  Objective: Community college students typically submit transfer applications
to multiple universities. However, each university may have differing
lower-division major requirements in order to transfer. Accordingly, our study
examined one pain point users may have with ASSIST, which is California's
official statewide database of articulation agreements. That pain point is
cross-referencing multiple articulation agreements to manually develop an
optimal academic plan. Optimal is defined as the minimal set of community
college courses that satisfy all the transfer requirements for the multiple
universities a student is preparing to apply to. Methods: To address that pain
point, we designed a low-fidelity prototype that lists the minimal set of
community college courses that a hypothetical optimization algorithm would
output based on the user's selected articulation agreements. 24 California
college students were tasked with creating an optimal academic plan using
either ASSIST (which requires manual optimization) or the optimization
prototype (which already provides the minimal set of classes). Results:
Experiment participants assigned to use the prototype had less optimality
mistakes in their academic plan, were faster in creating their plan, and
provided higher usability ratings compared to the ASSIST users. All differences
were statistically significant (p < 0.05) and had large effect sizes (d > 0.8).
Conclusions: Our preliminary experiment suggests manually developing optimal
academic plans can be error prone and that algorithm-generated academic plans
can potentially reduce unnecessary excess community college credits. However,
future research needs to move beyond our proof of value of a hypothetical
optimization algorithm and towards actually implementing an algorithm.
