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Newsletter: Spring 2018

Posted on June 4, 2018 by  in ()

This time of year, Advanced Technological Education (ATE) project evaluators are preparing evaluation reports for their projects, and principal investigators (PIs) and their project teams are preparing their annual reports for the National Science Foundation (NSF). EvaluATE has a lot of resources to take the mystery out of these activities and enhance the effectiveness and utility of your reports.

Writing Your NSF Annual Report

If you need help determining what should go in your NSF annual report and how to prepare it, see advice on Strategies for Writing Your NSF Annual Report, by Tara Sheffer, supervisor of grant projects at Columbus State Community College.

Reporting Evaluation Results in Your Annual Report to NSF

If you are working on your first annual report to NSF, you may be surprised that there isn’t a section in the online reporting system,, explicitly for reporting information from your external evaluation. EvaluATE offers straightforward guidance about how to include your evaluation results in your annual report in What Goes Where? Reporting Evaluation Results to NSF.

Guidance for Effective Evaluation Reporting

Starting an evaluation report from scratch can feel overwhelming. Evaluators can use EvaluATE’s Checklist for Program Evaluation Report Content to determine what content is appropriate for a report and how to organize the information in a coherent way. Project teams can use it to guide conversations with their evaluators about what should go in their evaluation reports.

Get the Word out with One-Page Reports

Don’t let the great information about your project stay buried in long reports that few will read. Highlight key results in a well-designed one-page report that your stakeholders will want—and have time—to read. One-page reports can be used to highlight grant achievements to college and industry stakeholders and to attract potential partners. EvaluATE’s recent webinar and several supporting resource materials clearly explain how to create attention-grabbing one-page reports. Visit our page on One-Page Reports.

ATE Evaluation Report Repository

Did you know EvaluATE has a growing repository of evaluation reports from funded ATE projects? Browse the collection to get a sense of how ATE projects and centers are evaluating their work, what they’re learning, and how they are communicating those results in evaluation reports. Reviewing the reports will also provide insights on what other ATE projects have been funded to do. If you’re an ATE principal investigator or evaluator, let us know if you have a report that we may add to the collection.

Newsletter: Winter 2018

Posted on February 5, 2018 by  in ()

Evaluation Data Resolutions: Newsletter Winter 2018

Whether you’ve been analyzing and collecting evaluation data for decades or you are brand new to this work, the beginning of a new year is a good time to take stock of this aspect of your work. In this issue of EvaluATE’s newsletter, Evaluation Data Resolutions, we encourage you to take a moment to reflect on your data collection practices and look for opportunities to freshen and strengthen your data.


Be Creative: written statement "be creative"

Surveys, interviews, and focus groups are probably the most common data collection methods across ATE project evaluations. But these may not always meet your need for data or be optimal for those providing information. Photolanguage, dotmocracy, and reputational monitoring are examples of nontraditional techniques for gathering information. You’ll find an inventory of 51 data collection methods on Better Evaluation’s website. The list includes short descriptions with links to detailed guidance. It may inspire you to go beyond traditional methods and get creative and innovation with your data collection.


Be Resourceful: computer

Developing a sound data collection instrument from scratch is time-intensive. You might be able to conserve resources by using an existing instrument that fits your context. Check out the instrument collection curated by the STEM Learning and Research Center (STELAR). STELAR supports the National Science Foundation’s Innovative Technology Experiences for Students and Teachers program, and several of the instruments are relevant to the ATE context. Examples include the STEM Semantics Survey, STEM Career Interest Questionnaire, Pre-College Annual Self-Efficacy Survey, Grit Scale, and 21st Century Skills Assessment.


Be Purposeful- focused picture

In the midst of data collection and analysis, it’s easy to lose sight of the big picture – why you collected the data in the first place. Use EvaluATE’s Data Collection Planning Matrix to align your data with your evaluation questions. This template also prompts you to record your plan for analyzing and interpreting data in ways that will help you answer your evaluation questions.


Be Careful: slippery when wet sign

Regardless of how you obtain your data or what you plan to do with it, it’s essential you take care to ensure it’s clean before you begin analysis. A systematic process of data cleaning involves identifying and correcting any issues related to data entry mistakes, duplicate records, format inconsistencies, and other problems that detract from the accuracy of the information or impair your ability to make sense of it. Check out Aleata Hubbard’s Six Data Cleaning Checks for guidance on how to make sure your data are ready for analysis.

Meet EvaluATE’s friendly new ATE survey coordinator

ATE Annual 2018 Survey Logo

Check out this short video to meet Lyssa Wilson Becho, your one-stop shop for questions about the annual survey of Advanced Technological Education (ATE) principal investigators, and to get the scoop on this year’s survey. In this video, Lyssa gives a quick introduction to the 2018 survey and how the findings are used throughout the ATE community.

Check out the new ATE evaluation report repository

Evaluation Report Repository

EvaluATE is building a repository of ATE evaluation reports. Check it out to get a sense of how ATE projects and centers are evaluating their work and what they’re learning. If you’re an ATE principal and investigator or evaluator, let us know if you have a report that should be added to the collection.


Newsletter: 2017 Summer

Posted on August 7, 2017 by  in ()

Proposals for the National Science Foundation’s Advanced Technological Education (ATE) program are due October 5. If you are submitting a proposal, now is the time to get your evaluation plan in order. This issue of EvaluATE’s newsletter points you to several resources to help you with this task.

New Evaluation Guidelines for ATE Proposals

The National Science Foundation has issued a new solicitation for Advanced Technological Education (ATE) proposals. It includes important changes in the evaluation guidance. Check out these resources to help you put together a winning evaluation plan for your ATE proposal:

Finding an Evaluator: Demystified

You won’t find “evaluator” in the Yellow Pages. There is no list of NSF-vetted evaluators. Yet there are thousands of professionals who identify as evaluators. This situation can leave prospective ATE PIs who need evaluators for their proposals feeling mystified and frustrated about how to locate and select an evaluator for their ATE projects. Read EvaluATE’s new guide to Finding and Selecting an Evaluator to learn how to streamline your search for an evaluator for your ATE proposal.

Evaluators- add your information to the ATE Central Evaluator Map (

Not Allowed to Name an Evaluator in Your ATE Proposal?

Some institutions do not allow their faculty and staff to name an evaluator in a proposal prior to an award being made. If that is your situation, check out EvaluATE’s advice for DIY evaluation planning, as well as grants specialist Jacqueline Rearick’s tips for dealing with administrative red tape.


EvaluATE will award ATE Evaluation Fellowships to four ATE evaluators to enable them to attend the 2017 ATE Principal Investigators Conference. Learn more and this opportunity and how to apply from the ATE PI Conference section of EvaluATE’s website.​

The ATE Principal Investigators Conference is THE must-attend event of the year for anyone involved in the ATE program. Come to learn and network. Plus, it’s a great opportunity to showcase your lessons learned to help your ATE peers–check out the Call for Sessions.

Recent EvaluATE Blogs:


Newsletter: 2016 Fall

Posted on October 19, 2016 by  in ()

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Happy New Year!

The calendar year may be coming to a close, but a new academic year just started and many ATE program grantees recently received their award notifications from the National Science Foundation. ‘Tis the season to start up or revisit evaluation plans for the coming year. This digital-only issue of EvaluATE’s newsletter is all about helping project leaders and evaluators get the new evaluation year off on the right track.

Don’t launch (or relaunch) your evaluation before taking these steps


Mentor-Connect’s one-page checklist tells project leaders what they need to do to set the stage for a successful evaluation.

You won’t hear this from anyone else


EvaluATE’s director, Lori Wingate, shares Three Inconvenient Truths about ATE Evaluation in her latest contribution to the EvaluATE blog. You may find them unsettling, but ignorance is not bliss when it comes to these facts about evaluation.

Is your evaluation on track?


Use the Evaluation Progress Checklist to make sure your evaluation is on course. It’s on pages 26-28 in Westat’s Guidelines for Working with Third Party Evaluators, which also includes guidance for resolving problems and other tips for nonevaluators.

Myth: All evaluation stakeholders should be engaged equally


Monitor, facilitate, consult, or co-create? Use our stakeholder identification worksheet to figure out the right way to engage different types of stakeholders in your evaluation.

EvaluATE at the ATE PI Conference: October 26-29

A Practical Approach to Outcome Evaluation: Step-by-Step
WORKSHOP: Wednesday 1-4 p.m.
DEMONSTRATION: Thursday 4:45-5:15 p.m.

SHOWCASES: We will be at all three showcase sessions.

Check out the conference program.

Next Webinar


Did you miss our recent webinars?

Check out slides, handouts, and recordings

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Shape the future of EvaluATE

EvaluATE has been refunded for another 4 years! Let us know how you would like us to invest our resources to advance evaluation in the ATE program.

Complete our two-minute survey today.

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Newsletter: Getting the Most out of Your Logic Model

Posted on July 1, 2016 by  in - ()

I recently led two workshops at the American Evaluation Association’s Summer Evaluation Institute. To get a sense of the types of projects that the participants were working on, I asked them to send me a brief project description or logic model in advance of the Institute. I received more than 50 responses, representing a diverse array of projects in the areas of health, human rights, education, and community development. While I have long advocated for logic models as a succinct way to communicae the nature and purpose of projects, it wasn’t until I received these responses that I realized how efficient logic models really are in terms of conveying what a project does, whom it serves, and how it is intended to bring about change.

In reviewing the logic models, I was able to quickly understand the main project activities and outcomes.  My workshops were on developing evaluation questions, and I was amazed how quickly I could frame evaluation questions and indicators based on what was presented in the models. It wasn’t as straight forward with the narrative project descriptions, which were much less consistent in terms of the types of information  conveyed and the degree to which the elements were linked conceptually.  When participants would show me their models in the workshop, I quickly remembered their projects and could give them specific feedback based on my previous review of their models.

Think of NSF proposal reviewers who have to read numerous 15-page project descriptions. It’s not easy to keep straight all the details of a single project, let alone that of 10 or more 15-page proposals. In a logic model, all the key information about a project’s activities, products, and outcomes is presented in one graphic. This helps reviewers consume the project information as a “package.”  For reviewers who are especially interested in the quality of the evaluation plan, a quick comparison of the evaluation plan against the model will reveal how well the plan is aligned to the project’s activities, scope, and purpose.  Specifically, mentally mapping the evaluation questions and indicators onto the logic model provides a good sense of whether the evaluation will adequately address both project implementation and outcomes.

One of the main reasons for creating a logic model—other than the fact it may be required by a funding agency—is to illustrate how key project elements logically relate to one another. I have found that representing a project’s planned activities, products, and outcomes in a logic model format can reveal weaknesses in the project’s plan. For example, there may be an activity that doesn’t seem to lead anywhere or ambitious outcomes that aren’t adequately supported by activities or outputs.  It is much better if you, as a project proposer, spot those weaknesses before an NSF reviewer does. A strong logic model can then serve as a blueprint for the narrative project description—all key elements of the model should be apparent in the project description and vice versa.

I don’t think there is such a thing as the perfect logic model. The trick is to recognize when it is good enough. Check to make sure the elements are located in the appropriate sections of the model, that all main project activities (or activity areas) and outcomes are included, and that they are logically linked. Ask someone from outside your team to review it; revise if they see problems or opportunities to increase clarity. But don’t overwork it—treat it as a living document that you can update when and if necessary

Download the logic model template from

Newsletter: What’s the Difference Between Outputs, Outcomes, and Impacts?

Posted on July 1, 2016 by  in - ()


A common source of confusion among individuals who are learning about logic models is the difference between outputs, outcomes, and impacts. While most people generally understand that project activities are the things that a project does, the other terms may be less straightforward.

Outputs are the tangible products of project activities. I think of outputs as things whose existence can be observed directly, such as websites, videos, curricula, labs, tools, software, training materials, journal articles, and books. They tend to be the things that remain after a project ends or goes away.

Outcomes are the changes brought about through project activities and outputs/products.  Outcomes may include changes in individual knowledge, skills, attitudes, awareness, or behaviors; organizational practices; and broader social/economic conditions.  In her blog post “Outputs are for programs, outcomes are for people” (, Sheila Robinson offers this guidance: “OUTCOMES are changes in program participants or recipients (aka the target population). They can be identified by answering the question:  How will program participants change as a result of their participation in the program?” This is a great way to check to see if your logic model elements are located in the right place.  If the outcomes in your logic model include things that don’t sound like an appropriate answer to that question, then you may need to move things around.

The term impact is usually used to refer to outcomes that are especially large in scope or the ultimate outcomes a project is seeking to bring about. Sometimes the terms impacts and long-term outcomes are used interchangeably.

For example, one of EvaluATE’s main activities are webinars. Outputs of these webinars include resource materials, presentation slides, and recordings. Short-term outcomes for webinar participants are expected to include increased knowledge of evaluation. Mid-term outcomes include modifications or changes in their evaluation practice. Long-term outcomes are improved quality and utility of ATE project evaluations. The ultimate intended impact is for ATE projects to achieve better outcomes through strategic use of high-quality evaluations.

Keep in mind that not all logic models use these specific terms, and not everyone adheres to these particular definitions. That’s OK! The important thing to remember when developing a logic model is to understand what YOU mean in using these terms and to use and apply them consistently in your model and elsewhere.  And regardless of how you define them, each column in your model should present new information, not a reiteration of something already communicated.

Newsletter: ATE Logic Model Template

Posted on July 1, 2016 by  in - ()

A logic model is a graphic depiction of how a project translates its resources into activities and outcomes. The ATE Project Logic Model Template presents the basic format for a logic model with question prompts and examples  to guide users in distilling their project plans into succinct statements about planned activities and products and desired outcomes. Paying attention to the prompts and ATE-specific examples will help users avoid common logic model mistakes, like placing outputs (tangible products) under outcomes (changes in people, organizations or conditions brought about through project activities and outputs).

The template is in PowerPoint so you may use the existing elements and start creating your own logic model right away—just delete the instructional parts of the document and input your project’s information.  We have found that when a document has several graphic elements, PowerPoint is easier to work in than Word.  Alternatively, you could create a simple table in Word that mirrors the layout in the template.

Formatting tips:

  • If you find you need special paper to print the logic model and maintain its legibility, it’s too complicated.  It should be readable on a 8.5” x 11” sheet of paper.  If you simply have too much information to include in a single page, include general summary statements/categories, and include detailed explanations in a proposal narrative or other project planning document.
  • You may wish to add arrows to connect specific activities to specific outputs or outcomes.  However, if you find that all activities are leading to all outcomes (and that is actually how the project is intended to work), there is no need to clutter your model with arrows leading everywhere.
  • Use a consistent font and font size.
  • Align, align, align! Alignment is one of the most important design principles. When logic model elements are out of alignment, it can make it seem messy and unprofessional.
  • Don’t worry if your logic model doesn’t capture all the subtle nuances of your project. It should provide an overview of what a project does and is intended to accomplish and  convey a clear logic as to how the pieces are connected.  Your proposal narrative or project plan is where the details go.

Download the template from

Newsletter: Project Spotlight: Geospatial Technician Education – Unmanned Aircraft Systems & Expanding Geospatial Technician Education through Virginia’s Community Colleges

Posted on July 1, 2016 by  in - ()

Chris Carter is the Deputy Director of the Virginia Space Grant Consortium, where he leads two ATE projects.

How do you use logic models in your ATE projects?

Our team recently received our fourth ATE award, which will support the development of academic pathways and faculty training in unmanned aircraft systems (UAS). UAS, when combined with geospatial technologies, will revolutionize spatial data collection and analysis.

Visualizing desired impacts and outcomes is an important first step to effective project management. Logic models are wonderful tools for creating a roadmap of key project components. As a principal investigator on two ATE projects, I have used logic models to conceptualize project outcomes and the change that our team desires to create. Logic models are also effective tools for articulating the inputs and resources that are leveraged to offer the activities that bring about this change.

With facilitation and guidance from our partner and external evaluator, our team developed several project logic models. We developed one overarching project logic model to conceptualize the intended outcomes and desired change of the regional project. Each community college partner also developed a logic model to capture its unique goals and theory of change while also articulating how it contributes to the larger effort. These complementary logic models allowed the team members to visualize and understand their contributions while ensuring everyone was on the same path.

Faculty partners used these logic models to inform their administrations, business partners, and employers about their work. They are great tools for sharing the vision of change and building consensus among key stakeholders.

Our ATE projects are focused on creating career pathways and building faculty competencies to prepare technicians. The geospatial and UAS workforce is a very dynamic employment sector that is constantly evolving. We find logic models helpful tools for keeping the team and partners focused on the desired outputs and outcomes. The models remind us of our goals and help us understand how the components fit together. It is crucial to identify the project inputs and understand that as these evolve, project activities also need to evolve. Constantly updating a logic model and understanding the relationships between the various sections are key pieces of project management.

I encourage all ATE project leaders to work closely with their project evaluators and integrate logic models. Our external evaluator was instrumental in influencing our team to adopt these models. Project evaluators must be viewed as team members and partners from the beginning. I cannot imagine effectively managing a project without the aid of this project blueprint.

Newsletter: Revisiting Intellectual Merit and Broader Impact

Posted on January 1, 2016 by  in - ()

If you have ever written a proposal to the National Science Foundation (NSF) or participated in a proposal review panel for NSF, you probably instantly recognize the terms Intellectual Merit and Broader Impacts as NSF’s merit review criteria. Proposals are rated and funding decisions are made based on how well they address these criteria. Therefore, proposers must describe the potential of their proposed work to advance knowledge and understanding (Intellectual Merit) and benefit society (Broader Impacts).

Like cramming for an exam and then forgetting 90 percent of what you memorized, it’s all too easy for principal investigators to lose sight of Intellectual Merit and Broader Impacts after proposal submission. But there are two important reasons to maintain focus on Intellectual Merit and Broader Impacts after an award is made and throughout project implementation.

First, the goals and activities expressed in a proposal are commitments about how a particular project will advance knowledge (Intellectual Merit) and bring tangible benefits to individuals, institutions, communities, and/or our nation (Broader Impacts). Simply put, PIs have an ethical obligation to follow through on these commitments to the best of their abilities.

Second, when funded PIs seek subsequent grants from NSF, they must describe the results of their prior NSF funding in terms of Intellectual Merit and Broader Impacts. In other words, proposers must explain how they used their NSF funding to actually advance knowledge and understanding and benefit society. PIs who have evidence of their accomplishments in these areas and can convey it succinctly will be well-positioned to seek additional funding. To ensure evidence of both Intellectual Merit and Broader Impacts are being captured, PIs should revisit project evaluation plans with their evaluators, crosschecking the proposal’s claims about potential Intellectual Merit and Broader Impacts in relation to the evaluation questions and data collection plan to make sure compelling evidence is captured.

Last October, I conducted a workshop on this topic at the ATE Principal Investigators Conference with colleague Kirk Knestis, an evaluator from Hezel Associates. Dr. Celeste Carter, ATE program co-lead, spoke about how to frame results of prior NSF support in proposals. She noted that a common misstep that she has seen in proposals is when proposers speak to results from prior support by simply reiterating what they said they were going to do in their funded proposals, rather than describing the actual outcomes of the grant. Project summaries (one-page descriptions that address a proposed project’s Intellectual Merit and Broader Impacts that are required as part of all NSF proposals) are necessarily written in a prospective, future-oriented manner because the work hasn’t been initiated yet. In contrast, the Results of Prior NSF Support sections are about completed work and therefore are written in past tense and should include evidence of accomplishments. Describing achievements and presenting evidence of the quality and impact of those achievements shows reviewers that the proposer is a responsible steward of federal funds, can deliver on promises, and is building on prior success.

Take time now, well before it is time to submit a new proposal or a Project Outcomes Report, to make sure you haven’t lost sight of the Intellectual Merit and Broader Impact aspects of your grant and how you promised to contribute to these national priorities.

Newsletter: How can PIs demonstrate that their projects have “advanced knowledge”?

Posted on January 1, 2016 by  in - ()

NSF’s Intellectual Merit criterion is about advancing knowledge and understanding within a given field or across fields. Publication in peer-reviewed journals provides strong evidence of the Intellectual Merit of completed work. It is an indication that the information generated by a project is important and novel. The peer review process ensures that articles meet a journal’s standard of quality, as determined by a panel of reviewers who are subject matter experts.

In addition, publishing in an academic journal is the best way of ensuring that the new knowledge you have generated is available to others, becomes part of a shared scientific knowledge base, and is sustained over time. Websites and digital libraries tend to come and go with staff and funding changes. Journals are archived by libraries worldwide and, importantly, indexed to enable searches using standard search terms and logic. Even if a journal is discontinued, its articles remain available through libraries. Conference presentations are important dissemination vehicles, but don’t have the staying power of publishing. Some conferences publish presented papers in conference proceedings documents, which helps with long-term accessibility of information presented at these events.

The peer review process that journals employ to determine if they should publish a given manuscript is essentially an evaluative process. A small group of reviewers assesses the manuscript against criteria established for the journal. If the manuscript is accepted for publication, it met the specified quality threshold. Therefore, it is not necessary for the quality of published articles produced by ATE projects to be separately evaluated as part of the project’s external evaluation. However, it may be worthwhile to investigate the influence of published works, such as through citation analysis (i.e., determination of the impact of a published article based on the number of times it has been cited—to learn more, see

Journals focused on two-year colleges and technical education are good outlets for ATE-related publications. Examples include Community College Enterprise, Community College Research Journal, Community College Review, Journal of Applied Research in the Community College, New Directions for Community Colleges, Career and Technical Education Research, Journal of Career and Technical Education, and Journal of Education and Work. (For more options, see the list of journals maintained by the Center of Education and Work (CEW) at the University of Wisconsin at

NSF’s Intellectual Merit criterion is about contributing to collective knowledge. For example, if a project develops embedded math modules for inclusion in an electrical engineering e-book, students may improve their understanding of math concepts and how they relate to a technical task—and that is certainly important given the goals of the ATE program. However, if the project does not share what was learned about developing, implementing, and evaluating such modules and present evidence of their effectiveness so that others may learn from and build on those advances, the project hasn’t advanced disciplinary knowledge and understanding.

If you are interested in preparing a journal manuscript to disseminate knowledge generated by your project, first look at the type of articles that are being published in your field (check out CEW’s list of journals referenced above). You will get an idea of what is involved and how the articles are typically structured. Publishing can become an important part of a PI’s professional development, as well as a project’s overall effort to disseminate results and advance knowledge.