Home⇛JPAIR Institutional Research Journal⇛vol. 20 no. 1 (2023)

Factors Affecting Student Compliance in Asynchronous Classes of the Grade 11 Students

JEENADINE M. GUAVIS | ADRIENNE M. ZABALLERO

Discipline: Education, Institutional Research

 

Abstract:

Students’ compliance has long been a recurring problem for educators in the academe. In Online Distance Learning, compliance-based activities are the main output teachers generate students’ grades from. Hence, students’ submissions during synchronous and asynchronous sessions are highly expected. This study aimed to ascertain the factors affecting Student Compliance, and the student’s commitment to submit tasks and outputs within the given time, as required in a course, in Asynchronous Classes of the Grade 11 Students of DLSZ-Vermosa. Eighty-six students and three English teachers were respondents to this mixedmethods research. Qualitative data collected from open-ended questions and focus group discussions among teachers and students were coded into categories thematically. Students’ performance in terms of compliance was measured through documentary analysis of the recorded number of output submissions via Google Classroom and Google Spreadsheet record, respectively. Factors affecting student compliance were determined through a researchers-constructed 5-point Likert scale questionnaire. Results revealed that Personal Constraints, Resource Constraints, and LMS/Instruction constraints do not significantly affect the students’ compliance in asynchronous classes. Among the four factors, only the environment or physiological constraints significantly influence the students’ compliance in asynchronous classes. Thus, the extent of the effect of the environment or physiological constraints is moderate to students’ compliance in asynchronous classes.



References:

  1. Adnan, M., & Adwar, K. (2020). Online learning amid the COVID-19 pandemic: Students perspectives. Journal of Pedagogical Sociology and Psychology, 1(2), 45–51. https://doi.org/10.33902/jpsp.2020261309
  2. Akram, A., Fu, C., Li, Y., Javed, M. Y., Lin, R., Jiang, Y., & Tang, Y. (2019). Predicting students’ academic procrastination in blended learning course using homework submission data. Ieee Access, 7, 102487-102498.
  3. Andriotis, B. N. (2017, March 30). The Flipped Classroom Approach: The Benefits and Challenges. eFront Blog. https://www.efrontlearning.com/blog/2017/03/benefits-challengesflipped-classroom-approach.html
  4. Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The internet and higher education, 27, 1-13.
  5. Brookhart, S. M. (2013). Develop a student-centered mind-set for formative assessment. Voices from the Middle, 21(2), 21.
  6. Cavanaugh, T., Lamkin, M. L., & Hu, H. (2012). Using a generalized checklist to improve student assignment submission times in an online course. Journal of Asynchronous Learning Networks, 16(4), 39-44.
  7. Chen, K. C., & Jang, S. J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in human behavior, 26(4), 741-752.
  8. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Routledge. https://www.utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf
  9. Cosmas, B. F., & Mbwette, T. S. (2009). Open and distance learning in developing countries: The past, the present and the future. Dar es salaam: Open University of Tanzania.
  10. Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.
  11. Dukewich, K. R., & Wood, S. C. (2016). “Can I have a grade bump?” The Contextual Variables and Ethical Ideologies that Inform Everyday Dilemmas in Teaching. Collected Essays on Learning and Teaching, 9, 97-110.
  12. Essau, C. A., Ederer, E. M., O’Callaghan, J., & Aschemann, B. (2008, October). Doing it now or later? Correlates, predictors and prevention of academic, decisional and general procrastination among students in Austria. In Proceedings of the 8th Alps-Adria Psychology Conference, Ljubljana, Slovenia (pp. 2-4).
  13. Gregory, K., & Morón-García, S. (2009). Assignment submission, student behaviour and experience. engineering education, 4(1), 16-28.
  14. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis 6th Edition.
  15. Limniou, M., Schermbrucker, I., & Lyons, M. (2018). Traditional and flipped classroom approaches delivered by two different teachers: The student perspective. Education and Information Technologies, 23, 797-817.
  16. Marzano, R. J. (2012). The many uses of exit slips. Educational Leadership, 70(2), 80-81.
  17. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
  18. Musingafi, M. C., Mapuranga, B., Chiwanza, K., & Zebron, S. (2015). Challenges for open and distance learning (ODL) students: Experiences from students of the Zimbabwe Open University. Journal of Education and Practice, 6(18), 59-66.
  19. Naturil-Alfonso, C., Peñaranda, D., Vicente, J., & Marco-Jiménez, F. (2018, October). Procrastination: the poor time management among university students. In 4th International Conference on Higher Education Advances (HEAD'18) (pp. 1-8). Editorial Universitat Politècnica de València.
  20. Nortey, S., & Bodjawah, E. K. (2014). Submission of art studio-based assignments: Students experience. Journal of Science and Technology (Ghana), 34(3), 88-99.
  21. Owen, D., & Sarles, P. (2012). Exit tickets: The reflective ticket to understanding. Library Media Connection, 31(3), 20-22.
    Smith, D. R., & Ayers, D. F. (2006). Culturally responsive pedagogy and online learning: Implications for the globalized community college. Community College Journal of Research and Practice, 30(5-6), 401-415.
  22. Strayer, J. F. (2012). How learning in an inverted classroom influences cooperation, innovation and task orientation. Learning environments research, 15, 171-193.
  23. Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in science education, 48, 1273-1296.
  24. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International journal of medical education, 2, 53.
  25. Yoo, W., Mayberry, R., Bae, S., Singh, K., He, Q. P., & Lillard Jr, J. W. (2014). A study of effects of multicollinearity in the multivariable analysis. International journal of applied science and technology, 4(5), 9.
  26. You, J. W. (2015). Examining the effect of academic procrastination on achievement using LMS data in elearning. Journal of educational technology & society, 18(3), 64-74

Tools


Copyright © 2024  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic