Intervention Support Program for Students at Risk of Dropping Out Using Fuzzy Logic-Based Prescriptive Analytics

Abstract

Education is perceived to be an inevitable impact in building one's nation and presumed to be a significant factor of one's success. However, the issue with increasing school dropouts in secondary schools continue to persist worldwide. This study aimed to design and develop an intervention support program for students at risk of dropping using prescriptive analytics for the Department of Education. Factors affecting students to drop include family, individual, community and school related factors were identified. Based from these factors, appropriate types of intervention programs were determined through focus group discussions with secondary school teachers and guidance counselors. A web-based intervention support program system was developed with the use of Fuzzy Logic-Based prescriptive analytics. First, the system predicts students at risk of dropping out through the identified factors as inputs. Second, based from the results of the prediction, the system's fuzzy inference mechanism determines both the intervention applicability and effectivity to provide suitable intervention prescription as the system's final output. Results show that students who are at risk of dropping out can be identified earlier with the correct inputs in the developed system and appropriate interventions vary from one student to another. Thus, making the study useful in addressing the issue with increasing school dropouts by prescribing suitable intervention programs.