CLASTERIZATION OF LECTURER'S PROFILE IN ONLINE LEARNING DURING THE COVID-19 PANDEMIC

Agnes Maria Polina(1*), Christiyanti Aprinastuti(2), Hari Suparwito(3),

(1) Sanata Dharma University, Indonesia
(2) Sanata Dharma University, Indonesia
(3) Sanata Dharma University, Indonesia
(*) Corresponding Author

Abstract


The learning process changed from classroom to online learning during the COVID-19 pandemic. One of the things that must be done is to analyze the readiness of lecturers in facing online learning. The purpose of this study is to cluster the profiles of lecturers dealing with online learning. The clustering method uses a Machine Learning approach with the K-means algorithm. Data were taken from 274 lecturers who returned questionnaires during April–June 2022. The questionnaire consisted of 27 questions on a Likert scale (1–4). The Boruta technique is used to determine the five most significant variables (Variable Importance) in the clustering. The results of the clustering show that the lecturers are divided into 2 large groups with the following criteria: focus on learning methods, learning materials, student independence, exploration of new knowledge, and online learning evaluation tools.


Keywords


Boruta, clustering, K-means, online learning, variable importance

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References


Al-Ansi, A. M., Garad, A., & Al-Ansi, A. (2021). ICT-based learning during COVID-19 outbreak: Advantages, opportunities, and challenges. Gagasan Pendidikan Indonesia, 2(1), 10-26.

Almazova, N., Krylova, E., Rubtsova, A., & Odinokaya, M. (2020). Challenges and opportunities for Russian higher education amid COVID-19: Teachers’ perspective. Education Sciences, 10(12), 368-379.

Baldwin, S.J., & Trespalacios, J. (2017) Evaluation instruments and good practices in online education. Retrieved on October 26, 2022, from https://scholarworks.boisestate.edu/cgi/viewcontent.cgi?article=1172&context=edtech_facpubs

Cavus, N. (2015). Distance learning and learning management systems. Procedia-Social and Behavioral Sciences, 191, 872-877.

Cope, B., & Kalantzis, M. (2016). Big data comes to school: Implications for learning, assessment, and research, AERA (American Education Research Association) Open. Retrieved from https://doi.org/10.1177/2332858416641907

Davidovitch, N., & Wadmany, R. (2021). The lecturer at a crossroads of teaching and learning in academia in Israel. Journal of Education and e-Learning Research, 8(3), 281-289.

Dobre, I. (2015). Learning management systems for higher education-an overview of available options for higher education organizations. Procedia-social and behavioral sciences, 180, 313-320.

Mulyatiningsih, E., Komariah, K., Lastariwati, B., Kartika, M. G., & Restiana, R. (2020). Eksplorasi faktor-faktor penentu keberhasilan pembelajaran daring di era revolusi industri 4.0. Retrieved from https://simppm.lppm.uny.ac.id/uploads/8729/laporan_akhir/laporan-akhir-8729-20201217-193903.pdf

Ghazal, T. M. (2021). Performances of K-means clustering algorithm with different distance metrics. Intelligent Automation & Soft Computing, 30(2), 735-742.

García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: Methods and prospects. Big Data Analytics, 1(1), 1-22.

Guri-Rosenblit, S. (2018). E-teaching in higher education: An essential prerequisite for e-learning. Journal of New Approaches in Educational Research, 7(2), 93-97. https://doi.org/10.7821/naer.2018.7.298

Han, J., Pei, J., & Kamber, M. (2012). Data mining: Concepts and techniques (pp.39-82). Waltham: Elsevier.

Humaira, H., & Rasyidah, R. (2018). Determining the appropiate cluster number using Elbow method for K-Means algorithm. In Proceedings of the 2nd Workshop on Multidisciplinary and Applications (WMA), 24-25 January 2018, Padang, Indonesia.

Hussin, A. A. (2018). Education 4.0 made simple: Ideas for teaching. International Journal of Education and Literacy Studies, 6(3), 92-98. https://doi.org/10.7575/aiac.ijels.v.6n.3p.92

Kim, K., & Bonk, C. J. (2006). The future of online teaching and learning in higher education. Educause quarterly, 29(4), 22-30.

Kislyakov, P. A., Shmeleva, E. A., Karaseva, T. V., & Silaeva, O. A. (2014). Monitoring of education environment according to the social-psychological safety criterion. Asian Social Science, 10(17), 285–291. https://doi.org/10.5539/ass.v10n17p285

Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S., Rashaideh, H., & Alharbi, H. (2022). The COVID-19 pandemic and E-learning: Challenges and opportunities from the perspective of students and instructors. Journal of Computing in Higher Education, 34(1), 21-38. https://doi.org/10.1007/s12528-021-09274-2

Naik, N., & Mohan, B. R. (2019). Stock price movements classification using machine and deep learning techniques-the case study of Indian stock market. In International Conference on Engineering Applications of Neural Networks, 445-452. Cham: Springer.

Purbojo, R. (2018). Role of the university lecturer in an online learning environment: An analysis of Moodle features utilized in a blended learning strategy. In Educational Technology to Improve Quality and Access on a Global Scale, 227-244. Cham: Springer.

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Sulisworo, D., Astuti, A. Y., & Fatimah, N. (2020). Online learning implementation during COVID-19 mitigation in Indonesia: Measuring the lecturers’ technology readiness. International Journal of Advanced Science and Technology, 29(7), 2252-2263.

Sunarto, M. J. (2021). The readiness of lecturers in online learning during the Covid-19 pandemic at the faculty of information technology and the faculty of economics and business. International Journal of Recent Educational Research (IJORER), 2(1), 54-63. https://doi.org/10.46245/ijorer.v2i1.70

Suparwito, H., Polina, A.M., & Budiraharjo, M. (2021). Student perceptions analysis of online learning: A machine learning approach. Indonesian Journal of Information Systems, 4(1), 64-75. https://doi.org/10.24002/ijis.v4i1.4594

Thurlings, M., Vermeulen, M., Bastiaens, T., & Stijnen, S. (2013). Understanding feedback: A learning theory perspective. Educational Research Review, 9, 1–15. https://doi.org/10.1016/j.edurev.2012.11.004

Turnbull, D., Chugh, R., & Luck, J. (2021). Transitioning to e-learning during the COVID-19 pandemic: How have higher education institutions responded to the challenge? Education and Information Technologies, 26(5), 6401-6419. https://doi.org/10.1007/s10639-021-10633-w

Vo, T. D., & Tran, M. D. (2021). The impact of Covid-19 pandemic on the global trade. International Journal of Social Science and Economics Invention, 7(1), 1-7. https://doi.org/10.23958/ijssei/vol07-i01/261

Suhirman, S., Herawan, T., Chiroma, H., & Mohamad Zain, J. (2014). Data mining for education decision support: A review. International Journal of Emerging Technologies in Learning (iJET), 9(6), 4–19. https://doi.org/10.3991/ijet.v9i6.3950




DOI: https://doi.org/10.24071/ijiet.v7i2.6495

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