


































Frontiers of Contemporary Education 
ISSN 2690-3520 (Print) ISSN 2690-3539 (Online) 

Vol. 1, No. 2, 2020 

www.scholink.org/ojs/index.php/fce 

23 
 

Original Paper 

The Effect of Distance Teaching on Academic Achievement in 

the Online Course of Introduction to Computers Science 

Ying-Sheng Kuo
1
 

1
 General Education Center, Open University of Kaohsiung, Kaohsiung City, Taiwan (R.O.C.) 

 

Received: September 18, 2020    Accepted: October 6, 2020    Online Published: October 22, 2020 

doi:10.22158/fce.v1n2p23              URL: http://dx.doi.org/10.22158/fce.v1n2p23 

 

Abstract 

For distance teaching, if the teacher can obtain the predicted scores that students may obtain in the 

final exam at this time, the students whose predicted scores do not meet the standard can be found. 

Teachers can strengthen the teaching of this type of students in the teaching process, which will greatly 

improve the overall teaching efficiency of teachers. The target of this research is the students of the 

course of the “Introduction to Computers Science” at Open University of Kaohsiung. This course is a 

distance teaching based on online teaching, and the questionnaire and final test are also completed on 

line. A total of 95 students filled out the questionnaire online, accounting for 77.24% of the electives. 

This study aims to assess the measurement of students’ pre-learning computer experience, software 

operation ability, learning motivation, and computer attitude, and analyze their relationship with the 

performance of subsequent distance teaching on the course of the “Introduction to Computers 

Science”, and find a multiple regression model to predict student performance. It was found that a 

multiple linear regression model combining 7 independent variables was statistically significantly 

related to the final test scores. 

Keywords 

pre-learning computer experience, learning motivation, online teaching, computer attitude, distance 

teaching, introduction to computers science 

 

1. Introduction 

The Open University of Kaohsiung (OUK) is an adult education university that provides lifelong 

learning. It provides a dreaming opportunity for people who drop out of school early and fail to receive 

higher education, so that people who want to obtain a university diploma do not need to pass any 

exams, and there are no admission qualification restrictions. As long as the people of Taiwan who are 

over 18 years old can register for school, the school play a very important role in solving the 



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knowledge gap of the relatively vulnerable people in Taiwan. With the advancement of the times, and 

Taiwan’s gradual entry into an advanced society, lifelong learning is a must for everyone today. The 

idea of living to old age and learning has prompted many people to study new knowledge instead of 

obtaining a university diploma. Many seniors who have retired in the workplace have chosen course at 

OUK. The OUK has made great contributions to improving people’s life knowledge and providing 

opportunities and places for lifelong learning for Taiwanese people. 

Students who take the course of the “Introduction to Computers Science” at OUK have different 

computer experience, motivation and basic background (age, education, social experience), and the 

course is online teaching, plus students are free to elective without age, education and grade restrictions, 

students’ computer knowledge background is very scattered (Kuo, 2020).  

In this course, the content of distance teaching is designed by teachers according to their own 

profession, and teachers have insufficient knowledge of students’ knowledge background and learning 

motivation. However, a good teaching will not be perfect if it cannot match the students’ starting 

behavior and learning motivation. It will have an impact on students’ learning effectiveness. Generally 

speaking, at the beginning of distance learning, teachers are almost completely unfamiliar with students. 

If the teacher can obtain the predicted scores that students may obtain in the final exam at this time, the 

students whose predicted scores do not meet the standard can be found. Teachers can strengthen the 

teaching of this type of students in the teaching process, which will greatly improve the overall 

teaching efficiency of teachers. 

This is a 3-credit course per semester and is conducted through distance learning. Teachers use 

multimedia systems to apply Information and Communication Technology (ICT) applications to 

provide course content in the form of digital audiovisual materials. The teaching content of 54 lectures 

was produced and placed on a digital teaching platform for students to learn online. In addition, there 

are online discussion areas and four back-to-school teaching activities. The online discussion area 

allows students to discuss teaching content and teachers can also participate. The four back-to-school 

face-to-face teaching activities (once a month, 100 minutes each time) are mainly used to supplement 

and guide the teaching content of distance learning. Finally, an online final quiz will be conducted. 

This research mainly explores the understanding of the computer experience, computer attitude and 

learning motivation of students in the “Introduction to Computers Science” course based on distance 

teaching. Combined with the final test scores, the paper discusses the relationship between the student’s 

computer experience before learning and the relationship between learning motivation and test scores. 

The research results are handed over to the curriculum planning decision-makers as a reference for 

planning advanced course, and hope to find a set of multivariate variables that are significantly related 

to the test scores, establish a prediction model, and help teachers predict the students’ learning 

achievements, as early as possible to strengthen the teaching of students with possible low grades. 

 

 



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Kim and Peterson (1992) believe that the computer introduction course plays a vital role in introducing 

basic computer concepts and skills to college students. The improvement of students’ computer 

abilities is based on the introduction of computers. They have solid computer basic knowledge, and 

they are competent for the use and discussion of computer-related equipment in the future. Ideally, 

students who complete such course should have sufficient computer literacy and the ability to acquire 

more complex computer skills in subsequent course or workplaces (Kim & Keith, 1994). To ensure that 

all students have a similar understanding of basic computer skills and concepts, introductory course 

usually assume that students have little relevant computer experience (Brock, Thomsen, & Kohl, 1999). 

Therefore, the introduction to computers can construct students' basic knowledge of computers. 

The students at OUK are all social people, and most of them have smartphones. Thus, they could 

browse, search for information, chat, leave messages, take photos, go to websites, and use mobile 

payment applications. They might even have used all the functions of ICT in smartphones. However, 

they may not understand the basic operations of traditional computers and the knowledge related to 

application software and computers (Kuo, 2020). Due to the diversified sources of the students in this 

course, teachers’ knowledge of students’ knowledge background, computer experience before class, 

computer attitude and learning motivation are quite weak. Therefore, it is necessary to study the 

background, computer attitude and motivation of students in the course of the “Introduction to 

Computers Science” based on distance teaching. 

 

2. Literature Review 

2.1 Computer Experience before Learning 

Smith, Caputi, Crittenden, Jayasuriya, and Rawstorne (1999) divided the previous computer experience 

into subjective and objective categories. Subjective refers to the feeling of like or dislike of the 

computer, that is, computer anxiety and computer attitude. Objective previous experience refers to 

externally observable variables that interact with computers including computer usage, usage 

opportunities, and usage diversity. Varma and Marler (2013) believe that previous computer experience 

has two dimensions: computer proficiency and computer frequency. Brock et al. (1999) found that 

almost any type of computer experience, especially video game experience, has improved the computer 

literacy level of freshmen to a certain extent. But, surprisingly, it was found that exposure to computer 

information systems at the high school or community college level had almost no significant impact on 

students’ computer literacy (Rex & Roth, 1998). Before students participate in the course, first evaluate 

the measurement of computer experience and computer self-efficacy, and analyze its relationship with 

the performance of subsequent course. Explore the relationship between the total years of computer 

experience, the current average computer hours per week, and the number of computer course 

completed before and the performance of subsequent course. It is found that none of these three 

measures can be directly used as an important predictor of subsequent course performance. They 

recommended to further understand the relationship between previous computer experience and 



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computer literacy and performance.  

This study will use the frequency of computer use, the number of computer course ever studied, the 

most familiar computer skills and other factors to explore the relationship between the students’ 

pre-learning computer experience and the learning performance of the course of the “Introduction to 

Computers Science” based on distance teaching. 

2.2 Learning Motivation 

According to human development professionals, there are two types of learning motivation, namely 

extrinsic motivation and intrinsic motivation. External motivation refers to the use of external things to 

induce students to engage in learning activities; internal motivation refers to the motivation caused by 

people's interest in learning itself. When a person likes to engage in a certain learning activity because 

he feels that this learning activity has “Interest”, “Like”, “Happy”, “Need”, “Target”, and no other 

external reasons, this potential internal force is “intrinsic motivation”. Lepper (1988) pointed out that 

external motivation refers to obtaining some kind of reward or avoiding some kind of punishment 

external to the activity itself, such as reward or teacher’s approval; internal motivation is for its own 

sake, and regards internal motivation as what it is the enjoyment provided, the learning allowed or the 

sense of accomplishment inspired. 

Afzal, Ali, Khan, and Hamid (2010) believe that students with intrinsic motivation prefer to use 

strategies that require more effort and make them process information more extremely, while students 

with external motivation tend to pay the least effort to obtain the maximum return. Stipek, Feiler, 

Daniels, and Milburn (1995) believe that learning motivation is a student’s motivation for achievement 

in learning, a psychological need for individual pursuit of success, and one of the main factors affecting 

academic achievement. The study found that learning motivation has a strong relationship with 

students' academic performance, and it is concluded that students with intrinsic motivation are 

academically better than students with extrinsic motivation (Afzal et al., 2010). 

The students of this course are all adults. Adult motivation is reflected in setting goals, maintaining 

attention in certain activities, and exerting effort and perseverance to achieve goals. According to the 

conclusion drawn by the Institute of Educational Psychology, it is pointed out that in the learning of 

adults, high positive motivation can play a compensatory role, especially when a person’s ability is low 

or knowledge reserves are insufficient (Lukianova, 2016). And what is the inner learning motivation of 

adults? According to M. Bryn and S. Mann, the inner motivation of adult learners is caused by the 

constant desire to learn, which can be characterized as an attitude: “I really want to do” Instead of 

“because I want to do it, so I do”, or “I need” (Lukianova, 2016). 

Therefore, before teaching this course of the “Introduction to Computers Science” based on distance 

teaching, it is necessary for adult students from all directions to thoroughly understand their learning 

motivations, determine their needs, interests and tendencies, because these usually have a significant 

impact on learning outcomes. 

 



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2.3 Learning Attitude and Performance 

Students’ learning attitudes have a considerable impact on academic performance, and most 

educational scholars and teachers agree that a positive learning attitude can improve students’ academic 

performance. Kiekkas et al. (2015) assessed the impact of biostatistics course on the statistical attitudes 

of nursing students, and explored the relationship between these attitudes and their performance in 

course examinations. The study found that the correlation between the overall scale score and 

examination performance was positive and significant, but weak; and previous research on medical 

students has reported that the relationship between the two is moderately related (Beurze, Donders, 

Zielhuis, de Vegt, & Verbeek, 2013; Zhang et al., 2012). However, Bidegain and Mujika (2020) 

explore the relationship between scientific attitudes and PISA (The Program for International Student 

Assessment) scientific performance. From a broader perspective (e.g., across countries and regions), 

the relationship between all attitude variables and science achievement using aggregated PISA scores 

was found to be negative. Hignite and Echternacht (1992) believed that computer attitude cannot be 

used to predict the existence of corresponding levels of computer literacy. But, Klein, Knupfer and 

Crooks (1993) evaluated the students’ attitudes towards computer learning, and checked the students’ 

computer knowledge and computer skills performance after attending the course, and found that 

students with positive learning attitudes had significantly better performance assessments. Shen, Wu 

and Lee (2014) proposed a case study to discuss the impact of their proposed system on college 

students’ attitudes towards computer science, and found that students’ attitudes towards computer 

science improved, and previous studies have shown that positive attitudes toward course tend to 

produce better results on achievement measures. Gopu (2016) focused on to explore the introduction of 

student’s attitude towards computer science. It was found that there was a strong correlation between 

students’ academic performance and their personal perceptions of competence during the course 

(Gomes, Santos, & Mendes, 2012); and there was a significant positive correlation between students’ 

attitudes and their achievements in programming (Baer, 2013). 

Most of the above studies on the relationship between learning attitudes and achievements are aimed at 

general students, and there is relatively little research on the relationship between computer learning 

attitudes and academic performance of adults who participate in distance teaching like OUK, so they 

are also included in this study. 

 

3. Methodology 

The main purpose of this research is to evaluate the measurement of students’ computer experience, 

software operation ability, learning motivation, and computer attitude before taking the course of the 

“Introduction to Computers Science” based on distance teaching, and analyze its relationship with 

subsequent course performance. Find a multiple regression model to predict student achievement in 

computer literacy course. The content of the course is that teachers use general computer content to 

conduct online teaching (Kuo, 2020). Based on the content of the lecture, 100 questions are given, 1 



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point for each question, and a final online test is taken, which is regarded as the score obtained by the 

students after taking the course. This research offers the following contributions to the literature: 

1) Understand the relationship between demographic variables and final test scores. 

2) Analyze the relationship between students' pre-learning computer experience, clerical software 

operation ability, learning motivation and computer attitudes and the final test. 

3) Identify a set of multivariate variables that have a significant relationship with the final test 

scores to build a predictive model. 

To verify the research questions, the following null hypothesis are assessed: 

1) There are no statistically significant differences at the level of (0.05) between final test scores 

and demographic variables. 

2) There are no statistically significant correlations at the level of (0.05) between final test scores 

and pre-learning computer experience. 

3) There are no statistically significant correlations at the level of (0.05) between final test scores 

and learning motivation. 

4) There are no statistically significant correlations at the level of (0.05) between final test scores 

and computer use experience. 

5) There are no statistically significant correlations at the level of (0.05) between final test scores 

and computer attitudes. 

6) There are no statistically significant correlations at the level of (0.05) between final test scores 

and clerical software operation ability. 

3.1 Design of Research Experiment and Evaluation Questionnaires 

This research questionnaire mainly includes four groups: demographic variables, computer learning 

experience, computer use experience, personal feelings and opinions, and includes a computer attitude 

scale and a clerical software operation ability scale. Demographic variables include five items 

including gender, age, education, OUK time already enrolled, and whether you have a personal 

computer. There are two questions for computer learning experience and personal feelings and opinions, 

and three questions for computer use experience.  

In addition to the two scales, there are 12 items in the questionnaire. The content of the questionnaire 

was modified from Lin (2007). The instrument of clerical software operation ability scale is the author's 

own proposition based on teaching experience. The above content is slightly modified by expert 

opinions. The computer attitude scale uses the scale used by Kuo (2020). The contents of the 12-items 

questionnaire are shown in Table 1. The clerical software operation capability scale is shown in Table 

2.  

These two scales were measured by a Likert scale from 1 strongly disagree to 5 strongly agree. After 

analysis, the Cronbach α of the computer attitude scale for this study was.949, the clerical software 

operation ability scale was .937. The Cronbach α of these two scales is >.9 in this study, which is an 

excellent reliability level according to the rules of George and Mallery (2003). 



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3.2 Participant and Data Analysis 

This research is aimed at students who take the course of the “Introduction to Computers Science” 

based on distance teaching. Students are notified and voluntarily encouraged to fill out the 

questionnaire online before the course begins. It takes approximately 15 minutes to complete the 

questionnaire. After the course is completed, take the final exam online. The total number of students 

taking this course is 123, and there are 110 students who took the final exam at the end. 95 of the 

students who took the final exam filled out the questionnaire. These 95 participants are the sample of 

this study. The sample size is about 77.2% of the original electives and about 86.4% of the students 

taking the final exam. The average score of 110 students who participated in the final exam was 83.9 

points, while the average score of 95 sample students who participated in the questionnaire was 83.76 

points, which is quite close. 

The computer statistics software SPSS for Windows (version 20.0) is used for the data analysis, and 

reliability analysis, Person correlation test, regression analysis, and analysis of variance are applied to 

test various hypotheses. 

 

Table 1. Questionnaire Content 

Category Item 

Demographic variables 

Gender 

Age 

Education 

OUK time already enrolled 

Do you have a dedicated computer? 

Computer learning experience 
Have you ever taken any computer-related course? 

What is your main motivation for learning computers? 

Computer use experience 

How long have you been using a computer? 

On average, how long do you spend on computer-related 

equipment (including smartphones) every day? 

What is your purpose of using computer-related equipment 

most often? 

Personal feelings and opinions 

What computer skills are you most familiar with? 

Do you think the technical skills you possess are 

sufficient? 

 

 

 

 



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Table 2. Clerical Software Operation Ability Scale 

Questions 

1. I can use clerical software to modify the font, size and color of the text in the document. 

2. I can use the page border of the clerical software as the cover of the report. 

3. I can use the Drop Caps function. 

4. I can insert a page number for the document. 

5. I can use Bullets and Numbers to input data. 

6. When aligning the data between line and line, I can use the TAB key at keyboard. 

7. I can set the positions of tabs in a paragraph. 

8. I can use preview printing before printing the document. 

9. I can use the function of Align Center. 

10. I can use the indent function to set the paragraph format. 

 

4. Discussion 

4.1 Demographic Analysis 

4.1.1 Gender 

Males have higher academic performance than Females, as shown in Table 3. Further analysis of the 

ANOVA test results in F=.469, P=.495, and the null hypothesis 1 cannot be rejected. It indicates that 

there is no obvious gender difference in the final grades of the students. 

 

Table 3. Gender and Final Test Scores 

Gender Number Percentage Average Score Standard Deviation 

Male 47 49.47% 84.9 13.64 

Female 48 50.53% 82.6 18.53 

 

4.1.2 Age 

Because there is no age limit for the selection of course for the OUK students. The age distribution of 

students who choose to take this course is very wide as shown in Table 4. It is found from Table 4 that 

the average scores of the 36-45, 46-55, and >56 age groups are 86.1, 93, and 87.7 points respectively, 

which are higher than the average score of the whole class, while the youngest 18-25 age group has the 

lowest average score of 79.1. As a result, it is clear that older students are not inferior to young students 

in the study of the course toward the “Introduction to Computers Science”. This is different from the 

current belief that young people’s acceptance of computer literacy is higher than that of older people. 

This also shows that older students are more serious in their studies than younger students. After the 

ANOVA test results in F=6.661, P=.012, because the P value was <0.05, null hypothesis 1 can be 

rejected. It shows that there is obvious age difference in the students’ final grades. 



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Table 4. Age and Final Test Scores 

Age Number Percentage Average Score Standard Deviation 

18-25 25 26.32% 79.1 18.9 

26-35 33 34.74% 82.0 17.4 

36-45 22 23.16% 86.1 12.3 

46-55 12 12.63% 93.0 7.6 

>56 3 3.16% 87.7 20.5 

 

4.1.3 Education 

Since the OUK has no restrictions on students’ qualifications for taking this course, the qualifications 

of students taking this course are also scattered. Table 5 shows the distribution of educational level and 

final test scores of students who choose course. The results show that the average final grades of 

secondary school and high school students are 96.3 and 84.8, respectively, which are higher than the 

average of the whole class. While the scores of college and university students are 79.6 and 78.8, 

respectively, which are lower than the average of the whole class. And found that academic 

qualifications and final test scores are inversely proportional, which shows that students with lower 

academic qualifications are more serious. Because OUK adopts registration for admission, as long as 

citizens who are 18 years old or older can enroll, provide an opportunity for early dropouts to receive 

higher education, and play the role of lifelong learning, fostering knowledge disadvantaged groups and 

people enrich knowledge. This one as a result, OUK did achieve this goal. After the ANOVA test 

results in F=3.538, P=.063, and null hypothesis 1 cannot be rejected. Although the P value is greater 

than 0.05, it is close to 0.05, so it is still one of the important factors affecting the test score. 

 

Table 5. Education and Final Test Scores 

Education Number Percentage Average Score Standard Deviation 

Secondary school 4 4.21% 96.3 3 

High school 64 67.37% 84.8 14.7 

College 21 22.11% 79.6 21.7 

University 6 6.32% 78.8 10.9 

 

4.1.4 OUK Time Already Enrolled 

OUK has no restrictions on school years. Students choose subject arbitrarily by themselves and record 

their learning status by accumulating credits. There is no grade system like general school, so we use 

the OUK time already enrolled as the equivalent of general school grades. Discuss the relevance of 

OUK time already enrolled and final test scores. It can be seen from Table 6 that the average scores of 

the two groups of students studying at OUK for one year and three years are 87.8 and 84.7, respectively, 



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which is higher than the class average. It indicates that the students of these two groups are most used 

to and familiar with the school’s on-line teaching model and learning method. It can also indicate that 

students in these two groups are studying more seriously. The average score of freshmen is 78 which is 

the lowest and much lower than the average of the whole class. It may be caused by freshmen who are 

not used to online learning. This part provides teachers with more attention to freshmen to improve 

their learning effect. After the ANOVA test results in F=1.456, P=.231, and null hypothesis 1 cannot be 

rejected. It shows that there is no obvious difference in the OUK time already enrolled for students’ 

final grades. 

 

Table 6. OUK Time Already Enrolled and Final Test Scores 

Years Number Percentage Average Score Standard Deviation 

new student 32 33.68% 78.0 18.6 

1 45 47.37% 87.8 14.7 

2-3 15 15.79% 84.7 13.7 

4-5 2 2.11% 79.5 7.8 

>5 1 1.05% 79.0  

 

4.1.5 Do You Have a Dedicated Computer? 

It is found from Table 7 that most of the students at OUK have personal computers. This is because 

OUK is a school mainly based on distance teaching. Students usually go online for distance learning. It 

can be seen from Table 7 that the performance of students who do not have a dedicated computer in the 

course of the “Introduction to Computers Science” is not lower than the students who have a dedicated 

computer. This is because the content of the course and the quizzes are not actually operated by the 

computer, but mainly the knowledge content of computer literacy. After the ANOVA test results in 

F=.429, P=.514, and null hypothesis1 cannot be rejected. It shows that there is no obvious difference in 

dedicated computers for students’ final grades. 

 

Table 7. Dedicated Computers and Final Test Scores 

Dedicated computers Number Percentage Average Score Standard Deviation 

Yes 76 80.00% 83.2 15.7 

No 19 20.00% 85.9 18.5 

 

 

 

 

 



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4.2 Descriptive Statistics Analysis 

4.2.1 Computer Learning Experience 

The discussion of students’ computer learning experience mainly investigates two questions. One is 

whether students have ever taken any computer-related course? The other is what are the main 

motivations for students to learn computers? We will discuss in order. 

4.2.1.1 Have Taken in Computer-Related Course 

Have you ever taken any computer-related course? This project is a multiple-choice question. The 

student can indicate that he has taken computer-related course. Whether the student has taken any 

computer-related course and the final test scores are shown in Table 8. It is found from Table 8 that the 

average score of students who have taken computer-related course before class is 84.61, and the 

average score of students who have not taken is 83.96. These scores are very close to the average score 

of 83.9 for the whole class. This shows that there is no big difference in whether the final test scores 

have taken computer-related course before the class. 

For students who answered that they have taken any computer-related course, further investigate their 

learning status. The problem of computer-related course that I have taken is multiple choice (a total of 

8 options), the results of multiple choices are not helpful for regression analysis. Therefore, we need to 

establish a single score to represent the measurement of a single student in the computer-related course 

that have been studied before we can perform subsequent regression analysis. The survey results are 

shown in Table 9, where the percentage is the ratio of the number of students who selected this option 

to the number of students who participated in the questionnaire, and the average score is the average 

score of the students who selected this option. The selected percentage represents the relative 

importance of this option. The higher the percentage, the more representative this option is, and the 

average score of students who choose this option is also an important indicator, so we will multiply the 

average score and the percentage. The result obtained by multiplication is used as the intensity index of 

the computer-related course that have been taken. This intensity index is called “Pre-learned scores”. 

The sum of the scores of all computer-related course ever taken by a single student is the student’s 

score on this question. We call this score “COURSE”. The student’s final grade and “COURSE” were 

subjected to Person correlation test analysis to obtain a correlation coefficient of 0.113. Regression 

analysis was performed to obtain R-Square=.013, F-statistics=1.2, P=.276, and null hypothesis 2 could 

not be rejected. 

 

Table 8. Have You Ever Taken Any Computer-Related Course and Final Test Scores 

Ever taken? Number Percentage Average Score Standard Deviation 

No 49 51.58% 82.96 17.73 

Yes 46 48.42% 84.61 14.66 

 



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Table 9. Taken Computer Course and Final Test Scores 

Taken computer course Number Percentage Average Score 
Pre-learned 

scores 

Basic Introduction to 

Computer 
24 25.26% 88.1 22.3 

Clerical processing 31 32.63% 84.4 27.5 

Briefing design and 

production 
13 13.68% 88.5 12.1 

Spreadsheet 5 5.26% 94.8 5.0 

Drawing software 11 11.58% 85.9 9.9 

Computer animation 3 3.16% 84.7 2.7 

Programming 1 1.05% 99.0 1.0 

Other __________ 1 1.05% 50.0 0.5 

 

4.2.1.2 Main Motivation for Learning Computers 

What are your main motivations for learning computers? This question is a multiple-choice question 

with a total of 9 motivation options. Each student’s learning motivation may have many items. The 

relationship between the main motivation for learning computers and the final test scores is shown in 

Table 10. From Table 10, it is found that the main motivation for students to learn computers is 

“Strengthen the ability to organize or handle life affairs” for 73.68% of the sample and “To browse and 

use the Internet function” to account for 54.74%. Since this project is a multiple-choice question, most 

students have many learning motivations, and it is found that the average score of students with 

multiple learning motivations is higher than the average score of the whole class, but the difference is 

not significant.  

In order to perform regression analysis, according to the method of dealing with any computer-related 

course that have been taken in, we established a motivation option intensity indicator called 

“Motivation scores”. The sum of all the scores of multiple motivations selected by a single student is 

the student's score on this question. We call this score “Motivation”. The student’s final score and 

“Motivation” were analyzed by Person correlation test to obtain a correlation coefficient of 0.104. 

Regression analysis was performed to obtain R-Square=.008, F-statistics=.780, P=.379. Unable to 

reject null hypothesis 3. It shows that students’ learning motivation has no significant relationship with 

final test scores. 

 

 

 

 



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Table 10. Main Motivations and Final Test Scores 

Main motivations Number Percentage 
Average 

Score 

Motivation 

scores 

Can keep up with the trend and avoid falling 

behind. 
31 32.63% 85.0 27.7 

Can understand the world where 

(grandchildren) children live. 
9 9.47% 89.0 8.4 

It’s an interesting thing to try new things. 47 49.47% 85.0 42.1 

Strengthen the ability to organize or handle 

life affairs. 
70 73.68% 85.6 63.1 

To browse and use the Internet function. 52 54.74% 84.2 46.1 

Want to try the computer’s entertainment 

features (e.g., computer games). 
26 27.37% 85.2 23.3 

Can connect with people more quickly and 

conveniently. 
39 41.05% 84.3 34.6 

Go to a community website (e.g., Facebook). 30 31.58% 83.3 26.3 

Other_________. 2 2.11% 69.5 1.5 

 

4.2.2 Computer Use Experience 

For the discussion of students’ computer use experience, it mainly investigates three questions. One is 

how long the students have used computers? Another is how long do students spend on 

computer-related equipment (including smartphones) on average every day? The other is what is the 

purpose of using computer-related equipment most often? We will discuss in order. 

4.2.2.1 How Long Have You Been Using the Computer? 

It is found from Table 11 that 76.84% of the students have used computers for more than five years, 

and students who have used computers for more than three years have higher average scores for the 

final test than the class average. It seems that students who have been using computers for longer will 

have better grades at the end of the course of the “Introduction to Computers Science”. After the 

ANOVA test results in F=.519, P=.473, and null hypothesis 2 cannot be rejected. It shows that the time 

that the computer has been used before the class has no obvious difference on the students’ final 

grades. 

 

 

 

 

 



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Table 11. How Long Have You Been Using Computer and Final Test Scores 

Years Number Percentage Average Score Standard Deviation 

<1 6 6.32% 79.2 16.2 

<3 11 11.58% 82.4 18.7 

<5 5 5.26% 85.2 23.4 

>5 73 76.84% 84.3 15.7 

 

4.2.2.2 On average, How Long Do You Spend on Computer-Related Equipment Every Day 

Table 12 shows the average daily time students spend on computer-related equipment (including 

smartphones) and final test scores. The answer to this question includes the time spent using 

smartphones. From Table 12, it is found that 25.26% of students spend more than 7 hours a day on 

computer-related equipment, 42.1% spend more than 5 hours, 74.73% spend more than 3 hours, and 

25.26% of the students spent less than 3 hour. Observing from the final test scores, the test scores of 

students did not increase with the use of computer-related equipment. After the ANOVA test results in 

F=.043, P=.837, and null hypothesis 2 cannot be rejected. It shows that the time spent in using 

computer-related equipment every day which before the course begins has no significant difference on 

the students’ final grades. 

 

Table 12. Average Daily Time Students Spend on Computer-Related Equipment (including 

Smartphones) and Final Test Scores 

Hours Number Percentage Average Score Standard Deviation 

<3 24 25.26% 84.1 17.1 

<5 31 32.63% 81.4 17.2 

<7 16 16.84% 88.1 13.9 

>7 24 25.26% 83.5 15.9 

 

4.2.2.3 Purpose of Using Computer Related Equipment 

The purpose of students using computer-related equipment and the final test results are shown in Table 

13. This question is a multiple-choice question. From Table 13, 73.68% of the students answered 

“Watch the OUK online teaching”, followed by 56.84% of the students answered “Searching 

information, watching news” and “Listening to music or watching videos online”, 51.58% of the 

answers to the “E-Mail”, 42.11% of the answers to the “Go to a community website” and 41.05% of 

the answers to “Use software such as clerical processing, briefing design and production, spreadsheet, 

and drawings”. Only 27.37% of the students answered “play online games”, and 22.11% of the students 

answered “Go to the chat room or discussion forum”. Observed from the final test scores, the purpose 

of the students’ use of the computer has no obvious relationship with the final test scores. 

 



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Refer to the previous way to deal with multi-option problems, and create a computer-used purpose 

option score called “Purpose scores”. The sum of all the scores of multiple items selected by a single 

student is the student's score on this question. We call this score “Purpose”. The student’s final grade 

and “Purpose” were analyzed by Person correlation test, and the correlation coefficient of -0.138 was 

obtained. Regression analysis was performed to obtain R-Square=.019, F-statistics=1.799, P=.183, and 

null hypothesis 4 could not be rejected. Explain that the students’ learning purpose has no significant 

relationship with the final test scores. 

 

Table 13. The Purpose of Students Using Computer-Related Equipment and Final Test Scores 

The purpose of using computer-related 

equipment 
Number Percentage 

Average 

Score 

Purpose 

scores 

Send and receive E-Mail. 49 51.58% 84.8 43.7 

Watch the OUK online teaching. 70 73.68% 83.3 61.4 

Go to the chat room or discussion forum. 21 22.11% 83.9 18.5 

Play online games. 26 27.37% 84.5 23.1 

Search information, watch news. 54 56.84% 83.8 47.6 

Listen to music or watch videos online. 54 56.84% 85.2 48.4 

Online shopping. 36 37.89% 82.2 31.2 

Go to a community website (such as 

Facebook). 
40 42.11% 85.0 35.8 

Use software such as clerical processing, 

briefing design and production, 

spreadsheet, and drawings. 

39 41.05% 81.8 33.6 

other____________. 28 29.47% 87.8 25.9  

 

4.2.3 Personal Feelings and Opinions 

For the discussion of students’ personal feelings and opinions on learning computers, it mainly 

investigates two questions. One is what computer skills the students are most familiar with? The other 

is do students feel that the technological skills they possess are sufficient? We will discuss in order. 

4.2.3.1 Computer Skills Most Familiar to Students 

Students think that they are most familiar with the computer skills and final test scores shown in Table 

14. This question is a multiple-choice question. From Table 14, 78.49% of the students answered that 

their most familiar computer skills were “Clerical processing”, 44.09% of the students answered “Basic 

Introduction to Computers”, and 32.26% of the students answered “Briefing design and production”. In 

response to “Basic Introduction to Computers” and other higher-skilled student groups, such as 

“spreadsheets”, “drawing software” and “programming”, the average final test score is much higher 



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than the average class score. Among them, 3 students answered “Other”, their average score was 58.7 

points, which was far lower than the average score of the whole class, 83.9 points. “Other” may be 

professional information system operation skills at work, such as counter registration and pricing staff 

in large hospitals, or the cashier in the store, etc., said that students familiar with keyboard and mouse 

operation skills similar to this type have no advantage over ordinary people in the course of the 

“Introduction to Computers Science”. This phenomenon is the same as Brock’s et al. (1999) research 

results. The use of computers only in the workplace does not affect computer literacy, because 

individuals may only learn one application, such as an electronic form or a single word processing 

program, and may even only know how to turn on the computer. “Computer” in simple English, they 

are typists who moved to detached by keyboards and never learned about the computer as we have 

defined computer literacy. 

Refer to the previous way to deal with multi-choice questions, establish a computer skills score that 

students are most familiar with is called “Skills scores”. The sum of all the scores of the most familiar 

computer skills selected by a single student is the student's score on this question. We call this score 

“Skills”. The student’s final grade and “Skills” were subjected to Person correlation test analysis to 

obtain a correlation coefficient of 0.103. Regression analysis was performed to obtain R-Square=.011, 

F-statistics=.995, P=.321, and null hypothesis 2 could not be rejected. This shows that there is no 

significant relationship between the computer skills that students are familiar with before the study and 

the final test scores. 

 

Table 14. Computer Skills Most Familiar to Students and Final Test Scores 

Most familiar Computer skills Number Percentage Average Score Skills scores 

Basic Introduction to Computer 41 44.09% 87.6 38.6 

Clerical processing 73 78.49% 83.6 65.6 

Briefing design and production 30 32.26% 83.6 27.0 

Spreadsheet 6 6.45% 86.2 5.6 

Drawing software 4 4.30% 94.8 4.1 

Computer animation 1 1.08% 87.0 0.9 

Programming 1 1.08% 94.0 1.0 

Other __________ 3 3.23% 58.7 1.9 

 

4.2.3.2 Do You Think the Technical Skills You Possess Are Sufficient? 

This question investigates whether the students think they have enough technical skills they have? The 

results are shown in Table 15. It is found from Table 15 that 10.53% of the students think that they 

possess sufficient technical skills, and 89.47% of the students think that they are not enough. The 

average score of the former group is 88.3 points, which is greater than the latter, and it is much higher 



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than the average score of the whole class. For the student groups who think that they do not have 

enough technological skills, we further investigate the course or training that students think they need. 

The results are shown in Table 16. From Table 16, students who answered that the higher-level skills 

were insufficient, their average score is higher than the class score, for example: students who 

answered “Drawing software” and “Computer animation”. In addition, 64.21% of students think they 

need “Briefing design and production”, 54.74% think “Spreadsheet”, 48.42% think “Clerical 

processing”, it can be seen that most of OUK students think they need to learn office software. Like 

Kuo’s (2020) research, this part can be used as a reference for decision makers in OUK course. 

 

Table 15. Do You Think the Technical Skills You Possess Are Sufficient? 

Sufficient? Number Percentage Average Score Standard Deviation 

Yes 10 10.53% 88.3 12.07 

No 85 89.47% 83.2 16.65 

 

Table 16. What Course or Training Do You Think You Need Most? 

The need most course Number Percentage Average Score 

Basic Introduction to Computer 28 29.47% 82.4 

Clerical processing 46 48.42% 81.2 

Briefing design and production 61 64.21% 83.5 

Spreadsheet 52 54.74% 83.7 

Drawing software 45 47.37% 85.6 

Computer animation 33 34.74% 87.2 

Programming 0 0.00% 0 

 

4.3 Computer Attitude 

The student’s final grade and the score of the computer attitude scale were analyzed by Person 

correlation test to obtain a correlation coefficient of 0.261. Regression analysis was performed to obtain 

R-Square=.068, F-statistics=6.793, P=.011. Below 95% confidence level, null hypothesis 5 was 

rejected. This shows that there is a clear relationship between computer attitude and student’s final 

grade. 

4.4 Clerical Software Operation Ability Scale 

The student’s final grade and the score of the clerical software operation ability scale were analyzed by 

Person correlation test to obtain a correlation coefficient of 0.047. Regression analysis was performed 

to obtain R-Square=.002, F-statistics=.21, P=.648, and null hypothesis 6 could not be rejected. This 

shows that there is no significant relationship between the measurement of clerical software operation 

ability and the final test scores of students before learning this course. 



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5. Prediction Model for Final Test 

From the results of the discussion of the individual independent variable and the dependent variable 

above, it is found that in terms of demographic variables, “Age” P<0.05 has the greatest impact on the 

final test scores, followed by “Education” P=0.063, and other variables have no significant impact on 

the Computers Science course based on distance teaching. In the analysis of descriptive statistics, it is 

found that only “Computer attitude scale measurement” has significant impact on the final test scores 

on the Computers Science course based on distance teaching, and the other variables have no 

significant impact. However, Afzal et al. (2010) found that there is no significant relationship between 

individual independent variables and dependent variables. But, there is a significant relationship 

between the model combining six independent variables and dependent variables. Rex and Roth (1998) 

found that none of the three measures of computer experience can be used as an important predictor of 

performance. However, after combining the computer literacy scores, there is a significant relationship 

between the four independent variable models and learning performance. Therefore, this study hope to 

establish a set of prediction models that has an important relationship with the final test score is based 

on the “Introduction to Computers Science” course based on distance teaching from all the independent 

variables discussed in this research. After multiple regression analysis, a predictive model is 

established. The multiple regression coefficients of the prediction model are shown in Table 17. This 

model combined seven independent variables, including “Age”, “Education”, “Computer attitude 

measurement”, “Motivation”, “Course”, “Clerical software operation ability scale measurement” and 

“Skills”. Table 17 shows the results of regression analyses with the values of R-Square=.238, 

F-statistics=3.889, and Significance=.001. The results reveal that the model is significant (p<.05) and 

there is a strong relationship between independent and dependent variables. 

 

Table 17. Prediction Model for Final Test 

Dependent Variables 

Unstandardized coefficient Std. coefficient 

t-Value Sig. 
Estimated value of B 

Std. 

error 

Beta 

distribution 

(Constant) 64.108 16.080  3.987 .000 

Education -7.861 2.381 -.343 -3.301 .001 

Age 3.296 1.444 .228 2.282 .025 

Motivation .307 .161 .188 1.906 .060 

Clerical software operation 

ability measurement 
-.049 .264 -.021 -.187 .852 

Computer attitude 

measurement 
.423 .157 .307 2.695 .008 

Skills -.017 .050 -.035 -.330 .742 

Course .129 .073 .188 1.764 .081 

R-Square  .238     

F-Statistic 3.889     

Significance  .001     



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6. Results, Limitations, and Future Research 

This study explores the relationship between the final test scores and factors, including “demographic 

variables”, “computer learning experience”, “computer use experience”, “Personal feeling and 

opinions”, “computer attitude scale measurement” and “clerical software operation ability scale 

measures” in the “Introduction to Computers Science” course based on distance teaching. In terms of 

the relationship between individual independent variables and final test scores, there is obvious age 

difference. The computer attitude scale measurement and academic performance show a statistically 

significant correlation. This result is similar to Klein et al. (1993), but different from Hignite and 

Echternacht (1992). In addition, there is no direct statistically significant association between the other 

independent variables and the final test result.  

In the questionnaire questions of this research, there are many questions with multiple options. There is 

no way to perform regression analysis on the data of such multiple options. This research uses 

weighting to quantify each option in order to obtain a single quantification value of the students in this 

multiple options question data. A final test score prediction model with 7 independent variables is 

established in the “Introduction to Computers Science” course based on distance teaching. This is just 

like previous studies (Afzal et al., 2010; Rex & Roth, 1998), combining some independent variables 

that have no significant relationship between individual independent variables and dependent variables 

to establish a significant relationship between the independent variable model and academic 

performance relationship. 

This study has limitations. Only 95 students participated in the questionnaire. The amount of data is 

slightly insufficient. If we can collect more classes or more semesters of accumulated data, in short, 

increase the amount of research data, the results obtained will be more practical. Another limitation is 

that for the data of multiple-choice questions, it is not possible to directly perform regression analysis. 

In this study, the weighted method is used to generate the option intensity scores of multiple-choice 

questions to perform regression analysis. This part increases the load of data consolidation and analysis, 

but it is also the study’s important contribution. 

From the above results, we know that there are complex and important relationships between factors 

such as pre-learning computer experience and computer motivation and the learning performance in the 

“Introduction to Computers Science” course based on distance teaching. Nowadays, in the era of big 

data, artificial intelligence technology is booming, and it has a strong ability to analyze and calculate 

complex multiple independent variables and dependent variables. If we can collect enough data, we can 

consider using machine learning, such as support vector machines, artificial neural networks or deep 

learning, are used to build predictive models. 

 

 

 

 

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7. Conclusion 

Due to the diverse sources of OUK students, teachers have insufficient understanding of students’ 

knowledge background, computer experience before class, and learning motivation. In the course of 

“Introduction to Computer Science” based on distance learning, it is necessary to evaluate students’ 

computer experience and learning motivation before the class. According to the results of this research, 

students can fill out the 7 independent variable questionnaires used in this research before class. Then, 

the teacher can predict the student's final academic performance at the beginning of the class based on 

the prediction model. This can help teachers obtain early warning effects and then provide counseling 

strategies for students who may not get enough grades at the end of the semester in the “Introduction to 

Computer Science” course based on distance learning. 

In addition to establishing a predictive model that is significantly related to final learning performance, 

this study also found that although 33% of the students have studied “Clerical processing”, 14% have 

studied “Briefing design and production” and 5% have studied “spreadsheet”. And 78% of students 

said that their most familiar computer skills were “Clerical processing”, 32% were for “Briefing design 

and production” and 6% were for “spreadsheet”. But 48% of students still think that the computer 

course they need most are “Clerical processing”, 64% are for “Briefing design and production” and 

55% are for “spreadsheet”. It can be seen that a large part of the students at OUK are unfamiliar with 

general office software and feel the need. This result is the same as the Kuo’s (2020) study. Similarly, 

in response to the government of the Republic of China (Taiwan) is promoting the Open Document 

Format (ODF) through its ODF-CNS15251 policy. And to meet the needs of students, it is 

recommended that OUK launch Libre Office free software-related course, such as Writer, Impress, and 

Calc software. Allow OUK students to better meet the expectations and goals of the government and 

society, and make themselves more competitive in the workplace and adapt to society. 

 

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