Update 2/11/2018: Replaced original concentric circles diagram with an updated, clearer version.
If my innovation plan is to be successful, it will need to be supported by a strategy to influence Sauk stakeholders to take part in the process. For this post, I am focusing on just one facet of the plan–collecting data as part of the advising process.
Currently, the developmental education process at Sauk is pretty cut and dried. Depending on their placement test scores, incoming students are placed in the appropriate course. Incorporating other methods of placement such as the college preparation course I am proposing, though, introduces complexity to the process and so it also increases the chance for error. In addition, judging the effectiveness of the process will depend on collecting and analyzing as much data as possible.
Following Grenny, Patterson, Maxfield, McMillan, and Switzler’s model in Influencer calls for finding a “vital behavior” to change and then applying six sources of influence. To achieve the result of having enough data to analyze the effectiveness of the plan, it is imperative that academic advisors collect as much relevant data as possible. While specific relevant measures still need to be defined, some examples of relevant data would include the following: high school GPA and specific course grades, ACT/SAT scores, at-risk markers, and participation indicators in college preparatory course (if applicable).
Therefore, for this influence plan, the vital behavior is to ensure that a complete data set is collected for at least 80% of incoming students. It is not reasonable to expect 100% collection as data may not be available for all students and some students may not be willing to provide all data points.
The Six Sources of Influence
To be successful, I will need to engage all of the six sources of influence from Grenny, et al. While the matrix in Influencer is useful, I prefer to think of the model as concentric circles as it shows the difficulty of penetrating all the way to the Personal level as well as the relationship between the Structural, Social, and Personal levels.
Changes are easiest to make at the structural level, as they are what McChesney, Covey, and Huling call a “stroke-of-the-pen strategy” , a change that can just be made by saying it needs to be done. Structural changes will most directly affect the social level, which will in turn affect the individuals at the personal level.
Structural, or external, motivation could be accomplished by providing printed or digital materials (posters, computer wallpapers, etc.) that remind advisors to ask for all the information, not just the minimum necessary to get the student’s immediate needs taken care of.
In addition, some silly rewards such as “most math scores this month” or “collected 500 high school GPAs” could be given at monthly staff meetings. In addition to turning the data collection into a game, it will also help to encourage…
…healthy peer pressure among advising staff. Between the healthy competition among advisors and the effect of seeing that other advisors are collecting the information, the social motivation will provide a powerful encouragement for advisors to remember to collect information.
Healthy peer pressure will help advisors to be personally motivated, but much more can and should be done to affect the personal motivation realm. One key way to do this will be to clearly contextualize the data by repeatedly discussing the overall goal of the project and sharing data as it becomes available. This will help advisors to see the results of their work and how it is helping to help students succeed.
To help advisors’ ability to collect the necessary information, the most important structural accommodation will be to make sure the database and collection forms are user-friendly and easily accessible.
Good database design will also enable multiple advisors (and other personnel) to collect and enter information, decreasing the load on each individual advisor.
Proper training, of course, is paramount to the success of any program like this. In addition to contextualizing the need for the data collection, training sessions can also equip advisors with clear descriptions of what data need to be collected and responses to common objections students may provide. A mnemonic device to help advisors remember the pieces of information that need to be collected could also be helpful.
I believe that, with these measures in place, 80% data collection is an achievable result and will contribute greatly to the success of the overall project.