Abstract:
As a developing country, dropout is the dominant obstacle for our educational
sectors. Therefore, it is important to develop the logical method for prediction of
the students at the risk point of dropping out, allowing a proactive process to
reduce this problem. This research work develops a prototype which can
automatically recognize either the students will continue his/her study or dropout,
using classification rules. Data were performed at one of the famous and
prestigious university named Daffodil international university, with the main goal
to reveal the high prospective of data mining applications. Data were collected
from the students mainly focused on their personal and Family problems and
university-performance. The responsible factors for dropping out were found
through the Association technique using Apriori algorithm. Pre-processed factors
were applied on the running students who were already completed one years or 3rd
semester of their study. Classification method can be highly supportive in
predicting student’s dropout reasons. Selected 10 best attributes using CFS which
were directly affected on the analysis. Finally, decision was making based
onC4.5, Naïve Bayes algorithms that a running student would continue their study
or would drop out. C4.5 algorithms was found the best classifier with 86.014%
accuracy whereas Naïve Bayes was 76.22% of accuracy.