An Information System for Converting of ’Merdeka Belajar Kampus Merdeka’ Programs
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Dian Ramadhani
*
Universitas Riau, Pekanbaru,
Indonesia
- Al Aminuddin Universitas Riau, Pekanbaru, Indonesia
- Yanti Andriyani Universitas Riau, Pekanbaru, Indonesia
- Evfi Mahdiyah Universitas Riau, Pekanbaru, Indonesia
- Riki Ario Nugroho Universitas Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Merdeka Belajar Kampus Merdeka (MBKM) program enables universities to convert independently the programs into department courses hence those can be considered as its course credits. MBKM program guideline requires universities, specifically departments, to carefully convert MBKM program since they have to consider Program Learning Outcomes (PLO) and relevancy of courses in the conversion process. To follow the guideline, departments must explore learning outcomes of each courses and learning outcome of the programs. The job is certainly time and resource consuming. Therefore it needs to be supported by a reliable strategy and system to manage efficiently and effectively relational data between MBKM program, PLO and courses. Through these data, departments then enable to identify quickly related courses with those programs. This paper introduces a model to represent the conversion strategy. We then developed in-house application called e-OBE to implement the model to enable the departments to map PLO, MBKM program and course data efficiently and effectively. The mapping data can be used to retrieve department courses quickly in which their PLO are linked with MBKM program PLO. Finally, a recommendation based on text similarity from courses and MBKM program can be generated automatically to support department decision in the conversion process.
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References
A. Junaidi, D. Wulandari, S. Arifin, and H. Soetanto, Panduan Penyusunan Kurikulum Pendidikan Tinggi (K. Direktorat Jenderal Pendidikan Tinggi (Ed.). Direktorat Jenderal Pendidikan Tinggi Kementerian Pendidikan dan Kebudayaan, 2020. [Online]. Available: https://dikti.kemdikbud.go.id/
C. Dawson, “Educational data mining,” A–Z Digit. Res. Methods, pp. 114–119, 2019, doi: 10.4324/9781351044677-18.
H. Ji, K. Park, J. Jo, and H. Lim, “Mining students activities from a computer supported collaborative learning system based on peer to peer network,” Peer-to-Peer Netw. Appl., vol. 9, no. 3, pp. 465–476, 2016, doi: 10.1007/s12083-015-0397-0.
S. B. Sailesh, K. J. Lu, and M. Al Aali, “Profiling students on their course-taking patterns in higher educational institutions (HEIs),” in Proceedings - 2016 International Conference on Information Science, ICIS 2016, 2017, pp. 160–167. doi: 10.1109/INFOSCI.2016.7845319.
E. Afros and C. F. Schryer, “The genre of syllabus in higher education,” J. English Acad. Purp., vol. 8, no. 3, pp. 224–233, 2009, doi: 10.1016/j.jeap.2009.01.004.
A. Garrido, L. Morales, and I. Serina, “On the use of case-based planning for e-learning personalization,” Expert Syst. Appl., vol. 60, pp. 1–15, 2016, doi: 10.1016/j.eswa.2016.04.030.
A. M. Tokatli and Y. Keşli, “Syllabus:how much does it contribute to the effective communication with the students?,” Procedia - Soc. Behav. Sci., vol. 1, no. 1, pp. 1491–1494, 2009, doi: 10.1016/j.sbspro.2009.01.263.
G. Demartini, I. Enchev, J. Gapany, and P. Cudré-Mauroux, “The bowlogna ontology: Fostering open curricula and agile knowledge bases for europe’s higher education landscape,” Semant. Web, vol. 4, no. 1, pp. 53–63, 2013, doi: 10.3233/SW-2012-0064.
M. E. Yuksel and H. Fidan, “A decision support system using text mining based grey relational method for the evaluation of written exams,” Symmetry (Basel)., vol. 11, no. 11, 2019, doi: 10.3390/sym11111426.
M. Tanana, K. A. Hallgren, Z. E. Imel, D. C. Atkins, and V. Srikumar, “A Comparison of Natural Language Processing Methods for Automated Coding of Motivational Interviewing,” J. Subst. Abuse Treat., vol. 65, pp. 43–50, 2016, doi: 10.1016/j.jsat.2016.01.006.
M. Topaz et al., “Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application,” Int. J. Nurs. Stud., vol. 64, pp. 25–31, 2016, doi: 10.1016/j.ijnurstu.2016.09.013.
A. J. P. Tixier, M. R. Hallowell, B. Rajagopalan, and D. Bowman, “Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports,” Autom. Constr., vol. 62, pp. 45–56, 2016, doi: 10.1016/j.autcon.2015.11.001.
R. Leaman, R. Khare, and Z. Lu, “Challenges in clinical natural language processing for automated disorder normalization,” J. Biomed. Inform., vol. 57, pp. 28–37, 2015, doi: 10.1016/j.jbi.2015.07.010.
K. Liu, W. R. Hogan, and R. S. Crowley, “Natural Language Processing methods and systems for biomedical ontology learning,” J. Biomed. Inform., vol. 44, no. 1, pp. 163–179, 2011, doi: 10.1016/j.jbi.2010.07.006.
D. D. Lewis and K. S. Jones, “Natural Language Processing for Information Retrieval,” Commun. ACM, vol. 39, no. 1, pp. 92–101, 1996, doi: 10.1145/234173.234210.
C. G. Abimanyu and N. A. Sanjaya ER, “Automatic Essay Answer Rating Using the Text Similarity Method,” JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 8, no. 4, p. 463, 2020, doi: 10.24843/jlk.2020.v08.i04.p12.
A. Aminuddin, R. Salambue, Y. Andriyani, and ..., “Aplikasi E-OBE Untuk Integrasi Komponen Kurikulum OBE (Outcome-Based Education),” JSI J. Sist. …, vol. 13, no. 1, pp. 2168–2182, 2021, [Online]. Available: https://ejournal.unsri.ac.id/index.php/jsi/article/view/13914
B. J. Oates, Researching Information Systems and Computing, vol. 37. 2006. [Online]. Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2836698&tool=pmcentrez&rendertype=abstract
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