Implementasi Algoritma K-Means pada Kesiapan Guru Menyelenggarakan Pembelajaran Daring
Abstract
Abstract: Teachers need certain characteristics to be ready to organize online learning. The purpose of this research is to see the readiness of teachers in conducting online learning. The characteristics of the teachers collected were gender, generational, last education, teacher tenure, teacher's internet self-efficacy level, learning environment and variations of online learning models. This study uses the K-Means algorithm for clustering, then the analysis of the relationship between variables is used by Spearman's rho. The K-Means algorithm was performed on a dataset of 285 teachers. The results of the K-Means analysis obtained 8 clusters with various characteristics of each cluster. Furthermore, the Spearmans Rho Correlation analysis obtained significant correlation results with probability values > 0.05 being "Generation" and "Teacher tenure" as well as "Environment" and "Variation of online learning models". The relationship between “Generation” and “Teacher tenure” is positive and moderate. Meanwhile, the relationship between "Environment" and "Variation of online learning models" is positive and moderate. That is, the better the learning environment, the teachers tend to use more varied online learning models.
Keyword: K-Means, Teacher characteristics, Online learning
Abstrak: Guru membutuhkan karakteristik tertentu supaya siap menyelenggarakan pembelajaran daring. Tujuan penelitian ini adalah melihat kesiapan guru dalam menyelenggarakan pembelajaran daring. Karakteristik guru yang dikumpulkan adalah gender, kelompok generasi, pendidikan terakhir, masa kerja guru, tingkat internet self efficacy guru, lingkungan pembelajaran dan variasi model pembelajaran daring. Penelitian ini menggunakan algoritma K-Means untuk clustering, selanjutnya analisis hubungan antar variabel digunakan Spearman’s rho. Algoritma K-Means dilakukan pada dataset 285 guru. Hasil analisis K-Means didapatkan 8 cluster dengan karakteristik tiap klaster yang beragam. Selanjjutnya analisis Korelasi Spearmans Rho diperoleh hasil korelasi yang signifikan dengan nilai probabilitas >0,05 adalah “Generasi” dan “Masa kerja guru” serta “Lingkungan” dan “Variasi model pembelajaran daring”. Hubungan antara “Generasi” dan “Masa kerja guru” bersifat positif dan sedang. Sedangkan, hubungan antara “Lingkungan” dan “Variasi model pembelajaran daring” bersifat positif dan sedang. Artinya, semakin baik lingkungan pembelajaran maka guru cenderung akan menggunakan model pembelajaran daring lebih variatif.
Kata kunci: K-Means, karakteristik guru, pembelajaran daring
Keywords
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Abadi, S., Mat The, K. S., Nasir, B. M., Huda, M., Ivanova, N. L., Sari, T. I., Maseleno, A., Satria, F., & Muslihudin, M. (2018). Application model of k-means clustering: Insights into promotion strategy of vocational high school. International Journal of Engineering and Technology(UAE), 7(2.27 Special Issue 27), 182–187. https://doi.org/10.14419/ijet.v7i2.11491
Aldino, A. A., & Darwis, D. (2018). The effect of mining data k-means clustering toward students profile model drop out potential The effect of mining data k-means clustering toward students profile model drop out potential.
Auliasari, K., & Kertaningtyas, M. (2019). Penerapan Algoritma K-Means untuk Segmentasi Konsumen Menggunakan R. Jurnal Teknologi Dan Manajemen Informatika, 5(2). https://doi.org/10.26905/jtmi.v5i2.3644
Ayu, P., Lestari, S., & Gunawan, D. (2020). The Impact of Covid-19 Pandemic on Learning Implementation of Primary and Secondary School Levels. In Indonesian Journal of Elementary and Childhood Education.
Bandura, A. (1986). Social foundations of thought and action : a social cognitive theory / Albert Bandura. New Jersey: Prentice-Hall, 1986.
Buabeng-Andoh, C. (2012). Factors influencing teachers ’ adoption and integration of information and communication technology into teaching: A review of the literature. International Journal of Education and Development Using Information and Communication Technology.
Cassidy, S., & Eachus, P. (2002). Developing the computer user self-efficacy (CUSE) scale: Investigating the relationship between computer self-efficacy, gender and experience with computers. Journal of Educational Computing Research. https://doi.org/10.2190/JGJR-0KVL-HRF7-GCNV
Dabbagh, N., & Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. Internet and Higher Education. https://doi.org/10.1016/j.iheduc.2011.06.002
Daniel, S. J. (2020). Education and the COVID-19 pandemic. Prospects. https://doi.org/10.1007/s11125-020-09464-3
Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. In Journal of Computer-Mediated Communication. https://doi.org/10.1111/j.1083-6101.2000.tb00110.x
Hsu, M. H., & Chiu, C. M. (2004). Internet self-efficacy and electronic service acceptance. Decision Support Systems. https://doi.org/10.1016/j.dss.2003.08.001
Hung, M. L. (2016). Teacher readiness for online learning: Scale development and teacher perceptions. Computers and Education. https://doi.org/10.1016/j.compedu.2015.11.012
Islam, H., & Haque, M. (2012). An Approach of Improving Student’s Academic Performance by using K-means clustering algorithm and Decision tree. International Journal of Advanced Computer Science and Applications, 3(8), 146–149. https://doi.org/10.14569/ijacsa.2012.030824
Joo, Y. J., Bong, M., & Choi, H. J. (2000). Self-efficacy for self-regulated learning, academic self-efficacy, and internet self-efficacy in web-based instruction. Educational Technology Research and Development. https://doi.org/10.1007/BF02313398
Kim, Y., & Glassman, M. (2013). Beyond search and communication: Development and validation of the Internet Self-efficacy Scale (ISS). Computers in Human Behavior. https://doi.org/10.1016/j.chb.2013.01.018
Kistner, S., Rakoczy, K., Otto, B., & Klieme, E. (2015). Teaching learning strategies : The role of instructional context and teacher beliefs. Journal for Educational Research Online.
McCool, M., Robison, A. D., & Reinders, J. (2012). K-Means Clustering. In Structured Parallel Programming. https://doi.org/10.1016/b978-0-12-415993-8.00011-6
Mumtaz, S. (2000). Factors affecting teachers’ use of information and communications technology: A review of the literature. Journal of Information Technology for Teacher Education. https://doi.org/10.1080/14759390000200096
Sam, H. K., Othman, A. E. A., & Nordin, Z. S. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the Internet: A study among undergraduates in Unimas. Educational Technology and Society.
Santosa, E. B., & Sarwanta, S. (2021). Pengaruh Tingkat Internet Self-Efficacy, Pengalaman Mengajar dan Usia Guru Terhadap Peguasaan Komputer dalam Strategi Pembelajaran Daring. Jurnal Pendidikan Edutama, 8(1), 41. https://doi.org/10.30734/jpe.v8i1.1489
Shen, D., Cho, M. H., Tsai, C. L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet and Higher Education. https://doi.org/10.1016/j.iheduc.2013.04.001
Wajdi, M. B. N., Iwan Kuswandi, Umar Al Faruq, Zulhijra, Z., Khairudin, K., & Khoiriyah, K. (2020). Education Policy Overcome Coronavirus, A Study of Indonesians. EDUTEC : Journal of Education And Technology. https://doi.org/10.29062/edu.v3i2.42
Wang, S. L., & Lin, S. S. J. (2007). The effects of group composition of self-efficacy and collective efficacy on computer-supported collaborative learning. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2006.03.005
Yeşilyurt, E., Ulaş, A. H., & Akan, D. (2016). Teacher self-efficacy, academic self-efficacy, and computer self-efficacy as predictors of attitude toward applying computer-supported education. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2016.07.038
Zimmerman, B. J. (1989). A Social Cognitive View of Self-Regulated Academic Learning. Journal of Educational Psychology. https://doi.org/10.1037/0022-0663.81.3.329
Abadi, S., Mat The, K. S., Nasir, B. M., Huda, M., Ivanova, N. L., Sari, T. I., Maseleno, A., Satria, F., & Muslihudin, M. (2018). Application model of k-means clustering: Insights into promotion strategy of vocational high school. International Journal of Engineering and Technology(UAE), 7(2.27 Special Issue 27), 182–187. https://doi.org/10.14419/ijet.v7i2.11491
Aldino, A. A., & Darwis, D. (2018). The effect of mining data k-means clustering toward students profile model drop out potential The effect of mining data k-means clustering toward students profile model drop out potential.
Auliasari, K., & Kertaningtyas, M. (2019). Penerapan Algoritma K-Means untuk Segmentasi Konsumen Menggunakan R. Jurnal Teknologi Dan Manajemen Informatika, 5(2). https://doi.org/10.26905/jtmi.v5i2.3644
Ayu, P., Lestari, S., & Gunawan, D. (2020). The Impact of Covid-19 Pandemic on Learning Implementation of Primary and Secondary School Levels. In Indonesian Journal of Elementary and Childhood Education.
Bandura, A. (1986). Social foundations of thought and action : a social cognitive theory / Albert Bandura. New Jersey: Prentice-Hall, 1986.
Buabeng-Andoh, C. (2012). Factors influencing teachers ’ adoption and integration of information and communication technology into teaching: A review of the literature. International Journal of Education and Development Using Information and Communication Technology.
Cassidy, S., & Eachus, P. (2002). Developing the computer user self-efficacy (CUSE) scale: Investigating the relationship between computer self-efficacy, gender and experience with computers. Journal of Educational Computing Research. https://doi.org/10.2190/JGJR-0KVL-HRF7-GCNV
Dabbagh, N., & Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. Internet and Higher Education. https://doi.org/10.1016/j.iheduc.2011.06.002
Daniel, S. J. (2020). Education and the COVID-19 pandemic. Prospects. https://doi.org/10.1007/s11125-020-09464-3
Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. In Journal of Computer-Mediated Communication. https://doi.org/10.1111/j.1083-6101.2000.tb00110.x
Hsu, M. H., & Chiu, C. M. (2004). Internet self-efficacy and electronic service acceptance. Decision Support Systems. https://doi.org/10.1016/j.dss.2003.08.001
Hung, M. L. (2016). Teacher readiness for online learning: Scale development and teacher perceptions. Computers and Education. https://doi.org/10.1016/j.compedu.2015.11.012
Islam, H., & Haque, M. (2012). An Approach of Improving Student’s Academic Performance by using K-means clustering algorithm and Decision tree. International Journal of Advanced Computer Science and Applications, 3(8), 146–149. https://doi.org/10.14569/ijacsa.2012.030824
Joo, Y. J., Bong, M., & Choi, H. J. (2000). Self-efficacy for self-regulated learning, academic self-efficacy, and internet self-efficacy in web-based instruction. Educational Technology Research and Development. https://doi.org/10.1007/BF02313398
Kim, Y., & Glassman, M. (2013). Beyond search and communication: Development and validation of the Internet Self-efficacy Scale (ISS). Computers in Human Behavior. https://doi.org/10.1016/j.chb.2013.01.018
Kistner, S., Rakoczy, K., Otto, B., & Klieme, E. (2015). Teaching learning strategies : The role of instructional context and teacher beliefs. Journal for Educational Research Online.
McCool, M., Robison, A. D., & Reinders, J. (2012). K-Means Clustering. In Structured Parallel Programming. https://doi.org/10.1016/b978-0-12-415993-8.00011-6
Mumtaz, S. (2000). Factors affecting teachers’ use of information and communications technology: A review of the literature. Journal of Information Technology for Teacher Education. https://doi.org/10.1080/14759390000200096
Sam, H. K., Othman, A. E. A., & Nordin, Z. S. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the Internet: A study among undergraduates in Unimas. Educational Technology and Society.
Santosa, E. B., & Sarwanta, S. (2021). Pengaruh Tingkat Internet Self-Efficacy, Pengalaman Mengajar dan Usia Guru Terhadap Peguasaan Komputer dalam Strategi Pembelajaran Daring. Jurnal Pendidikan Edutama, 8(1), 41.
https://doi.org/10.30734/jpe.v8i1.1489
Shen, D., Cho, M. H., Tsai, C. L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet and Higher Education. https://doi.org/10.1016/j.iheduc.2013.04.001
Wajdi, M. B. N., Iwan Kuswandi, Umar Al Faruq, Zulhijra, Z., Khairudin, K., & Khoiriyah, K. (2020). Education Policy Overcome Coronavirus, A Study of Indonesians. EDUTEC : Journal of Education And Technology. https://doi.org/10.29062/edu.v3i2.42
Wang, S. L., & Lin, S. S. J. (2007). The effects of group composition of self-efficacy and collective efficacy on computer-supported collaborative learning. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2006.03.005
Yeşilyurt, E., Ulaş, A. H., & Akan, D. (2016). Teacher self-efficacy, academic self-efficacy, and computer self-efficacy as predictors of attitude toward applying computer-supported education. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2016.07.038
Zimmerman, B. J. (1989). A Social Cognitive View of Self-Regulated Academic Learning. Journal of Educational Psychology. https://doi.org/10.1037/0022-0663.81.3.329
DOI: http://dx.doi.org/10.30734/jpe.v9i2.2589
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