Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

Daniel Febrian Sengkey, Angelina Stevany Regina Masengi


Numerous approaches have been developed to group learners’ behavior in an online/blended learning environment. However, most clustering analyses in this particular field only consider numeric features despite the existence of categoric features that are found important in other studies. In this study, we compare K-Means and K-Prototypes algorithms to cluster learners’ behavior in a flipped classroom implementation. From the model selection, we found that the model produced by the K-Prototypes algorithm — which included categoric features — is a better one. The statistical analysis of the clustering results of the selected K-Prototypes model shows significant differences in most of the inter-cluster comparisons, implying a good separation of the data. More importantly, we can identify the behavior in each cluster which then can be used to help learners in achieving better results in learning.


learning behavior analysis; clustering; mixed-type variables; blended learning; flipped classroom


“The rise of online learning during the COVID-19 pandemic | World Economic Forum. ”https://www.weforum.org/agenda/2020/04/coronavirus-education-global-covid19-online-digital-learning/ (accessed Mar. 21, 2022).

M. Aparicio, F. Bacao, and T. Oliveira, “An e-Learning Theoretical Framework,” J. Educ. Technol. Syst., vol. 19, no. 1, pp. 292–307, 2016, [Online]. Available: http://www.jstor.org/stable/jeductechsoci.19.1.292.

P. Qiao, X. Zhu, Y. Guo, Y. Sun, and C. Qin, “The Development and Adoption of Online Learning in Pre- and Post-COVID-19: Combination of Technological System Evolution Theory and Unified Theory of Acceptance and Use of

Technology,” J. Risk Financ. Manag. 2021, Vol. 14, Page 162, vol. 14, no. 4, p. 162, Apr. 2021, doi: 10.3390/JRFM14040162.

N. M. Almusharraf and S. H. Khahro, “Students Satisfaction with Online Learning Experiences during the COVID-19 Pandemic,” Int. J. Emerg. Technol. Learn., vol. 15, no. 21, pp. 246–267, Nov. 2020, doi: 10.3991/IJET.V15I21.15647.

J. L. Moore, C. Dickson-Deane, and K. Galyen, “e-Learning, online learning, and distance learning environments: Are they the same?,” Internet High. Educ., vol. 14, no. 2, pp. 129–135, Mar. 2011, doi: 10.1016/j.iheduc.2010.10.001.

A. A. Okaz, “Integrating Blended Learning in Higher Education,” Procedia - Soc. Behav. Sci., vol. 186, pp. 600–603, May 2015, doi: 10.1016/j.sbspro.2015.04.086.

S. Hubackova and I. Semradova, “Evaluation of Blended Learning,” Procedia - Soc. Behav. Sci., vol. 217, pp. 551–557, Feb. 2016, doi: 10.1016/j.sbspro.2016.02.044.

S. D. E. Paturusi, T. Usagawa, and A. S. M. Lumenta, “A study of students’ satisfaction toward blended learning implementation in higher education institution in Indonesia,” in 2016 International Conference on Information & Communication Technology and Systems (ICTS), 2016, pp. 220–225, doi: 10.1109/ICTS.2016.7910302.

T. Usagawa and K. Ogata, “Potential of e-Learning for Enhancing Graduate and Undergraduate Education,” IPTEK J. Proceeding Ser., no. 1, pp. KS2-3-KS2-6, 2015, [Online]. Available: http://iptek.its.ac.id/index.php/jps/article/view/1115.

S. D. E. Paturusi, Y. Chisaki, and T. Usagawa, “Development and Evaluation of the Blended Learning Courses at Sam Ratulangi University in Indonesia,” Int. J. e-Education, e-Business, e-Management e-Learning, vol. 2, no. 3, pp. 242–246, 2012, doi: 10.7763/IJEEEE.2012.V2.118.

H. B. Seta, T. Wati, A. Muliawati, and A. N. Hidayanto, “E-Learning Success Model: An Extention of DeLone & McLean IS’ Success Model,” Indones. J. Electr. Eng. Informatics, vol. 6, no. 3, pp. 281–291, Sep. 2018, doi: 10.52549/IJEEI.V6I3.505.

C. T. Gozali, S. D. E. Paturusi, and A. M. Sambul, “Studi Preferensi Mahasiswa terhadap Jenis Media Pembelajaran Daring,” J. Tek. Inform., vol. 13, no. 4, pp. 39–46, 2018, Accessed: May 18, 2019. [Online]. Available: https://ejournal.unsrat.ac.id/index.php/informatika/article/view/24115.

D. F. Sengkey, S. D. E. Paturusi, and A. M. Sambul, “Identifying Students’ Pre-Classroom Behaviors in a Flipped Learning Environment,” J. Sustain. Eng. Proc. Ser., vol. 1, no. 2, pp. 143–149, Sep. 2019, doi: 10.35793/joseps.v1i2.19.

D. F. Sengkey, S. D. E. Paturusi, and A. M. Sambul, “Perbandingan Akses Mahasiswa terhadap Media Pembelajaran Daring dalam Penerapan Flipped Classroom,” J. Tek. Elektro dan Komput., vol. 9, no. 1, pp. 31–38, Jun. 2020, doi: 10.35793/JTEK.9.1.2020.28634.

D. F. Sengkey, S. D. E. Paturusi, and A. M. Sambul, “Correlations between Online Learning Media Types, First Access Time, Access Frequency, and Students’ Achievement in a Flipped Classroom Implementation,” J. Sist. Inf., vol. 17, no. 1, pp. 44–57, Apr. 2021, doi: 10.21609/jsi.v17i1.1008.

L. E. Sherman, M. Michikyan, and P. M. Greenfield, “The Effects of Text, Audio, Video, and In-person Communication on Bonding between Friends,” Cyberpsychology J. Psychosoc. Res. Cybersp., vol. 7, no. 2, Jul. 2013, doi: 10.5817/CP2013-2-3.

L. P. Macfadyen and S. Dawson, “Mining LMS data to develop an ‘early warning system’ for educators: A proof of concept,” Comput. Educ., vol. 54, no. 2, pp. 588–599, Feb. 2010, doi: 10.1016/j.compedu.2009.09.008.

Y. Zhang, A. Ghandour, and V. Shestak, “Using Learning Analytics to Predict Students Performance in Moodle LMS,” Int. J. Emerg. Technol. Learn., vol. 15, no. 20, pp. 102–115, Oct. 2020, doi: 10.3991/IJET.V15I20.15915.

G. Akçapınar, “Profiling Students’ Approaches to Learning through Moodle Logs,” in Proceedings of Multidisciplinary Academic Conference on Education, Teaching and Learning (MAC-ETL 2015), 2015, pp. 9–15, Accessed: Jan. 17, 2022. [Online]. Available: https://citeseerx.ist.psu.edu/viewdoc/download?doi=

S. S. Kusumawardani and S. A. I. Alfarozi, “Kajian Penggunaan Data Log Mahasiswa untuk Berbagai Permasalahan Analisis Pembelajaran,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 4, pp. 365–374, Dec. 2020, doi: 10.22146/jnteti.v9i4.779.

A. Charitopoulos, M. Rangoussi, and D. Koulouriotis, “Educational data mining and data analysis for optimal learning content management: Applied in moodle for undergraduate engineering studies,” in 2017 IEEE Global Engineering Education Conference (EDUCON), Apr. 2017, pp. 990–998, doi: 10.1109/EDUCON.2017.7942969.

Riandini, S. A. Aditya, R. N. Wardhani, and S. Setiowati, “Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks,” Indones. J. Electr. Eng. Informatics, vol. 10, no. 2, pp. 452–462, Jun. 2022, doi: 10.52549/IJEEI.V10I2.3500.

A. Aldholay, O. Isaac, A. N. Jalal, F. A. Anor, and A. M. Mutahar, “Towards a better understanding of the Organizational Characteristics that affect Acceptance of Big Data Platforms for Academic Teaching,” Indones. J. Electr. Eng. Informatics, vol. 9, no. 3, pp. 766–773, Sep. 2021, doi: 10.52549/IJEEI.V9I3.2902.

C. Li and J. Yoo, “Modeling student online learning using clustering,” in Proceedings of the 44th annual southeast regional conference on - ACM-SE 44, 2006, vol. 2006, p. 186, doi: 10.1145/1185448.1185490.

M. Köck and A. Paramythis, “Activity sequence modelling and dynamic clustering for personalized e-learning,” User Model. User-adapt. Interact., vol. 21, no. 1–2, pp. 51–97, Apr. 2011, doi: 10.1007/s11257-010-9087-z.

J. Chen, K. Huang, F. Wang, and H. Wang, “E-learning behavior analysis based on fuzzy clustering,” 3rd Int. Conf. Genet. Evol. Comput. WGEC 2009, pp. 863–866, 2009, doi: 10.1109/WGEC.2009.214.

M. A. Hogo, “Evaluation of e-learning systems based on fuzzy clustering models and statistical tools,” Expert Syst. Appl., vol. 37, no. 10, pp. 6891–6903, Oct. 2010, doi: 10.1016/J.ESWA.2010.03.032.

K. R. Koedinger, K. Cunningham, A. Skogsholm, and B. Leber, “An open repository and analysis tools for fine-grained, longitudinal learner data,” in Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings, 2008, no. May 2014, pp. 157–166.

K. R. Koedinger, R. S. J. d. Baker, K. Cunningham, A. Skogsholm, B. Leber, and J. Stamper, “A Data Repository for the EDM Community: The PSLC DataShop,” in Handbook of Educational Data Mining, CRC Press, 2010, pp. 65–78.

M. Jovanovic, M. Vukicevic, M. Milovanovic, and M. Minovic, “Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study,” Int. J. Comput. Intell. Syst., vol. 5, no. 3, pp. 597–610, Jun. 2012, doi: 10.1080/18756891.2012.696923.

A. Bovo, S. Sanchez, O. Heguy, and Y. Duthen, “Clustering moodle data as a tool for profiling students,” in 2013 2nd International Conference on E-Learning and E-Technologies in Education, ICEEE 2013, 2013, pp. 121–126, doi: 10.1109/ICELETE.2013.6644359.

S. Liu and M. D’Aquin, “Unsupervised learning for understanding student achievement in a distance learning setting,” in 2017 IEEE Global Engineering Education Conference (EDUCON), Apr. 2017, pp. 1373–1377, doi: 10.1109/EDUCON.2017.7943026.

J. Kuzilek, M. Hlosta, D. Herrmannova, Z. Zdrahal, J. Vaclavek, and A. Wolff, “OU Analyse: analysing at-risk students at The Open University,” Mar. 2015, Accessed: Oct. 25, 2022. [Online]. Available: http://www.laceproject.eu/learning-analyticsreview/analysing-at-risk-students-at-open-university/.

A. Triayudi and I. Fitri, “A new agglomerative hierarchical clustering to model student activity in online learning,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 17, no. 3, pp. 1226–1235, Jun. 2019, doi: 10.12928/TELKOMNIKA.V17I3.9425.

A. Moubayed, M. Injadat, A. Shami, and H. Lutfiyya, “Student Engagement Level in an e-Learning Environment: Clustering Using K-means,” Am. J. Distance Educ., vol. 34, no. 2, pp. 137–156, Apr. 2020, doi: 10.1080/08923647.2020.1696140.

K. Palani, “Identifying At-Risk Students in Virtual Learning Environment using Clustering Techniques,” National College of Ireland, Dublin, 2020.

K. Palani, P. Stynes, and P. Pathak, “Clustering Techniques to Identify Low-engagement Student Levels,” in Proceedings of the 13th International Conference on Computer Supported Education, Apr. 2021, pp. 248–257, doi: 10.5220/0010456802480257.

G. Nalli, D. Amendola, A. Perali, and L. Mostarda, “Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses,” Appl. Sci., vol. 11, no. 13, p. 5800, Jun. 2021, doi: 10.3390/app11135800.

G. Ge et al., “Analyzing Differences between Online Learner Groups during the COVID-19 Pandemic through K-Prototype Clustering,” J. Data Anal. Inf. Process., vol. 10, no.1, pp. 22–42, Dec. 2021, doi: 10.4236/JDAIP.2022.101002.

I. Dhaiouir, M. Ezziyyani, and M. Khaldi, “Smart Model for Classification and Orientation of Learners in a MOOC,” Int. J. Emerg. Technol. Learn., vol. 17, no. 05, pp. 19–35, Mar. 2022, doi: 10.3991/IJET.V17I05.28153.

A. Talasbek, A. Serek, M. Zhaparov, S. Moo-Yoo, Y. K. Kim, and G. H. Jeong, “Personality Classification Experiment by Applying k-Means Clustering,” Int. J. Emerg. Technol. Learn., vol. 15, no. 16, pp. 162–177, Aug. 2020, doi: 10.3991/IJET.V15I16.15049.

Z. Huang, “Clustering Large Data Sets with Mixed Numeric and Categorical Values,” in Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference, 1997, pp. 21–34.

D. F. Sengkey, S. D. E. Paturusi, A. M. Sambul, and C. T. Gozali, “A Survey on Students’ Interests toward On-line Learning Media Choices (A Case Study from the Operations Research Course in the Department of Electrical Engineering, UNSRAT),” Int. J. Educ. Vocat. Stud., vol. 1, no. 2, pp. 146–152, Jun. 2019, doi: 10.29103/ijevs.v1i2.1527.

D. F. Sengkey, A. M. Sambul, and S. D. E. Paturusi, “Penilaian Mahasiswa terhadap Jenis Media Pembelajaran dalam Penerapan Flipped Classroom,” J. Tek. Elektro dan Komput., vol. 8, no. 2, pp. 103–110, Aug. 2019, doi: 10.35793/JTEK.8.2.2019.25029.

G. Ke et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” Adv. Neural Inf. Process. Syst., vol. 30, 2017, Accessed: Oct. 26, 2022. [Online]. Available: https://github.com/Microsoft/LightGBM.

K. Kurniabudi, D. Stiawan, Darmawijoyo, M. Y. Bin Idris, B. Kerim, and R. Budiarto, “Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset,” Indones. J. Electr. Eng. Informatics, vol. 9, no. 2, pp. 498–511, May 2021, doi: 10.52549/IJEEI.V9I2.3028.

M. J. Pendekal and S. Gupta, “An Ensemble Classifier Based on Individual Features for Detecting Microaneurysms in Diabetic Retinopathy,” Indones. J. Electr. Eng. Informatics, vol. 10, no. 1, pp. 60–71, Mar. 2022, doi: 10.52549/IJEEI.V10I1.3522.

Y. Nohara, K. Matsumoto, H. Soejima, and N. Nakashima, “Explanation of Machine Learning Models Using Improved Shapley Additive Explanation,” pp. 546–546, Sep. 2019, doi: 10.1145/3307339.3343255.

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