1. Home
  2. Vol 5, No 1 (2021)
  3. Muhammad Anwar

Prediction of the graduation rate of engineering education students using Artificial Neural Network Algorithms

  • Abstract Views: 482
  • PDF Downloads: 26
  • June 10, 2021
Corresponding Author

Keywords:

Engineering Education; Graduation; Artificial Neural Network; Particle Swarm Optimization; Forward Selection.

Abstract

The graduation rate of engineering education students on time dramatically affects the quality of learning. The purpose of this study is to predict the graduation rate of engineering education students. The method uses an artificial neural network algorithm combined with particle swarm optimization and forward selection, with 234 samples. The test results with Artificial Neural Network obtained 82.61% accuracy with predictions on time 149 and not on time 62. Artificial Neural Network with Particle Swarm Optimization obtained 91.30% accuracy with predictions on time 165, not on time 69. Furthermore, Artificial Neural Network with Particle Swarm Optimization and reduced by forwarding selection obtained 95.65% accuracy with predictions of the number of graduations on time 165 and not on time 69. Thus, the combination of the three algorithms can predict the graduation rate of engineering education students with high accuracy.

Full Text:

References

Adekitan, A. I., & Salau, O. (2019). The impact of engineering students' performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), 01250.

Aldossari, Abdulaziz Salem. (2020). Vision 2030 and Reducing the Stigma of Vocational and Technical Training among Saudi Arabian Students. Empirical Research in Vocational Education and Training, 12(1). doi: 10.1186/s40461-020-00089-6.

Andrianis, R., Anwar, M., & Zulwisli, Z. (2018). Pengaruh Model Pembelajaran Berbasis Projek Terhadap Hasil Belajar Pemrograman Web Dinamis Kelas Xi Rekayasa Perangkat Lunak Di Smk Negeri 2 Padang Panjang. VoteTEKNIKA: Jurnal Vocational Teknik Elektronika dan Informatika, 6(1).

Anwar, M. (2019). Kontribusi Self Efficacy Dan Self Regulated Terhadap Kesiapan Kerja Siswa Kelas Xii Teknik Audio Vidio Smk N 1 Padang. Jurnal Kapita Selekta Geografi, 2(10), 1-15.

Anwar, M. (2021). Problem Solving Skills Analysis of Vocational Engineering Teacher Candidates in Term of Several Variables. Journal of Education Technology, 5(1). doi:http://dx.doi.org/10.23887/jet.v5i1.33624

Aryanti, L., Anwar, M., & Zulwisli, Z. (2017). Pengaruh Penerapan Model Pembelajaran Inkuiri Terhadap Hasil Belajar Teknik Elektronika Dasar Siswa Kelas X SMKN 5 Padang. VoteTEKNIKA: Jurnal Vocational Teknik Elektronika dan Informatika, 5(2).

Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & Education, 113, 177-194.

Bayhan, Hasan Gokberk, and Ece Karaca. (2020). Technological Innovation in Architecture and Engineering Education - an Investigation on Three Generations from Turkey. International Journal of Educational Technology in Higher Education, 17(1). doi: 10.1186/s41239-020-00207-0.

Behrendt, Stefan, Elmar Dammann, Florina Ştefănică, Bernd Markert, and Reinhold Nickolaus. (2015). Physical-Technical Prior Competencies of Engineering Students. Empirical Research in Vocational Education and Training, 7(1). doi: 10.1186/s40461-015-0013-9.

Birhan, Amare Tesfie, and Merso, Tsehaye Alene. (2021). Supporting Engineering Education through Internship Mentoring Program: Approaches, Perceptions and Challenges. Journal of Technical Education and Training, 13(1), 185–94. doi: 10.30880/jtet.2021.13.01.020.

Cao, Yusong. (2021). Portrait-Based Academic Performance Evaluation of College Students from the Perspective of Big Data. International Journal of Emerging Technologies in Learning, 16(4), 95–106. doi: 10.3991/ijet.v16i04.20475.

Chachashvili-Bolotin, S., Milner-Bolotin, M., & Lissitsa, S. (2016). Examination of factors predicting secondary students’ interest in tertiary STEM education. International Journal of Science Education, 38(3), 366-390.

Gambo, Omobola, Adelokun Adedapo, Ishaya Gambo, and Damilola Ejalonibu. (2021). Planning and Designing Online Vocational Skill Showcasing Platform: From an Educational Perspective. Journal of Technical Education and Training, 13(1), 22–34. doi: 10.30880/jtet.2021.13.01.003.

Ganefri, Hidayat, H., Kusumaningrum, I., & Mardin, A. (2017). Needs Analysis of Entrepreneurship Pedagogy of Technology and Vocational Education with Production Based Learning Approach in Higher Education. International Journal of Advanced Science, Engineering and Information Technology, 7, 1701-1707. http://dx.doi.org/10.18517/ijaseit.7.5.1510

Ganefri, G., Hidayat, H., Yulastri, A., Mardin, A., Sriwahyuni, D., & Zoni, A. A. (2018). Perangkat Pembelajaran Pedagogi Entrepreneurship Dengan Pendekatan Pembelajaran Berbasis Produk di Pendidikan Vokasi. In Prosiding Seminar Nasional & Internasional, 1(1).

Hidayat, H. (2015). Production based Learning: An Instructional Design Model in the context of vocational education and training (VET). Procedia-Social and Behavioral Sciences, 204, 206-211.

Hidayat, H. (2017a). How is the Application and Design of a Product-Based Entrepreneurship Learning Tools in Vocational Higher Education?. In International Conference on Technology and Vocational Teachers (ICTVT 2017) (pp. 223-228). Atlantis Press.

Hidayat, H. (2017b). Impact of learning with the production-based learning model in vocational school. International Journal of Research in Engineering and Social Sciences, 7(2), 1-6.

Hidayat, H., Herawati, S., Tamin, B. Y., & Syahmaidi, E. (2018a). How is the practicality of technopreneurship Scientific learning model design in vocational higher education?. International Journal of Scientific Research and Management, 6(09).

Hidayat, H., Herawati, S., Syahmaidi, E., Hidayati, A., & Ardi, Z. (2018b). Designing of technopreneurship scientific learning framework in vocational-based higher education in Indonesia. International Journal of Engineering and Technology (UAE), 7(4), 123-127.

Hidayat, H., & Yuliana. (2018). The Influence of Entrepreneurship Education and Family Background on Students’ Entrepreneurial Interest in Nutritious Traditional Food Start Ups in Indonesia. International Journal of Engineering and Technology(UAE). 7(4), 118-122. https://doi.org/10.14419/ijet.v7i4.9.20631

Hidayat, H., Tamin, B. Y., Herawati, S., Khairul, K., & Syahmaidi, E. (2019a). The contribution of technopreneurship scientific learning and learning readiness towards the entrepreneurship learning outcomes in higher vocational education. Jurnal Pendidikan Vokasi, 9(1), 21-32.

Hidayat, H., Ardi, Z., Yuliana, & Herawati, S. (2019b). Exploration of the need analysis for technopreneurship scientific learning models in higher vocational education. International Journal of Economics and Business Research, 18(3), 356-368.

Hidayat, H., Tamin, B.Y., Herawati, S., Hidayati, A., Muji, A.P. (2019c). Implementation of technopreneurship scientific learning for produce electronic product prototypes in engineering education. International Journal of Innovative Technology and Exploring Engineering, 8(11), 2842-2846. http://dx.doi.org/10.35940/ijitee.K2406.0981119

Hidayat, H., Tamin, B. Y., Herawati, S., Ardi, Z., & Muji, A. P. (2020). The Contribution of Internal Locus of Control and Self-Concept to Career Maturity in Engineering Education. Int. J. Adv. Sci. Eng. Inf. Technol, 10(6), 2282-2289.

Imran, Muhammad, Shahzad Latif, Danish Mehmood, and Muhammad Saqlain Shah. (2019). Student Academic Performance Prediction Using Supervised Learning Techniques. International Journal of Emerging Technologies in Learning, 14(14), 92–104. doi: 10.3991/ijet.v14i14.10310.

Jia, Sujuan, and Pang, Yajing. (2018). Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm. International Journal of Emerging Technologies in Learning, 13(10), 146–57. doi: 10.3991/ijet.v13i10.9460.

Laugerman, M., Rover, D. T., Shelley, M. C., & Mickelson, S. K. (2015). Determining graduation rates in engineering for community college transfer students using data mining. International Journal of Engineering Education, 31(6A), 1448.

Li, Na. (2020). Curriculum Data Association Organization and Knowledge Management Method for Unstructured Learning Resources. International Journal of Emerging Technologies in Learning, 15(6), 79–94. doi: 10.3991/IJET.V15I06.13173.

Lopez, C., & Jones, S. J. (2017). Examination of factors that predict academic adjustment and success of community college transfer students in STEM at 4-year institutions. Community College Journal of Research and Practice, 41(3), 168-182.

Mansur, Mansur, Toni Prahasto, and Farikhin Farikhin. (2014). Particle Swarm Optimization Untuk Sistem Informasi Penjadwalan Resource Di Perguruan Tinggi. Jurnal Sistem Informasi Bisnis, 4(1), 11–19. doi: 10.21456/vol4iss1pp11-19.

Mason, C., Twomey, J., Wright, D., & Whitman, L. (2018). Predicting engineering student attrition risk using a probabilistic neural network and comparing results with a backpropagation neural network and logistic regression. Research in Higher Education, 59(3), 382-400.

Melendez-Armenta, Roberto Angel, N. Sofia Huerta-Pacheco, Luis Alberto Morales-Rosales, and Genaro Rebolledo-Mendez. (2020). How Do Students Behave When Using A Tutoring System? Employing Data Mining to Identify Behavioral Patterns Associated to The Learning of Mathematics. International Journal of Emerging Technologies in Learning, 15(22), 39–58. doi: 10.3991/ijet.v15i22.17075.

Moscoso-Zea, O., Saa, P., & Luján-Mora, S. (2019). Evaluation of algorithms to predict graduation rate in higher education institutions by applying educational data mining. Australasian Journal of Engineering Education, 24(1), 4-13.

Mubarak, A. A., Cao, H., & Zhang, W. (2020). Prediction of students’ early dropout based on their interaction logs in online learning environment. Interactive Learning Environments, 1-20.

Naseer, M., Zhang, W., & Zhu, W. (2020). Early Prediction of a Team Performance in the Initial Assessment Phases of a Software Project for Sustainable Software Engineering Education. Sustainability, 12(11), 4663.

Nyoman Sukajaya, I., I. Ketut Eddy Purnama, and Mauridhi Hery Purnomo. (2015). Intelligent Classification of Learner’s Cognitive Domain Using Bayes Net, Naïve Bayes, and j48 Utilizing Bloom's Taxonomy-Based Serious Game. International Journal of Emerging Technologies in Learning, 10(2), 46–52. doi: 10.3991/ijet.v10i1.4451.

Ramamuruthy, Viji, Dorothy Dewitt, and Norlidah Alias. (2021). The Need for Technical Communication for 21st Century Learning in Tvet Institutions: Perceptions of Industry Experts. Journal of Technical Education and Training, 13(1), 148–58. doi: 10.30880/jtet.2021.13.01.016.

Rolansa, Freska, Yunita Yunita, and Suheri Suheri. (2020). Sistem Prediksi Dan Evaluasi Prestasi Akademik Mahasiswa Di Program Studi Teknik Informatika Menggunakan Data Mining. Jurnal Pendidikan Informatika Dan Sains, 9(1), 75. doi: 10.31571/saintek.v9i1.1696.

Salihoun, Mohammed. (2020). State of Art of Data Mining and Learning Analytics Tools in Higher Education. International Journal of Emerging Technologies in Learning, 15(21), 58–76. doi: 10.3991/ijet.v15i21.16435.

Sari, P. P., Ganefri, G., & Anwar, M. (2020). The Contribution Of Principal Leadership Style, Teachers’professional Competence And School Climate On The Quality Of Learning Outcomes At Vocational High School In Padang. Jurnal Pendidikan Teknologi Kejuruan, 3(1), 26-30.

Wang, Lanzhong. (2016). Personalized Teaching Platform Based on Web Data Mining. International Journal of Emerging Technologies in Learning, 11(11), 15–20. doi: 10.3991/ijet.v11i11.6253.

Yu, Jing. (2021). Academic Performance Prediction Method of Online Education Using Random Forest Algorithm and Artificial Intelligence Methods. International Journal of Emerging Technologies in Learning, 16(5), 45–57. doi: 10.3991/ijet.v16i05.20297.

Yulastri, A., Hidayat, H., Ganefri, Ayu, R., & Ardi, Z. (2019). An Empirical Study on The Effects of Pedagogy Learning Tools Entrepreneurship With Product-Based Learning Approach, Learning Readiness, and Locus of Control: A Case From Engineering Education in Indonesia. International Journal of Scientific & Technology Research, 8(9), 1722-1727.

Yustisia, Henny, Nizwardi Jalinus, Fahmi Rizal, and Fadhillah. (2021). A New Approach of Students’ Industrial Field Experience Program in the Digital Age. Journal of Technical Education and Training, 13(1), 167–75. doi: 10.30880/jtet.2021.13.01.018.

Zhang, Yuan, and Wenbo Jiang. (2018). Score Prediction Model of MOOCs Learners Based on Neural Network. International Journal of Emerging Technologies in Learning, 13(10), 171–82. doi: 10.3991/ijet.v13i10.9461.

Refbacks

  • There are currently no refbacks.