New Orleans, Louisiana
June 26, 2016
June 26, 2016
August 28, 2016
Educational Research and Methods
In this paper, we demonstrate the details of a case study in which we used data mining and process mining methods to assess the success of an intervention method on the student population within ABC University. In addition to assessing the intervention method, we also suggest a set of decision making rules that if supplemented with the current intervention method can potentially improve the assessment outcomes.
Within ABC University, a proportion of undergraduate students who satisfy a minimum Cumulative Grade Point Average (CGPA) of 3.4 (out of 4.0) are placed in a special intervention program called DEF. DEF participants are offered general education courses with smaller class sizes, are assigned additional counselors, and are provided more scholarship opportunities. All DEF participants are monitored after each semester and if a DEF participant’s CGPA decreases to lower than 3.4, the participant is dropped from the intervention program.
The success of the DEF intervention program is measured by the percentage of its participants who keep their CGPA above 3.4 (called CGPA criterion) as well as the percentage of its participants who graduate from ABC university within 6 years of beginning (called graduation criterion).
Our study included the analysis of more than 17,000 students of ABC University who entered the university as freshmen between 2005 to 2009. Out of these 17,000 students, more than 2,500 attended the DEF intervention program for at least one semester out of their first four semesters. The remaining students were not exposed to the intervention program. We created a trace (a set of events) for each student by tracking their CGPA in their first 4 semesters. In each semester, if a student’s CGPA was lower than 3.4, we recorded an N (non-satisfactory) event for that student. A CGPA of 3.4 or higher generated an S (satisfactory) event. As the output (final) event for each student’s trace, we recorded a D (showing that the student dropped from the university) or a G showing that the student graduated. The D and G events did not necessarily occur in the first 4 semesters for each student. The D and G events were available for all students as our data set included only the students who entered ABC University as a freshman between 2005 to 2009.
We used visualization, data mining, and process mining tools to analyze the success of the intervention program. Our analysis revealed that the DEF program did not have a significant impact on the success of its participant who attended the program in their first semester. However, for groups of students with certain trace structures, the DEF program was very effective. For these groups, both the CGPA and the graduation criteria were significantly better compare to the students who were never exposed to the DEF program. Based on the trace structures that were significantly effective, we developed a set of rules to help the DEF administrators to decide how to admit or dismiss students from this program to improve the intervention success and effectiveness.
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