Teacher Education Freshmen Applicants’ Current State in the New Normal’s Learning Delivery Platforms

Mark Angelo C. Reotutar


The online learning platform (OLS) is currently the new normal learning setting amidst the Covid-19 pandemic. Teachers need to look on the other side of the traditional classroom-based learning mode to make teaching and learning in the new normal possible. It aimed to analyze the current state of the teacher education freshmen applicants concerning the new normal learning platforms. This study employed a descriptive method of research and considered a sample of 85 freshmen applicants in the College of Teacher Education in the academic year 2020-2021. The frequencies and percent value was used to analyze the data gathered. The following are the verdicts of the study, the bulk of the respondents belong to low-income families with farming as their family source of income. Most of the respondents have their mobile phones while the great majorities are using mobile data only. All of the respondents do not have any idea about the different platforms in online learning. Based on the findings, the researcher concluded that the freshmen applicants in the College of Teacher Education cannot totally survive and are not yet ready to embrace the new normal learning platforms due to poverty and lack of resources. It is therefore recommended that the University administration needs to open other sources of learning platforms such as the use of printed learning materials of which will be delivered door-to-door to the students. Besides, the College of Teacher Education should plan and initiate on how to make learning flexible and more engaging.


COVID-19 pandemic; freshmen applicants' profile; new normal; readiness on the online learning platform; survey research; quantitative method

Full Text:



Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.

Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.

Collis, B., & Moonen, J. (2012). Flexible learning in a digital world: Experiences and expectations. London and New York: Routledge, Taylor & Francis Group.

Compeau, D., & Higgins, C. (1995). Computer Self-efficacy: Development of a Measure and Initial Test. Management Information Systems Quarterly, 19(1), 9.

Demir, O. (2015). The Investigation of E-learning Readiness of Students and Faculty Members: Hacettepe University Faculty of Education Example [Master Thesis]. Ankara: Hacettepe University.

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., et al. (2014). Active Learning Increases Student Performance in Science, Engineering, and Mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410e8415.

Garrison, R., & Kanuka, H. (2004). Blended Learning: Uncovering its Transformative Potential in Higher Education. Internet and Higher Education, 7, 95–105.

Geng, S., Law, K.MY., and Niu, B. (2019). Investigating self-directed learning and technology readiness in blending learning environment. International Journal of Educational Technology in Higher Education. Retrieved from https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-019-0147-0.

Horton, W. (2006). E-learning by Design. San Francisco: Pfeiffer.

IATF (2020). Resolution No. 56 Series 2020. Retrieved from https://www.officialgazette.gov.ph/downloads/2020/07jul/20200716-IATF-RESOLUTION-NO-56.pdf.

Khan, I. M. (2009). An analysis of the Motivational Factors in Online Learning. Doctoral Dissertation. Arizona: University of Phoenix.

Makhroji, I. (2020). Improving Character Education Strengthening through EDMODO-Based E-learning. Budapest International Research and Critics Institute (BIRCI Journal): Humanities. Volume 3, No. 3: 2262-2267.

Milligan, C., and Littlejohn, A. (2014). Supporting Professional Learning in a Massive Open Online Course. The International Review of Research in Open and Distributed Learning, 15(5). https://doi.org/10.19173/irrodl.v15i5.1855.

Parasuraman, A. (2000). Technology Readiness Index (TRI) a Multiple-item Scale to Measure Readiness to Embrace New Technologies. Journal of Service Research, 2(4), 307–320.

Rovai, A. P., and Jordan, H. (2004). Blended Learning and Sense of Community: a Comparative Analysis with Traditional and Fully Online Graduate Courses. The International Review of Research in Open and Distributed Learning, 5 (2). Retrieved from https://doi.org/10.19173/irrodl.v5i2.192.

Stone. (2020). Computer in the Classroom: Desktop vs. Laptop vs. Tablet. Retrieved from https://www.stonegroup.co.uk/insights/computers-in-the-classroom.

Tumapon, T.T. (2020). Education and the ‘New Normal.’ Manila Times. Retrieved from https://www.manilatimes.net/2020/06/04/campus-press/education-and-the-new-normal/729288.

Wahid, R., Pribadi, F., and Wakas, B. E. (2020). Digital Activism: Covid-19 Effects in Capus Learning. Budapest International Research and Critics in Linguistics and Education. (BIRLE-Journal) Volume 3, No. 3: 1336-1342.

Retrieved from http://www.bircu-journal.com/index.php/birle/article/view/1174.

World Health Organization (2020). Coronavirus. Retrieved from https://www.who.int/health-topics/coronavirus#tab=tab_1.

Yilmaz, R. (2016). Knowledge sharing behaviors in the e-learning community: Exploring the role of academic self-efficacy and sense of community. Computers in Human Behavior, 63, 373e382.

Zimmerman, B. J. (2000). Attaining self-regulation: A social-cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation, (pp. 13–39). San Diego: Academic.

DOI: https://doi.org/10.33258/birle.v3i3.1227

Article Metrics

Abstract view : 38 times
PDF - 12 times


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.