students.
The gynecology and obstetrics students were
questioned about their interest in working on other
cases using the tool; 73.33% answered YES and
26.67% said NO. In an interview with the tutoring
professor, she considered the tool ready for wider-
scale tests. These tests would be conducted with the
students solving clinical cases separately. This
would be considered the final step in the
development stages.
At the end of the system development stages, it was
confirmed that the system could be used by a larger
number of students. In order to validate the system,
20 medical students undergoing gynecology
tutorials took part in the process.
After the system was validated, the discussion of
cases by the students was studied. It was believed
that the students who had added and removed
variables were analyzing all possibilities in order to
form a diagnosis, in the same manner as they did in
the classroom during the tutorials.
Lastly, clinical case comparisons were made. After
filling out the entire clinical case, the complete
solved case solution is displayed, after which the
gynecology and obstetrics student can select other
answered cases in order to visually compare case
solutions. The student may not return to the case to
alter it after finishing the solution to the clinical
case.
At this stage and after analyzing the project
(Rodrigues, 2006), it was verified that there is no
comparison between the paths taken by the medical
student and that predetermined by the professor,
which was recorded according to an analysis by the
faculty member responsible for the Gynecology and
Obstetrics discipline. The data are stored inside the
system, but their analysis was not done directly.
Based on that need, we observed the
importance of proposing a solution that aims to
automate or semi-automate a process that can make
a match between the path taken by the student, step-
by-step, form beginning to end, regarding the
clinical case and the solution proposed by the
professor.
3.2 Analysis and Discussion
In item 2.1, we showed that in the current decade,
several ICTs have contributed to the education field,
and that the Internet has been decisive in presenting
new solutions in the teaching / learning relationship.
Moreover, the use of the PBL methodology, in
addition to diversifying the areas in which it is
applied, new virtual platforms and software
programs are integrating solutions and adaptations
for the development of PBL-based teaching and
learning, thus demonstrating an ever-greater
integration between the PBL methodology and ICTs.
According to items 2.2 and 2.3 and after a
detailed analysis of the obtained results, it was
verified that there is no automated or semi-
automated comparison between the paths taken by
the medical student and the predetermined path set
by the system, which was recorded according to an
analysis by the faculty member responsible for the
Gynecology and Obstetrics discipline. As the
students’ data and the professor’s answer sheet are
stored in the system data base, it is possible to match
between the student’s data and the professor’s
answer sheet automatically or semi-automatically.
With the use of math-based tools, it is possible to
compare these two paths, thus obtaining an adequate
solution to this problem: automating or semi-
automating the process that can make a match
between the path taken by the student, step-by-step,
form beginning to end, regarding the clinical case
and the solution proposed by the professor.
The main characteristic of the PBL methodology
is the use of clinical cases as study objects. The
Learning Program in Gynecology and Obstetrics at
PUCPR has been a great ally to PBL, as both
students and professors adapted efficiently to the
process. This methodology offers students the
conditions to experience in their studies what would
really be happening in their professional lives.
In order to integrate the student with reality, and
so that individual performance can be monitored and
recorded, the support system for the teaching of
gynecology and obstetrics can be further improved
to include automation or semi-automation of the
analysis of similarity indices between the solution
found by the student and the professor’s answer
sheet.
In order to solve this problem, the system
(Rodrigues, 2006) was provided with a module
where information related to the patient’s anamnesis
was stored. The System features logs, and was built
as follows: at each variable inclusion or exclusion
request by the user, a record is made. To that end,
two types of system logs were created: one that
records navigation, case description visualizations,
the possibility of the user solving the case, fill out
diagnostic hypothesis and compare clinical case. The
second log records activities that take place while
the variables are being selected, named requests,
Figure 1.
The log represented in Figure 3 demonstrates all
the requests made by the student and recorded in the
HEALTHINF 2009 - International Conference on Health Informatics
334