Learning Behavioral Characteristics and Teaching Intervention Strategies of Midwifery Students in Higher Vocational Colleges Using K-means Clustering Algorithm
Main Article Content
Abstract
To address the difficulty of integrating multi-source heterogeneous learning behavior data and the broadness of conventional teaching intervention strategies, this paper proposes a data-driven analysis framework based on K-means clustering for higher vocational midwifery education. The study collects multidimensional behavioral data from online learning logs, smart classroom interaction records, and practical training assessments, and constructs a 12-dimensional feature system covering cognitive input, behavioral activity, practical competence, and comprehensive performance. Zscore standardization is used to eliminate dimensional differences, and the optimal cluster number K=3 is determined by combining the elbow rule and silhouette coefficient. Students are classified into actively balanced, theoretically weak, and practically lagging groups. Experimental results show that targeted interventions based on clustering profiles significantly improve core competency indicators in theoretical and practical dimensions. Hotelling’s T2 tests and external evaluation indicators confirm the statistical significance and educational interpretability of the clustering structure. The framework can also support engineering training environments using smart classrooms, wireless sensing, and electromagnetic-compatible data acquisition systems for precise learning diagnosis and intervention.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
J. Li and E. Xue, “Dynamic interaction between student learning behaviour and learning environment: Meta-analysis of student engagement and its influencing factors,” Behavioral Sciences, vol. 13, no. 1, pp. 59-74, 2023, doi: 10.3390/bs13010059.
A. P. Rahayu and Y. Dong, “The relationship of extracurricular activities with students’ character education and influencing factors: a systematic literature review,” Al-Ishlah: Jurnal Pendidikan, vol. 15, no. 1, pp. 459-474, 2023, doi: 10.35445/alishlah.v15i1.2968.
K. Nikolopoulou and G. Zacharis, “Blended learning in a higher education context: Exploring university students’ learning behavior,” Education Sciences, vol. 13, no. 5, pp. 514-528, 2023, doi: 10.3390/educsci13050514.
C. J. Arizmendi, et al., “Predicting student outcomes using digital logs of learning behaviors: Review, current standards, and suggestions for future work,” Behavior research methods, vol. 55, no. 6, pp. 3026-3054, 2023, doi: 10.3758/s13428-022-01939-9.
I. Suciati, et al., “Character and moral education based learning in students’ character development,” International Journal of Evaluation and Research in Education, vol. 12, no. 3, pp. 1185-1194, 2023, doi: 10.11591/ijere.v12i3.25122.
Y. Luo, X. Han, and C. Zhang, “Prediction of learning outcomes with a machine learning algorithm based on online learning behavior data in blended courses,” Asia Pacific Education Review, vol. 25, no. 2, pp. 267-285, 2024, doi: 10.1007/s12564-022-09749-6.
Y. Shengyao, et al., “Emotional intelligence impact on academic achievement and psychological well-being among university students: the mediating role of positive psychological characteristics,” BMC psychology, vol. 12, no. 1, pp. 389-406, 2024, doi: 10.1186/s40359-024-01886-4.
Y. Chen and L. Zhai, “A comparative study on student performance prediction using machine learning,” Education and Information Technologies, vol. 28, no. 9, pp. 12039-12057, 2023, doi: 10.1007/s10639-023-11672-1.
K. Kaspar, K. Burtniak, and M. Rüth, “Online learning during the Covid-19 pandemic: How university students’ perceptions, engagement, and performance are related to their personal characteristics,” Current Psychology, vol. 43, no. 18, pp. 16711-16730, 2024, doi: 10.1007/s12144-023-04403-9.
H. Murcahyanto and M. Mohzana, “Evaluation of Character Education Program Based on School Culture,” IJE: Interdisciplinary Journal of Education, vol. 1, no. 1, pp. 38-52, 2023, doi: 10.61277/ije.v1i1.8.
A. Rachman, et al., “Building Religious Character of Students in Madrasah Through Moral Learning,” Tafkir: Interdisciplinary Journal of Islamic Education, vol. 4, no. 1, pp. 78-94, 2023, doi: 10.31538/tijie.v4i1.261.
R. Li, N. C. Hassan, and N. Saharuddin, “College student’s academic help-seeking behavior: A systematic literature review,” Behavioral Sciences, vol. 13, no. 8, pp. 637-675, 2023, doi: 10.3390/bs13080637.
W. H. Prasetiyo, et al., “Promoting digital citizenship among student-teachers: The role of project-based learning in improving appropriate online behaviors,” Participatory Educational Research, vol. 10, no. 1, pp. 389-407, 2023, doi: 10.17275/per.23.21.10.1.
G. A. A. Purnadewi and I. W. Widana, “Improving student’s science numeration capability through the implementation of PBL model based on local wisdom,” Indonesian Journal of Educational Development (IJED), vol. 4, no. 3, pp. 307-317, 2023, doi: 10.59672/ijed.v4i3.3252.
D. T. K. Ng, et al., “Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach,” British Journal of Educational Technology, vol. 55, no. 3, pp. 1082-1104, 2024, doi: 10.1111/bjet.13411.
R. J. Kapoh, et al., “Analyzing the Teacher’s Central Role in Effort to Realize Quality Character Education,” Journal of Education Research, vol. 4, no. 2, pp. 452-459, 2023, doi: 10.37985/jer.v4i2.176.
N. T. Kerman, et al., “Online peer feedback in higher education: A synthesis of the literature,” Education and Information Technologies, vol. 29, no. 1, pp. 763-813, 2024, doi: 10.1007/s10639-023-12273-8.
W. Xu, N. Zhang, and M. Wang, “The impact of interaction on continuous use in online learning platforms: a metaverse perspective,” Internet Research, vol. 34, no. 1, pp. 79-106, 2024, doi: 10.1108/INTR-08-2022-0600.
Y. Xie, et al., “Design and effects of the teacher-student interaction model in the online learning spaces,” Journal of Computing in Higher Education, vol. 35, no. 1, pp. 69-90, 2023, doi: 10.1007/s12528-022-09348-9.
R. Pillai, et al., “Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education,” Information Technology & People, vol. 37, no. 1, pp. 328-355, 2024, doi: 10.1108/ITP-02-2021-0152.
A. Hariyadi, et al., “Building of the Pancasila character with religious harmony in the globalization era,” AL-ISHLAH: Jurnal Pendidikan, vol. 15, no. 2, pp. 2126-2133, 2023, doi: 10.35445/alishlah.v15i2.3208.
N. A. Johar, et al., “Learning analytics on student engagement to enhance students’ learning performance: A systematic review,” Sustainability, vol. 15, no. 10, pp. 7849-7874, 2023, doi: 10.3390/su15107849.
S. Darma, et al., “Implementation of Child Friendly School Governance in Junior High Schools Country 3 Labuhan Deli Satu Atap,” Journal Of Education And Teaching Learning (JETL), vol. 5, no. 1, pp. 91-100, 2023, doi: 10.51178/jetl.v5i1.1214.
L. Qin, W. Zhong, and H. C. Davis, “The evaluation algorithm of English teaching ability based on big data fuzzy K-Means clustering,” International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), vol. 18, no. 2, pp. 1-15, 2023, doi: 10.4018/IJWLTT.325348.
M. Mustafiyanti, et al., “A form of independent curriculum, an overview of independent learning at State Elementary School 05 Gelumbang Muaraenim,” Pengabdian: Jurnal Abdimas, vol. 1, no. 2, pp. 82-96, 2023, doi: 10.55849/abdimas.v1i2.185.
N. I. Mohd Talib, N. A. Abd Majid, and S. Sahran, “Identification of student behavioral patterns in higher education using K-means clustering and support vector machine,” Applied Sciences, vol. 13, no. 5, pp. 3267-3281, 2023, doi: 10.3390/app13053267.
N. Hendrasty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM), vol. 3, no. 1, pp. 46-56, 2024, doi: 10.58602/jima-ilkom.v3i1.26.
S. J. Sultan Alalawi, I. N. Mohd Shaharanee, and J. Mohd Jamil, “Clustering student performance data using k-means algorithms,” Journal of Computational Innovation and Analytics (JCIA), vol. 2, no. 1, pp. 41-55, 2023, doi: 10.32890/jcia2023.2.1.3.
S. Kim, et al., “Statistical assessment on student engagement in asynchronous online learning using the k-means clustering algorithm,” Sustainability, vol. 15, no. 3, pp. 2049-2063, 2023, doi: 10.3390/su15032049.
L. Q. Huatangari, M. A. Cueva Ríos, and R. T. Huerta Camones, “Segmenting the eating behaviour of university students using the K-means algorithm,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 4, pp. 2363-2371, 2023, doi: 10.11591/eei.v12i4.4543.
L. Pamungkas, N. A. Dewi, and N. A. Putri, “Classification of Student grade data using the K-means clustering method,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 13, no. 1, pp. 86-91, 2024, doi: 10.32736/sisfokom.v13i1.1983.
M. Annas and S. N. Wahab, “Data mining methods: K-means clustering algorithms,” International Journal of Cyber and IT Service Management, vol. 3, no. 1, pp. 40-47, 2023, doi: 10.34306/ijcitsm.v3i1.122.
Y. Ariyanto, W. I. Sabilla, and Z. S. As Sidiq, “Recommendation System for Clustering to Allocate Classes for New Students Using The K-Means Method,” Compiler, vol. 13, no. 1, pp. 27-38, 2024, doi: 10.21015/vtse.v11i4.1633.
A. Yudistira and R. Andika, “Pengelompokan Data Nilai Siswa Menggunakan Metode K-Means Clustering,” Journal of Artificial Intelligence and Technology Information (JAITI), vol. 1, no. 1, pp. 20-28, 2023, doi: 10.58602/jaiti.v1i1.22.
D. P. Rahmawati, et al., “Prospective New College Student Dashboard: Insights from K-Means Clustering with Principal Component Analysis,” Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 9, no. 2, pp. 137-144, 2024, doi: 10.25139/inform.v9i2.8462.
L. F. Casinillo, “Mathematics Students’ Coping Behaviour, Happiness, and Self-efficacy in the New Normal: Correlation and K-means Cluster Analysis,” Canadian Journal of Family and Youth/Le Journal Canadien de Famille et de la Jeunesse, vol. 15, no. 3, pp. 51-62, 2023, doi: 10.29173/cjfy29931.