Evaluation of the Effectiveness of Artificial Intelligence System in Higher Education Curriculum Management

Anri Naldi (1), Nurkadri Nurkadri (2), Mansyur Srisudarso (3), Didik Cahyono (4), Suyitno Suyitno (5)
(1) Universitas Medan Area, Indonesia,
(2) Universitas Negeri Medan, Indonesia,
(3) Universitas Singaperbangsa Karawang, Indonesia,
(4) Universitas Mulawarman, Indonesia,
(5) Universitas Gresik, Indonesia

Abstract

Background. The learning of natural sciences at Islamic elementary schools in Central Ternate has not yet been integrated with the verses of the Koran. There is still a dichotomy between general knowledge and religious knowledge, even though all of this knowledge originates from the Al-Quran.


Purpose. This study aims to dig deeper into the integration of verses from the Koran in science learning at Islamic Elementary Schools in Central Ternate City. This study uses a phenomenological approach to the triangulation model. Respondents in this study were 15 people consisting of 3 school principals, 3 class teachers, and 9 grade 4 students of SD Islamiyah in Central Ternate City.


Method. Data collection techniques in this study are observation, documentation, and in-depth interviews related to research variables. The research data were analyzed descriptively.  


Results. The results showed that based on the results of observations of learning activities in class 4 SD Islamiyah in Ternate City, it had not been found integrating science learning with verses from the Qur'an, meaning that learning only focused on science subject matter, had nothing to do with verses. Al-Qur'an related to science material being taught, but students' character building can be seen clearly when learning activities take place.


Conclusion. The same thing was also obtained from the results of interviews with school principals, class teachers, and students that the implementation of learning by integrating verses of the Qur'an had not been carried out because the school was still carrying out learning by referring to the established curriculum, so there was no connection between Al -Qur'an and any subject matter conveyed by the class teacher.

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References

Acikkar, M., & Akay, M. F. (2009). Support vector machines for predicting the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University. Expert Systems with Applications, 36(3), 7228–7233. https://doi.org/10.1016/j.eswa.2008.09.007

Adamson, D., Dyke, G., Jang, H., & Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs. International Journal of Artificial Intelligence in Education, 24(1), 92–124. https://doi.org/10.1007/s40593-013-0012-6

Agaoglu, M. (2016). Predicting Instructor Performance Using Data Mining Techniques in Higher Education. IEEE Access, 4, 2379–2387. https://doi.org/10.1109/ACCESS.2016.2568756

Ahmad, S. F., Alam, M. M., Rahmat, Mohd. K., Mubarik, M. S., & Hyder, S. I. (2022). Academic and Administrative Role of Artificial Intelligence in Education. Sustainability, 14(3), 1101. https://doi.org/10.3390/su14031101

Alamri, H., Lowell, V., Watson, W., & Watson, S. L. (2020). Using personalized learning as an instructional approach to motivate learners in online higher education: Learner self-determination and intrinsic motivation. Journal of Research on Technology in Education, 52(3), 322–352. https://doi.org/10.1080/15391523.2020.1728449

Alicia, V., & Rani, I. H. (2022). KONTRIBUSI APLIKASI SISTEM MANAJEMEN PEMBELAJARAN BERBASIS SIBER TERHADAP KOMPLEKSITAS MANAJEMEN TINDAKAN KELAS. Jurnal Pendidikan, 23(1), 24–42. https://doi.org/10.33830/jp.v23i1.2611.2022

Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020). Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews, 130, 109899. https://doi.org/10.1016/j.rser.2020.109899

Aparicio, F., Morales-Botello, M. L., Rubio, M., Hernando, A., Muñoz, R., López-Fernández, H., Glez-Peña, D., Fdez-Riverola, F., De La Villa, M., Maña, M., Gachet, D., & Buenaga, M. D. (2018). Perceptions of the use of intelligent information access systems in university level active learning activities among teachers of biomedical subjects. International Journal of Medical Informatics, 112, 21–33. https://doi.org/10.1016/j.ijmedinf.2017.12.016

Aspegren, K. (1999). BEME Guide No. 2: Teaching and learning communication skills in medicine-a review with quality grading of articles. Medical Teacher, 21(6), 563–570. https://doi.org/10.1080/01421599978979

Baker, R. S. (2016). Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education, 26(2), 600–614. https://doi.org/10.1007/s40593-016-0105-0

Bond, M. (2020). Facilitating student engagement through the flipped learning approach in K-12: A systematic review. Computers & Education, 151, 103819. https://doi.org/10.1016/j.compedu.2020.103819

Ding, Y. (2021). Performance analysis of public management teaching practice training based on artificial intelligence technology. Journal of Intelligent & Fuzzy Systems, 40(2), 3787–3800. https://doi.org/10.3233/JIFS-189412

Dosilovic, F. K., Brcic, M., & Hlupic, N. (2018). Explainable artificial intelligence: A survey. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 0210–0215. https://doi.org/10.23919/MIPRO.2018.8400040

Ertefaie, A., Small, D. S., & Rosenbaum, P. R. (2018). Quantitative Evaluation of the Trade-Off of Strengthened Instruments and Sample Size in Observational Studies. Journal of the American Statistical Association, 113(523), 1122–1134. https://doi.org/10.1080/01621459.2017.1305275

Faculty of Education, University of Osijek, Cara Hadrijana 10, 31 000 Osijek, Croatia, & ?ur?evi? Babi?, I. (2017). Machine learning methods in predicting the student academic motivation. Croatian Operational Research Review, 8(2), 443–461. https://doi.org/10.17535/crorr.2017.0028

George, B., & Wooden, O. (2023). Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sciences, 13(9), 196. https://doi.org/10.3390/admsci13090196

Guerin, S. H. (2009). Internationalizing the Curriculum: Improving Learning Through International Education: Preparing Students for Success in a Global Society. Community College Journal of Research and Practice, 33(8), 611–614. https://doi.org/10.1080/10668920902928945

Hamilton, A. B., & Finley, E. P. (2019). Qualitative methods in implementation research: An introduction. Psychiatry Research, 280, 112516. https://doi.org/10.1016/j.psychres.2019.112516

Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education. Mathematical Problems in Engineering, 2022, 1–19. https://doi.org/10.1155/2022/5215722

Lau, C. L., & Al-Hawamdeh, S. (2002). Knowledge Management Education and Curriculum Development. Journal of Information & Knowledge Management, 01(02), 99–118. https://doi.org/10.1142/S021964920200042X

Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., Cao, K., Liu, D., Wang, G., Xu, Q., Fang, X., Zhang, S., Xia, J., & Xia, J. (2020). Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology, 296(2), E65–E71. https://doi.org/10.1148/radiol.2020200905

Lukita, C., Suwandi, S., Harahap, E. P., Rahardja, U., & Nas, C. (2020). Curriculum 4.0: Adoption of Industry Era 4.0 as Assessment of Higher Education Quality. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(3), 297. https://doi.org/10.22146/ijccs.57321

Maesaroh, S., Lubis, R. R., Husna, L. N., Widyaningsih, R., & Susilawati, R. (2022). Efektivitas Implementasi Manajemen Business Intelligence pada Industri 4.0. ADI Bisnis Digital Interdisiplin Jurnal, 3(2), 1–8. https://doi.org/10.34306/abdi.v3i2.764

Mohamed, S. (2023). The development of an Arabic curriculum framework based on a compilation of salient features from CEFR level descriptors. The Language Learning Journal, 51(1), 33–47. https://doi.org/10.1080/09571736.2021.1923781

Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1

Rofiah, C., & Bungin, B. (2021). QUALITATIVE METHODS: SIMPLE RESEARCH WITH TRIANGULATION THEORY DESIGN. Develop, 5(1), 18–28. https://doi.org/10.25139/dev.v5i1.3690

Somasundaram, M., Junaid, K. A. M., & Mangadu, S. (2020). Artificial Intelligence (AI) Enabled Intelligent Quality Management System (IQMS) For Personalized Learning Path. Procedia Computer Science, 172, 438–442. https://doi.org/10.1016/j.procs.2020.05.096

Yin, H., Lee, J. C.-K., & Wang, W. (2014). Dilemmas of leading national curriculum reform in a global era: A Chinese perspective. Educational Management Administration & Leadership, 42(2), 293–311. https://doi.org/10.1177/1741143213499261

Yustiasari Liriwati, F. (2023). Transformasi Kurikulum; Kecerdasan Buatan untuk Membangun Pendidikan yang Relevan di Masa Depan. Jurnal IHSAN?: Jurnal Pendidikan Islam, 1(2), 62–71. https://doi.org/10.61104/ihsan.v1i2.61

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Zinser, R. (2012). A curriculum model of a foundation for educating the global citizens of the future. On the Horizon, 20(1), 64–73. https://doi.org/10.1108/10748121211202080

Authors

Anri Naldi
anrinaldi@staff.uma.ac.id (Primary Contact)
Nurkadri Nurkadri
Mansyur Srisudarso
Didik Cahyono
Suyitno Suyitno
Naldi, A., Nurkadri, N., Srisudarso, M., Cahyono, D., & Suyitno, S. (2024). Evaluation of the Effectiveness of Artificial Intelligence System in Higher Education Curriculum Management. International Journal of Educational Narratives, 2(2), 189–198. https://doi.org/10.70177/ijen.v2i2.792

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