Proceedings

if this was not their first option when faced with choosing their future specialization,
as they were in the past.
We should also mention the fact that out of the total 367 questionnaires that were
implemented we only ended up using 316. This was due to some questionnaires being
incompletely filled and eliminated from the analysis, as explained within the research
design part of the paper. Moreover, as Table 1 shows, the number of cases being
analyzed when considering different clusters might also differ from the total number
of questionnaires finally being kept for analysis within the study. We refer here to
clusters comprising less than the 316 students due to eliminating those students who
did not answer the question that actually represented the factor that helped us establish
a certain cluster. For example, 12 students forgot to mention their gender when
answering the questionnaire. As a consequence, Panel B only comprises a number of
304 students.
Moving forward, another aspect that should be discussed is students’ academic
performance. We have appealed within our study to using two distinctive instruments
in order to measure academic success or performance. One on hand we used students’
final grade as assessed by the educator (the SAFG variable) and on the other their
self-acknowledged grade (the SSAG variable). We meanwhile consider that both
instruments being used in measuring students’ academic performance actually
represent two distinctive perceptions upon the same reality. Therefore, the main
objective of our study would be to question and analyze these two perceptions on the
evaluation process while also making use of Bloom’s taxonomy. The obtained results
are interpreted in correlation and based on variables obtained through the
implemented questionnaire.
A first step of our study would be to analyze the correlation degree between the two
grades used in measuring accounting students’ academic performance while also
considering cluster analysis. We furthermore analyzed the manner in which students’
final grades as assessed by the educator were formed when considering the levels of
Bloom’s taxonomy. We therefore calculated the Percentage representing the weight
of each level of Bloom’s taxonomy in generating students’ final grade (SAFG).
We might say that some of the obtained results are unexpected and reflect
particularities of the analyzed environment. First, when referring to the whole group
of students being analyzed (Panel A) we maybe expected a high level of students’
subjectivity in assessing their self-acknowledged grade to make it difficult to establish
a correlation between their assessment (SSAG) and that of the educator (SAFG).
Obtained results document that such a correlation exists and records a medium level.
The analyzed correlation could not be documented for the group of students that were
not planning on pursuing a future career in the field of accounting (Panel E) and also
for the group of students that presented a negative attitude towards the accounting
specialization as being unsatisfied with it (Panel G). When looking at the graph that
reflects grades’ distribution for the two considered measurement instruments of
accounting students’ academic performance (SAFG and SSAG) we can conclude that
students’ expectations for their grades as based on their self-acknowledgement is
higher in comparison to the actual grade being granted by the educator. This for sure
we kind of expected as a result of the comparison. When considering students’ final
grades as assessed by the educator our sample records a mean grade of 4.93, while the
mean for students’ self-acknowledged grades is 7.50. We consider the difference
~ 874 ~