Students’ perception of online learning amidst the Covid-19 pandemic: A study of junior, senior high school and college students in a remote area

Introduction

The COVID-19 pandemic has had a major impact on various aspects of peoples’ lives, namely in the economic, socio-cultural, and educational aspects. It is a global problem affecting educational institutions. Since the start of this pandemic, it has caused shock and disruption to students. The pandemic has forced schools to close and lessons that were carried out face-to-face have shifted to the online world. The use of the Internet and many other significant technologies to create materials for educational purposes, educational distribution, and program management constitute online learning (Fry, 2001). All educators are asked to make a transition, due to the closure of school buildings. There is no other choice but to apply online learning; even though many feel unprepared during this transitional period, students must adjust themselves while trying to build meaning amid various challenges related to the pandemic. Even though learning is carried out online, it is hoped that learning outcomes will remain maximal. There is some evidence that online learning can lead to higher student success (Kurucay & Inan, 2017). A great amount of evidence indicates that there is no substantial difference in the efficacy of well-designed online learning relative to well-designed face-to-face learning (Clark, 2007).

However, the reality is not as expected since not all students respond positively to the implementation of online learning. Today, the majority of colleges and universities still face virtual learning difficulties (Talidong & Toquero, 2020). For example, not all educators and students can use e-learning applications, especially those in remote areas. They feel that they are not optimal in learning. During online learning, they deal with several obstacles such as more assignments that make them feel burdened. This happens since teachers or lecturers in charge assign them two or three tasks for every lesson. Additionally, network connection disturbance in rural areas affects their attendance of online learning. Online learning also influences the students’ motivation in doing assignments. Therefore, the objectives of online learning goals are not always achieved effectively. Students who succeed in learning are those who are active and always follow the learning. Parents of students also confirmed that their children were too lazy to learn online. This situation gives a bad picture of the learning attitudes of students.

In Anna Ya Ni’s research titled “A profile of MPA students’ perceptions of online learning: What MPA students value in online education and what they think would improve online learning experiences”, it is suggested that the use of the video chat software Zoom has the greatest potential to improve classes in order to meet student concerns. Zoom is one of the most frequently used applications in online learning to replace conventional face-to-face classes (Ni, Wart, Medina, Collins, Kimberly, & Pei, 2020) . The problems associated with online learning, especially in remote areas, motivated the authors to conduct this study. Therefore, this current study aims to identify the reasons why students in remote areas perceive that online learning during the COVID-19 pandemic is not effective.

Literature

The development of information and communication technology at this time provides many benefits for human life, so the mastery of such technology is no longer an option but has become a necessity. Through the existence of Internet networks, the use of technology in the educational environment has opened new avenues for educators; face-to-face learning has been transformed into e-learning or online learning (Bernard et al., 2009). In addition to other electronic media, such as CD-ROM, satellite, and television, some experts classify e-learning as ‘education delivered via the Internet’, while online education is described as ‘education delivered only via the Internet or web-based media’ (Lee, 2017). When used interchangeably, online education or e-learning is commonly defined as bridging the space between teachers and students through the use of web-based technology (Ryan & Young, 2015).

The presence of the Internet facilitates human work in many ways, especially in the field of education. The current learning process requires teachers and students to use technology. However, not all students can accept and adapt to these changes. The acceptance of changes in the learning process differs among students. This can be influenced by age, thinking ability, and students’ interest in technology. Students of all ages seem to react differently to the practice of online learning, with older students showing greater appreciation. There are still major variations in how learners view their online interactions during learning (Koohang, Paliszkiewicz, Nord, & Ramim, 2014). There are also concerns about the online learning environment’s efficacy (Hashem, 2011).

Students’ seriousness in taking online learning can be assessed by how they participate in ongoing learning. Participation in online learning requires three dimensions, namely cognitive participation, emotional participation, and behavioral participation (Fredricks, Blumenfeld, & Paris, 2004). These three dimensions are explained as follows: (1) Cognitive participation is the cognitive effort of a student to acquire skills in the online learning process. (2) Emotional participation is described as students’ positive emotions towards teachers, peers, and online learning. (3) Behavioral participation is participation that is manifested by activities that pay attention to learning when studying online (Jung & Jeongmin, 2018).

Methods

An online-based questionnaire study was conducted in a remote area, North Tapanuli, Indonesia. The main objectives of this study were as follows:

Ethics

This research project was approved by the Research Ethics Committee. Ethical Approval Involving Human Respondent from tertiary education (Approval number: 1437.1/Ikn.01/TL.01/09/2020), from junior high school education (Approval number: 086/SMP-SM/IX/2020), and senior high school education, (Approval number: 422.1/063/SMA N 1TRT/2020). Written informed consent from all subjects involved was obtained for participation in the study and subsequent publication.

Data collection

Primary data was collected through an online survey (see Table 1). The survey included 20 items on a four-point Likert scale, from 1 (disagree), 2 (neutral), 3 (agree), 4 (strongly agree). The survey was conducted for over a week. Students were asked to participate in a web-based survey. Of the 75 students surveyed, only 30 students submitted their answers to the online survey, namely 10 students from junior high school education, 10 students from senior high school education and 10 undergraduate students from tertiary education. In this case, gender demography is an important factor to be analyzed.

Instrument

Data in this study were collected through the use of questionnaires. Questionnaires consisted of four indicators; 1) Teachers’ methods of online learning; 2) Students’ convenience in online learning; 3) Motivation to learn online; 4) The effectiveness of online learning. Then the indicators were translated into 20 questionnaire items (Table 1).

Statistical analysis

Data were collected, coded, checked for completeness and input into SPSS Version 25 IBM (SPSS Statistics, RRID:SCR_019096). R is an open-source alternative software that can also be used to do the same analysis. Descriptive statistics (frequency, percentage, mean and standard deviation) were used to describe variables. One-way analysis of variance (ANOVA) was used to determine differences in perceptual scores about learning online for junior high school, senior high school, and college students. In all experiments in this report, we applied an alpha level of.05.

Results

Based on the results of the calculation of the data obtained, the value of each questionnaire indicator was as follows: The teacher’s method in online learning (score = 89.8; average = 2,992; percentage = 74.83%), student comfort in online learning (score = 87.83; average = 2,928; percentage = 73.19%), learning motivation in online learning (score = 86.5; average = 2.883; percentage = 72.08%), effective online learning (score = 85.33; average = 2.846; percentage =71, 11%). After being calculated, the average percentage score = 72.96%. So, based on the hypothesis H1: p ≥ 85% (effective), H0: p ≤ 85% (less effective) indicates that students’ perceptions towards online learning in remote areas are less effective (Table 2).

The conditions that must be met to process data in a One-way ANOVA test are the data must be normally distributed, and the variance must be homogeneous. After our data were processed, the normality test met the first of these requirements, namely, a significance value of.103 > 0.05 (Shapiro-Wilk) thus the data was declared to be normally distributed (Table 3).

Results of the homogeneity of variance test obtained a significance value of.093 > 0.05. Thus, we can be confident that our data distribution was homogeneous (Table 4).

The output in the descriptive section shows the average value of students’ perceptions about online learning: student at junior high school (mean) = 58.10, student at senior high school (mean) = 55.30 and college student (mean) = 61.70. The highest score stating that online learning is less effective than face-to-face learning is that of college students, n = 30, 95% confidence interval for mean, total min = 48 and max = 73 (Table 5).

The ANOVA output in the descriptive section shows sum of squares total = 724,967; df = 29; mean square = 102,933 and 19.226; F = 5.354 and a significance value of 0.011 <0.05, meaning that the average value of student perceptions of the three levels of education about online learning is not significantly different. Students as a whole report the same perception that online learning is less effective in a remote area (Table 6).

Then, the authors conducted a follow-up ANOVA test using Duncan’s test to determine the perceived significance value between junior high school with senior high school students and senior high school with college students. Duncan’s test results have two subsets, namely in the first subset, the significance value was 0.077 > 0.005 of senior and junior high school students’ perceptions, meaning that their perceptions about online learning are not significantly different. In the second subset, the significance value was 0.165 > 0.05 of senior high school and college students’ perceptions, meaning that their perceptions about online learning were also not significantly different. So, the results of Duncan’s test concluded that there was no significant difference between students’ perceptions of online learning, meaning that they had the same perception (Table 7).

In this study, more male students answered that online learning was less effective than female students. The result of data calculation showed that the frequency of male students’ answers was 66.7%, while the frequency of female students’ answers was 33.3% (Table 8).

Discussion

The overall mean score obtained in this study p = 72,96% thus (H0:72, 96% ≤ 85%) indicates that students’ perception of online learning in remote areas is that it is less effective than face-to-face learning. The resulting score needs to be improved for the achievement of learning objectives. The indicators used to recruit perception data include the following:

Conclusions

According to the previous explanations, students generally have more fun when learning is done face-to-face. With face-to-face learning, students can directly get answers to their curiosity about the material being studied. After conducting this research, the assumptions about the displeasure or reduced effectiveness of online learning in this area were proved correct and significant. Times have changed. With the COVID-19 pandemic, students and teachers are required to use technology in learning since learning must now be done remotely to prevent crowds from gathering, to break the chain of the pandemic. Willingly or unwillingly, online learning must be practiced. However, this is also a call for the government to improve Internet networks and infrastructure in remote areas in order to facilitate online education.

The results of this research indicate that online learning is less effective according to the perception of students in remote areas. So, educators are expected to redesign and implement procedures for online learning so that students can still learn as much as possible. From the teacher’s side, it is hoped that teachers will improve methods of teaching, by designing models and other approaches to provide variation in learning in order to raise students’ interest and willingness to learn online. To achieve higher levels of academic success, teachers must ensure there is a complete curricular plan that is tailored to goals, avoiding a large number of student burdens that are practically impossible to meet (Oliveira & Magalhães, 2020).

The results of this research provide additional insight to all those involved in the implementation of education. However, further research is needed to obtain a more complete explanation.

Data availability