# A Machine Learning Framework for Student Retention Policy Development: A Case Study
## 4 Experimental Results and Discussion
### Using Prediction Results to Enhance Student Retention Policies
#### 4.3.2 Example Scenario: Using Clustering and Decision Trees for Student Retention  
Establishing a support system and modifying regulations and practices for learning processes has been employed successfully to decrease the dropout rates at universities [9]. Once the students at risk of dropout are identified, they may be analyzed to design tailored policies and support programs to prevent their dropout. A possible policy-making scenario using only the information available in the decision tree is presented below. The decision tree provides a human-interpretable presentation of the cluster definitions. Note that this discussion is provided as an example of how the policy-making process may be supported by the proposed framework. The ability to describe the clusters and in effect the students in each cluster allows HEI administrators to design tailored policies for each cluster. Examining the DT reveals the main characteristics of the clusters that can be mapped to some common retention strategies as summarized in Table 7. It should be  
Figure 13: Decision tree showing clusters of at-risk students. emphasized that in this study, both the analysis for policy generation require domain experts at decision-making levels within an HEI. The three clusters of students at risk of dropout and a corresponding retention policy tailored for the cluster are defined in more detail regarding the information in the first three levels of the decision tree as follows:  
_Cluster 0: Students with consistent challenges_  
The students who are repeating two or more courses and have failed three or more courses in the second semester are placed in cluster 0. These are the students who are consistently failing two or more courses in both semesters. Another group of students who are placed in this cluster have failed less than two courses in the second semester, but they have repeated more than four courses. This indicates that they had very bad performance in their first semester. _Retention Policy_  
Since these are students whose academic performance is consistently low, they may be struggling with the major they have chosen. These students may be offered academic and career counseling that recommends a change in major. Intensive academic support implementing personalized academic advising and personalized tutoring may also be offered to help these students. The students may be scrutinized to see if the consistent failure is due to a deficiency in the academic foundation. For such students, bridging programs may be offered to help them improve their academic performance and transition into their studies more effectively. For instance, in this study, an analysis of the students in cluster 0 showed that most of them are engineering students who were admitted to the university based on their provided credentials, such as high school diplomas, academic performance, and other exam results. However, since these students did not take an official entrance exam, they may have a weaker foundational background compared to the students who were admitted through official entrance exams. Therefore, these students might be given a choice of changing majors or taking bridge courses. Resilience and social adaptation have been linked with academic success and retention [48]. This suggests the design of social and psychological support programs for the students in this cluster as a possible proactive measure to prevent their dropout. _Cluster 1: Students with moderate or declining performance_  
The leftmost branch in Figure 13 corresponds to students who have not repeated any courses or who repeated at most one course but have passed a maximum of three courses in the second semester and at the most five courses in the first semester. Since these are students who have not repeated more than one course, it means they did not fail more than one course in the first semester showing a moderate performance compared to the students in cluster 0. This analysis indicates that these are the students whose performance is declining.