CAREER TRACK PREDICTION USING DEEP LEARNING MODEL BASED ON DISCRETE SERIES OF QUANTITATIVE CLASSIFICATION
Rowell HERNANDEZ
rowell.hernandez@g.batstate-u.edu.phBatangas State University (Computer Science), Philippines, National Research Council of the Philippines – Engineering and Industrial Research (Philippines)
Robert ATIENZA
Batangas State University (Information Technology) (Philippines)
Abstract
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 prediction 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 students to determine which Senior High School track is suitable for students with the utilization of the DNN model.
Keywords:
track prediction, deep learning, educationReferences
Abarro, J. O. (2016). Factors Affecting Career Track and Strand Choices of Grade 9 Students in the Division of Antipolo and Rizal, Philippines. International Journal of Scientific and Research Publications, 6(6), 51–2250.
Google Scholar
Abu Zohair, L. M. (2019). Prediction of Student’s performance by modelling small dataset size. International Journal of Educational Technology in Higher Education, 16(1), 27. https://doi.org/10.1186/s41239-019-0160-3
Google Scholar
Al-Barrak, M. A., & Al-Razgan, M. (2016). Predicting Students Final GPA Using Decision Trees: A Case Study. International Journal of Information and Education Technology, 6(7), 528–533. https://doi.org/10.7763/ijiet.2016.v6.745
Google Scholar
Al-Radaideh, Q. A., Ananbeh, A. A., & Al-Shawakfa, E. M. (2011). A classification model for predicting the suitable study track for school students. IJRRAS, 8(2), 18788963.
Google Scholar
Alzhrani, N., & Algethami, H. (2019). Fuzzy-Based Recommendation System for University Major Selection. In Proceedings of the 11th International Joint Conference on Computational Intelligence – Volume 1: FCTA (pp. 317–324). Vienna, Austria. https://doi.org/10.5220/0008071803170324
Google Scholar
Asif, R., Merceron, A., & Pathan, M. K. (2015). Investigating performance of students: A longitudinal study. LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 108–112). ACM International Conference Proceeding Series. https://doi.org/10.1145/2723576.2723579
Google Scholar
Bendangnuksung, & Prabu, D. (2018). Students’ Performance Prediction Using Deep Neural Network. International Journal of Applied Engineering Research, 13(2), 1171–1176.
Google Scholar
Bin Mat, U., Buniyamin, N., Arsad, P. M., & Kassim, R. A. (2014). An overview of using academic analytics to predict and improve students’ achievement: A proposed proactive intelligent intervention. 2013 IEEE 5th International Conference on Engineering Education: Aligning Engineering Education with Industrial Needs for Nation Development (ICEED) (pp. 126–130). IEEE. https://doi.org/10.1109/ICEED.2013.6908316
Google Scholar
Durosaro, I. A., & Nuhu, M. A. (2012). An evaluation of the relevance of career choice to school subject selection among school going adolescents in ondo state. Asian Journal Of Management Sciences And Education, 1(2), 140–145.
Google Scholar
Gestiada, G., Nazareno, A., & Roxas-Villanueva, R. M. (2017). Development of a senior high school career decision tool based on social cognitive career theory. Philippine Journal of Science, 146(4), 445-455.
Google Scholar
Gorad, N., Zalte, I., Nandi, A., & Nayak, D. (2017). Career Counseling using Data Mining. International Journal of Engineering Science and Computing, 7(4), 10271–10274.
Google Scholar
Goyal, P., Kukreja, T., Agarwal, A., & Khanna, N. (2015). Narrowing awareness gap by using e-learning tools for counselling university entrants. 2015 International Conference on Advances in Computer Engineering and Applications (pp. 847–851). IEEE. https://doi.org/10.1109/ICACEA.2015.7164822
Google Scholar
Grewal, D.S., & Kaur, K. (2015). Developing an Intelligent Recommendation System for Course Selection by Students for Graduate Courses. Business and Economics Journal, 7(2), 1000209. https://doi.org/10.4172/2151-6219.1000209
Google Scholar
Hamsa, H., Indiradevi, S., & Kizhakkethottam, J. J. (2016). Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm. Procedia Technology, 25, 326–332. https://doi.org/10.1016/j.protcy.2016.08.114
Google Scholar
Hasan, R., Palaniappan, S., Raziff, A. R. A., Mahmood, S., & Sarker, K. U. (2018). Student Academic Performance Prediction by using Decision Tree Algorithm. 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society (ICCOINS) (pp. 1–5). IEEE. https://doi.org/10.1109/ICCOINS.2018.8510600
Google Scholar
Jauhari, F., & Supianto, A. A. (2019). Building student’s performance decision tree classifier using boosting algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 14(3), 1298–1304. https://doi.org/10.11591/ijeecs.v14.i3.pp1298-1304
Google Scholar
Khasanah, A. U., & Harwati. (2017). A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques. IOP Conference Series: Materials Science and Engineering, 215(1), 012036. https://doi.org/10.1088/1757-899X/215/1/012036
Google Scholar
Laguador, J. (2014). Examination of Influence and Intention towards Lyceum of the Philippines University and Career Choice of General Engineering Students. International Journal of Management Sciences, 3(11), 847–855.
Google Scholar
Li, L., & Zhang, X. (2010). Study of data mining algorithm based on decision tree. 2010 International Conference on Computer Design and Applications (V1-155-V1-158). IEEE. https://doi.org/10.1109/ICCDA.2010.5541172
Google Scholar
Mhetre, V., & Nagar, M. (2018). Classification based data mining algorithms to predict slow, average and fast learners in educational system using WEKA. Proceedings of the International Conference on Computing Methodologies and Communication (ICCMC 2017) (pp. 475–479). IEEE. https://doi.org/10.1109/ICCMC.2017.8282735
Google Scholar
Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques. International Journal of Modern Education and Computer Science, 8(11), 36–42. https://doi.org/10.5815/ijmecs.2016.11.05
Google Scholar
Natividad, M. C. B., Gerardo, B. D., & Medina, R. P. (2019). A fuzzy-based career recommender system for senior high school students in K to 12 education. IOP Conference Series: Materials Science and Engineering, 482(1), 012025. https://doi.org/10.1088/1757-899X/482/1/012025
Google Scholar
Nazareno, A. L., Lopez, M. J. F., Gestiada, G. A., Martinez, M. P., & Roxas-Villanueva, R. M. (2019). An artificial neural network approach in predicting career strand of incoming senior high school students. Journal of Physics: Conference Series, 1245(1), 012005. https://doi.org/10.1088/1742-6596/1245/1/012005
Google Scholar
Oancea, B., Dragoescu, R., & Ciucu, S. (2013). Predicting students ’ results in higher education using neural networks. International Conference on Applied Information and Communication Technology (pp. 190–193). Jelgava (Latvia).
Google Scholar
Obsie, E. Y., & Adem, S. A. (2018). Prediction of Student Academic Performance using Neural Network, Linear Regression and Support Vector Regression: A Case Study. International Journal of Computer Applications, 180(40), 975–8887.
Google Scholar
Okubo, F., Yamashita, T., Shimada, A., & Konomi, S. (2017). Students’ performance prediction using data of multiple courses by recurrent neural network. Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings (pp. 439–444). Asia-Pacific Society for Computers in Education.
Google Scholar
Pal, S. (2011). PER-07: A prediction for performance improvement using classification. India – Chapter III. Student Related Variables, 9(4).
Google Scholar
Piad, K. C., Dumlao, M., Ballera, M. A., & Ambat, S. C. (2016). Predicting IT employability using data mining techniques. 2016 3rd International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) (pp. 26–30). IEEE. https://doi.org/10.1109/DIPDMWC.2016.7529358
Google Scholar
Qamhieh, M., Sammaneh, H., & Demaidi, M. N. (2020). PCRS: Personalized Career-Path Recommender System for Engineering Students. IEEE Access, 8, 214039–214049. https://doi.org/10.1109/ACCESS.2020.3040338
Google Scholar
Rafanan, R. J. L., De Guzman, C. Y., & Rogayan, D. V. (2020). Pursuing stem careers: Perspectives of senior high school students. Participatory Educational Research, 7(3), 38–58. https://doi.org/10.17275/per.20.34.7.3
Google Scholar
Razak, T. R., Hashim, M. A., Noor, N. M., Halim, I. H. A., & Shamsul, N. F. F. (2014). Career path recommendation system for UiTM Perlis students using fuzzy logic. 2014 5th International Conference on Intelligent and Advanced Systems: Technological Convergence for Sustainable Future (pp. 1–5). IEEE. https://doi.org/10.1109/ICIAS.2014.6869553
Google Scholar
Rizvi, S., Rienties, B., & Khoja, S. A. (2019). The role of demographics in online learning; A decision tree based approach. Computers and Education, 137(January), 32–47. https://doi.org/10.1016/j.compedu.2019.04.001
Google Scholar
Roy Montebon, D. T. (2014). K12 Science Program in the Philippines: Student Perception on its Implementation. International Journal of Education and Research, 2(12), 153–164.
Google Scholar
Sarvepalli, S. S. K. (2015). Deep Learning in Neural Networks: The science behind an Artificial Brain. https://doi.org/10.13140/RG.2.2.22512.71682
Google Scholar
Sulaiman, M. S., Tamizi, A. A., Shamsudin, M. R., & Azmi, A. (2019). Course recommendation system using fuzzy logic approach. Indonesian Journal of Electrical Engineering and Computer Science, 17(1), 365–371. https://doi.org/10.11591/ijeecs.v17.i1.pp365-371
Google Scholar
Sulaiman, S. (2020). Prediction Students ’ Performance in Elective Subject Using Decision Tree Method. Journal of Asian Islamic Higher Institutions (JAIH), 5(1).
Google Scholar
Sulaiman, S., Shibghatullah, A. S., & Rahman, N. A. (2017). Prediction of students’ performance in elective subject using data mining techniques. Proceedings of Mechanical Engineering Research Day 2017 (pp. 222–224).
Google Scholar
Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., & Schmidt-Thieme, L. (2010). Recommender system for predicting student performance. Procedia Computer Science, 1(2), 2811–2819. https://doi.org/10.1016/j.procs.2010.08.006
Google Scholar
Varade, R. V., & Thankanchan, B. (2021). Academic Performance Prediction of Undergraduate Students using Decision Tree Algorithm. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 13(SUP 1), 97–100. https://doi.org/10.18090/samriddhi.v13is1.22
Google Scholar
Vijayalakshmi, V., & Venkatachalapathy, K. (2019). Comparison of Predicting Student‘s Performance using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications, 11(12), 34–45. https://doi.org/10.5815/ijisa.2019.12.04
Google Scholar
Yi, H., Shiyu, S., Duan, X., & Chen, Z. (2017). A study on Deep Neural Networks framework. Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 (pp. 1519–1522). IEEE. https://doi.org/10.1109/IMCEC.2016.7867471
Google Scholar
Authors
Rowell HERNANDEZrowell.hernandez@g.batstate-u.edu.ph
Batangas State University (Computer Science), Philippines, National Research Council of the Philippines – Engineering and Industrial Research Philippines
Authors
Robert ATIENZABatangas State University (Information Technology) Philippines
Statistics
Abstract views: 327PDF downloads: 33
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Robert KARPIŃSKI, Przemysław KRAKOWSKI, Józef JONAK, Anna MACHROWSKA, Marcin MACIEJEWSKI, COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES , Applied Computer Science: Vol. 19 No. 4 (2023)
- Tomasz CHMIELEWSKI, Katarzyna ZIELIŃSKA, SURVEY OF REMOTELY CONTROLLED LABORATORIES FOR RESEARCH AND EDUCATION , Applied Computer Science: Vol. 13 No. 1 (2017)
- Marcin TOMCZYK, Barbara BOROWIK, Mariusz MIKULSKI, IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD , Applied Computer Science: Vol. 14 No. 4 (2018)
- Michał BIAŁY, Marcin SZLACHETKA, CRANK-PISTON MODEL OF INTERNAL COMBUSTION ENGINE USING CAD/CAM/CAE IN THE MSC ADAMS , Applied Computer Science: Vol. 13 No. 1 (2017)
- ABDERRAHIM BAHANI, El Houssine Ech-Chhibat, Hassan SAMRI, Laila AIT MAALEM , Hicham AIT EL ATTAR , INTELLIGENT CONTROLLING THE GRIPPING FORCE OF AN OBJECT BY TWO COMPUTER-CONTROLLED COOPERATIVE ROBOTS , Applied Computer Science: Vol. 19 No. 1 (2023)
- Qingyu Liu, Roben A. Juanatas, MASK FACE INPAINTING BASED ON IMPROVED GENERATIVE ADVERSARIAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Mariano LARIOS, Perfecto M. QUINTERO-FLORES , Mario ANZURES-GARCÍA , Miguel CAMACHO-HERNANDEZ , APPLICATION OF THE REAL-TIME FAN SCHEDULING IN THE EXPLORATION-EXPLOITATION TO OPTIMIZE MINIMUM FUNCTIONS OBJECTIVES , Applied Computer Science: Vol. 19 No. 2 (2023)
- Damian KRASKA, Tomasz TRZEPIECIŃSKI, FINITE ELEMENT BASED PREDICTION OF DEFORMATION IN SHEET METAL FORMING PROCESS , Applied Computer Science: Vol. 14 No. 3 (2018)
- Anna MACHROWSKA, Robert KARPIŃSKI, Józef JONAK, Jakub SZABELSKI, NUMERICAL PREDICTION OF THE COMPONENT-RATIO-DEPENDENT COMPRESSIVE STRENGTH OF BONE CEMENT , Applied Computer Science: Vol. 16 No. 3 (2020)
- Tomasz SEDERYN, Małgorzata SKAWIŃSKA, COMPUTATIONAL ANALYSIS OF PEM FUEL CELL UNDER DIFFERENT OPERATING CONDITIONS , Applied Computer Science: Vol. 19 No. 4 (2023)
You may also start an advanced similarity search for this article.
