbers are being read a text by analytical software then calculations will be impossible (text and numbers are different field types).

Data management can be a team job. If you're not sure how to get your ducks in a row, reach out and find someone who can help.

5. Data often can tell lots of different stories

When you analyze data, you are generally looking for patterns. These patterns may tell a story about what has happened, or what's most common, or something else.

Related: How to Tell a Story With Data (A Guide for Beginners)

Most patterns will come down to the relationships between two or more variables, which are the "things" that are being counted, measured, or described. Here are a few examples:

People who are on bowling leagues versus people who are on tennis teams

Grocery stores in low-income neighborhoods and grocery stores in high-income neighborhoods (note: place-based or geographic data includes map coordinates in addition to other variables)

How people feel about cats versus how they feel about dogs

For any one of these examples, you might discover in the data a variety of patterns. Let's consider the last example listed. In looking at people's different sentiments, you might discover that people who are older like cats more, or people with kids like dogs more.

The point is that data alone doesn't say anything. It's only through analysis that you can begin to uncover insights. And there may be many to uncover, so it's worth it to keep digging and not merely settle for the first "aha" you stumble upon. Pay attention to outliers and anomalies-these are significantly different data points that can indicate errors in the data and/or places for further study and analysis. Be aware of your cognitive biases and potential pitfalls as you conduct your analysis.

6. When you know your story, share it responsibly

First, what are you hoping to share with others?

Descriptions of the data, such as frequencies or percentages

Distributions of the data, such as ranges or averages

Comparisons in the data, such as changes over time or correlations between variables

Another big no-no is to equate correlation with causation in any way. There may be a correlation between ice cream and sunscreen sales, but that doesn't mean that selling more ice cream will lead to more sunscreen, it may mean that both sales are a result of a third variable which is time of year.

Once you know what you're hoping to share, you can choose a visual that will best help someone else understand key takeaways.

7. You don't need to know everything

But you do need to keep learning! There's a lot to learn so if you need help prioritizing what to nail first, it's super important to understand how data can be misused to mislead. If nothing else, make sure you aren't doing that.

Congrats! You are now well on your way to overcoming your data phobias and becoming more data literate. I hope you will take what you've learned here and begin to apply it immediately.

Just how once the best way for you to learn to read was to pick up books, the best way to build your data literacy is to practice working with data and sharing it with others. When you do, you'll quickly come to realize the immense value of doing so.

Simple data visualization templates to build your data literacy

You can get started by using some simple templates to create some data visualizations with Venngage. Here are a few templates to get you going.

Want to see more? Check out our templates library and start creating your own infographic for free with our easy-to-edit templates and drag-and-drop editor.

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Data Literacy: 7 Things Beginners Need to Know - Venngage

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Are you confident you're data literate? Read to learn more about data literacy and 7 things beginners should know to build their data literacy skills.

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While the value of data literacy is widely recognized, many teachers find it challenging to develop curricular activities that are relevant and engaging for their students, a problem amplified during the pandemic. This social media-based avenue for student and teacher learning evolved out of a GLOBE-based professional development program for preservice and inservice K-12 teachers. It supports science-based cross disciplinary learning that engages students in observing and analyzing their local environment.

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Place-Based Data Literacy: Protecting the Platte

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While the value of data literacy is widely recognized, many teachers find it challenging to develop curricular activities that are relevant and engaging for their students, a problem amplified duri...

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Hoping to broaden the scope of our work and reach a larger audience, we have created an Instagram account (@multi_stem). We will be posting relevant STEM educational pieces to engage teachers and students! Give us a follow!

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The STEM Equity Initiative, Inc. provides a new model for the classroom educator to understand and enact efforts to increase access to and success within academic programs, particularly for traditionally underrepresented or marginalized groups. Working with secondary schools, community colleges, federal and state agencies, nonprofits, and the business community, the new model has demonstrated a measurable impact for increasing the participation, engagement, and completion of students.

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Closing Disparities in Academic Performance

"Evidence shows that providing educators with the necessary knowledge and tools needed to create an equitable learning environment broadens student participation and their academic success. A new model for education is needed if we are to meet the needs of businesses today and for the future."

- Claudia Morrell

Our Focus on Equity in Career Technical Education

Ensuring equity in education for all students in schools, classrooms, and communities throughout the U.S. and the world requires both knowledge of equity and practical experience in public education, community colleges, and universities. The process of changing the culture in an education environment requires meeting educators and administrators where they are in their beliefs and values. It also requires all stakeholders to work collaboratively to address the economic and educational disparities that exist for children, families, and communities across the U.S. and the world.

STEM Equity Initiative, Inc. provides a tailored approach to supporting educators' and administrators' efforts to meet students' unique needs in the classroom, including consideration of students' cultures, races, ethnicities, genders, languages, classes, disabilities, incomes, religions, and geography. STEM Equity Initiative, Inc.'s new systemic change model for creating Equitable Learning Environments helps educators, administrators, and communities to understand the conditions needed to create an environment where every student can thrive and grow personally and academically. The model introduces the four NEIR © indicators that help educators easily and clearly design, build, implement and measure effective practices based on quality research and the wisdom of the practice. The goal is to improve the academic outcomes for every student.

What is the STEM Equity Initiative, Inc.?

The STEM Equity Initiative, Inc. provides a new model for the classroom educator to understand and enact efforts to increase access to and success within academic programs, particularly for traditionally underrepresented or marginalized groups. Working with secondary schools, community colleges, federal and state agencies, nonprofits, and the business community, the new model has demonstrated a measurable impact for increasing the participation, engagement, and completion of students in informal and formal science, technology, engineering, and mathematics (STEM) courses and programs (including career and technical education). Published, peer-reviewed journal articles provide evidence that every educator using research-based effective practices can create a pathway for students to satisfying and family-supporting STEM careers.

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Closing Disparities in Academic Performance Ensuring equity in education for all students in schools, classrooms, and communities throughout the U.S. and the world requires both knowledge of equity and practical experience in public education, community colleges, and universities. The process of changing the culture in an education environment requires meeting educators and administrators where they are in their beliefs and values.

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Children's Defense Fund- Promoting Equity in STEM Education

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Children's Defense Fund- Promoting Equity in STEM Education

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Representation matters. Black professionals are underrepresented in STEM (science, technology, engineering, and math) fields, while exciting opportunities in STEM careers continue to grow at a frantic pace. The CDF Freedom Schools® program is doing its part to help produce the next generation of leaders in STEM by introducing Black children to the wide world of possibilities in STEM careers.

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Promoting Equity in STEM Education Representation matters. Black professionals are underrepresented in STEM (science, technology, engineering, and math) fields, while exciting opportunities in STEM careers continue to grow at a frantic pace. The CDF Freedom Schools® program is doing its part to help produce the next generation

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Not just because it's the right thing to do (it is), but because
we know that individuals from a broad range of backgrounds
working together are better able to solve the toughest and
most complex scientific challenges.

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Creating a Successful Diversity and Inclusion Program

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Creating a Successful Diversity and Inclusion Program

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1. Focus on intervention, not just bias reduction
2. Invite non-managers to foster communication across
the organization
3. Keep the focus on workplace issues, not personal ones
4. Keep the conversation going to stay accountable
5. Be
exible, in both content and delivery

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Executive Summary

Companies today spend millions of dollars on workplace diversity programs and outreach, often with little to show for it. A team at the University Corporation for Atmospheric Research (UCAR), a large, federally funded research institute, kept this in mind when creating a workplace diversity training program. The program is now a routinely offered, four-part course that covers topics related to power and privilege, gender, and race, and includes a three-hour bystander intervention training. They found it has increased collaboration among participants, helped research labs create more inclusive environments, and made employees more actively engaged in diversity-related issues throughout the organization. After three years of iterating, evaluating, and improving UNEION, they've learned five key practices for how to implement a successful workplace diversity program: 1) Focus on intervention, not just bias reduction; 2) Invite non-managers to foster communication across the organization; 3) Keep the focus on workplace issues, not personal ones; 4) Keep the conversation going to stay accountable; and 5) Be flexible, in both content and delivery.

Nick Purser/Getty Images

Companies today spend millions of dollars on workplace diversity programs and outreach, often with little to show for it. Research has found that most workplace diversity programs fail to produce meaningful diversity and inclusion, and some have actually increased bias among individual employees. In STEM fields, both the private and public sectors continue to struggle with recruiting and retaining a diverse workforce. As of 2017, nearly 75% of those in computing and mathematical fields were men and fewer than 15% were black or Hispanic.

We kept this in mind when creating our own workplace diversity program. Three of us (Brinkworth, Aponte, and Young) work at the University Corporation for Atmospheric Research (UCAR), a large, federally funded institute that focuses on producing research and supporting scholars in the atmospheric and earth sciences. Like many technical workplaces, UCAR, which has approximately 1400 employees, has struggled to recruit and retain women and people of color. But UCAR very strongly believes that diversity, equity, and inclusion are fundamental to producing our best science.

In 2015, UCAR appointed Brinkworth as the Director for Diversity, Education, and Outreach (DEO), (later called the Chief Diversity Officer), and was tasked with making UCAR more diverse and inclusive. Shortly after that, a small group of employee researchers led by Young asked about the lack of spaces within the organization to discuss diversity-related topics - a critical issue for employee development and retention. Brinkworth worked with these employees to co-create a diversity training program called UNEION, which stands for UCAR|NCAR Equity and Inclusion.

UNEION is now a routinely offered, four-part course that covers topics related to power and privilege, gender, and race, and includes a three-hour bystander intervention training. The goal of the program is twofold: first, to train participants on how to build inclusive teams, facilitate diversity-related conversations within their divisions, and identify other practices that can promote a positive workplace culture; second, the program serves as a community for those interested in fostering equity and offers a venue for action. More than 80 employees have completed UNEION, and 10 have participated as organizers or "lead learners." We've found that UNEION has increased collaboration among participants, helped research labs create more inclusive environments, and made employees more actively engaged in diversity-related issues throughout the organization.

Participation in the program is voluntary and any employee can join. Whereas many similar diversity programs target only managers and HR, we have managers, administrative, and scientific staff participate together in mixed groups to capture multiple perspectives. Cohorts range from 10 to 25 participants, and the sessions incorporate a mix of discussion and hands-on activities. The course meets once a month for four months, although many participants choose to informally communicate, both online and in person, between sessions.

The sessions are developed by organizers or "lead learners" who act as participants, rather than as instructors or trainers, in order to maintain a flat hierarchy. They create the reading list, activities, and discussion topics for UNEION. Most have formal training or personal experience with diversity-related issues, although this is not required to participate. Materials from both academic literature and popular media, such as TED talks, are incorporated into the sessions, and participants are encouraged to share their personal experiences within UCAR.

While we still meet resistance from employees who do not understand how diversity and inclusion are related to their job in a scientific organization, this resistance is becoming less common as we continue to engage in change management and make the case for inclusivity across UCAR. After three years of iterating, evaluating, and improving UNEION, we've learned five key practices for how to implement a successful workplace diversity program:

Focus on intervention, not just bias reduction

Many workplace diversity programs have focused only on bias reduction. Studies have shown that when employers require bias reduction training, hostilities can actually increase. In voluntary programs such as UNEION, research suggests that those who elect to participate already see themselves as "pro-diversity." That's why we move beyond attempting to reduce bias and toward putting inclusion into action.

We learned that the majority of UNEION participants were already aware of societal biases and workplace barriers that women, people of color, and other marginalized groups face. Because research suggests having high levels of awareness before training can lead to more engagement in diversity-related programs, UNEION focuses on 1) equipping participants to intervene when they see bias or harassment unfolding, and 2) training people on how to talk to others about organizational diversity.

UNEION leaders dedicate one session to in-depth bystander intervention training, so people know how to step in when they observe instances of bias and discrimination. The training begins with a demonstration of different intervention techniques, with lead learners role-playing a scenario (based on real instances that had been reported at UCAR), asking the participants for interventions, then acting those suggestions out. For example, in one prompt, the group is asked to respond to a situation where a white researcher tells an Asian colleague that they "work well together because... well, you're white in my book!" Participants then separate into small groups to review the scenarios, devise a strategy to intervene, and act it out in front of others.

Participants consistently report this session as the most impactful, having boosted their confidence to intervene appropriately with peers, supervisors, and upper management. Follow-up surveys have found that 80% of past participants reported they did intervene in inappropriate workplace situations after receiving this training.

While UNEION does include readings and activities designed to challenge participants' views of workplace inclusion, the goal is to put those ideas into action. At each session, lead learners introduce community resources for improving diversity and inclusion. For example, participants not only discussed why systemic racial inequalities and sexism can lead to fewer women and people of color in STEM, but also how to improve UCAR's student outreach programs and local organizations that could support those efforts.

Alongside these pieces, UCAR has made significant structural efforts to be a more inclusive organization, including undertaking a comprehensive workplace culture study, developing a diversity, equity, and inclusion strategic plan, expanding an outreach and mentoring program for underrepresented students, revamping hiring procedures, and reviewing policies to ensure they are equitable for all. These efforts have significantly shifted the conversation about diversity and inclusion at UCAR. An optional full-day retreat in November 2017 attracted more than 10% of UCAR's staff and approximately 85% of our senior leadership to discuss future diversity and inclusion efforts and strategic planning.

Invite non-managers to foster communication across the organization

Many workplace diversity trainings tend to target only managers. Because previous research shows there are benefits to recruiting diverse groups in terms of race and gender for trainings, UNEION lead learners also emphasize recruiting from all levels of the organization, including non-technical and clerical staff. Research has also shown that inviting non-managers to diversity and inclusion workshops can help organizations better identify points of conflict and possible resolutions. Approximately one-third of past participants have been research staff, and two-thirds have been administrative employees (many of whom have scientific training but are working in program or education-related roles). Over 40% of UNEION participants are in management roles. And over half of past participants we surveyed said they formed collaborations with people in different research areas or departments from their cohort.

At a recent Diversity Action Summit at UCAR, over 140 employees convened to collectively identify UCAR's unique challenges and opportunities for diversity and inclusion, as well as develop responsive strategies and short-term action steps to create a more inclusive environment in each laboratory and workgroup. We followed up on these plans with customized workshops for each lab, program, and department to help them identify priority areas in order to see positive cultural change.

Keep the focus on workplace issues, not personal ones

Personal issues and career paths are inevitably intertwined. The lack of diversity and inclusion in workplaces can also be due to personal decisions or other non-workplace factors. For example, research from the Center for Talent Innovation shows that more women than men have to pause their careers to take care of children or aging parents. People of color and LGBT individuals also face additional challenges, both in and out of the workplace, to advancing their technical careers.

So initially, UNEION embraced the overlap between work and home and included readings and discussions related to topics such as childhood socialization and parenting. However, feedback from those sessions indicated that participants wanted the focus to be on workplace issues and inclusion at all levels of the organization. We also saw research suggesting that diversity training be solely focused on business issues.

The session was reworked to acknowledge external challenges that can impact work performance, advancement, and career choice, while keeping the conversation away from the explicitly personal, such as parenting choices. This kept the issues grounded in the context of work, an important feature of successful diversity programs, while maintaining a forum for people to discuss the ways in which personal identity can affect one's experience in the organization.

Keep the conversation going to stay accountable

Research shows that the most successful workplace diversity programs are those with higher levels of continued engagement and accountability, such as task forces, diversity managers, and mentoring programs. So, during and after the course, lead learners began holding one-on-one meetings, workshops, and town halls, and encouraging participation in diversity-related outreach programs. Lead learners also promote a cohort mentality among participants by encouraging collaboration and informal information sharing.

Many past participants have ongoing relationships with UNEION leaders and the Office of Diversity and Inclusion at UCAR. Nearly 90% of those surveyed who completed UNEION have incorporated diversity and inclusion into their team building activities, outreach efforts, and recruitment and retention plans. And we've found that many people get introduced to UNEION and other diversity-related programs at UCAR through UNEION alumni.

Be flexible, in both content and delivery

There is no one-size fits all curriculum for workplace diversity programs. Each organization and even each group of participants will have different needs, so facilitators should be flexible in their content, delivery, and structure.

UNEION is now designed so only the introduction has set content. The remaining sessions are developed by the lead learners based on pre-workshop surveys that provide information about the interests, challenges, and biases of participants. This means readings and activities vary greatly by cohort based on the needs of the group. For example, in one cohort, many people raised questions about so-called "reverse racism" in the pre-course survey, a concept not previously addressed in the course. The flexible structure allowed lead learners to change the content to specifically address "reverse racism," which resulted in a 23% decrease in the number of people in that cohort who felt "reverse racism" was an issue at UCAR.

Each cohort brings new challenges and learning opportunities. Although we strive to both expand our course offerings and keep previous cohorts engaged, as a small office with resource constraints, we have to make compromises. We want to respect the time of our lead learners, who participate in UNEION in addition to their normal workplace responsibilities. Besides being the "right thing to do," we know that building a more diverse and inclusive community of researchers, educators, and support staff will help UCAR produce more creative and innovative scientific outcomes.

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5 Things We Learned About Creating a Successful Workplace Diversity Program

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Executive Summary Companies today spend millions of dollars on workplace diversity programs and outreach, often with little to show for it. A team at the University Corporation for Atmospheric Research (UCAR), a large, federally funded research institute, kept this in mind when creating a workplace diversity training program.

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Citizen Science with the GLOBE Observer App

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Citizen Science with the GLOBE Observer App

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GLOBE Observer invites you to make environmental
observations that complement NASA satellite observations to
help scientists studying Earth and the global environment.

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GLOBE Observer - GLOBE Observer - GLOBE.gov

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Download the app and follow the instructions to get started. Always follow guidelines from your local officials, and only participate in GLOBE activities or use the GLOBE Observer app if it is safe to do so.

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GLOBE International STEM Network Members (GISN)

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GLOBE International STEM Network Members (GISN)

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The GLOBE International STEM Network (GISN) is an international network of STEM professionals (Science, Technology, Engineering, Mathematics) that work with GLOBE students around the world conducting science. STEM professionals mentor students and teachers, present scientific ideas, and/or collaborate on scientific research. Each relationship between a STEM professional and a GLOBE school is unique, and is determined by the STEM professional and the school.

Check Out The Website for Career Connections:
https://www.globe.gov/web/globe-international-stem-network/overview/gisn-members

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Feel free to post any thoughts or remarks you have regarding our video, the showcase, or our work so we can continue this discussion!

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Feel free to post any thoughts or remarks you have regarding our video, the showcase, or our work so we can continue this discussion!

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The Global Learning and Observation to Benefit the Environment (GLOBE) program is a worldwide hands-on, primary and secondary school-based science and education program.

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One intriguing way that teachers can change their instructional methods is by integrating place-based education (sometimes also called as expeditionary learning or experiential learning). Place-based education centers the educational process in the environment. This form of learning requires that teachers place students in their communities so that they can solve community-based problems.

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Researchers, Indigenous Peoples, and Place-Based Learning Communities

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Researchers, Indigenous Peoples, and Place-Based Learning Communities

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Relations between external researchers and indigenous communities have been increasingly strained by differences in understanding and in expectation about the relevance of research. In the field of resource management, the potential for conflict over research is increased by the politics surrounding control over the resource management decision making processes. In this article, we propose the creation of dialogic networks that engage researchers and indigenous people as collaborators in a process of knowledge production. Such an applied research process can produce context-specific knowledge networks that support management and planning decisions by indigenous people; these networks we refer to as place-based learning communities. We present a researcher's perspective on this approach through our experience with the Shoal Lake Resource Institute of Iskatewizaagegan No. 39 Independent First Nation located in northwestern Ontario.

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Location, location, location! See what happens when schoolwork explores students' connections with local history, environment, culture, or economy.

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Location, location, location! See what happens when schoolwork explores students' connections with local history, environment, culture, or economy.

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A global community dedicated to creating
a data-literate world

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How Data Literate Are You?

Being data literate doesn't require becoming an expert in statistics, we have identified four personas for skills needed in different job roles, and tailored our courses to strengthen each one.

We're here to change that and give you all the support you need to become data brilliant.

Find out where you are on your journey

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Being data literate doesn't require becoming an expert in statistics, we have identified four personas for skills needed in different job roles, and tailored our courses to strengthen each one. We're here to change that and give you all the support you need to become data brilliant.

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Data Literacy for Teachers: Bridging the Gap between Education Policy Makers and Educators

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Data Literacy for Teachers: Bridging the Gap between Education Policy Makers and Educators

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The fight for equal opportunity in education has uncovered the need to promote data literacy among teachers. Brennan McMahon Parton, director of policy and advocacy for the Data Quality Campaign, experienced firsthand how teachers can use student data to create a better curriculum.

Practicing tiered warm-ups, with three different levels of questions, was just one of the ways Parton's school figured out how to analyze student data and apply it to classroom assignments, according to EdSurge. To promote a deeper learning in her students, Parton first looked at the right qualitative and quantitative indicators to ensure the students were progressing. If students fell behind, she could then use that data to get them back on track. Parton says, "Data breathes life into standards and curriculum."

What Is Data Literacy?

Data literacy in education is the ability to collect student data-attendance, grades, test scores, behavior, and motivation-and create actionable instruction materials, according to the Harvard Educational Review. Educators can then incorporate these materials into a new curriculum, with the ultimate goal of improving student outcomes.

Every teacher's level of data literacy is different, so policy makers and school leaders need to build on their established data literacy, the District Administration explains. As the teacher grows in data fluency, they create better assessments and standards. Additionally, they can track discipline-specific knowledge and how students learn, according to the Harvard Educational Review.

Why Is Data Literacy Important?

When teachers use data literacy to assess their students and adjust their lessons, they see an increase in student learning and understanding. According to the Data Literacy Campaign, teachers can apply data gathered from students' tests, homework, and attendance to inform their lesson planning.

Focusing on the individual needs of students increases their chances of success. Analyzing student data can give teachers valuable insights into their students, helping them improve their overall educational experience and prepare them for successful futures.

Being data literate can also help teachers get to the root of student issues-including absenteeism. EdTech reported that one in seven students missed 15 or more school days which can result in failing classes, lacking mastery of a skill like reading, and a higher drop-out rate in high school. Only six schools in the U.S. kept records of absenteeism. Johns Hopkins University took that data, found the early warning signs among students, and implemented immersive classroom technologies to help boost attendance. This is just one example of how analyzing data can change the course of a student's education.

Learning Data Literacy

Teachers must gain certain skills before entering a data-driven classroom. As the NWEA highlights, data-literate educators know the different kinds of data and when to apply them. They can evaluate data's accuracy and transform it into actionable information. Furthermore, they can hold themselves ethically accountable for data-driven decisions.

American University's EdD in Education Policy and Leadership teaches educators how to collect and evaluate data, as well as how to apply it when crafting curricula. EdD students learn how to transform pre-K-12 classes to increase equity and inclusivity in education. They also gain insight into working with data and ways to apply research and policy skills in the classroom.

Students in this program have the opportunity to take courses such as Applied Research Methods I: Enacting Critical Research, which focuses on enhancing skills with an anti-racist lens (analyzing how the power of majority groups has brought about inequity in education). They identify a problem of practice-such as the lack of data literacy in education-and produce scholarly research. Throughout the course, students explore improvement science, or problem-solving centered around continuous learning; participatory action research; ethnography; and developmental, qualitative, and quantitative methods.

Education Policy Analysis, another course offered in the EdD program, explores themes that relate to changing data literacy policy, such as agenda setting, problem definition, policy design, and policy implementation.

Courses such as these give educators the tools to apply data in the classroom and in policy development, as well as promote data literacy for teachers.

How Can Educational Leaders Encourage Data Literacy?

Educating teachers is the first step toward making data literacy a significant part of the education system. Competent and strong educational leaders, such as superintendents and principals, can encourage teachers to become data literate.

According to the District Administration, leaders address the challenges of data literacy by supporting professional development opportunities for teachers. In doing so, they will drive change to policy surrounding educational data literacy.

The South Carolina Department of Education created an instructional data literacy series to help principals and instructional leaders increase their data literacy and improve their use of data in making instructional decisions. By developing their own abilities, leaders clarify their expectations of teachers and better serve them.

Explore American University's EdD in Education Policy and Leadership program and discover how educational leaders can encourage teachers to become data literate, changing and improving students' lives in the classroom and beyond.

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Data Literacy for Teachers: Bridging the Gap between Education Policy Makers and Educators

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The fight for equal opportunity in education has uncovered the need to promote data literacy among teachers. Brennan McMahon Parton, director of policy and advocacy for the Data Quality Campaign, experienced firsthand how teachers can use student data to create a better curriculum.