Main Article Content

Abstract

The study used a quantitative descriptive approach. The results showed that the CSI value obtained through the analysis was 75.67% for the level of student satisfaction, 81.75% for the level of satisfaction of lecturers, 75.15% for the level of satisfaction of education personnel. Students consider that in implementing MBKM, the priority is the issue of funds and the completeness of learning instruments. Things that are considered necessary to be maintained because of their  satisfactory performance are the study program services in providing human resources for course lecturers, mentors from partners, coordinator lecturers and guardian lecturers who are declared capable, responsive, accommodating, communicative, cooperative, helpful, in administering courses and guiding problem solving. The HR Hospitality of academic staff also supports the satisfaction of the MBKM implementation so as to encourage the high interest of students to take MBKM courses in the internship program. Lecturers, staff and students agreed that the socialization and information about MBKM learning had been carried out properly so that it was considered not a priority, besides the three parties argued that the lecturers were in accordance with their competencies and the work environment and infrastructure were in accordance with the needs.  

Keywords

Internship Student Competence Polic

Article Details

How to Cite
1.
Hermawan F, Damayanti J, Nelfia LO, Puspitasari P, Kurniyaningrum E, Kuswanda GF, Margaretta F. Statistical Analysis with Moment Products with a Quantitative Descriptive Approach for Competence in MBKM Implementation . EKSAKTA [Internet]. 2024Dec.30 [cited 2025Jan.15];25(04):508-1. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/350

References

  1. Adarkwah, M. A. (2021). “I’m not against online teaching, but what about us?”: ICT in Ghana post Covid-19. Education and Information Technologies, 26(2), 1665–1685.
  2. Ahmad, F., & Karim, M. (2019). Impacts of knowledge sharing: a review and directions for future research. Journal of Workplace Learning.
  3. Ahmed, V., & Opoku, A. (2022). Technology supported learning and pedagogy in times of crisis: the case of COVID-19 pandemic. Education and Information Technologies, 27(1), 365–405.
  4. Alam, A. (2021). Should robots replace teachers? Mobilisation of AI and learning analytics in education. 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3), 1–12.
  5. AlOmari, F. (2021). Measuring gaps in healthcare quality using SERVQUAL model: Challenges and opportunities in developing countries. Measuring Business Excellence, 25(4), 407–420.
  6. Arnó-Macià, E., Aguilar-Pérez, M., & Tatzl, D. (2020). Engineering students’ perceptions of the role of ESP courses in internationalized universities. English for Specific Purposes, 58, 58–74.
  7. Buchholz, A. C., Vanderleest, K., MacMartin, C., Prescod, A., & Wilson, A. (2020). Patient simulations improve dietetics students’ and interns’ communication and nutrition-care competence. Journal of Nutrition Education and Behavior, 52(4), 377–384.
  8. Cho, H. J., Zhao, K., Lee, C. R., Runshe, D., & Krousgrill, C. (2021). Active learning through flipped classroom in mechanical engineering: improving students’ perception of learning and performance. International Journal of STEM Education, 8, 1–13.
  9. Cochran-Smith, M., Grudnoff, L., Orland-Barak, L., & Smith, K. (2020). Educating teacher educators: International perspectives. The New Educator, 16(1), 5–24.
  10. Demir, A., Maroof, L., Sabbah Khan, N. U., & Ali, B. J. (2021). The role of E-service quality in shaping online meeting platforms: a case study from higher education sector. Journal of Applied Research in Higher Education, 13(5), 1436–1463.
  11. Ellizar, E., Putri, S. D., Azhar, M., & Hardeli, H. (2019). Developing a discovery learning module on chemical equilibrium to improve critical thinking skills of senior high school students. Journal of Physics: Conference Series, 1185(1), 12145.
  12. Fields, N. L., Miller, V. J., Cronley, C., Hyun, K. K., Mattingly, S. P., Khademi, S., Nargesi, S. R. R., & Williams, J. (2020). Interprofessional collaboration to promote transportation equity for environmental justice populations: A mixed methods study of civil engineers, transportation planners, and social workers’ perspectives. Transportation Research Interdisciplinary Perspectives, 5, 100110.
  13. Girouard, A., & Kang, J. (2021). Reducing the UX skill gap through experiential learning: Description and initial assessment of collaborative learning of usability experiences program. Human-Computer Interaction–INTERACT 2021: 18th IFIP TC 13 International Conference, Bari, Italy, August 30–September 3, 2021, Proceedings, Part II 18, 481–500.
  14. Guo, P., Saab, N., Post, L. S., & Admiraal, W. (2020). A review of project-based learning in higher education: Student outcomes and measures. International Journal of Educational Research, 102, 101586.
  15. Jatmika, S., Pramita, E., Setyawati, L., & Narimo, S. (2020). The Inhibiting Factors of 2013 Curriculum Implementation in Vocational High Schools (Case Study of Public and Private Vocational High Schools, Surakarta, Indonesia). International Conference on Progressive Education (ICOPE 2019), 236–241.
  16. Kulal, A., & Nayak, A. (2020). A study on perception of teachers and students toward online classes in Dakshina Kannada and Udupi District. Asian Association of Open Universities Journal, 15(3), 285–296.
  17. Lai, C. S., Tao, Y., Xu, F., Ng, W. W. Y., Jia, Y., Yuan, H., Huang, C., Lai, L. L., Xu, Z., & Locatelli, G. (2019). A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty. Information Sciences, 470, 58–77.
  18. Makkonen, T., Tirri, K., & Lavonen, J. (2021). Engagement in learning physics through project-based learning: A case study of gifted Finnish upper-secondary-level students. Journal of Advanced Academics, 32(4), 501–532.
  19. Payong, M. R., & Pantaleon, K. V. (2021). Education Post Covid-19 Pandemic: Build Partnerships to Strengthen Quality and Competitiveness of Higher Education in Indonesia. ICHELAC 2021: First International Conference on Humanities, Education, Language and Culture, ICHELAC 2021, 30-31 August 2021, Flores, Indonesia, 32.
  20. Purwanti, E. (2021). Preparing the implementation of merdeka belajar–kampus merdeka policy in higher education institutions. 4th International Conference on Sustainable Innovation 2020–Social, Humanity, and Education (ICoSIHESS 2020), 384–391.
  21. Putri, A. V. A., Khofiyah, N. A., & Sutopo, W. (2021). Location design of electric vehicles charging facility: distance‐based method approach. Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management Singapore, March 7‐11.
  22. Roy, R., & Naidoo, V. (2021). Enhancing chatbot effectiveness: The role of anthropomorphic conversational styles and time orientation. Journal of Business Research, 126, 23–34.
  23. Sharma, S., Kumar, N., & Kaswan, K. S. (2021). Big data reliability: A critical review. Journal of Intelligent & Fuzzy Systems, 40(3), 5501–5516.
  24. Shim, T. E., & Lee, S. Y. (2020). College students’ experience of emergency remote teaching due to COVID-19. Children and Youth Services Review, 119, 105578.
  25. Shin, M., & Hickey, K. (2021). Needs a little TLC: Examining college students’ emergency remote teaching and learning experiences during COVID-19. Journal of further and higher education, 45(7), 973-986.
  26. Sürücü, L., & MASLAKÇI, A. (2020). Validity and reliability in quantitative research. Business & Management Studies: An International Journal, 8(3), 2694–2726.
  27. Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses Association, 22(2), 27-30.
  28. Uncuoglu, E., Citakoglu, H., Latifoglu, L., Bayram, S., Laman, M., Ilkentapar, M., & Oner, A. A. (2022). Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems. Applied Soft Computing, 129, 109623.
  29. Colkesen, I., Sahin, E. K., & Kavzoglu, T. (2016). Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. Journal of African Earth Sciences, 118, 53-64.
  30. Wiranto, R., & Slameto, S. (2021). Alumni satisfaction in terms of classroom infrastructure, lecturer professionalism, and curriculum. Heliyon, 7(6), e06679.