Good day everyone. My name is Joshua Manzano and I am presenting our paper entitled, using Boolean satisfiability solvers to help
reduce cognitive load, and improve decision making when creating common academic schedules. Our paper focuses on student
scheduling, which is defined as assigning students to sections of each course such that they have no schedule conflicts. Many
universities employ a method wherein they let the students pick their own courses and create their own schedules. This entails
students having to pick from a list of courses and creating their own schedules. This usually leaves them having to deal
with picking from a lot of courses and also coordinating it with a lot of peers. This leads to mental stress and high cognitive
load which then impairs their decision making and makes them create poor quality schedules. Related literature around this
problem shows that scheduling tends to be a complex task due to the constraints involved. And due to this complexity the quality
of the final decision, and in this case, the schedule is more likely to be stifled and bad schedules can affect the grade and
performance as well as the motivation of a student. Constraint satisfiability has been proposed as a technique that can be used
to solve the student scheduling problem. An example of such technique is the usage of a satisfiability solver, which has
been used in course scheduling, employee timetabling and tournament scheduling. There is an opportunity to integrate a
sat solver in a semi automated system to tackle the problem of student scheduling. There have been minimal studies in tackling
the student scheduling problem, especially with a sat solver. So our research questions are as follows. First, what impact is the
use of a sat solver based system have on the decision making process and schedule creation? Next is what impact does the use
of a sat solver based system have on the cognitive load of users? To answer our research questions, we have the following
methodology. First, we do needfinding to identify the preferences and the necessary design goals needed. Next, we
design the system and configure the sat solver. And next, we evaluate our proposed system to existing methods. For our
needfinding we were able to gather 54 respondents and from these 54 we were able to interview 16 respondents who had unique and
unexpected answers. To further understand we got another set of five respondents for contextual inquiry. From this we were able
to create our preference taxonomy, which is used as a basis for our sat solver. We had the following design goals which is first
to improve efficiency, to reduce stress, and to minimize mental load. Thus, our system was designed with these design goals in
mind. To evaluate our system, we recruited seven friend groups, with 2 to 4 participants each for a total of 42 participants.
These participants were separated in control group, which were allowed to use any method or system they want. And the
experimental group which used the proposed system. They were tasked to create their schedule individually and coordinate with
others. To measure the different aspects in schedule making, we used multiple metrics for each aspect to ensure that we
correctly capture each aspect. For our results, we found that students experienced less difficulty when using our proposed
system. This can be seen in subjective mental effort questionnaire, and in NASA TLX. It is interesting to note that
students in the experimental group found that they experienced less physical demand than those in the control group, even
though the task in general is not really physically demanding. We also found that students were able to decide faster and
create their schedules faster. We saw that students experienced slight confusion with the first group task, and this was
attributed to the lack of feedback and confusion with how some of the automated features were used. We also found that students
experienced less stress. For the user experience we found that in general, in most aspects in the user experience
questionnaire, the proposed system had good and excellent scores, with an exception for perspicuity, which is just above
average, which can be attributed to the aforementioned confusion. When asked to pick between schedules created by the
control group and the experimental group, we found that students favored schedules created using the proposed system which is the
experimental group. In general, students were also more satisfied and think that their method was more efficient when
using the proposed system. For design recommendations when creating a user centric semi automated system for creating
personal schedules, we provide the following recommendation. Is to first, provide informative feedback with semi automation.
This due to the fact that feedback reduces the likelihood of perceived ambiguity and confusion with automated results, and
also aids with decision making. Next is to visualize and contextualize content through the use of visualization such as a
calendar weekly view with color highlights, as these can simplify the task of deciding a common schedule. Next is to
always strike a balance between automation and autonomy, as too much automation often leads to the user getting confused and
actually harms their decision making and their efficiency. For future work. We would like to explore more collaborative
features, to benchmark against other automation algorithms, and to explore course timetabling in tandem with a semi automated
student scheduling system.
That is all. Thank you for listening!