President, Magnolia Consulting, LLC
Stephanie B. Wilkerson, President of Magnolia Consulting, brings over 20 years of experience working on research and evaluation studies at national, state, and local levels. She has served as the principal evaluator for the NSF-funded Advanced Technological Education (ATE) project with the Virginia Space Grant Consortium and several Virginia community colleges that aim to enhance geospatial technician education to meet workforce demands. Stephanie has directed 32 randomized controlled trials and quasi-experimental evaluations in the areas of STEM, reading, and English language proficiency using mixed-method designs with student assessment data, student interest surveys, instructional practices surveys, and implementation fidelity measures.
Too often, people begin developing infographics by playing with templates, images, and data visualizations. And who can blame them? It’s fun! But while this process will produce an infographic, it might not result in a story that connects with your audience. A better approach is to begin by making intentional decisions about your infographic: clearly defining your audience, purpose, and message constitutes three foundational and critical steps for developing an effective infographic.
Read the full blog at: bit.ly/10stepsInfographic
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This information is from Stephanie Wilkerson at Magnolia Consulting.
Evaluations are most useful when evaluators make relevant findings available to project partners at key decision-making moments. One approach to increasing the utility of evaluation findings is by collecting real-time data and providing immediate feedback at crucial moments to foster progress monitoring during service delivery. Based on our experience evaluating multiple five-day professional learning institutes for an ATE project, we discovered the benefits of providing real-time evaluation feedback and the vital elements that contributed to the success of this approach.
What did we do?
With project partners we co-developed online daily surveys that aligned with the learning objectives for each day’s training session. Daily surveys measured the effectiveness and appropriateness of each session’s instructional delivery, exercises and hands-on activities, materials and resources, content delivery format, and session length. Participants also rated their level of understanding of the session content and preparedness to use the information. They could submit questions, offer suggestions for improvement, and share what they liked most and least. Based on the survey data that evaluators provided to project partners after each session, partners could monitor what was and wasn’t working and identify where participants needed reinforcement, clarification, or re-teaching. Project partners could make immediate changes and modifications to the remaining training sessions to address any identified issues or shortcomings before participants completed the training.
Why was it successful?
Through the process, we recognized that there were a number of elements that made the daily surveys useful in immediately improving the professional learning sessions. These included the following:
- Invested partners: The project partners recognized the value of the immediate feedback and its potential to greatly improve the trainings. Thus, they made a concentrated effort to use the information to make mid-training modifications.
- Evaluator availability: Evaluators had to be available to pull the data after hours from the online survey software program and deliver it to project partners immediately.
- Survey length and consistency: The daily surveys took less than 10 minutes to complete. While tailored to the content of each day, the surveys had a consistent question format that made them easier to complete.
- Online format: The online format allowed for a streamlined and user-friendly survey. Additionally, it made retrieving a usable data summary much easier and timelier for the evaluators.
- Time for administration: Time was carved out of the training sessions to allow for the surveys to be administered. This resulted in higher response rates and more predictable timing of data collection.
If real-time evaluation data will provide useful information that can help make improvements or decisions about professional learning trainings, it is worthwhile to seek resources and opportunities to collect and report this data in a timely manner.
Here are some additional resources regarding real-time evaluation:
Articulating project outcomes is easier said than done. A well-articulated outcome is one that is feasible to achieve within the project period, measurable, appropriate for the phase of project development, and in alignment with the project’s theory of change. A project’s theory of change represents causal relationships – IF we do these activities, THEN these intended outcomes will result. Understandably, project staff often frame outcomes as what they intend to do, develop, or provide, rather than what will happen as a result of those project activities. Using logic models to situate intended outcomes within a project’s theory of change helps to illustrate how project activities will result in intended outcomes.
Since 2008, my team and I have served as the external evaluator for two ATE project cycles with the same client. As the project has evolved over time, so too have its intended outcomes. Our experience using logic models for program planning and evaluation has illuminated four critical roles we as evaluators have played in partnership with project staff:
- Educator. Once funded, we spent time educating the project partners on the purpose and development of a theory of change and intended outcomes using logic models. In this role, our goal was to build understanding of and buy-in for the need to have logic models with well-articulated outcomes to guide project implementation.
- Facilitator. Next, we facilitated the development of an overarching project logic model with project partners. The process of defining the project’s theory of change and intended outcomes was important in creating a shared agreement and vision for project implementation and evaluation. Even if the team includes a logic model in the proposal, refining it during project launch is still an important process for engaging project partners. We then collaborated with individual project partners to build a “family” of logic models to capture the unique and complementary contributions of each partner while ensuring that the work of all partners was aligned with the project’s intended outcomes. We repeated this process during the second project cycle.
- Methodologist. The family of logic models became the key source for refining the evaluation questions and developing data collection methods that aligned with intended outcomes. The logic model thus became an organizing framework for the evaluation. Therefore, the data collection instruments, analyses, and reporting yielded relevant evaluation information related to intended outcomes.
- Critical Friend. As evaluators, our role as a critical friend is to make evidence-based recommendations for improving project activities to achieve intended outcomes. Sometimes evaluation findings don’t support the project’s theory of change, and as critical friends, we play an important role in challenging project staff to identify any assumptions they might have made about project activities leading to intended outcomes. This process helped to inform the development of tenable and appropriate outcomes for the next funding cycle.
There are several resources for articulating outcomes using logic models. Some of the most widely known include the following:
Worksheet: Logic Model Template for ATE Projects & Centers: https://www.evalu-ate.org/resources/lm-template/
Education Logic Model (ELM) Application Tool for Developing Logic Models: http://relpacific.mcrel.org/resources/elm-app/
University of Wisconsin-Extension’s Logic Model Resources: http://www.uwex.edu/ces/pdande/evaluation/evallogicmodel.html
W.K. Kellogg Foundation Logic Model Development Guide: https://www.wkkf.org/resource-directory/resource/2006/02/wk-kellogg-foundation-logic-model-development-guide