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#NoStudentsLeftBehind: The Role of Digital Inclusion in the Academic Performance of Senior High School Students Amid the
Pandemic
Karina Leancel A. Dapal
1, Gaea Ayn Maui E. Entico
1, Kimberly Anne A. Lontok
1, Ariana Mariel D. Magdalena
1, and Wilfred Luis Clamor
1*1 De La Salle University Integrated School (Manila)
Abstract:
As society continually progresses in the 21st century, there is evident advancement in the utilization of digital devices and the internet. With this, digital inclusion, or an individual’s ability to use and access information and communications technology (ICT), is particularly relevant in the present day. Furthermore, the COVID-19 pandemic has only exacerbated the need for adequate digital inclusion levels among students. This paper describes the relationship between the digital inclusion levels and the academic performance of senior high school students during the pandemic, and determines the sociodemographic and socioeconomic characteristics that influence both digital inclusion and academic performance. Using a sample of 203 senior high school students residing in Metro Manila, data was collected by means of an online survey questionnaire utilizing a 5-point Likert scale conducted through Google Forms. The data obtained were viewed and analyzed through a descriptive-quantitative research approach, mainly utilizing inferential statistics, to exhaust and determine the correlation between the primary variables of the study. The findings indicate that there is a significant relationship between digital inclusion levels and academic performance with all three digital inclusion levels expressing significant correlations. Moreover, data present that digital access has the largest effect on the academic performance of the students during the pandemic. Overall, the study suggests that the sociodemographic characteristics and digital inclusion levels significantly contribute to the academic performance of Metro Manila senior high school students amid the COVID-19 pandemic. Due to insufficient data, this research has yet to correlate socioeconomic characteristics and the digital inclusion levels.
Keywords: digital inclusion; academic performance; sociodemographic; socioeconomic; COVID-
19 pandemic
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1. INTRODUCTION 1.1. Background of the Study
Digital inclusion is the ability of individuals and groups to utilize and access information and communication obtained through the use of technology (Institute of Museum and Library Services, 2011). According to Herbert (2017), the digital inclusion agenda aims to decrease, and possibly close the gaps in access and adoption of information and communications technology (ICT) services, especially for mobile devices and the internet. It is a crucial aspect particularly to the aim of “leaving no one behind”.
In the literature, analyzing digital inclusion is frequently seen in digital divide studies. Digital divide studies present the different gaps and disparities influencing digital inclusion. According to Gómez (2018), there are three levels of digital divide: first, which includes the access gap and the quality of access; second, the use gap which covers skills, motivation and emotional gap, and lastly; utility gap, which includes offline outcomes and benefits. Mossberger (2013) states that barriers to access to technology vary across communities and different demographic groups. Ortega (2019) states that digital divide implies disparities between societies and economic opportunities and progress wherein social class continues to set barriers in terms of access to ICTs.
Communities that experience extreme poverty are more likely to be left out and not benefit from modern technology and the internet, with some of the causes being the high cost of devices. Ordinario (2016) states that in the Philippines, social injustice of digital divide has been an impediment to Filipinos in terms of digital inclusion, with only 63 million Filipinos having mobile access. In accordance with the three levels and the concept of social relativity of the digital divide, this study measures digital inclusion through skills, access, and use (Van Deursen and Helsper, 2015).
As modernization progresses, technology and digital devices continue to play a crucial role in education. A study conducted by Apple (2015) concludes that technology helps accomplish student achievement. The use of technology further enhances the provision of the solid foundation of basic skills needed in education. The same study determined that students who have access to technology to acquire and organize information have shown a higher performance level
in terms of comprehension. Despite initially being an alternative method of learning due to the physical restrictions the pandemic has brought, the application of online learning in education has become the new standard. Even before the pandemic, several digital learning means have been widely implemented, such as using language applications, virtual tutoring, virtual conferencing tools, and online learning software.
Despite these, digital inclusion on the academic performance of students during the pandemic has not been further elucidated. Only few studies have been made particularly in evaluating the personal and social aspects of an online setting in a quantitative manner (Junior et al., 2015);
thus, resources and information concerning the study may be limited.
1.2. Objectives of the Study
The study describes the level of digital inclusion among senior high school students in the Philippines and how it influences their academic performance during the COVID- 19 pandemic. Furthermore, the study describes the sociodemographic and socioeconomic characteristics that influence the levels of digital inclusion and academic performance. Specifically, it answers the following questions:
1. What are the sociodemographic (SDC) &
socioeconomic (SEC) characteristics of senior high school students?
2. What is the level of their digital inclusion?
3. What is their academic performance level?
4. How do SDC & SEC influence both their digital inclusion levels & academic performance level ? 5. How does their level of digital inclusion influence
their academic performance level?
2. METHODOLOGY
2.1. Research Design and Setting
This study used a descriptive-quantitative research approach to determine the relationship between the primary variables, namely, digital inclusion and academic performance. Similarly, through using this design, this study exhausted possible factors that contribute to the correlation
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To accomplish this, the study analyzed the sociodemographic and socioeconomic factors of respondents, as well as the dimensions of digital inclusion, their skill, access, and use.
2.2. Population and Sampling
A total of 203 respondents studying in Metro Manila and residing within the Philippines were utilized in this study.
The target population used in this study consisted of senior high school (SHS) students in the Philippines who are currently experiencing and receiving education through the online learning set-up. As an adolescent prepares for adulthood, Harper (2018) states that the various biological changes provide an avenue of learning experiences depending on their environments. Consequently, this study accumulated data and responses from Grade 11 and 12 SHS students with age ranges from 15-19. Respondents were chosen through a purposive-quota sampling method, wherein the selection of participants was highly dependent based on the criteria presented by the researchers relevant to the study.
2.3. Profile of Respondents
The participants of the study included 203 senior high students from schools within Metro Manila. Based on the sample, 155 were females, which constituted more than half (76.35%) of the respondents. 48 males, which comprise 23.65% of the respondents answered the survey. A high preponderance of the respondents reside in urban areas (91.63%), while the remaining 8.37% respondents expressed that they live in rural areas. The average age among respondents is 17.57 and ranges from 15 to 19 years old.
Among them, 67 respondents (33.0%) expressed that they are minors (>18 years old) while on the other hand, 136 respondents (67.0%) answered that they are over the age of 18 (<18 years old).
2.4. Instrumentation
The self-administered survey, which serves as this study’s instrumentation, is divided into four key parts. These sections elaborate and ask questions relating to the respondents sociodemographic and socioeconomic status as well as their digital inclusion levels, specifically digital skill, use, and access, along with their academic performance.
Specifically, section one tackles the sociodemographic characteristics of the respondents while section two focuses on the socioeconomic characteristics. The succeeding section, which focuses on the respondents’ digital inclusion levels, comprises three subsections, mainly their skills, access, and use. In each subsection designated for the respondents’ digital inclusion levels, a Likert scale will be utilized ranging from 1 to 5 (1 = completely disagree; 5 = strongly agree). The last and final section of the survey is allotted for the academic performance of the respondent.
Similar to the previous section, a 5-point Likert scale will be used and will ask for their general average from previous school terms.
2.5. Data Analysis
The first level of data analysis concerns the use of descriptive statistics used to summarize, describe, and analyze the different variables of the study. Descriptive statistics such as frequency counts and percentages were used to describe sociodemographic characteristics. Means and standard deviations will also be used to describe digital inclusion levels and academic performance. The following scores were used in order to serve as the standard measures in analyzing the obtained scores of the respondents: Low = 1.00-2.33, Moderate = 2.34-3.66, High = 3.67-5.00.
This study utilized independent t-tests in describing differences among sociodemographic characteristics in relation with digital inclusion. Moreover, Pearson's correlation coefficient test was used to analyze the relationship between the variables involved in this study. Lastly, in order to determine the relative importance and relationship of each independent variable in the academic performance of a student, Multiple Linear Regression was used.
2.6. Test of Assumptions
The independent variables presented in the study expressed significant correlation scores under a coefficient score of 0.80, which reflects that no multicollinearity was present among the independent variables in this study. The normality of histograms were relatively skewed in nature, hence a bootstrapping method was used to bootstrap 1000 samples. This stipulates that respondents expressed extreme opinions and choices when answering the survey questionnaire.
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3. RESULTS AND DISCUSSION 3.1. Descriptive Statistics and Analysis
Table 1 presents the descriptive statistics garnered for this study. Both females and males exhibited high measures for all variables involved in this study with scores ranging from 3.87 to 4.53. In terms of location, respondents residing in urban areas expressed high measures in terms of their mean scores. The same can be said for those living in rural areas, wherein the measures of the mean scores range from 3.91 to 4.50. Although all variables exhibited high mean measures, there is still a significant difference in the mean scores of digital access as compared to digital skills and digital use in terms of gender and location. While digital skills and digital use both displayed mean scores within the range of 4.15 to 4.50, digital access exhibited values that range from 3.87 to 3.94 which indicate a significant difference from digital skills and digital use.
Inferred values from the conducted reliability analysis indicated that the survey questions were reliable as the variables expressed a significant alpha score wherein α > .70.
These stipulated values can be seen in Table 1.
Table 1.
Descriptive Statistics (n=203) Variable Attrib
utes Mean Scores of DIL Measures Skills Acce
ss Use Acad.
Perf.
Gender p 0.416 0.475 <.001 0.964
Mean Male Female
4.15 4.22
3.87 3.94
4.27 4.53
3.68 3.68
Location p 0.375 0.913 0.761 0.204
Mean Urban Rural 4.22
4.10 3.92 3.91 4.46
4.50 3.66 3.86 Cronbach’s α
General Mean Score
0.854 4.21
0.787 3.92
0.859 4.47
0.797 3.68
General Standard Deviation 0.522 0.810 0.775 0.797
3.2. Pearson’s R Correlation Results
Table 2 presents the correlation matrix among the primary variables. Evidently, all correlation values are significant and positive. Academic performance, the outcome variable, notably has significant and moderate correlations with digital skills, access, and use. These results are especially notable in the context of the COVID-19 pandemic.
The correlation between academic performance and digital skills is significant and moderate (r = 0.532, p < .001).
Similarly, Leung and Lee (2012) found that adolescents with higher informational digital skills perform better both in overall grades and in academic competence, but this was also affected by their age and family’s socioeconomic status.
Meanwhile, the correlation between academic performance and digital access is significant and moderate (r = 0.587, p <
.001). This provides new insight into this relationship, because of the lack of research in the context of the pandemic. Due to the recency of full-time online learning, most studies focus on technology integration in the classroom and home access rather than the reliance on technology in the current pedagogical scene (Hirsch, 2014; D’Angelo, 2018; Apple, 2015; Leung and Lee, 2012). Lastly, the correlation between academic performance and digital use is significant and moderate (r = 0.559, p < .001). This agrees with findings from Jackson et al. (2011), but negates those from Akhter (2013) due to the divisive nature of the subject. Jackson et al. found that more Internet use is associated with better reading skills and higher grade point averages, while Akhter found that Internet addiction is negatively and significantly related to academic performance among undergraduates.
Table 2.
Pearson’s Correlation of Variables, n=203
5 Skills Access Use Acad.
Perf.
Digital
Skills Pearson’s r —
p-value —
Digital
Access Pearson’s r 0.489*** —
p-value < .001 — Digital
Use Pearson’s r 0.737*** 0.531*** — p-value < .001 <. 001 — Acad.
Perf. Pearson’s r 0.532*** 0.587*** 0.559
*** —
p-value < .001 < .001 <
.001 — Note. ***p < .001
3.3. Multiple Linear Regression Results
As presented in Table 4, the three digital inclusion levels namely digital skills, digital access, and digital use all have moderately significant effects on the academic performance of Metro Manila senior high school students amid the pandemic accounting for 45% of the variance, F(3,199)=53.1, p<0.001. Furthermore, the data in Table 4 shows that the independent variables: digital skills, digital use, and digital access are statistically significant predictors having positive and moderate correlations with each other. Relatively, amongst the three, digital access exhibits the highest importance based on the estimates of the model, B=0.395, p<0.001.
The Multiple Linear Regression results in this study conclude that the three digital inclusion levels significantly affect the academic performance of the Metro Manila SHS students during the pandemic. Particularly, it highlights digital access being the most significant predictor that affects the academic performance of the students amongst the three
digital inclusion levels. These claims are supported by Muller (2022), who states that in terms of educational opportunities, the lack of access to the internet and devices hinder students from having a higher quality of education via the internet.
Furthermore, the results of the study are also in line with the findings of UWA (2020), stating that enhancing digital literacy and skills are beneficial for learners, putting them at the center of an increasingly networked social world. In terms of academic engagement and performance, these findings also coincide with the results of a study conducted by Kim, Hong, and Song (2019) wherein digital inclusion levels predict the academic performance of students through their perception of online learning. Findings in a study conducted by KewalRamani et. al (2018) concluded that students who had access to the internet and competent knowledge about ICTs significantly performed better than students who manifested academic difficulties and achievement gaps due to the lack of access to home internet and other personal variables such as sociodemographic and socioeconomic characteristics.
Table 3.
Multiple Linear Regression Results, n=203
Predictor Estimates p R2 f df
< .001 0.445 53.1 (3, 199)
Digital
Skills 0.219 0.022
Digital
Access 0.395 < .001 Digital
Use 0.294 0.007
4. CONCLUSIONS
This study shows that there is a significant relationship between digital inclusion levels and academic performance. The results of this study exhibit that the three digital inclusion levels namely digital skills, digital access, and digital use, contribute 45% of the variance in the academic performance of the respondents. Due to the limitations brought about by the COVID-19 pandemic, the
6 collection of data regarding the socioeconomic status of each respondent was insufficient, which impeded the analysis for this specific dimension and proves to be a limitation for this study. As such, this research still has yet to correlate socioeconomic characteristics and the digital inclusion levels of a student.
While all variables of the digital inclusion levels lead to better academic performance, this study presents that amongst the three digital inclusion levels, digital access appears to have the largest impact on the academic performance of the students during the pandemic. In line with the findings of the study, Rotas and Cahapay (2020) identify digital inclusion concerns such as internet connectivity, inadequate learning resources, electric power interruptions, poor peer communication, poor learning environment, financial related problems, and physical health compromises, amongst others, as difficulties in the remote learning of the students in the wake of the COVID-19 pandemic. By all counts, the digital inclusion levels significantly contribute to the academic performance of Metro Manila senior high school students amid the pandemic.
5. ACKNOWLEDGMENTS
The authors would like to use this opportunity to express their deepest gratitude to those who have helped in the creation of this paper. Furthermore, the authors would like to thank their friends and family for their unwavering support and words of appreciation in times of hardship. Lastly, the authors would like to thank the Almighty God for making this opportunity happen, and for another fruitful academic year, making the 4th Senior High School Research Congress of De La Salle University possible.
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