Intervention support program for students at risk of dropping out using fuzzy logic-based prescriptive analytics

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creativework.keywordsFuzzy logic; Education; Buildings; fuzzy logic; prescriptive analytics; Interventions
dc.contributor.authorDe Jesus, Cindy
dc.contributor.authorLedda, Mark Kristian
dc.date.accessioned2026-09-01T08:13:11Z
dc.date.available2026-09-01T08:13:11Z
dc.date.issued2021-03
dc.descriptionFull text
dc.description.abstractEducation 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 despite this notion. 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. It identified factors affecting students to drop such as family, individual, community and school related factors. 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 through the identified factors as inputs such as written work, performance task, quarterly exam, tardiness, absences, and results from the students’ guidance profiling. Second, based from the results of the prediction, the system’s fuzzy inference mechanism determines both the intervention applicability and effectivity in order to provide suitable intervention prescription as the system’s final output. The study found out that students who are at risk of dropping can be identified earlier with the correct inputs in the developed system and appropriate interventions vary from one student to another. Thus, the study is found to be useful in addressing the issue with increasing school dropouts by prescribing suitable intervention programs.
dc.identifier.citationDe Jesus, C. G., & Ledda, M. K. C. (2020). Intervention Support Program for Students at Risk of Dropping Out Using Fuzzy Logic-Based Prescriptive Analytics, 2021 IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA). Institute of Electrical and Electronics Engineers Inc. 144-149. doi: 10.1109/CSPA52141.2021.9377304.
dc.identifier.doiDOI:10.1109/CSPA52141.2021.9377304
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2439
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.urihttps://ieeexplore.ieee.org/document/9377304
dc.rights.licenseCC BY-NC 4.0
dc.sdgSDG 4
dc.sdgSDG 1
dc.sdgSDG 8
dc.sdgSDG 9
dc.sdgSDG 10
dc.subjectSOCIAL SCIENCES::Social sciences::Education
dc.subjectHigh school dropouts -- Prevention
dc.subjectDropouts -- Prevention -- Data processing
dc.subjectPrescriptive analytics
dc.subjectFuzzy logic
dc.subjectEducational decision making -- Data processing
dc.subjectEducation, Secondary -- Philippines -- Baguio City
dc.subject.ddcEarly school leavers (Secondary Education)
dc.subject.ddcEducational tests and measurements
dc.subject.ddcEvaluation
dc.subject.ddcAcademic predictive models
dc.subject.ddcArtificial intelligence (Fuzzy logic)
dc.subject.lcshHigh school dropouts -- Prevention -- Data processing
dc.subject.lcshDropouts -- Prevention -- Philippines -- Baguio
dc.subject.lcshEducational indicators -- Data processing
dc.subject.lcshFuzzy logic -- Educational applications
dc.subject.lcshEducational evaluation -- Data processing
dc.titleIntervention support program for students at risk of dropping out using fuzzy logic-based prescriptive analytics
dc.typeArticle
oaire.citation.endPage149
oaire.citation.startPage144
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