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American Journal of  Smart 
Technology and Solutions (AJSTS)

Computer Self-Efficacy and Effectiveness of  Quipper Learning Management System
Jude Rafael S. Alayacyac1, Jobert C. Regidor1, John Harry S. Caballo1*, Jelly E. Abellanosa1, Giebe Joshua G. Monajan1

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

DOI: https://doi.org/10.54536/ajsts.v3i1.2428
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: January 20, 2024

Accepted: March 04, 2024

Published: March 07, 2024

This quantitative study aimed to determine the relationship between computer self-
efficacy and the effectiveness of  the Quipper learning management system among 
senior high school students. The study utilized a descriptive-correlational design. Data 
on computer self-efficacy and Quipper LMS effectiveness were collected from 290 
senior high school students using survey questionnaires. The results showed high overall 
computer self-efficacy (M=4.18, SD=0.652) and high effectiveness of  the Quipper LMS 
(M=3.97, SD=0.622) among students. A significant positive correlation (r=0.414, p=.000) 
between computer self-efficacy and Quipper LMS effectiveness indicates a low positive 
relationship between the two variables. Computer self-efficacy has a direct relationship 
with the effectiveness of  the Quipper LMS. As computer self-efficacy increases, the 
effectiveness of  the Quipper LMS also increases among senior high school students.

Keywords
Computer Self-Efficacy, 
Learning Management System, 
Quipper LMS

1 The University of  Mindanao, Davao City, Philippines
* Corresponding author’s e-mail: harrycaballo@gmail.com

INTRODUCTION
Most institutions have increasingly adopted online 
learning to facilitate teaching and learning as a continuum 
to the traditional face-to-face approach. Most of  these 
institutions utilize Learning Management Systems, 
which contain features intended to make students active 
participants by delivering learning resources to learners 
and providing the environment for effective interaction 
in the learning process. It has been implemented in some 
universities worldwide to help connect students and 
lecturers without the confines of  the traditional classroom. 
It is an environment with digital software designed to 
manage user learning interventions and deliver learning 
content and resources to students (Adzharuddin, 2013). 
Several studies have proven the effectiveness of  LMS 
on undergraduate courses, specifically Information 
Technology courses (Nel, 2010), Engineering courses 
(Kurata, 2017), and English courses (Salahuddin & 
Saira, 2020). Vesin et al. (2009) and Chaubey et al. (2015) 
highlighted the role of  LMS in specific programs, such 
as vocational education and programming in Java, in 
promoting active and cooperative learning and providing 
access and flexibility in higher education. Further, Rahman 
et al. (2019) and Lasmanawati et al. (2021) emphasized 
the potential of  LMS to enhance learning by providing 
a user-friendly interface, facilitating independent and 
creative learning, and enabling learning anytime and 
anywhere however; other studies have opposing views 
on the effectiveness of  learning management systems, 
highlighting its features are underutilized by students. 
However, students face several challenges preventing 
them from actively participating in online learning. There 
is a lack of  individualized feedback, a lack of  depth of  
learning, and a lack of  interpersonal interactions using 
LMS (Reed, 2014; Araka et al., 2021). 
Students’ computer self-efficacy can influence their use 

of  the LMS. Students with high computer self-efficacy 
will find using computers easily and be more inclined 
to use them. In contrast, students with low computer 
self-efficacy lack confidence in their computer skills and 
may avoid using computers (Binyamin et al., 2018). This 
suggests that computer self-efficacy would influence 
students’ perception of  e-learning since it is a critical 
predictor of  perceived learning (Alqurashi, 2019). It is 
also a key predictor in accepting e-learning (Tarhini et al., 
2015; Fathema et al., 2015). Even in the attitude toward 
artificial intelligence of  university students, self-efficacy 
has a significant effect (Obenza, 2023). Additionally, 
computer self-efficacy was discovered to be a significant 
predictor of  student satisfaction in LMS (Ghazal et al., 
2018; Hammouri & Abu-Shanab, 2018). As students’ 
computer self-efficacy grows, so does their perception 
that the system is simple to use and, therefore, their level 
of  satisfaction with the learning management system 
(Ghazal et al., 2018).
Several studies have examined the effectiveness of  the 
Quipper Learning Management System (LMS) and its 
benefits for both teachers and students. Morron (2015) 
stated that Quipper LMS is an effective online platform for 
improving student educational performance. Supporting 
this, Ghilay (2017) found that users view the LMS as 
useful for convenient learning and positive assistance in 
the learning process. Jamil et al. (2019) also noted that 
students see Quipper School as a useful learning tool, as it 
activates interest in learning English more and interacting 
with the language. Additionally, Mahariyanti and Suyanto 
(2018) found Quipper School enables students to 
enhance their knowledge and assess their aptitude and 
understanding through provided questions. It increased 
student engagement and eagerness to learn in their study.
The selection of  a learning management system is critical 
to student success. That selection needs to be based on 



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the online course’s objectives and the student’s needs. The 
LMS must have components allowing the instructor to 
create a course emphasizing active learning experiences 
(Lewis et al., 2005). There are numerous studies 
investigating the effectiveness of  LMS per se. However, 
the researchers have not found studies specifically about 
Quipper LMS. There is also limited research correlating 
computer self-efficacy and the effectiveness of  LMS. 
Thus, the researchers are motivated to pursue this 
research that may contribute to the existing knowledge 
about the learning management system. This may provide 
substantial data that would encourage the developers to 
improve the platform to a standard suited to the needs of  
the students.

Theoretical Framework
The study is anchored on the Technology Acceptance 
Model developed by Davis (1986). The goal of  TAM is 
to explain the general causes of  computer acceptance, 
which will lead to a better understanding of  users’ 
behavior across a wide range of  end-user computing 
technologies and user groups. Further, this study is also 
anchored on the First Learning Effectiveness Model 
developed by Lee (2008). The study suggested that the 
effectiveness of  a learning platform can be determined 
by the willingness of  the student to participate in any 
learning. Also, it highlights the importance of  the learning 
platform’s educational task content and the amount of  
support given by the organization. Moreover, this study 
is supported by the proposition of  Yusoff  (2009), who 
postulated that computer self-efficacy, a significant factor 
relating to achieving information and computing literacy 
skills, can lead to the ease of  educational technology.

Research Questions
This study aimed to determine the relationship between 
computer self-efficacy and the effectiveness of  the 
Quipper learning management system on senior high 
school students in a non-sectarian private institution. 
Specifically, this research study sought to answer the 
following research questions:
1.What is the level of  computer self-efficacy of  senior 
high school students in terms of:

1.1. Basic Computer skills;
1.2. Media Related skills; and
1.3. Web-Based skills?  

2. What is the level of  effectiveness of  the Quipper 
learning management system on senior high school 
students in terms of:

2.1. Perceived self-efficacy;
2.2. Perceived satisfaction;
2.3. Perceived usefulness;
2.4. Behavioral intention;
2.5. e-learning system quality;
2.6. Interactive learning activities;
2.7. E-learning effectiveness; and
2.8. Multimedia instruction?

3. Is there a significant relationship between computer 

self-efficacy and the effectiveness of  the Quipper learning 
management system on senior high school students?

MATERIALS AND METHODS
This research used a descriptive-correlational quantitative 
design. Correlational research is used in research studies 
to determine a relationship between two or more variables 
and the degree of  the relationship. In the correlational 
research design, researchers use the statistical correlation 
test to describe and measure the extent of  association 
(or relationship) between two or more variables or sets 
of  scores (Creswell, 2012). Simon and Goes (2011) also 
stated that descriptive and correlational studies examine 
variables in their natural environment and do not include 
treatments required by researchers. In this study, the 
researchers aimed to determine the relationship between 
computer self-efficacy and the effectiveness of  the 
Quipper learning management system of  senior high 
school students.
A simple random sampling was used to determine the 
respondents for this study. In this type of  sampling, the 
respondents are selected randomly and purely by chance. 
Hence, the selection quality is not affected as every member 
has an equal chance of  being selected in the sample. 
This type of  sampling is best for a highly homogenous 
population (Bhardwaj, 2019). Specifically, the respondents 
of  this study were 290 grade 11 and 12 students from 
Accountancy, Business, and Management Strand (ABM), 
Humanities and Social Sciences Strand (HUMSS), and 
Science, Technology, and Mathematics Strand (STEM). 
To evaluate the relationship between Computer self-
efficacy and Effectiveness of  the Quipper Learning 
management system, the researchers used an adapted 
modified survey questionnaire from the study of  
Amankwah et al. (2017) titled: Computer Self-Efficacy 
Among Senior High School Teachers in Ghana and 
the Functionality of  Demographic Variables on their 
Computer Self-efficacy, for computer self-efficacy survey 
questionnaire. Also, the researchers used an adapted 
modified survey questionnaire from the study of  Liaw 
and Huang (2007) titled: Investigating Students’ perceived 
satisfaction, behavioral intention, and Effectiveness 
of  e-learning: A case study of  the Blackboard System, 
for the Effectiveness of  Quipper learning management 
system survey questionnaire.
In addition, the researchers performed a pilot test to 
assess the credibility and reliability of  the questionnaire 
before the questionnaire was distributed for the 
conduction of  the survey. Also, the researchers selected 
50 individuals to participate and answer the questionnaire, 
and its preparation for the actual administration and 
survey was validated. This was made sure through the 
use of  Cronbach’s Alpha. The result for Computer self-
efficacy is 0.764, which signifies acceptable and reliable 
(11) questions within computer self-efficacy’s indicators. 
Moreover, the result for the effectiveness of  the Quipper 
learning management system is 0.959, which means 
it is excellent, reliable, and internally consistent with 



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(26) questions within the effectiveness of  the Quipper 
learning management system’s indicators.   
To conduct the study, the researchers wrote a permission 
letter to the Senior High School Principal and questionnaires 
the respondents would fill out. The researchers asked the 
respondents to sign an informed consent before starting 
each survey; this form indicated their authorization to 
be included as respondents in the research study. The 
researchers ensured that the respondents understood 
their rights and the implications of  participating. The 
survey questionnaires were distributed to the target 
respondents, grade 11 and 12 senior high school students. 
The role of  the researchers then was to organize, present, 
and analyze the questionnaire data accordingly. The data-
gathering process followed proper procedures to collect 
quality data. 
The researchers used the following statistical tools to 
assess and evaluate data:

Mean
This statistical concept is useful in figuring out the overall 
trend of  a data set or presenting a rapid image of  your 
data, generally referred to as average. This was used to 
measure the level of  students’ computer self-efficacy and 
the Quipper learning management system.

Standard Deviation
It refers to the measurement of  the gap of  the determined 
values from the mean (Ilola, 2018). As used in this 
study, this tool was used to measure the dispersion of  
a dataset relative to the computed mean on the level of  
student’s computer self-efficacy and the Quipper learning 
management system.

Pearson-r Correlation Coefficient (r-value)
It is a statistical measure of  the strength of  the 
relationship among two variables. A typical correlation 
coefficient ranging from -1.00 to 1.00 shows a strong 
negative and effective association (Ganti, 2020). This 
was used to measure the significant relationship between 
Computer Self-Efficacy and the Effectiveness of  the 
Quipper Learning Management System on Senior High 
School Students.

RESULTS AND DISCUSSION
Level of  Computer Self-Efficacy
The data presented in Table 1 shows the level of  
computer self-efficacy of  Senior high school students 
as regards basic computer skills, media-related skills, and 
web-based skills.

The results in Table 1 revealed a high overall level of  
computer self-efficacy among students, with a mean 
score of  4.18 (SD = 0.652). Students expressed the 
greatest confidence in their web-based skills (M=4.43, 
SD=0.684), while media-related skills received the lowest 
scores (M=3.70, SD=0.804). Basic computer skills were 
rated highly (M=4.38, SD=0.692). These findings support 
previous research on computer self-efficacy. Binyamin et 
al. (2018) found that students with high computer self-
efficacy are likelier to use computers easily, while those 
lacking confidence may avoid using them. Hammouri 
and Abu-Shanab (2018) underscored that self-confidence 
with technology affects perceptions of  difficulty and 
usefulness. Moreover, Khan (2018) noted that computer 
use enables efficient assessment and online learning.

Level of  Effectiveness of  Quipper Learning 
Management System
The data gathered from the survey corresponds to the 
level of  effectiveness of  the Quipper learning management 
system in terms of  perceived self-efficacy, perceived 
satisfaction, perceived usefulness, behavioral intention, 
e-learning system quality, interactive learning activities, 
e-learning effectiveness, and multimedia instruction among 
senior high school students are presented in table 2.

Table 1: Level of  Computer Self-Efficacy
Indicators x̄ SD
Basic Computer Skills 4.38 0.692
Media Related Skills 3.70 0.804
Web-based Skills 4.43 0.684
Overall 4.18 0.652

Table 2: Level of  Computer Self-Efficacy
Indicators x̄ SD
Perceived Self-efficacy 4.09 0.762
Perceived Satisfaction 4.00 0.658
Perceived Usefulness 4.21 0.676
Behavioral Intention 4.14 0.691
e-Learning system quality 3.78 0.740
Interactive learning activities 3.82 0.752
e-Learning effectiveness 3.93 0.715
Multimedia Instruction 3.61 0.818
Overall 3.97 0.622

Table 2 presented an overall high level of  effectiveness 
for the Quipper LMS, with a mean of  3.97 (SD = 0.622). 
All indicators have a high descriptive level. Perceived 
usefulness got the highest level, with a mean of  4.21 and 
a standard deviation of  0.676. Moreover, multimedia 
instruction has the lowest level, with a mean of  3.61 and 
a standard deviation of  0.818. Perceived self-efficacy 
got a mean of  4.09 and a standard deviation of  0.762.  
Perceived satisfaction got a mean of  4.00 and a standard 
deviation of  0.658. Behavioral intention got a mean score 
of  4.14 and a standard deviation of  0.691.
Furthermore, e-learning system quality obtained a mean 
of  3.78 and a standard deviation of  0.740. Interactive 
learning activities got a mean of  3.82 and a standard 
deviation of  0.752. And e-Learning effectiveness got 
a mean of  3.93 and a standard deviation of  0.715. 
Prior research aligns with the current findings, which 



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demonstrate the effectiveness of  the Quipper LMS 
for enhancing student learning, engagement, and 
performance through its interactive features and 
resources. Morron (2015) found that the Quipper LMS 
is an effective online platform for improving students’ 
academic performance. Ghilay (2017) added that users 
find the LMS helpful for the convenience of  learning 
and that it positively contributes to the learning process. 
Moreover, Jamil et al. (2019) stated that Quipper School 
is an effective learning tool because it triggered students’ 
interest in learning English. Mahariyanti and Suyanto 
(2018) also stated that Quipper School allows students 
to enhance their knowledge of  topics and assess their 
understanding of  information through teacher-provided 
materials and questions.                                                                                                     

Significant Relationship between Computer Self-
Efficacy and Effectiveness of  Quipper Learning 
Management System
Table 3 shows that the computed r-value of  computer 
self-efficacy and effectiveness of  the Quipper learning 
system is 0.414 (p-value=.000). This means a significant 
relationship exists between the two at a five percent 
level of  significance. Further, the computed r-value of  
Computer self-efficacy to the effectiveness of  the Quipper 
learning management system is 0.414, which indicates a 
low positive relationship. This shows that there is a direct 
relationship between the two variables, which means that 
Computer self-efficacy increases, the effectiveness of  the 
Quipper learning management system increases as well, 
and vice versa. The result supports Alqurashi (2019), 
who stated that computer self-efficacy levels influence 
students’ perception of  e-learning benefits since self-
efficacy is a critical predictor of  perceived learning. Also, 
Ghazal et al. (2018) found that as students’ computer 
self-efficacy increases, so does their perception that the 
learning management system is easy to use. Moreover, 
the result aligns with the proposition of  Yusoff  (2009), 
who postulated that computer self-efficacy, an important 
factor in achieving information and computing literacy, 
can lead to ease in using educational technology.

learning management system received a satisfactory rating 
from the students. They were pleased with Quipper LMS’ 
functions, contents, and multimedia instruction. This 
indicates the students have the knowledge and skills to 
use Quipper effectively as a learning management system. 
Moreover, there is a low but significant positive correlation 
between senior high school students’ computer self-
efficacy and the effectiveness of  the Quipper learning 
management system. This shows that there is a direct 
relationship between the two variables, which means that 
computer self-efficacy increases, and the effectiveness of  
the Quipper learning management system increases as 
well, and vice versa.

RECOMMENDATION
Based on the results of  this study, the following 
recommendations can be made:

• Continue using the Quipper learning management 
system in senior high schools, as students find it 
satisfactory and effective overall. The system should 
be maintained and updated regularly to ensure optimal 
performance.

• Provide training and support for teachers on fully 
utilizing all features and content of  the Quipper LMS to 
enhance instruction and student engagement.

• Develop supplemental digital literacy programs to 
further build students’ computer self-efficacy, particularly 
in web-based skills. 

• Share study findings with other senior high schools 
to encourage Quipper LMS adoption and highlight its 
benefits for supporting student learning and self-efficacy.

• Consider research on the relationship between 
computer self-efficacy and learning management system 
effectiveness in other educational contexts and age groups

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Amankwah, F., Konin, D., Sarfo, F. (2017). Computer 
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Table 3: Significant Relationship between Computer 
Self-Efficacy and Quipper Learning Management System
Variables r – value p-value
Computer Self-Efficacy .414 .000
Effectiveness of  Quipper 
Learning Management System

CONCLUSION
This study found that senior high school students in a 
non-sectarian private institution have a generally good 
level of  computer self-efficacy. This includes competence 
in basic computer skills, media-related skills, and web-
based skills. Students find computer use straightforward 
and generally understand computing concepts and 
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