The factors affecting ueh students’ engagement in learning when applying gamification in education

ĐỀ TÀI MÔN HỌC XUẤT SẮC UEH500 NĂM 2022 TEN CONG TRINH: THE FACTORS AFFECTING UEH STUDENTS’ ENGAGEMENT IN LEARNING WHEN APPLYING GAMIFICATION IN EDUCATION DE TAI THUOC KHOA/VIEN: TOAN

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- Helps students absorb knowledge more easily

- Helps students get better results

- Helps students to be more motivated to focus

- Is useful in students! learning

- Is a good idea to use in teaching

- Is and enjoyable learning and playing process

- Is expected by students to experience in the following subjects and in many other aspects

- Is flexible and easy to use

- Has clear and intuitive functions and interface

- Has easy and understandable interaction

- Has easy and playable games

Gamification applied in education helps students:

-Take better notes in class

- Focus more on listening to lectures

- Regularly review the lesson to master he knowledge

Interaction Gamification applied in education helps students:

- Actively discuss in group questions

CHAPTER III: THE METHODOLOGY 3.1 Research methods of the topic

3.1.1 Methods of analysis and synthesis

To analyze the effectiveness of the application of gamification in teaching for UEFH students, the authors have separated each smaller factor to understand the research object more including the following elements: The Perceived Usefulness of applying gamification in education, The Perceived Ease of Use of applying gamification, Students’ Attitude towards gamification, Students’ Skill Engagement, Students’ Interaction Engagement In each element, the author's team also divided it into questions so that the characteristics and nature can be clearly seen

The synthetic method is the opposite of the analytical method After having the judgments and understanding of the nature of the factors, the group conducted a synthesis to test the influence of the factors on the effectiveness of the application of gamification in teaching students UEH students

3.1.2 The method of data collection

This is the method most people apply and implement in scientific research papers and the authors have used this method of data collection by referencing and synthesizing data from relevant research articles that have content related to the influence of gamification on student's engagement in learning In addition to collecting from previous research articles, the authors created a questionnaire and collected directly from the opinions and evaluation levels of UEH students

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Cronbach’s Alpha Eliminate variables with correlation coefficient of total variables, Cronbach's Alpha coefficient if deletion does not meet

EFA check Eliminate bad variables, create representative factors

Pearson correlation analysis dependent variables

- Check normal distribution of residuals

- Check the hypothesis and write regression equation

Check the correlation between independent and

All UEH students from batch 44 to batch 46 who join sessions applied gamification in education

The authors’ group took a survey sample of 180 students.

In this study, two methods of non-probability sampling will be applied:

- Convenience sampling method: The authors create a questionnaire about “The factors affecting the effectiveness of applying gamification in education on UEH students' engagement” and post it on Facebook groups, classes and fan pages with a large number of UEH students such as UEH Study Group, UEH K46 Official

- Snowball method: The authors shared the questionnaire for the friends at UEH and asked them to survey and share with their friends at UEH

To ensure the sample size of N = 180 students, the authors used Internet survey tools, shared on social networking sites, and survey groups with a large number of UEH students, group class, and friends

Since we have the result from the Google Form, we recheck the data and upload it to the SPSS.26 for further processing and data analysis Specifically, as follow: 3.4.1 Cronbach’s Alpha Reliability Test

Cronbach’s Alpha: The coefficient used to check the reliability of the scale and remove the observed variables that do not ensure reliability based on the following criteria:

- Using Cronbach’s alpha to test the variability of each measurement scale

- Cronbach's Alpha coefficient of the scale greater than 0.6 1s accepted

- In case the Cronbach’s Alpha is less than 0.6, then we need to remove the variables In order to help the Cronbach's Alpha or Cronbach's Alpha If Item Deleted coefficient of the variable to be the largest and continue to run until the Cronbach's Alpha coefficient of the scale is qualified from 0.6

- Remove the variables with the total correlation coefficient or Corrected Item - Total Correlation less than 0.3.

After testing reliability and removing unqualified variables, the authors continue to do the EFA to check the variability of measurement scales EFA will reduce the number of observed variables and group observed variables into factors based on the following criteria:

- KMO coefficient (Kaiser-Meyer-Olkin) is used to consider the suitability of the factor The KMO coefficient must have a value of 0.5 or more

- Bartlett's test of sphericity is used to examine the correlation between the observed variables in a factor and has Bartlett's Test sig coefficient < 0.05 (statistically significant)

- Eigenvalue is used to determine the number of factors in EFA analysis With this criterion, only factors with Eigenvalue > | are kept in the model

- Total Variance Explained > 50% shows that the EFA model is suitable Considering the variation is 100%, this value shows how much % of the extracted factors are condensed and how many % of the observed variables are lost

- Factor Loading, also known as the factor weight, represents the correlation relationship between the observed variable and the factor The higher the factor loading coefficient, the greater the correlation between that observed variable and the factor and vice versa And the authors take the load factor 0.4 as the standard level (with N = 180) so that the observed variable has good statistical significance

When completing the EFA test, the authors create representative factors of each group of observed variables and use data of representative factors to continue doing Pearson Correlation analysis to consider the correlation between independent variables and dependent variable, and identify some cases where dynamic collinearity may occur based on the following criteria:

- The Sig value is less than 0.05 and the absolute value of the Pearson correlation coefficient is greater than 0, the authors will conclude that there is a correlation between the independent variable and the dependent variable and vice versa

- In addition, question the phenomenon of multicollinearity between independent variables if the Sig value is less than 0.05 and high Pearson correlation coefficient

After concluding the correlation between the independent variables and the group dependent variable, the authors continue to do a multivariable regression analysis to clarify this correlation Testing the hypothesis of the model proposed by the authors, and making a conclusion about the multicollinearity question includes the following steps:

- Testing the appropriation of the model through the adjusted R-squared coefficient (taking 0.5 as a landmark to distinguish between the good model and the bad model) At the same time, the sig value in the ANOVA table is less than 0.05 (with statistical significance)

- Residual normal distribution test based on Histogram, Normal P-P Plot

- The conclusion of the multicollinearity question is based on the VIF coefficient (less than 10)

- Provide the regression equations (standardized and unstandardized) based on the obtained results to evaluate the influence of the factors on the dependent variable.

CHAPTER IV: RESEARCH RESULT 4.1 Descriptive statistics of the survey sample

We have conducted a survey asking 180 UEH students through Google Forms questionnaire survey At the end of the survey, the research team checked and eliminated unsatisfactory or duplicate answer samples, finally we obtained 180 complete answer samples of 180 UEH students Our team's statistical results are displayed in charts with the following details:

Figure 4.1 details information about the batch of UEH students who have answered our survey K46 accounted for the highest proportion with 56.67% of the total

180 answers The percentage of K44 1s only 0.56%, equivalent to | student K45 and K47 accounted for 14.44% (26 subjects) and 28.33% (51 subjects), respectively.

Figure 4.2 represents different majors of the respondents in UEH University

International Business achieves the highest proportion with 72 subjects (accounting for 40%) The percentage of Marketing is 11.1%, which means it is in the second place Commercial Business and Business Administration percentage are 7.2% and 8.3%, respectively Finance - Banking and Investment Economics account for almost equal proportions (5.6% and 5% accordingly) and the percentage of other majors is 22.8% equivalent to 41 students.

4.1.3 The application of gamification in class

Figure 4.3 The application of gamification in class

Figure 4.3 shows if the respondent's class applies the gamification element or not The answer “Yes” accounts for an extremely high percentage of 92.78% of 180 answers samples (167 subjects) Only 13 students don’t seem to have their lecturers apply gamification elements in their classes.

4.1.4 Percentage of class applying gamification

Figure 4.4, Percentage of class applying gamification

This figure demonstrates the percentage of UEH student classes that apply gamification In general, the percentage of answers are not so different Only 23.89%

(43 subjects) of 180 answers is below 20% 20% to 40% Percentage of class applying gamification accounts for 33.33% of the total answers The highest proportion is “60% to 80%” of 42.78%.

Figure 4.5 Games used in class

Figure 3.10 provides information about what games are used in UEH students’ class Overall, the proportion of Kahoot accounts for more than half of 180 answers which is 54.22%, Quizizz and Quizlet percentage are 30.19% and 15.6% respectively 4.2 Analytical data from key questions

Table 4.1 Table of results to evaluate the reliability of the scale “Perceived

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Cronbach’s Alpha if item Deleted

Perceived Usefulness and Attitude Scale: Cronbach’s Alpha = 0.823

PUALI Helps me 25.14 8.969 0.600 0.794| Suitable absorb variable knowledge quickly

PUA2 Increases 25.36 9.437 0.399 0.826] Suitable my learning variable outcome

Motivates me to variable focus on the lecture

Gamification is variable useful in my learning

PUAS I think 24.97 8.815 0.618 0.791} Suitable it’s a good idea variable to apply gamification in teaching

PUAG6 I enjoy 25.21 8.622 0.597 0.793} Suitable learning and variable playing at the same time

PUAT I look 25.08 8.653 0.618 0.790} Suitable forward to variable gamification

(Source: The results of the data analysis of the research group)

Pearson Correlation ố ố

4.3.1 The correlation between Perceived Usefulness and Attitude and Students’ Engagement

By observing the results of analyzing Pearson correlation: the Pearson correlation coefficient r = 0.621 and sig = 0.000 (having statistical meaning) The authors, hence, conclude that there is a positive correlation between Perceived Usefulness and Attitude, and Students’ Engagement

4.3.2 The correlation between Skills Engagement and Students’ Engagement

By observing the results of analyzing Pearson correlation: the Pearson correlation coefficient r = 0.505 and sig = 0.000 (having statistical meaning) The authors, hence, conclude that there is a correlation between Skills Engagement and Students’ Engagement

4.3.3 The correlation between Perceived Ease of Use and Students’ Engagement

By observing the results of analyzing Pearson correlation: the Pearson correlation coefficient r = 0.362 and sig = 0.000 (having statistical meaning) The authors, hence, conclude that there is a positive correlation between the Perceived Ease of Use and Students’ Engagement

4.3.4, The correlation between Interaction Engagement and Students’ Engagement

After considering the linear correlation between the 4 independent variables and the dependent variable Students’ engagement, the authors continues to perform a regression analysis to draw conclusions about the influence of 5 independent variables on the dependent variable Students’ Engagement

To test the suitability of the regression model, we ran multivariate regression analysis data on SPSS between the independent variables: Perceived Ease of Use (PEU), Skills Engagement (S), Interaction Engagement (IE), Perceived Usefulness and Attitude (PUA), and the dependent variable: Students’ Engagement (SE)

Model R R square Adjusted R_ | Std Error of |} Durbin-

1 0.6972 0.485 0.473 0.38388 1.924 a Predictors: (Constant), Percetved Ease of Use, Skills, Interaction

Engagement, Perceived Usefulness and Attitude b Dependent Variable: Students’ Engagement

The data processing results are in the model summary table above The table shows that the R square is 0.485 and the adjusted R square is 0.473 The adjusted R- squared value of 0.473 shows that the independent variables included in the regression analysis affect 47.3% of the variation of the dependent variable, the remaining 52.7% are due to out-of-model variables and random errors The conclusion counts much more on the adjusted R square as it reflects the fit of the model more accurately than the coefficient R2

On the other hand, the results of this table also give Durbin—Watson values to evaluate the phenomenon of first-order series autocorrelation The value DW = 1.924, 1s in the range of 1.5 to 2.5 Therefore, the results do not violate the assumption of first- order series autocorrelation (Yahua Qiao, 2011).

Table 4.14, Analysis of variance (ANOVA) table

Model Sum of df Mean Square F Sig

Total 50.354 179 a Dependent Variable: Students’ Engagement

Engagement, Perceived Usefulness and Attitude b Predictors: (Constant), Perceived Ease of Use, Skills, Interaction

(Source: The results of the data analysis of the research group) From the ANOVA table, we get the result that the Sig value of the F-test is 0.00

< 0.05, so the regression model is built with statistical significance.

Figure 4.6 Normalized Residual Frequency chart Histogram

Histogram Dependent Variable: Students' Engagement

From the histogram, we can see that the Mean is -2.09E-15 The standard deviation is 0.989 - which is close to 1 From a general perspective, the columns of residuals have a bell-shaped distribution We can say that the distribution is approximately normal, assuming the normal distribution of the residuals is not violated.

Figure 4.7 Normalized Residual Frequency chart Normal P-P Plot

Normal P-P Plot of Regression Standardized Residual

Most of the residual data points are concentrated quite close to the diagonal Thus, the residuals have an approximately normal distribution, assuming the normal distribution of the residuals is not violated.

Figure 4.8 Normalized Residual Frequency chart Scatterplot

The scatter plot shows that: The distributed normalized residuals are centered on the zero line In addition, they tend to form parallel lines Thus, the assumption of a linear relationship is not violated

Model Unstandardized | Standardized | t Sig Collinearity

Perceived 0.008] 0.062 0.008 | 0.132 |0.895 0.730} 1.370 Ease of Use a Dependent Variable: Students’ Engagement

The variable Perceived Ease of Use (PEU) has sig test sig value equal to 0.895 > 0.05, so this variable is not significant in the regression model In other words, this variable has no impact on the dependent variable Students’ Engagement (SE) The remaining variables including Perceived Usefulness and Attitude (PUA), Skills (S), and Interaction Engagement (IE) all have sig test t less than 0.05, so these variables are all statistically significant and all affect the dependent variable Students’ Engagement (SE) From the table above, we can conclude that Perceived Usefulness and Attitude (PUA) is the independent variable that has the most impact on the dependent variable Students’ Engagement (SE)

On the other hand, we also recognize the attendance of the VIF value The variance exaggeration factor (VIF) is an indicator of collinearity in a regression model The smaller the VIF, the less likely there is to be multicollinearity - which may skew the regression estimates Hair et al (2009) suggested that a VIF threshold of 10 or more would result in strong multicollinearity The VIF value of the model is smaller than 10 They are even smaller than 2 Therefore, we can conclude that there will no scenario of multicollinearity in this case.

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By observing the results of analyzing Pearson correlation: the Pearson correlation coefficient r = 0.505 and sig = 0.000 (having statistical meaning) The authors, hence, conclude that there is a correlation between Skills Engagement and Students’ Engagement

4.3.3 The correlation between Perceived Ease of Use and Students’ Engagement

4.3.4, The correlation between Interaction Engagement and Students’ Engagement

After considering the linear correlation between the 4 independent variables and the dependent variable Students’ engagement, the authors continues to perform a regression analysis to draw conclusions about the influence of 5 independent variables on the dependent variable Students’ Engagement

To test the suitability of the regression model, we ran multivariate regression analysis data on SPSS between the independent variables: Perceived Ease of Use (PEU), Skills Engagement (S), Interaction Engagement (IE), Perceived Usefulness and Attitude (PUA), and the dependent variable: Students’ Engagement (SE)

Model R R square Adjusted R_ | Std Error of |} Durbin-

1 0.6972 0.485 0.473 0.38388 1.924 a Predictors: (Constant), Percetved Ease of Use, Skills, Interaction

Engagement, Perceived Usefulness and Attitude b Dependent Variable: Students’ Engagement

The data processing results are in the model summary table above The table shows that the R square is 0.485 and the adjusted R square is 0.473 The adjusted R- squared value of 0.473 shows that the independent variables included in the regression analysis affect 47.3% of the variation of the dependent variable, the remaining 52.7% are due to out-of-model variables and random errors The conclusion counts much more on the adjusted R square as it reflects the fit of the model more accurately than the coefficient R2

On the other hand, the results of this table also give Durbin—Watson values to evaluate the phenomenon of first-order series autocorrelation The value DW = 1.924, 1s in the range of 1.5 to 2.5 Therefore, the results do not violate the assumption of first- order series autocorrelation (Yahua Qiao, 2011).

Table 4.14, Analysis of variance (ANOVA) table

Model Sum of df Mean Square F Sig

Total 50.354 179 a Dependent Variable: Students’ Engagement

Engagement, Perceived Usefulness and Attitude b Predictors: (Constant), Perceived Ease of Use, Skills, Interaction

(Source: The results of the data analysis of the research group) From the ANOVA table, we get the result that the Sig value of the F-test is 0.00

< 0.05, so the regression model is built with statistical significance.

Figure 4.6 Normalized Residual Frequency chart Histogram

From the histogram, we can see that the Mean is -2.09E-15 The standard deviation is 0.989 - which is close to 1 From a general perspective, the columns of residuals have a bell-shaped distribution We can say that the distribution is approximately normal, assuming the normal distribution of the residuals is not violated.

Figure 4.7 Normalized Residual Frequency chart Normal P-P Plot

Normal P-P Plot of Regression Standardized Residual

Most of the residual data points are concentrated quite close to the diagonal Thus, the residuals have an approximately normal distribution, assuming the normal distribution of the residuals is not violated.

Figure 4.8 Normalized Residual Frequency chart Scatterplot

The scatter plot shows that: The distributed normalized residuals are centered on the zero line In addition, they tend to form parallel lines Thus, the assumption of a linear relationship is not violated

Model Unstandardized | Standardized | t Sig Collinearity

Perceived 0.008] 0.062 0.008 | 0.132 |0.895 0.730} 1.370 Ease of Use a Dependent Variable: Students’ Engagement

The variable Perceived Ease of Use (PEU) has sig test sig value equal to 0.895 > 0.05, so this variable is not significant in the regression model In other words, this variable has no impact on the dependent variable Students’ Engagement (SE) The remaining variables including Perceived Usefulness and Attitude (PUA), Skills (S), and Interaction Engagement (IE) all have sig test t less than 0.05, so these variables are all statistically significant and all affect the dependent variable Students’ Engagement (SE) From the table above, we can conclude that Perceived Usefulness and Attitude (PUA) is the independent variable that has the most impact on the dependent variable Students’ Engagement (SE)

On the other hand, we also recognize the attendance of the VIF value The variance exaggeration factor (VIF) is an indicator of collinearity in a regression model The smaller the VIF, the less likely there is to be multicollinearity - which may skew the regression estimates Hair et al (2009) suggested that a VIF threshold of 10 or more would result in strong multicollinearity The VIF value of the model is smaller than 10 They are even smaller than 2 Therefore, we can conclude that there will no scenario of multicollinearity in this case.

CONCLUSION AND RECOMMENDATIƠN .c

Conclusion

From the result of the data analysis, the research team has come to the conclusion: There are 3 factors that affect the engagement of UEH’s students in the lectures when gamification is applied Particularly, those factors include: Perceived Usefulness and Attitude (1); Skill Engagement (2); and Interaction Engagement (3) That means UEH’s students get fonder of lectures as they perceive the general usefulness of gamification and enjoy using it Moreover, they seem to enjoy the benefit of getting more skills and chances for interaction from gamification

The research also found that the engagement of UEH’s students toward gamification in university is quite high Among the three factors that affect the students’ engagement, “Perceived Usefulness and Attitude” is the most positively-affecting factor The second factor is “Skill Engagement’ Last but not least is “Interaction Engagement”

Based on the conclusion, our group proposed some recommendations

- In order to improve the engagement of UEH’s students toward lectures through gamification, lecturers should be more prone to apply gamification to teaching

A wide variety of students perceive the usefulness of gamification, and their attitude of them toward it is quite good Therefore, the frequency growth would be highly likely to positively affect the engagement of students generally

- The number of gamification applications is still limited A recommendation of increasing the number of applications being used, and diversifying the type of activities, should be brought into consideration The more kinds of activities and games are brought into the application, the higher chance that more people will experience the usefulness of gamification - which means the more likely it will be that more students would be engaged

- Gamification can be applied to not only normal lectures but also can be used for many other purposes in the university Such as Skill enhancement; Team building; Meetings; This recommendation aims at increasing the benefits delivered to the students and the chances for them to interact with the lecturers and their classmates.

- The lecturers could periodically check their student's results and use them as an indicator that assesses the effectiveness of the gamification application.

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In the process of conducting surveys to obtain research results, access to audience segments is still limited Geographic distance makes it difficult to reach a wide audience, so survey respondents are often familiar with and tend to be in the same environment (for example, in the same class or with the same teacher) Therefore, the possibility that these people share the same experience is very large and survey samples tend to be duplicated in the way they respond In addition, the income sample from K46 students is mainly, so it is not possible to cover all students of UEH Therefore, these samples do not reflect the population leading to limitations in the study known as

"sampling bias" In this case, the respondents to the survey questions may not actually be arandom sample

Besides, there are cases where students who responded to the survey did not answer honestly, instead they just randomly selected the answer without carefully considering before answering, leading to the errors that affect the objectivity of the research results

In the process of constructing the questionnaire and selecting variables, our team encountered difficulties in selecting the appropriate sub variable for the scale, leading to the removal of unsatisfactory or non-discriminatory variables

Finally, the research paper is only within the scope of UEH, so there is no comprehensive coverage of the application of gamification in education by other universities and the practical level is not high enough to be widely applied But other schools can also consult and develop further research to apply appropriate solutions in applying gamification to their education in order to bring efficiency to the teaching and learning process

With the desire to inherit and develop the topic more, we propose the following development directions:

- Directly survey UEH students and survey in large numbers instead of conducting through online channels so that the results obtained are more accurate Besides, the survey of a variety of courses is also essential to obtain more objective and in-depth results instead of favoring a small group

- Exploiting and researching more with a larger scale to discover the observed variables that our research has not mentioned in the research paper From that base, inherit and develop to create new more complete models and cover all the factors affecting students' engagement in learning when applying gamification in education

- Expanding scale not only within UEH University but also universities, colleges nationwide to explore more deeply about the application of gamification in education and how it affects the engagement of students in learning today in our country From there, propose the best measures to improve the quality of teaching and learning.

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APPENDIX Appendix 1: The content of the survey Xin chảo mọi người!!

Chúng mình là nhóm sinh viên đến từ lớp IBC0I, khoá 46, thuộc Khoa Kinh doanh Quốc tế - Marketing của Trường Đại học Kinh tế TP.HCM Hiện nay nhóm chúng mình đang thực hiện nghiên cứu khảo sát về đề tài "MỨC ĐỘ HIỆU QUÁ CỦA VIỆC ÁP DỤNG GAME HÓA VÀO GIÁO DỤC ĐÓI VỚI SINH VIÊN UEH"

Một trong những vấn đề quan trọng nhất mà các nhà giáo dục phải đối mặt ngày nay là lam thé nao dé thu hút học sinh sinh viên tham gia vào quá trình dạy và học Dựa trên làn sóng sáng tạo đang tồn tại sẵn có, liệu "Game hóa" có phải là giải pháp cho vấn đề này? Nhóm chúng mình muốn tìm hiểu xem liệu đây có phải là phương pháp hiệu quả hay không và cách thức mà nó có thể ảnh hưởng đến sự tham gia của sinh viên UEH vào các bài giảng và các môn học Đề thực hiện được mục đích đó, rất mong các anh/chị và các bạn dành ít thời gian hỗ trợ chúng mình hoàn thành phiêu khảo sát này

Xin chân thành cảm ơn!

DAC DIEM DOI TUONG NGHIEN CUU

1 Ban la sinh viên khóa?

2 Ngành học của bạn là:

Hệ thống thông tin quản lí

Logistics và Quản lí chuỗi cung ứng

Quản trị dịch vụ du lịch và lữ hành

3 Các buổi học bạn tham gia có được áp dụng yếu tổ game không?

4 Trong một tuần, tỉ lệ tham gia vào các buôi học (theo thời khóa biểu) có áp dụng yếu tố game của bạn là? dưới 20%

5 Cac game ma giang vién cua bạn thường sử dụng là gì?

MUC DO HIEU QUA CUA VIEC AP DUNG GAME HOA VAO GIANG DAY DOI VOI SINH VIEN UEH

Bạn vui lòng đánh dâu “X” vào ô tương ứng thê hiện mức độ đồng ý của bạn đôi với mỗi phát biêu theo quy ước sau:

` Không đông ý | Binh thường Đông ý ` dong y dong y

Lưu ý: Mỗi hàng duy nhất, chỉ chọn duy nhất một mức độ đồng ý trong 5 mức độ

1 Mức độ hữu ích của game

Bình thường Đồng ý Hoàn toàn đồng ý

Vận dụng game trong các buổi học khiến mình tiếp thu kiến thức đễ dàng hơn

Việc vận dụng game trong giảng dạy giúp mình có kết quả tốt hơn

Khi biết cuối buổi học có game để tông kết bài, điều đó giúp mình có động lực tập trung hơn

Nhin chung, minh thay viéc ung dung game vao giang day là hữu ích

2 Mức độ dễ tiếp cận

Rất Không Binh Đồng ý Hoàn không đồng ý thường toàn đồng ý đồng ý

Minh thấy các game được ứng dụng trong buôi học khá linh hoạt và dễ dùng

Các chức năng và giao diện của game được thiết kế rõ ràng và trực quan

Việc tương tac trong game dé hiểu và không tốn nhiều thời gian suy nghĩ

Nhìn chung, mình thấy các game được sử dụng đều dễ chơi

3 Thái độ sinh viên đối với việc áp dụng game vào các buôi học

Rất Không Binh Đồng ý | Hoàn không déngy | thường toàn đồng ý đồng ý

Tôi nghĩ rằng việc ứng dụng game vào giảng dạy là một ý tưởng hay

Tôi thấy hứng thú trong quá trình vừa học vừa chơi này

Tôi mong được trải nghiệm việc ứng dụng game này trong các môn học sau vả trong nhiều khía cạnh khác nữa

4 Khả năng bồ trợ kỹ năng ( Việc ứng dụng game tạo động lực cho mình)

Rất Không Binh Đồng ý | Hoàn không déngy | thường toàn đồng ý đồng ý

Ghi chú tốt hon trong budi học

Tập trung nghe giảng hơn

Thường xuyên ôn lai bai dé năm vững kiên thức

5 Mức độ tương tác ( Việc chơi những game như thế này giúp mình)

Rất Không Binh Đồng ý | Hoàn không đồngý | thường toàn đồng ý đồng ý

Cảm thấy vui trong buồi học đó

Chủ động thảo luận hơn trong các câu hỏi theo nhóm

Tăng khả năng tương tác với bạn củng lớp

Tăng khả năng tương tác với giảng viên

6 Mức độ hứng thú của sinh viên đối với việc học

Rất Không Binh Đồng ý | Hoàn không đồngý | thường toàn đồng ý đồng ý Áp dụng game hoá trong lớp học làm tôi cảm thấy hào hứng hơn với các hoạt động học tập trong lớp Áp dụng game hoá trong lớp học làm tôi tích cực tham gia vào các hoạt động học tập trong lớp Áp dụng game hoá trong lớp học làm tôi cảm thấy tập trung hơn vào việc học hơn với môn học Áp dụng game hoá trong lớp học làm tôi trở nên hứng thú

Scale: Perceived Usefulness and Attitude

Appendix 2: Result of Cronbach Alpha gamification

Scale Mean if Variance if ltem-Total Alpha if Item Item Deleted Item Deleted Correlation Deleted

PU1 Helps me absorb 25.14 8.969 600 794 knowledge quickly

PU2 Increases my 25.36 9.437 399 826 learning outcome

PU3 Motivates me to 25.23 8.333 594 794 focus on the lecture

PU4 Gamification is 25.09 9.013 545 802 useful in my learning

A1 | think it's a good idea 24.97 8.815 618 791 to apply gamification in teaching

A2 | enjoy learning and 25.21 8.622 597 793 playing atthe same time

Scale: Perceived Ease of Use

PEU1 The game is 12.41 2.947 535 722 flexible and easy to use

PEU2 The game's 12.56 2.896 533 723 functions and interface are clear and intuitive

PEU3 The interaction in 12.53 2.731 577 700 the game is easy to understand

PEU4 | find the games 12.51 2.732 605 684 used are easy to play

$1 Take better notes 7.98 1.681 536 586 during the lesson

IE1 Makes me happy in 11.99 3.966 495 791 class

IE2 Motivates me to 12.13 3.788 575 754 discuss more

IE3 Increases my 11.97 3.279 718 679 interaction with classmates

IE4 Increases my 12.06 3.410 627 728 interaction with lecturers

SE1 Makes me feel 12.42 2.781 650 722 excited

SE2 Motivates me to join 12.49 2.877 579 754 activities in class

SE3 Makes me feel more 12.57 2.560 573 762 focused

SE4 Make me feel more 12.54 2.607 626 730 involved in the lesson

Appendix 3: Result of EFA Results of KMO and Bartlett tests of independent variables (1)

KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy .862 Bartlett's Test of Approx Chi-Square 1111.729

Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of Variance | Cumulative % Total % of Variance | Cumulative % Total % of Variance | Cumulative %

Extraction Method: Principal Component Analysis

PUA7 | look forward to gamification 754

PUA1 Helps me absorb knowledge quickly 714

PUAG | enjoy learning and playing at the same time 703

PUA3 Motivates me to focus on the lecture 690

PUAS | think it's a good idea to apply gamification in 639 teaching

PUA4 Gamification is useful in my learning 558

PUA2 Increases my learning outcome 411

IE3 Increases my interaction with classmates B51

IE2 Motivates me to discuss more 757

IE4 Increases my interaction with lecturers 753

IE1 Makes me happy in class 401 596

PEU3 The interaction in the game is easy to understand 768

PEU4 | find the games used are easy to play 751

PEU2 The game's functions and interface are clear and 714 intuitive

PEU1 The game is flexible and easy to use 629

$1 Take better notes during the lesson 788

Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations

Results of KMO and Bartlett tests of independent variables (2)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy

Bartlett's Test of Approx Chi-Square

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Component Total % of Variance | Cumulative % Total % of Variance | Cumulative % Total % of Variance Cumulative %

PUA7 | look forward to gamification 766

PUA1 Helps me absorb knowledge quickly 720

PUA6 | enjoy learning and playing at the same time 708

PUA3 Motivates me to focus on the lecture 690

PUAS | think it's a good idea to apply gamification in teaching 651

PUA4 Gamification is useful in my learning 572

PUA2 Increases my learning outcome 429

PEU3 The interaction in the game is easy to understand

PEU4 | find the games used are easy to play

PEU2 The game's functions and interface are clear and intuitive

PEU1 The game is flexible and easy to use

IE3 Increases my interaction with classmates

IE2 Motivates me to discuss more

IE4 Increases my interaction with lecturers

$1 Take better notes during the lesson

Extraction Method: Principal Component Analysis 766 757 107 636 861 792 745 781 778 657

Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations

Factor Analysis Results for Dependent Variable Scales

Extraction Method: Principal Component Analysis a 1 components extracted

KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 794

Bartlett's Test of Approx Chi-Square 205.321

Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance | Cumulative % Total % of Variance | Cumulative %

SE1 Makes me feel excited 820

SE4 Make me feel more involved in the lesson 805

SE2 Motivates me to join activities in class 767

SE3 Makes me feel more focused 761

Appendix 4: Result of Pearson Correlation and Regression Analysis Result of Pearson Correlation

Correlations Perceived Students’ Usefulness Perceived Interaction Engagement and Attitude Skills Ease of Use Engagement

Perceived Usefulness Pearson Correlation 621 1 432 453 389 and Attitude Sig (2-tailed) 000 000 000 000

Perceived Ease of Use Pearson Correlation 362 453 326 1 380

™ Correlation is significant at the 0.01 level (2-tailed)

Adjusted R Std Error of Durbin-

Model R R Square Square the Estimate Watson

Ay 697° 485 473 38488 1.924 a Predictors: (Constant), Perceived Ease of Use, Skills, Interaction

Engagement, Perceived Usefulness and Attitude b Dependent Variable: Students' Engagement

Model Squares df Mean Square F Sig

Total 50.354 179 a Dependent Variable: Students' Engagement b Predictors: (Constant), Perceived Ease of Use, Skills, Interaction Engagement,

Standardized Unstandardized Coefficients Coefficients Collinearity Statistics

Model B Std Error Beta t Sig Tolerance VIF

Perceived Usefulness 221 034 427 6.429 000 BBB 1.501 and Attitude

Perceived Ease of Use 008 082 008 132 895 730 1.370 a Dependent Variable: Students' Engagement

Normal P-P Plot of Regression Standardized Residual Dependent Variable: Students' Engagement

Tiêu đề	The factors affecting UEH students’ engagement in learning when applying gamification in education
Trường học	University of Economics Ho Chi Minh City
Chuyên ngành	Economics
Thể loại	Graduation Project
Năm xuất bản	2022
Thành phố	Ho Chi Minh City

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Số trang	74
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