Career track prediction using deep learning model based on discrete series of quantitative classification

• Autorzy: Hernandez Rowell , Atienza Robert

Identyfikatory

DOI

10.23743/acs-2021-29

• Języki publikacji: EN

Abstrakty

EN

In this paper, a career track recommender system was proposed using Deep Neural Network model. This study aims to assist guidance counselors in guiding their students in the selection of a suitable career track. It is because a lot of Junior High school students experienced track uncertainty and there are instances of shifting to another program after learning they are not suited for the chosen track or course in college. In dealing with the selection of the best student attributes that will help in the creation of the predictive model, the feature engineering technique is used to remove the irrelevant features that can affect the performance of the DNN model. The study covers 1500 students from the first to the third batch of the K-12 curriculum, and their grades from 11 subjects, sex, age, number of siblings, parent’s income, and academic strand were used as attributes to predict their academic strand in Senior High School. The efficiency and accuracy of the algorithm depend upon the correctness and quality of the collected student’s data. The result of the study shows that the DNN algorithm performs reasonably well in predicting the academic strand of students with a predic-tion accuracy of 83.11%. Also, the work of guidance counselors became more efficient in handling students’ concerns just by using the proposed system. It is concluded that the recommender system serves as a decision tool for counselors in guiding their stu-dents to determine which Senior High School track is suitable for students with the utilization of the DNN model.

Słowa kluczowe

EN

track prediction; deep learning; education

PL

przewidywanie torów; głębokie uczenie; edukacja

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2021
• Tom: Vol. 17, no 4
• Strony: 55--74
• Opis fizyczny: Bibliogr. 41 poz., fig., tab.

Twórcy

autor

Hernandez Rowell

• Batangas State University (Computer Science), Philippines, National Research Council of the Philippines – Engineering and Industrial Research

• https://orcid.org/0000-0002-8748-6271

autor

Atienza Robert

• Batangas State University (Information Technology), Philippines

• https://orcid.org/0000-0001-7351-0325

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).