Archive: project spotlight

Newsletter: Project Spotlight: Manufacturing Associate Degree Education in Northwestern Connecticut

Posted on October 1, 2015 by  in Newsletter - ()

Professor, Biology, Northwestern Connecticut Community College

A conversation with Sharon Gusky, an ATE PI at Northwestern Connecticut Community College.

Q: Your ATE project started just over a year ago. What do you know now that you wish you’d known then about project evaluation?

A: I wish I had a better understanding of information that is useful to collect before the start of the grant so we would have been better prepared to capture baseline and impact data. This is our first NSF grant and it allowed us to start a new manufacturing program. The community was excited about and very supportive of it. The first year we received many requests to speak at events, do radio and cable TV shows, and visit high schools, but we did not have a way to capture the impact of these activities.

Q: What advice do you have for new PIs with regard to working with an evaluator?

A: Start working with your evaluator early and set clear timelines for checking in and reviewing and analyzing the data as it is collected. The information that you collect along the way can help shape the program. We learned early on through student interviews that they did not like the course schedule, which required them to wait a semester or summer to take the second technical course in a sequence.  We used their feedback to revise the schedule so that each course ran for eight weeks during a semester.  If we had waited until the end of the spring semester to find this out, it would have been too late to implement the change for fall.

Q: What challenges did you face in getting the evaluation off the ground?

A: We faced a number of scheduling challenges and miscommunication with regard to data collection.  We hadn’t clearly defined the roles of the various people involved—external evaluator, institutional research director, PI, and co-PIs.  We needed to sit down together and work out a plan so that the data we needed was being collected and shared.

Newsletter: Project Spotlight: E-MATE

Posted on July 1, 2015 by  in Newsletter - ()

Professor and chair of engineering and technology at Brookdale Community College, E-MATE

A conversation with Mike Qaissaunee, E-MATE’s principal investigator

Q: How did you work with your evaluator during proposal development?

A: As PI and an experienced evaluator, I wrote the initial plan and selected a longtime colleague to act as external evaluator. The proposal was funded with the understanding that we would select a new evaluator, as panelists felt the initial evaluator was too close to me (the PI) and would have difficulty being objective. We selected a new evaluator with significant experience with NSF, ATE, and community colleges. Through a number of calls and meetings, we discussed the proposal, detailed our goals and objectives, answered a number of really good questions, and identified the key things we hoped to learn. Our new evaluator was able to build on my original evaluation plan, developing a rich evaluation framework and logic model.

Q: What advice do you have for communicating an evaluation plan in a proposal?

A: As proposals are fairly short, it’s important to keep the evaluation plan brief and specific to the project, rather than boilerplate. If possible, communicate information in a table and/or graphic. Evaluation metrics and tasks can also be included in tables detailing timelines, activities, and goals and objectives.

Q: How did you integrate evaluation results from a prior project into your proposal?

A: I’ve found that the most powerful approach to including evaluation results in a proposal is a judicious mix of qualitative and quantitative data. Quantitative data demonstrates past success and capacity for future work, while qualitative results bring the proposal to life and engages readers. Evaluation results can also be used to highlight areas with limited success and new areas for investigation. I don’t shy away from addressing evaluation data as it demonstrates that the project team is learning and adapting.