Exploring Information Technology Industry Programming Language Trends with Non-Negative Matrix Factorization Topic Modelling
DOI:
https://doi.org/10.37134/ejsmt.vol10.1.5.2023Keywords:
Topic Modelling, NMF, T, Industry, ProgrammingAbstract
The study aimed to determine the preferred programming language among information technology professionals using non-negative matrix factorization topic modelling technique. The results showed that the majority of participants were software developers and programmers who commonly used C#, Java, and Python, and agreed that Java, C, or C++ would be the best language to start with when learning programming. The application of topic modelling revealed key themes such as online video tutorials as effective learning resources, hands-on activities as key to learning programming, importance of feedback, effective teaching strategies, and problem-solving skills as crucial for success in programming. Results of the study may serve as baseline data for the improvement of curricular offerings.
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Mercado, P. A., Granadino, G. A., Pasco, E. M., & Rosete, M. A. (2021). The Impact of ICT on Service Sector’s Productivity and Employment in the Philippines. Journal of Economics, Finance and Accounting Studies, 3(2), 288-299.
Adene, G., Mbonu, C. E., Alade, S. M., & Mba, C. J. Programming Language Preference among Undergraduate Students in Nigeria.
Preston, R. (2020). Why Demand of Software Engineers Is High (Plus Job Tips). Retrieved from https://www.indeed.com/: https://www.indeed.com/career-advice/finding-a-job/demand-of-software-engineers
Scott, C. (2022). Demand for software developers doubled in 2021. Retrieved from https://www.infoworld.com/: https://www.infoworld.com/article/3654480/demand-for-software-developers-doubled-in-2021.html
Fernández, E., & Rennolds, N. (2022). Jobs in these 10 industries will grow most in demand in 2022, from 'behavioral health' to cybersecurity. Retrieved from https://www.businessinsider.com/: https://www.businessinsider.com/10-jobs-that-will-be-growing-in-2022-have-openings-2021-12
Burning Glass. (2016). Why Should Students Learn to Code? Retrieved from Oracle: https://academy.oracle.com/pages/infographics/why-learn-code.html
Fedorenko, E., Ivanova, A., Dhamala, R., & Bers, M. U. (2019). The language of programming: a cognitive perspective. Trends in cognitive sciences, 23(7), 525-528.
CHED Memorandum Order 25 series of 2015
Zhang, J. (2016). Selection and Improvement of Computer Programming Language. ISME 2016 - Information Science and Management Engineering IV (ISME 2016), pages 242-245
Creswell JW, Clark VL. 2017. Designing and conducting mixed methods research. 2nd ed. Thousand Oaks. CA:SAGE Publications; 2017. p. 1-520.
Alburo, H. R., Romana, C. L. C. S., & Feliscuzo, L. S. (2021). Sentiment analysis of the academic services of ESSU salcedo campus using plutchik model and latent dirichlet allocation
S. Vijayarani, M. J. Ilamathi, and M. Nithya, “Preprocessing techniques for text mining-an overview,” International Journal of Computer Science & Communication Networks, vol. 5, no. 1, pp. 7-16, 2015
C. Ramasubramanian and R. Ramya, “Effective pre-processing activities in text mining using improved porter‟s stemming algorithm,” International Journal of Advanced Research in Computer and Communication Engineering ,vol. 2, issue 12, December 2013.
George, S., & Vasudevan, S. (2020). Comparison of LDA and NMF topic modeling techniques for restaurant reviews. Indian J. Nat. Sci, 10 Algorithm. International Journal of Recent Technology and Engineering, 9(6), 176-183.
J. Demsar, T. Curk, A. Erjavec, C. Gorup, T. Hocevar, M. Milutinovic, M. Mozina, M. Polajnar, M. Toplak, A. Staric, M. Stajdohar, L. Umek, L. Zagar, J. Zbontar, M. Zitnik, and B. Zupan, “orange: data mining toolbox in python,” Journal of Machine Learning Research, vol. 14, pp. 2349−2353, 2013.
Cass Stephen. (2022, August 23). Top Programming Languages 2022. Retrieved from IEEE Spectrum: https://spectrum.ieee.org/top-programming-languages-2022
Statista. (2022, August 9). Most used programming languages among developers worldwide as of 2022. Retrieved from Statista: https://www.statista.com/statistics/793628/worldwide-developer-survey-most-used-languages/
Rodrigues, G., Monteiro, A. F., & Osório, A. (2022). Introductory Programming in Higher Education: A Systematic Literature Review. In Third International Computer Programming Education Conference (ICPEC 2022). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
Zhan, Q., Wang, J., Pan, X., Ding, Y., & Liu, Y. (2022). Teaching Model Design of Computer Programming Courses for Digital Media Technology Students. Wireless Communications and Mobile Computing, 2022, 1-5.
Kadar, R., Wahab, N. A., Othman, J., Shamsuddin, M., & Mahlan, S. B. (2021). A study of difficulties in teaching and learning programming: a systematic literature review. International Journal of Academic Research in Progressive Education and Development, 10(3), 591-605.
Maziriri, E. T., Gapa, P., & Chuchu, T. (2020). Student Perceptions towards the Use of YouTube as an Educational Tool for Learning and Tutorials. International Journal of Instruction, 13(2), 119-138.
Okechukwu, O., Orie, M., Eziokwu, P., Onovo, N., Okeke, H., & Ukeh, B. (2020). Integrating YouTube Tutorials in teaching of Computer Programming course: Effects on Students’ Achievement and Interest. International Journal of Integrated Research in Education (IJIRE), 111-117.
Kadriu, A., Abazi-Bexheti, L., Abazi-Alili, H., & Ramadani, V. (2020). Investigating trends in learning programming using YouTube tutorials. International Journal of Learning and Change, 12(2), 190-208.
von Hausswolff, K. (2022). Practical thinking in programming education: Novices learning hands-on (Doctoral dissertation, Acta Universitatis Upsaliensis).
Bosse, Y., Redmiles, D., & Gerosa, M. A. (2019, July). Pedagogical content for professors of introductory programming courses. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (pp. 429-435).
Yang, S., Paul, S. D., & Bhunia, S. (2021). Hands-On Learning of Hardware and Systems Security. Advances in Engineering Education, 9(2), n2.
Brown NCC, Wilson G (2018) Ten quick tips for teaching programming. PLoS Comput Biol 14(4): e1006023. https://doi.org/10.1371/journal.pcbi.1006023
Alammary, A. (2019). Blended learning models for introductory programming courses: A systematic review. PloS one, 14(9), e0221765.
Fueiredo, J., & García-Peñalvo, F. J. (2020, April). Increasing student motivation in computer programming with gamification. In 2020 IEEE Global Engineering Education Conference (EDUCON) (pp. 997-1000). IEEE.
López-Pernas, S., Gordillo, A., Barra, E., & Quemada, J. (2019). Examining the use of an educational escape room for teaching programming in a higher education setting. IEEE Access, 7, 31723-31737.
Nouri, J., Zhang, L., Mannila, L., & Norén, E. (2020). Development of computational thinking, digital competence and 21st century skills when learning programming in K-9. Education Inquiry, 11(1), 1-17.
Skolverket. (2017).Få syn på digitaliseringen på grundskolenivå. Commentary material. Retrieved from https://www.skolverket.se/publikationer?id=3783
Koehler, J. (2019, May 1). 5 Skills to Help You Thrive in Computer Programming. Retrieved from University of Arkansas Grantham: https://www.uagrantham.
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Copyright (c) 2023 Niel Francis Casillano, Karen Cantilang
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