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American Journal of  Smart 
Technology and Solutions (AJSTS)

Freshmen’s Technological Expertise and Distance Learning Readiness: A Convergent 
Parallel Design

Jordan B. Mallari1, Ivy T. Lozarita1, John Harry S. Caballo1*, Jobert C. Regidor1

Volume 3 Issue 1, Year 2024
ISSN: 2837-0295 (Online)

DOI: https://doi.org/10.54536/ajsts.v3i1.2423
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: January 31, 2024

Accepted: March 08, 2024

Published: March 11, 2024

This convergent parallel mixed methods study primarily focused on technological 
expertise and distance learning readiness of  freshmen students. The study had two phases: 
the quantitative phase and the qualitative phase. In the quantitative phase, results found 
that technological expertise and distance learning readiness were highly correlated with 
each other, with an R-value of  .828 and a p-value of  0.000. In the qualitative phase, 
four themes were extracted: Technological Tools as a Significant Factor in Distance 
Education, Technological Expertise as Influenced by Minor Skills and Educational 
Courses, Technological Use Due to Education’s Status Quo, Challenges in Technological 
Availability, and Technological Background. The results showed a merging and converging 
characteristic of  both data. The lived experiences of  the freshmen students showed that 
technological tools, which are an indicator of  technological expertise, are also a factor to 
be considered in distance learning readiness.

Keywords
Technological Expertise, 
Distance Learning Readiness, 
Mixed Method

1 University of  Mindanao Senior High School, Bolton Campus, Davao City, Philippines
* Corresponding author’s e-mail: harrycaballo@gmail.com

INTRODUCTION
The COVID-19 pandemic prompted a significant 
overhaul of  the education system worldwide, with 
countries implementing distance learning measures such 
as online tools, television broadcasts, and video lectures 
(UNESCO, 2020; Bana et. al). Mitchell (2020) noted 
that only a third of  global universities offer full online 
courses, impacting students significantly. UNESCO’s 
data (2020) indicated that at its peak in April 2020, 
over 91% of  students globally were affected by school 
closures, highlighting the pandemic’s profound impact on 
education. Gabor (2020) emphasized the dual challenges 
of  school closures and students’ lack of  technological 
access and skills. Llego’s (2020) report revealed a decline 
in student enrollment for the school year 2019-2020, 
indicating difficulties in adjusting to the new learning 
mode. A study among medical students (Baticulon, 2020) 
showed that only 41% felt capable of  engaging in online 
learning, with technological barriers cited. The data also 
highlighted many students in Region XI facing potential 
challenges due to a lack of  technological expertise.
Freshmen students, being more susceptible to changes in 
learning (Olutola et al., 2016), require careful examination 
of  their readiness for distance learning. Studies (Kaymak, 
2013; Beyth-Marom et al., 2013; Kitsantas & Chow, 2010) 
emphasized the relationship between online learning 
readiness, learning preferences, and psychological 
attitudes toward technology use. Learning motivation, 
study habits, and technological attributes were identified 
as key factors influencing distance learning performance 
(Horzum et al., 2015; Joosten & Cusatis, 2020; Olutola et 
al., 2016).
The sudden shift to online learning, particularly affecting 
first-year tertiary-level students, raised concerns at 
international, national, and local levels. Urgent action 

is needed to understand how freshmen’s technological 
expertise correlates with their learning readiness (Olutola 
et al., 2016). Recognizing the gaps between students’ 
readiness and technological aspects is crucial for the 
successful implementation of  e-learning (Olutola et 
al., 2016). This paper aims to help higher education 
institutions assess student readiness in the online learning 
environment by considering technological factors.

Research Questions
This study investigates the correlation between freshmen 
students’ technological expertise and readiness for 
distance learning. Additionally, it seeks to derive codes 
and themes from participants’ experiences and ultimately 
integrate and validate both quantitative and qualitative 
data. Specifically, the study will address the following 
objectives:
1. What is the level of  technological expertise in terms of:

1.1 Technological spaces;
1.2 Computer skills; and
1.3 Perception of  the use of  technology-enabled learning?

2. What is the level of  distance learning readiness in terms of:
2.1 Self-direction;
2.2 Learning preference;
2.3 Study habit;
2.4 Technology skills; and
2.5 Computer equipment capabilities?

3. Is there a significant relationship between the level of  
technological expertise and the level of  distance learning 
readiness?
4. Based on the experiences of  participants, what is the 
role of  technological expertise in the distance learning 
readiness of  freshmen students?
5. How do the qualitative data corroborate with the 
quantitative data?



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Am. J. Smart. Technol. Solutions 3(1) 22-31, 2024

LITERATURE REVIEW
Technological Expertise
Farley (2019) provides a comprehensive definition of  
technological expertise, encompassing the skills necessary 
for intricate computer and physical technology operations 
within diverse industries. He specifically highlights its 
significance in enhancing students’ learning experiences 
and extending their capabilities in mathematical research. 
Additionally, Beers (2011) underscores the importance of  
integrating technological expertise into students’ learning 
preferences for e-learning encounters, defining it as the 
ability to utilize technology efficiently, effectively, and 
ethically for accessing and managing information.
Ziefle and Schaar (2010) highlight the positive 
impact of  technological expertise on understanding 
modern technologies, particularly in the information 
and communication technology field. Larson and 
Miller (2011) argue that increasing expertise in digital 
technologies is essential as technology becomes more 
prevalent in society, especially in researching, organizing, 
assessing, and communicating information. However, 
the Australian Council for Educational Research (2016) 
notes that despite its growing importance, technological 
expertise faces challenges due to revisions in hierarchical 
structures and production methods, leading to 
uncertainty, particularly in critical economic sectors like 
financial services.
The level of  technological expertise has three indicators: 
technological spaces, computer skills, and perception of  
technology-enabled learning.

Technological Spaces
As defined by Srivastava (2015), encompass the availability 
of  technology equipment for students, representing 
an essential factor in education. These spaces facilitate 
efficient learning and effective teaching by providing 
technical capacity for applying external knowledge 
(Ottenbreit-Lefwich, 2014). Alsied and Pathan (2013) 
liken technological spaces to the dominance of  the 
English language, emphasizing their role in creating 
opportunities for efficient learning and teaching. Despite 
government initiatives, the shortage of  computer 
resources can impede technology experiences, leading to 
a decrease in technological expertise (Buabeng-Andoh, 
2012). Gilakjani (2013) supports this, noting the shortage 
of  information technology as a contextual factor affecting 
computer hardware/software for learners and instructors. 
The Office of  Educational Technology (2016) highlights 
that technology-enabled learning environments, or 
technological spaces, enable less experienced learners 
to access specialized communities, fostering increased 
engagement in learning. The societal context plays a 
crucial role in the use of  technology in distance learning, 
establishing a direct association between technological 
spaces and the independent variable.

Computer Skills
Refer to the ability and speed to locate, gather, and 

provide data in the graphical user interface of  computers, 
enabling individuals to perform basic operations 
(Goldhammer et al., 2013). Israel (2010) emphasizes the 
significance of  computer skills in the effective use of  the 
Internet. Paino and Renzulli (2012) underscore the role 
of  computer skills in perpetuating inequality in schools, 
while Guclu (2020) views them as crucial skill sets for 
technological proficiency and career success. Baker 
(2013) supports this by characterizing computer skills 
as a set of  abilities encompassing the use of  programs 
for accounting, databases, email, web, programming, 
spreadsheets, and word processing. Lowe’s (2019) report 
suggests that computer skills acquired in high school 
contribute to success in college, particularly in processing 
and presenting information, aligning with the concept of  
technological expertise in the current study.

Perception of  Use of  Technology-Enabled Learning
Perceived usefulness, as defined by Peart, et al. (2017), is a 
relevant factor influencing the acceptance of  technology. It 
guides research in understanding students’ characteristics 
and how they define usefulness, encompassing their 
attitude and acceptance of  e-learning training methods. 
These factors are crucial for the effectiveness of  
instructional processes, influenced by individual learning 
styles, available technical tools, learners’ opinions on 
technological assistance, and technology sophistication in 
online learning (Parai et al., 2015). Beckman et al. (2014) 
case study of  12 students from Australian secondary 
schools supports these findings. The study provides 
detailed accounts of  students’ technology experiences 
in various contexts, highlighting the heterogeneous and 
complex network of  factors influencing technology 
practices. The study concludes that students’ perceptions 
of  technology use directly impact their technological 
expertise for learning. In the current study, students’ 
perceptions of  technology are used as an indicator of  
the independent variable, aligning with the claim that 
perception is a direct factor in determining the level of  
technological expertise. All the previous studies presented 
reiterate the researchers’ aim to regard technological 
expertise as a determinant variable. However, it is notable 
that these studies only involved a minimal description of  
the level of  technical experience and how it correlates to 
the level of  distance learning readiness that this current 
study will attempt to provide.

Distance Learning Readiness
Distance learning readiness, as specified by Hashim and 
Tasir (2014), refers to the preparedness of  an institution 
or organization for various aspects of  e-learning 
technology before implementing the entire e-learning 
environment for diverse purposes. Ngampornchai 
and Adams (2016) highlight that sufficient e-learning 
readiness can offer educational opportunities to students 
who cannot access higher education, contributing to 
a country’s competitiveness in the Southeast Asian 
Nations Association. This readiness variable significantly 



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influences students’ perspectives on education, as 
suggested by Yilmaz’s study (2017), which indicates 
that student motivation is directly affected by the level 
of  distance learning readiness. Bovermann et al. (2018) 
identify technological components and computer 
skills as critical predictors of  social interaction, social 
communication, and learning outcomes in online learning. 
Additionally, Rasouli et al. (2016) highlight the flexibility 
in methodology, content management, synchronous 
and asynchronous interactions, organization of  courses, 
educational projects, student projects, and assessment 
between teachers and students facilitated by distance 
learning readiness.
The level of  technological expertise has five indicators: 
self-direction, learning preference, study habits, 
technological skills, and computer equipment capabilities.

Self-Direction
Self-direction in education, as highlighted by Petro 
(2017), encompasses diverse activities such as exploring 
new knowledge, critical thinking, active participation in 
learning groups, and creating one’s learning path. Boyer 
et al. (2014) emphasize the efficiency of  self-direction 
in engaging and training adult learners. Self-directed 
learning, as described by Garland (2017), allows students 
to decide what and how they learn with teacher guidance. 
Murad et al. (2010) consider it a suitable approach to 
keep practitioners informed about recent literature. The 
effectiveness of  self-directed learning extends to medical 
students, patients, physicians, nurses, and healthcare 
practitioners. Schiller’s (2020) findings emphasize the 
importance of  self-direction in a student’s ability to 
adapt to distance learning, particularly in completing 
assignments and handling technological tasks. Online 
classes, as mentioned in the research, require students 
to navigate virtual spaces independently, indicating the 
relevance of  self-direction to students’ distance learning 
readiness.

Learning Preference
Learning preference, as defined by Liew, Barua, and 
Sidhu (2015), refers to the adopted learning style or 
methods preferred by learners for acquiring, evaluating, 
and interpreting insight. James, Thomas, and D’Amore 
(2011) characterize learning preference as an individual’s 
favored way of  acquiring knowledge and comprehending 
information, aligning with the psychological type theory 
of  Jung. Jain, Sharma, and Joachims (2015) suggest that 
student learning preferences are complex and influenced 
by the context of  various objects and human interactions 
in their environment. Battalio’s study (2010) indicates that 
learners with healthy learning preferences exhibit more 
success and better adaptation to online courses compared 
to other learning dimensions tested. This underscores 
that learning preference serves as a valuable indicator of  
students’ readiness and performance in distance education.

Study Habit

Study habits, as defined by Uju and Paul (2017), encompass 
how an individual approaches their studies, emphasizing 
the importance of  good study habits for academic 
success. Rabia et al. (2017) further explain that study 
habits determine the extent of  learning, one’s academic 
goals, and the level of  achievement. Olatula et al. (2016) 
note that study habits can be systematic, efficient, or 
inefficient, with good study habits contributing to better 
academic performance and a lack thereof  leading to 
inadequacies. Somuah and Dankyi’s study (2014) focused 
on the study habits of  University of  Cape Coast Distance 
Education learners in Ghana. The results revealed habits 
such as reading while lying in bed, studying for less than 
three hours a day, not seeking additional materials from 
the internet, and reviewing notes before face-to-face 
meetings. The study suggests that study habits play a 
role in the distance learning readiness of  students, with 
implications for online learning, where unhealthy study 
habits may be manifested.

Technology Skills
Computer skills, defined as the ability to use computers 
and technology effectively (Resume.com, 2019), involve 
acquiring expertise in performing physical or digital tasks 
(Indeed Career Guide, 2017). Wikiversity (2020) stated 
that technological abilities, especially computer skills, 
are crucial for effectively utilizing computers and related 
technology. In the study by Somuah and Dankyi (2014) on 
University of  Cape Coast Distance Education learners, 
computer skills were considered vital, particularly in the 
context of  distance learning education. The study suggests 
that those who do not develop their computer expertise 
may lag behind in the digital revolution, emphasizing 
computer skills as fundamental predictors of  a student’s 
readiness for engagement in distance learning.

Computer Equipment Capabilities
Digital accessibility, as defined by Kulkarni (2018), 
pertains to the extent to which people with disabilities or 
special needs can access and navigate products, devices, 
services, or environments. Foley and Ferri (2012) note 
that accessible technologies enhance access and flexibility 
for students and adults. However, Haines et al. (2018) 
suggest that technology availability has minimal and 
fleeting effects on group development.
Mitchell (2020) emphasizes the importance of  computer 
capabilities in online learning, where distance education 
relies on electronic communications. The research 
indicates that a lack of  computer capabilities among 
students can result in decreased performance and 
engagement in online learning, suggesting a direct 
correlation between computer equipment capabilities and 
distance learning readiness. Notably, while many studies 
on online learning readiness focus on college students 
and their relationship with variables like perception 
and satisfaction, research on the potential connection 
between online learning readiness and technological 
expertise is limited. The researchers aim to address this 



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gap and contribute to the growing literature on the topic. 
Correlation between Variables
Halili’s (2019) study, “Technological Advances in 
Education,” explores the use of  advanced technology in 
teaching and learning to enhance the education system 
and engage students in distance learning. The research 
suggests that the efficient utilization of  advanced 
technology is crucial for student engagement in online 
schooling, contributing to the readiness for distance 
learning. A quantitative approach revealed a lack of  
efficient technology usage in Malaysia’s education system, 
urging improvements for global competitiveness. Cigdem 
and Yildirim’s (2014) study investigates the relationship 
between students’ technological approaches and online 
learning readiness at a vocational college. The research 
implies that students’ technological expertise may 
positively relate to their readiness for online learning, 
affecting dimensions of  online learning readiness.
Wei and Chou’s (2020) study explores the structural 
model determining the impact of  distance learning 
perceptions and readiness on students’ online learning 
performance. Results suggest that students’ computer 
efficacy and motivation positively influence online 
performance, indicating a positive relationship between 
computer skills (a component of  technological expertise) 
and online readiness. Contrary to these findings, Mafenya 
(2013) presents empirical evidence opposing the idea 
that technological spaces or resources significantly 
impact students’ readiness for e-learning. The study 
at the University of  South Africa indicates a high level 
of  acceptance despite resource shortages, challenging 
the assumption that technological spaces directly affect 
readiness.
Lai, Wang, and Lei (2012) suggest in their study that 
adapting technology indirectly affects computer self-
efficacy and perceived utility in online education. This 
contradicts the notion that technology use positively 
relates to student performance and engagement in online 
learning, providing an alternative perspective. Raes and 
Dapaepae’s (2019) study explores how exposure to 
technology can improve students’ performance in school. 
The results suggest that students exhibit a positive 
predisposition to educational technology’s usefulness and 
ease of  use, with perceptions significantly improving after 
technology exposure.
In summary, these studies offer varying perspectives 
on the relationship between technological expertise, 
readiness for online learning, and the impact of  
technology on students’ performance and engagement in 
distance education.

MATERIALS AND METHODS
A convergent parallel mixed-method design was utilized 
in this study. Creswell and Clark (2011) stated that mixed 
methods research includes at least one quantitative and 
one qualitative strand. For the quantitative phase, this 
study employed a descriptive correlational research model 
to explore relationships between variables, specifically 

examining if  changes in one variable corresponded to 
changes in another (Tan, 2014). In the qualitative phase, 
a phenomenological design was employed, concentrating 
on individual experiences through in-depth, occasionally 
repeated interviews with participants. This approach 
allowed researchers to extract insights for generating 
themes and codes, facilitating cross-referencing of  data 
from both researches.
This study focused on freshmen students from diverse 
courses and schools in Davao City during the 2020-
2021 academic year. For the quantitative phase, due 
to the unspecified total freshmen population, the 
researchers utilized a purposive snowball sampling 
method, particularly the maximum variation sampling 
technique, resulting in a sample size of  one hundred 
(100) freshmen students. Purposive sampling, also 
known as judgmental or selective sampling, involves 
researchers using their judgment to choose participants 
based on prior knowledge of  the study’s purpose (Foley, 
2018). By combining this with snowball sampling, where 
participants nominate others, the researchers ensured 
diversity in the sample. Maximum variation sampling, 
guided by researchers’ judgment, was employed to select 
participants with diverse characteristics (Dudovskiy, 
2010). For the qualitative phase, fourteen participants 
were purposively chosen, nine for in-depth interviews 
and 5 for focus group discussion. The selection criteria 
ensured maximum variation of  responses, aligning 
with the vulnerability of  freshmen students to learning 
changes (Olutola et al., 2016). Open-ended, discovery-
oriented interviews were conducted to explore 
participants’ feelings and perspectives deeply (Guion 
et al., 2015). Focus group discussions, recommended 
for understanding issues at a deeper level than surveys 
(Prasad & Garcia, 2015), complemented the qualitative 
aspect of  the research.
The data analysis method applied in this study, as per 
Spaulding (2011), was unilaterally chosen and had a 
significant impact on the questionnaire’s form and 
purpose. The research instrument, the questionnaire, was 
adapted from Das and Mishra (2016) for technological 
expertise and Penn State University (2015) for distance 
learning readiness. The adapted questionnaire comprised 
two sections, with the first divided into three segments and 
the second into five, each addressing specific indicators 
of  the independent and dependent variables. Each 
category contained thirty questions, totaling sixty in the 
questionnaire. Respondents expressed their agreement 
with statements on a scale from one to five, reflecting 
varying levels of  agreement. Three experts validated the 
questionnaire, yielding a mean score of  4.57 out of  5, 
indicating excellent quality. For determining technological 
expertise, a test questionnaire adapted from Das and 
Mishra (2016) was used, and the level was calculated 
based on the mean scores of  the independent variable 
indicators. For assessing distance learning readiness, a 
standardized questionnaire from Penn State University 
(2015) was adopted, and the level was determined using 



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the mean scores of  the dependent variable. Pilot testing 
with fifty individuals ensured the questionnaire’s credibility 
and reliability, as confirmed by a Cronbach’s alpha of  .928, 
indicating excellent reliability and internal consistency 
for all sixty items (Institute for Digital Research and 
Education, 2020). In the qualitative phase, the researchers 
employed an interview guide for in-depth and focus group 
discussions. This guide, according to Menzies et al. (2016), 

is a list of  high-level topics with corresponding questions 
designed to address specific aspects during the interview. 
The interview guide utilized in this study consisted of  one 
central question with seven sub-questions. 

RESULTS AND DISCUSSION
Technological Expertise of  Freshmen Students
Table 1 shows the level of  technological expertise 

Table 1: Technological Expertise of  Freshmen Students
Indicators Mean Standard Deviation Descriptive Level
Technological spaces 3.6460 .66049 High
Computer skills 3.5492 .74251 High
Perception of  the use of  technology-enabled learning 3.8143 .86038 High
Overall 3.6698 .64627 High

concerning its three (3) indicators: technological spaces, 
computer skills, and perception of  the use of  technology-
enabled learning. All indicators had a mean of  more than 
3.49 but less than 4.50, which gave us a high overall mean 
of  3.6698 for the independent variable. This indicated 
that the level of  technological expertise was manifested 
by the freshmen students oftentimes. Further, students 
had a strong foundation of  expertise in using technology 
in distance learning.
Table 2 mirrored the level of  distance learning readiness 

concerning its three (5) indicators: self-direction, 
learning preference, study habit, technological skills, and 
computer equipment capabilities. All indicators except 
for the computer equipment capabilities had more than 
3.49 but less than 4.50, which gave us a high overall mean 
of  3.7018 for the dependent variable. This indicated 
that the freshmen’s level of  distance learning readiness 
is manifested oftentimes. Further, this meant that the 
students had a high readiness level for distance learning 
education.

Table 2: Distance Learning Readiness of  Freshmen Students
Indicators Mean Standard Deviation Descriptive Level
Self  – Direction 3.8660 .79952 High
Learning Preference 3.8871 .61865 High
Study Habit 3.5386 .67001 High
Technological Skills 3.7940 .83434 High
Computer Equipment Capabilities 3.4233 .90435 Moderate
Overall 3.7018 .61266 High

Table 3 illuminates a robust and statistically significant 
relationship between freshmen’s technological expertise 
and their distance learning readiness, as evidenced by the 
high R-value of  .828. This substantial correlation suggests 
a direct proportionality, signifying that an increase in 
technological expertise corresponds to an elevated level 
of  distance learning readiness, and conversely, a decrease 
in technological expertise corresponds to a decline in 
distance learning readiness. This observed relationship 
holds true for freshmen students at the tertiary level. 

Notably, the p-value of  0.000 underscores the significance 
of  the relationship between technological expertise and 
distance learning readiness. This low p-value affirms that 
the observed association is not due to chance, further 
reinforcing the validity and importance of  the identified 
link between these two variables.

Experiences and Insights on Technological 
Expertise and Distance Learning Readiness
The data gathered from in-depth interviews and focus 

Table 3: Significant Relationship between Technological Expertise and Distance Learning Readiness
Variables r - value p-value Decision Interpretation
Technological Expertise .828 .000 Reject H0 Significant
Distance Learning Readiness

group discussions were transcribed, coded, and organized 
into themes. For this particular research question on 
technological expertise and distance learning readiness, 
four themes were generated. The themes are as follows: 

Technological Tools as a Significant Factor in Distance 
Education, Technological Expertise as Influenced by 
Minor Skills and Educational Courses, Technological 
Use Due to Education’s Status Quo, and Challenges in 



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Technological Availability and Technological Background.
Technological Tools as a Significant Factor in 
Distance Education
When freshmen students were interviewed about their 
technological expertise and distance learning readiness, 
their responses reflected that Technological Tools as 
a Significant Factor in Distance Education became 
a reoccurring theme. The said theme includes three 
codes, namely: Efficiency of  Technological Tools, Time 
Management as an Effect of  Utilizing Technology, and 
Learning Independence during Distance Education.

Technological Expertise as Influenced by Minor 
Skills and Educational Courses
When the participants were asked what experiences 
influenced them to have the level of  technological 
expertise they have at the moment, researchers found out 
that their experiences are influenced by two major factors. 
First, are their minor skills from using the internet and 
technological tools. Secondly, are the educational courses 
they went through back in Junior High School and 
Senior High School. Besides, two codes were generated 
from the theme: Minor Technological Skills Influences 
Students’ Technological Learning Skills and Technology 
Education in High School as Major Influence to Students 
Technological Expertise.

Technological Use Due to Education’s Status Quo
When freshmen students were interviewed about their 
technological expertise and distance learning readiness, 
their responses reflected that the technological use due to 

education’s status quo became a reoccurring theme. The 
said theme has two codes: More Frequent Technological 
Use and LMS as Common Factor of  Technological Use.

Challenges in Technological Availability and 
Technological Background
When the participants were interviewed about their 
difficulty in using technology during their distance 
learning education, their responses revealed that particular 
challenges in technological availability and technological 
background became an essential theme.
This theme has two codes which include accessibility of  
technology affects the level of  difficulty, and students’ 
insufficient technological knowledge affects the level of  
difficulty.

Corroboration of  the Qualitative and Quantitative 
Findings
Merging
The nature of  data in the given focal point is merging. 
The results from the quantitative and qualitative phases 
were integrated using a joint display.
In the process, the researcher evaluates and analyzes first 
the quantitative findings and then discusses them with the 
qualitative findings to either confirm or not the statistical 
results (Todd, 2016). According to Creswell (2006), the 
data are merged when the researcher takes the two data 
sets and explicitly brings them together or integrates 
them.

Converging

Table 6: Joint Display of  Salient Qualitative and Quantitative Findings
Aspects/Focal Point Quantitative Data Findings Qualitative Data 

Findings
Nature of  Data 
Integration

Axiological 
Implications

Relationship Between 
Technological 
Expertise and 
Distance Learning 
Readiness

R-value is .828 which 
indicates that the two 
variables are highly correlated 
by 82.8%
The P-value is 0.000 which 
indicates that the relationship 
between two variables is 
significant.
Thus, rejecting the null 
hypothesis, means that there 
is a significant relationship 
between the freshmen’s 
technological expertise and 
distance learning readiness.

Technological Tools 
as a Significant Factor 
in Distance Education 
with Codes:
• Efficiency of  
Technological Tools
• Time management 
as an Effect of  
Utilizing Technology
• Learning 
Independence during 
Distance Education

Merging - 
Converging

Freshmen 
students 
should have 
enough 
technological 
knowledge 
and skills 
to be ready 
in distance 
learning.

The findings of  the study show that the integration of  the 
quantitative and qualitative data is converging. This trend 
occurs when both data are collected at different times and 
evaluates them separately. Afterwards, equating the result 
to approve the findings (Demir & Pismek, 2017). 

Relationship between Technological Expertise and 
Distance Learning Readiness

As illustrated in the table, column 2 provides quantitative 
results, which shows that there is a significant relationship 
between the technological expertise and distance learning 
readiness of  students. There is a high correlation between 
the two variables with 82.8% of  correlation. Their 
relationship is also significant with a 0.000 p-value. 
The corroborated findings approved the mentioned 
study by Halili (2019), where they found that the usage of  



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technology can improve student’s engagement in online 
learning. Further, Cigdem and Yildirim’s (2014) report 
supported the claim as it was inferred from their findings 
that technological expertise could be positively related to 
their learning readiness in an online setting. Moreover, the 
current study’s findings complemented the results of  Wei 
and Chou’s (2014) study. Their results showed that computer 
skills relate to the independent variable of  the current study 
as a reason or a mediating variable for a high level of  distance 
learning readiness among freshmen students.

CONCLUSION
Based on the findings of  the study, freshmen students 
manifested a very strong foundation of  technological 
expertise. Students also showed sufficient technological 
availability in their learning environment. Further, 
freshmen students had enough computer skills and had 
a positive perception of  technology-enabled learning.  
Moreover, students also showed a very strong readiness 
level for distance learning. To elucidate, they manifested 
healthy self-directed thinking towards distance education. 
Freshmen students also had a good learning preference 
and healthy study habits during their education in the 
distance learning mode. Further, their skills in utilizing 
technologies were also sufficient. However, their usage of  
computer equipment was not that highly manifested all 
the time. The lived experiences of  freshmen students, on 
the other hand, imply that they become more efficient, 
time-bounded, and independent while learning online 
with respect to their high level of  technological expertise. 
Thus, it corroborates positively with the statistical results 
from the quantitative phase. Therefore, freshmen students 
possess technological skills such that they become more 
ready to engage in distance learning. These skills have 
been used to help them be more effective despite the 
struggles they encounter online.
Based on the findings that freshman students have 
a strong technological foundation and readiness for 
distance learning, the researchers would make the 
following research recommendations:
Expand the study to include students from various 
demographics and backgrounds to see if  there are 
differences in technological expertise and distance learning 
readiness based on other factors. Survey professors and 
university administrators about their perspectives on 
students’ technology skills and needs related to distance 
learning. Identify any gaps between student capabilities 
and instructor expectations. Investigate the technology 
training and support provided by colleges/universities 
to identify gaps and areas for improvement in preparing 
freshman students for online learning. Research the 
influence of  high school experiences with technology 
on freshman students’ university preparation. Determine 
what capabilities students gain before college.

REFERENCES
Alsied, S. M., & Pathan, M. M. (2013). The Use 

of  Computer Technology in EFL Classroom: 

Advantages and International Journal of  English 
Language & Translation Studies. International Journal 
of  English Language & Translation Studies (2308-5460). 

Australian Council for Educational Research. (2016). 
A Global measure of  digital and ICT literacy skills. 
UNESDOC Digital Library, 1-26. Retrieved September 
18, 2020, from https://unesdoc.unesco.org/
ark:/48223/pf0000245577

Baker, W. M. (2013, August 26). Empirically Assessing the 
Importance of  Computer Skills. Journal of  Education 
for Business, 88(6), 345-351. https://doi.org/10.1080/
08832323.2012.725433

E. Bana, E., A. Basanes, R., & Malabarbas, G. T. (2022). 
Online Learning Readiness of  Public Secondary 
Schools: The Perspective Between School Heads and 
Teachers. American Journal of  Education and Technology, 
1(2), 36–45. https://doi.org/10.54536/ajet.v1i2.393

Batallio, J. (2010). Success in Distance Education: 
Do learning Styles and Mutiple Formats Matter? 
Journal of  Distance Education, 71-78. https://doi.
org/10.1080/08923640902854405

Baticulon, R. E., Sy, J. J., Alberto, N. R. I., Baron, M. B. 
C., Mabulay, R. E. C., Rizada, L. G. T., ... & Reyes, J. 
C. B. (2021). Barriers to online learning in the time 
of  COVID-19: A national survey of  medical students 
in the Philippines. Medical science educator, 31, 615-626. 
https://doi.org/10.1101/2020.07.16.20155747

Beckman, K., Bennett, S., & Lockyer, L. (2014). 
Understanding students’ use and value of  technology 
for learning. Learning, Media and Technology, 39(3), 346-
367. https://doi.org/10.1080/17439884.2013.87835
3

Beers, S. Z. (2011). 21st Century Skills: Preparing Students 
for THEIR Future. STEM. Retrieved September 18, 2020, 
from https://cosee.umaine.edu/files/coseeos/21st_
century_skills.pdf

Beyth-Marom, R., Chajut, E., Roccas, S., & Sagiv, L. 
(2013). Internet-assisted versus traditional distance 
learning environments: factors affecting students’ 
preferences. Computers & Education, 65-76.  http://
www.elsevier.com/locate/compedu/a4.3d

Bovermann, K., Weidlich, J., & Bastiaens, T. (2018). 
Online learning readiness and attitudes. Bovermann 
et al. International Journal of  Educational Technology in 
Higher, 15(27). https://doi.org/10.1186/s41239-018-
0107-0

Boyer, S. L., Edmondson, D. R., Artis, A. B., & Fleming, 
D. (2014). Self-Directed Learning: A Tool for. Journal 
of  Marketing Education, 36(1), 20-32. https://sisu.
ut.ee/sites/default/files/opikasitus/files/boyer_
et_al._2014._self-directed_learning_a_tool_for_
lifelong_learning.pdf

Buabeng-Andoh, C. (2012). Factors influencing teachers’ 
adoption and integration of  information and 
communication technology into teaching: A review 
of  the literature. 8(1). https://www.learntechlib.
org/p/188018/

Chee, J. D. (2015). Pearson’s Product-Moment Correlation: 



Pa
ge

 
29

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 3(1) 22-31, 2024

Sample Analysis. https://doi.org/10.13140/
RG.2.1.1856.2726

Cigdem, H., & Yildirim, O. G. (2014). Effects of  Students’ 
Characteristics. Turkish Online Journal of  Distance 
Education-TOJDE, 1-14. https://files.eric.ed.gov/
fulltext/EJ1043667.pdf

Creswell, J. (2006). Choosing a Mixed Method Design. 83. 
Retrieved April 20, 2021, from https://www.sagepub.
com/sites/default/files/upm-binaries/10982_
Chapter_4.pdf

Creswell, J., & Clark, P. (2011). Mixed-Methods Research. 
Das, A., & Mishra, S. (2016). Questionnaire on Learner 

Use of  Technology. (A. Kirkwood, & L. Price, 
Eds.) Commonwealth of  Learning. https://
www.researchgate.net/publication/305650994_
Questionnaire_on_Learner_Use_of_Technology

Demir, S. B., & Pismek, N. (2017). A Convergent Parallel 
Mixed-Methods Study of  Controversial Issues in 
Social Studies Classes. Educational Sciences: Theory 
& Practice, 119-148. https://doi.org/0.12738/
estp.2018.1.029

Dudovskiy, J. (2010). Purposive sampling. Retrieved October 
12, 2020, from https://research-methodology.net/
sampling-in-primary-data-collection/purposive-
sampling/

Ericsson, G. (2015). Expertise Theory. https://www.
learning-theories.com/expertise-theory-ericsson-
gladwell.html

Farley, A. (2019). Technical Skills. https://www.
investopedia .com/ter ms/t/technica l -sk i l l s.
asp#:~:text=Technical%20skills%20refer%20
to%20the,diverse%20group%20of%20other%20
enterprises.

Foley, A., & Ferri, B. A. (2012). Technology for people, 
not disabilities: Ensuring access and inclusion. Journal 
of  Research in Special Educational Needs, 12(4). https://
doi.org/0.1111/j.1471-3802.2011.01230.x

Frost, J. (2020). Degrees of  Freedom in Statistics.
Gabor, A. (2020). Schools Aren’t Ready for Online 

Learning. https://www.bloombergquint.com/gadfly/
coronavirus-shutdowns-highlight-weakness-of-online-
u-s-schooling

Garland, W. A. (2017). Self-Directed Learning: Definition 
& Strategies. https://study.com/academy/lesson/
self-direct-learning-definition-strategies.html

Gilakjani, A. P. (2013). Factors Contributing to Teachers’ 
Use of  Computer Technology in the Classroom. 
Universal Journal of  Educational Research, 262-267. 
https://doi.org/10.13189/ujer.2013.010317

Goldhammer, F., Naumann, J., & Keßel, Y. (2013). 
Assessing Individual Differences in Basic Computer 
Skills. European Journal of  Psychological Assessment, 263-
275. https://doi.org/10.1027/1015-5759/a000153

Guion, L. A., Diehl, D. C., & McDonald, D. (2015). 
Conducting an In-depth Interview. 

Haines, R., Scamell, R. W., & Shah, J. R. (2018). The 
Impact of  Technology Availability and Structural 
Guidance on Group Development in Workgroups 

Using Computer-Mediated Communication. 
Informations System Management, 346-368. https://doi.
org/10.1080/10580530.2018.1503805

Halili, S. H. (2019). Technological Advancements in 
Education 4.0. The Online Journal of  Distance Education 
and e-Learning, 7(1). Retrieved September 18, 2020, 
from http://tojdel.net/journals/tojdel/articles/v07i01/
v07i01-08.pdf

Hall, S. V. (2012). Applying the Principle of  Readiness 
to Shorthand Instruction. The Journal of  Business 
Education, 357-361. Retrieved October 1, 2020, from 
https://shorturl.at/aDKRS

Hashim, H., & Tasir, Z. (2014). E-Learning Readiness: 
A Literature Review. International Conference 
on Teaching and Learning in Computing and 
Engineering, 267-271. 

Horzum, M. B., Kaymak, Z. D., & Gungoren, O. C. 
(2015, April 17). Structural Equation Modeling 
Towards Online Learning. (1303-0485). https://doi.
org/10.12738/estp.2015.3.2410

Indeed Career Guide. (2017). Technical Skills: Definitions 
and Examples. https://www.indeed.com/career-
advice/resumes-cover-letters/technical-skills

Institute for Digital Research and Education. (2020). 
What does Cronbach’s alpha mean? https://stats.
idre.ucla.edu/spss/faq/what-does-cronbachs-alpha-
mean/

Israel, O. (2010). Undergraduates’ Computer Skills and 
the Use of  Online Information Resources: A Case 
Study of  Library and Information Science Students 
of  Delta State University, Nigeria. 1-13. http://irjlis.
com/wp-content/uploads/2016/05/15-IR-333-54.
pdf

Jain, A., Sharma, S., & Joachims, T. (2015). Learning 
preferences for manipulation tasks from online 
coactive feedback. The International Journal 
of  Robotics and Research, 34(10). https://doi.
org/10.1177/0278364915581193

James, S., Thomas, T., & D’Amore, A. (2011). Learning 
preferences of  first year nursing and midwifery 
students: Utilizing VARK. Nurse Education Today, 31(4), 
417-423. https://doi.org/10.1016/j.nedt.2010.08.008

Jian, P., & Chen, L. (2011). The effects of  education 
compatibility and technological expectancy on 
e-learning acceptance. https://www.sciencedirect.
com/science/article/abs/pii/S0360131511000479

Joosten, T., & Cusatis, R. (2020). Online Learning Readiness. 
American Journal of  Distance  Education(1538-9286 ). 
https://doi.org/10.1080/08923647.2020.1726167

Kitsantas, A., & Chow, A. (2010). College students 
perceived threat and preference for seeking help 
in traditional, distributed, and distance learning 
environments. Science Direct, 383-395. 

Kulkarni, M. (2018). Digital Accessibility: Challenges 
And Opportunities. IIMB Management Review, 13(1). 
https://10.1016/j.iimb.2018.05.009

Lai, C., Wang, Q., & Lei, J. (2012). What factors predict 
undergraduate students’ use of  technology for 



Pa
ge

 
30

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 3(1) 22-31, 2024

learning? A case from Hong Kong. 59(2), 569-579. 
https://doi.org/10.1016/j.compedu.2012.03.006

Larson, L.  C., & Miller, T.  N. (2011). 21st Century 
Skills: Prepare Students for the Future. 
Kappa Delta Pi Record, 47(3), 121-123. htt
ps://10.1080/00228958.2011.10516575

Liew, S.-C., Barua, A., & Sidhu, J. (2015). The 
relationship between learning preferences (styles 
and approaches) and learning outcomes among pre-
clinical undergraduate medical students. 44. https://
bmcmededuc.biomedcentral.com/articles/10.1186/
s12909-015-0327-0#Sec1

Llego, M. A. (2020). DepEd Basic Education Statistics 
for School Year 2019-2020. https://www.teacherph.
com/deped-basic-education-statistics-school-
year-2019-2020/

Lowe, J. (2019). Importance of  Computer Skills in 
College. https://education.seattlepi.com/computer-
courses-beginners-1612.html

Mafenya, P. N. (2013). An Investigation of  First-Year 
Students’ Pedagogical Readiness to E-Learning 
and. Mediterranean Journal of  Social Sciences, 4(13), 1. 
https://10.5901/mjss.2013.v4n13p353

Menzies, T., Williams, L., & Zimmermann, T. (2016). 
Interview Guide. Perspectives on Data Science for 

Software Engineering. https://www.sciencedirect.com/
book/9780128042069/perspectives-on-data-science-
for-software-engineering

Mitchell, N. (2020). Universities not ready for online learning 
– U-Multirank. https://www.universityworldnews.
com/post.php?story=20200609183303614

Murad, M. H., Coto‐Yglesias, F., Varkey, P., Prokop, 
L. J., & Murad, A. L. (2010). The effectiveness 
of  self‐directed learning in health professions 
education: a systematic review. Wiley Online Library, 
44(11), 1057-1068. https://doi.org/10.1111/j.1365-
2923.2010.03750.x

Ngampornchai, A., & Adams, J. (2016). Students’ 
acceptance and readiness for. International Journal of  
Educational Technology in, 13(14), 1-13. https://doi.
org/10.1186/s41239-016-0034-x

Office of  Educational Technology. (2016). Engaging and 
Empowering Learning Through Technology. https://
tech.ed.gov/netp/learning/

Olutola, A. T., Olatoye, O. O., & Olatoye, R. A. (2016). 
Assessment of  Social Media Utilization and Study 
Habit of  Students of  Tertiary Institutions in Katsina 
State. Journal of  Education and Practice, 7(3).

Ottenbreit-Leftwich, A. T., & Ertmer, P. A. (2014). 
Teacher Technology Change. Journal of  Research on 
Technology in Education 42(3), 255-284. https://doi.or
g/10.1080/15391523.2010.10782551

Paino, M., & Renzulli, L. A. (2012). Digital Dimension 
of  Cultural Capital: The (In)Visible Advantages for 
Students Who Exhibit Computer Skills, 86(2), 124-
138. https://doi.org/10.1177/0038040712456556

Parai, M., Shenoy, P., & Loh, K. (2015). Students’ 
perception of  technology-assisted learning in 

undergraduate medical education – A survey. The Social 
Science Journal, 52, 78-82. https://doi.org/10.1016/j.
soscij.2014.08.007

Peart, D. J., Penny, L., Rumbold, S., Keane, K. M., & Allin, 
L. (2017). Student use and perception of  technology 
enhanced learning in a mass lecture knowledge-rich 
domain first year undergraduate module. International 
Journal of  Educational Technology in Higher Education, 14. 
https://link.springer.com/article/10.1186/s41239-
017-0078-6

Penn State University. (2015). Online Learning Readiness 
Questionnaire. http://tutorials.istudy.psu.edu/
learningonline/ORQ/ORQ.htm?fbclid=IwAR0okv
XkAaMSECevIO67JXuAwp6F7OtqjBzcVxt15LI1
aX-OVGUX1GeOjDU

Petro, L. (2017). How to Put Self-Directed Learning to 
Work in Your Classroom. https://www.edutopia.org/
discussion/how-put-self-directed-learning-work-
your-classroom

Prasad, M., & Garcia, C. (2015). How to Conduct a 
Successful Focus Group Discussion. Humans of  
Data. 

QuestionPro. (2020). Snowball Sampling: Definition, 
Method, Advantages and Disadvantages. https://
www.questionpro.com/blog/snowball-sampling/

Rabia, M., Mubarak, N., Tallat, H., & Nasir, W. (2017). 
A Study-on-Study Habits and Academic Performance 
of  Students. 7. https://doi.org/0.18488/
journal.1.2017.710.891.897

Raes, A., & Depaepe, F. (2019). A longitudinal study to 
understand students’ acceptance of  technological 
reform. When experiences exceed expectations. 533-
552. https://link.springer.com/article/10.1007/
s10639-019-09975-3

Rasouli, A., Rahbania, Z., & Attaran, M. (2016). Students’ 
Readiness for E-learning. 4(3). www.mojet.net

Resume.com. (2019). How to List Computer Skills on a 
Resume (Computer Skill Examples). https://www.
resume.com/career-advice/resumes/how-to-list-
computer-skills-on-a-resume-with-examples/

Salkind, N. J. (2010). Encyclopedia of  research design. (N. 
J. Salkind, Ed.) 1-0.

Schiller, N. (2020). Self-Directed Learning for Online 
Class.

Somuah, B. A., & Dankyi, L. A. (2014). An Investigation 
into the Study Habits of  Distance Learners: 
Implications for Guidance and Counseling Services. 
Mediterranean Journal of  Social Sciences, 5(6), 273-282. 
https://doi.org/10.5901/mjss.2014.v5n6p273

Spaulding, A. P. (2011,). Action Research Methods: The 
Pro-forma. https://alexandrapspaulding.wordpress.
com/2011/03/16/action-research-methods-the-pro-
forma/

Srivastava, M. K., Gnyawali, D. R., & Hatfield, D. E. 
(2015). Behavioral implications of  absorptive capacity: 
The role of  technological effort and technological 
capability in leveraging. Technological Forecasting and 
Social Change, 92, 346-358. https://sci-hub.tw/



Pa
ge

 
31

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 3(1) 22-31, 2024

https://doi.org/10.1016/j.techfore.2015.01.010
Tan, L. (2014). Correlational study. 269-271. https://

us.sagepub.com/enus/nam/music-in-the-social-and-
behavioral-sciences/book240878

Todd, M. (2016). Joint Display Analysis. 
Uju, E., & Paul, O. (2017). Study habit and its impact on 

secondary school students’ academic performance 
in biology in the Federal Capital Territory, Abuja. 
Academic Journals, 12(10). https://doi.org/10.5897/
ERR2016.3117

Wei, H.-C., & Chou, C. (2020). Online learning 
performance and satisfaction: do perceptions and 
readiness matter? Distance Education, 41(1), 48-69. 
https://doi.org/10.1080/01587919.2020.1724768

Wikiversity. (2020). Computer Skills. https://
en.wikiversity.org/wiki/Computer_Skills

Yilmaz, R. (2017). Exploring the role of  e-learning 
readiness on student satisfaction and. Computers in 
Human Behavior, 70, 251-260. 

Ziefle, M., & Schaar, A. K. (2010). Technical expertise 
and its influence on the acceptance of  future medical 
technologies: what is influencing what to which 
extent?. In HCI in Work and Learning, Life and Leisure: 
6th Symposium of  the Workgroup Human-Computer 
Interaction and Usability Engineering, USAB 2010, 
Klagenfurt, Austria, November 4-5, 2010. Proceedings 6 
(pp. 513-529). Springer Berlin Heidelberg. https://
doi.org/10.1007/978-3-642-16607-5_40


