Data Driven Decision Making
The program that I work here at CSUSB implements data driven decision making constantly. Our funding is generated by the number of student teachers working as interns in different school districts across the inland empire. Therefore, it is important for us to maintain constant communication with our students in order to satisfy their needs. Last year the CCTC distributed a survey to be conducted by all intern programs requesting information from interns across the state. The survey collected information about the intern’s opinions about the program overall, which included their experience in the classroom as a student as well as a teacher in their district. The information collected would help the CCTC provide supporting evidence for their requests in creating legislations that would provide more funding to intern programs across the state. This information assisted not only our program, but the also the College of Education in understanding the student’s feelings about courses, faculty, staff, and specific program requirements.
The positive use of data driven decision making in this example is that it can possibly increase the funding for intern programs in institutions across the state. Also, this information can help intern programs improve their student’s experience in the program by making the appropriate changes to satisfy student’s concerns and needs.
The negative use of data driven decision making can be the collection process for this information. In our university we conduct many surveys to collect this information. Students are constantly bombarded with surveys and evaluations that are used to conduct data driven decision making and that can generate a negative response from students as they takes time away from their responsibilities. When we conducted our survey for example, the College of Education had sent two other surveys to exiting credential students that resulted in a low response rate.
Leadership is important in these situations. In our case the CCTC demanded above 80% response from all programs. Our program achieved this by creating a raffle in which we conducted three prize drawings on established deadlines so that by the final drawing most of the surveys were collected.
Proposal and presentation for Ed-Media 2007.
Project 2
Project 2 Powerpoint
Weekly tasks
Listened to podcast and posted response.
Posted comments on other blogs
Finalize and posted Project 2
Continue work on Project 3
1 comment:
So in your negative example, the problem is actually too much data collection, much of it duplicated, is going on. Very good point. This no doubt results from the relative anarchy of a University. We all tend to operate in isolation and on our own schedules.
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