First Learning Outcome: Gain a better understanding of our self-audit and decision-making process to automate.
Second Learning Outcome: Learn the steps we took to ensure collaboration, buy-in, and measurable outcomes.
Third Learning Outcome: Learn the basic tools required to automate these processes.
Intended Audience: General Audience
Presenter(s):
David Granda Florida International University
Hitting the Jackpot with Automation
Category
Transfer > Session
Description
Florida International University (FIU) is 4th in the nation in transfer student enrollment, which translates to nearly 9,000 new transfers annually. An important driver in the commitment of these students to matriculate is the evaluation of transfer credit. During the past two years, Transfer and Transition Services staff spent considerable time reviewing the original build of our transfer rules processes to determine where automation was possible. In this presentation, we will detail two of these processes, including timelines, collaborators, results, and learnings.
At FIU, when non-articulated courses are transferred, they are initially awarded a generic pseudo or “fake” course signifying that the course has been accepted (TRF 1000/3000). Following the build of TRF, TTS staff, in consultation with discipline-faculty, evaluate these courses to award comparable FIU courses, as appropriate. This process has been done manually for the past 15 years. Manual entry is not only time consuming, it is also more prone to human error. Today, we have a new, daily process to identify courses with no articulated rules and automatically build them as TRF. We have seen immediate impact of this implementation.
The second process we will automate is related to advisors’ requests to equate upper-division courses to comparable FIU courses. These requests are made via a digital form on PeopleSoft, which allows advisors to note whether the equivalency is to be built as a general transfer rule for all (future) students or as an exception for a specific student. Upon review of data, we learned that the majority of transfer rules requested by advisors were exceptions. Because student-specific rules will not impact other students, we developed a process to identify these requests and automatically build the approved transfer rule. We anticipate positive outcomes (e.g., quicker turnover of advisor requests, and greater accountability at the level of the academic unit).
Submission ID:
4499
Day:
Sunday, July 14, 2019
Time:
1:00 PM - 2:00 PM
Room:
Neopolitan I