Evaluasi Penilaian Otomatis Pemrograman Web Laravel pada Platform LAIBA
DOI:
10.33395/jmp.v14i2.15641Keywords:
Laravel, Penilaian Otomatis, Pemrograman Web, Umpan BalikAbstract
Laravel adalah framework PHP open-source yang memudahkan pengembangan aplikasi web, namun beberapa sistem pembelajaran yang ada belum memberikan umpan balik yang efektif. Learning Application in Balanced Assessment (LAIBA) diusulkan sebagai aplikasi pembelajaran berbasis Laravel dengan fitur penilaian otomatis dan umpan balik terarah untuk meningkatkan pemahaman mahasiswa. Dalam sistem ini, mahasiswa menyelesaikan studi kasus menggunakan Laravel, kemudian mengirimkan kode untuk diperiksa dan dinilai oleh sistem. Data penilaian digunakan untuk mengidentifikasi topik yang perlu dipahami lebih lanjut. Pengujian dilakukan dengan metode pre-test dan post-test, yang menunjukkan peningkatan pemahaman mahasiswa secara keseluruhan. Hasil pengujian secara umum, LAIBA mampu meningkatkan pemahaman mahasiswa berdasarkan nilai rata-rata dengan signifikansi sebesar 0,294. Walaupun belum dapat dinyatakan mendapatkan hasil peningkatan secara signifikan secara umum, tetapi untuk data cluster nilai rata-rata rendah naik sebesar 26% dan menunjukkan peningkatan performa yang signifikan pada kelompok tersebut.
References
Chandrasekara, C., & Herath, P. (2021). Hands-on GitHub Actions: Implement CI/CD with GitHub Action Workflows for Your Applications. Apress. https://doi.org/10.1007/978-1-4842-6464-5
Chen, X., Ji, Z., Fan, Y., & Zhan, Y. (2017). Restful API Architecture Based on Laravel Framework. Journal of Physics: Conference Series, 910, 012016. https://doi.org/10.1088/1742-6596/910/1/012016
Delgado‐Pérez, P., & Medina‐Bulo, I. (2020). Customizable and scalable automated assessment of C/C++ programming assignments. Computer Applications in Engineering Education, 28(6), 1449–1466. https://doi.org/10.1002/cae.22317
Gong, B. (2010). Using Balanced Assessment Systems To Improve Student Learning and School Capacity: An Introduction.
González-Carrillo, C. D., Restrepo-Calle, F., Ramírez-Echeverry, J. J., & González, F. A. (2021). Automatic Grading Tool for Jupyter Notebooks in Artificial Intelligence Courses. Sustainability, 13(21), 12050. https://doi.org/10.3390/su132112050
Grenander, M., Belfer, R., Kochmar, E., Serban, I. V., St-Hilaire, F., & Cheung, J. C. K. (2021). Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15534–15544. https://doi.org/10.1609/aaai.v35i17.17829
Hagerer, G. J., Lahesoo, L., Anschütz, M., Krusche, S., & Groh, G. (2021). An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder. 2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET), 01–09. https://doi.org/10.1109/ITHET50392.2021.9759809
Haldeman, G., Tjang, A., Babeş-Vroman, M., Bartos, S., Shah, J., Yucht, D., & Nguyen, T. D. (2018). Providing Meaningful Feedback for Autograding of Programming Assignments. Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 278–283. https://doi.org/10.1145/3159450.3159502
Hao, Q., Smith Iv, D. H., Ding, L., Ko, A., Ottaway, C., Wilson, J., Arakawa, K. H., Turcan, A., Poehlman, T., & Greer, T. (2022). Towards understanding the effective design of automated formative feedback for programming assignments. Computer Science Education, 32(1), 105–127. https://doi.org/10.1080/08993408.2020.1860408
Harms, K. J., Rowlett, N., & Kelleher, C. (2015). Enabling independent learning of programming concepts through programming completion puzzles. 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 271–279. https://doi.org/10.1109/VLHCC.2015.7357226
Higgins, C. A., Gray, G., Symeonidis, P., & Tsintsifas, A. (2005). Automated assessment and experiences of teaching programming. Journal on Educational Resources in Computing, 5(3), 5. https://doi.org/10.1145/1163405.1163410
Jailia, M., Kumar, A., Agarwal, M., & Sinha, I. (2016). Behavior of MVC (Model View Controller) based Web Application developed in PHP and .NET framework. 2016 International Conference on ICT in Business Industry & Government (ICTBIG), 1–5. https://doi.org/10.1109/ICTBIG.2016.7892651
Jivani, P., Chopara, C., & Prashant, M. (2013). Over All Idea about MVC: How to use Model- View-Controller (MVC). International Journal of Innovations in Engineering and Technology.
Kavita. (2025). Automated Grading and Feedback Systems for Programming in Higher Education Using Machine Learning. Journal of Informatics Education and Research, 5(1). https://doi.org/10.52783/jier.v5i1.2142
Laaziri, M., Benmoussa, K., Khoulji, S., Mohamed Larbi, K., & Yamami, A. E. (2019). A comparative study of laravel and symfony PHP frameworks. International Journal of Electrical and Computer Engineering (IJECE), 9(1), 704. https://doi.org/10.11591/ijece.v9i1.pp704-712
Marion, S., Thompson, J., Evans, C., Martineau, J., & Dadey, N. (2019). THE CHALLENGES AND OPPORTUNITIES OF BALANCED SYSTEMS OF ASSESSMENT: A POLICY BRIEF.
Purohit, K. (2020). Executing DevOps & CI/CD, Reduce in Manual Dependency. 5(9).
Subecz, Z. (2021). Web-development with Laravel framework. Gradus, 8(1), 211–218. https://doi.org/10.47833/2021.1.CSC.006
Sumintono, B. (2016). Aplikasi Pemodelan Rasch pada Asesmen Pendidikan: Implementasi Penilaian Formatif (assessment for learning)
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