Verification and analysis of factors influencing mobile learning behavior | Статья в журнале «Молодой ученый»

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Автор:

Рубрика: Педагогика

Опубликовано в Молодой учёный №49 (183) декабрь 2017 г.

Дата публикации: 08.12.2017

Статья просмотрена: 19 раз

Библиографическое описание:

Ли Я. Verification and analysis of factors influencing mobile learning behavior // Молодой ученый. — 2017. — №49. — С. 374-379. — URL https://moluch.ru/archive/183/46935/ (дата обращения: 24.10.2019).



On the basis of constructing the model of factors influencing the mobile learning of liberal arts students in colleges and universities, the samples are measured, the reliability and validity are analyzed, and the constructed mobile learning model is verified.

Key words:TAM3; Mobile learning; Reliability and Validity Analysis

Introduction

To ensure the validity of the questionnaire, we conducted a pre-survey before the questionnaire was issued. Initially issued seven questionnaires, the questionnaire recovery and statistics, found the problem described in the questionnaire. After revising the questions of the questionnaire, finalize the questionnaire and sample plan, and issue and recall the questionnaire formally.

1.Sample description

We take the second grade undergraduates majoring in Chinese Language and Literature in the College of Liberal Arts as the research object, and issue and recycle paper questionnaires. A total of 80 questionnaires were distributed and 73 questionnaires were returned, of which 68 were valid, and the effective rate was 85 %. The data collected by a single sample t test, the statistics are Table 1, we can see that the variable confidence interval up and down the smaller floating, the sample has further description of the feasibility and reliability of the analysis.

Table 1

Independent samples t-test

Independent samples t-test

T

df

Significance(two-tailed)

The average difference

95 % difference between the number of confidence intervals

Lower limit

Upper limit

ID1

19.632

67

0.000

2.10294

1.8891

2.3168

ID2

7.436

67

0.000

0.82353

0.6025

1.0446

ID3

10.114

67

0.000

1.35294

1.0859

1.6200

ID4

14.360

67

0.000

1.79412

1.5447

2.0435

ID5

12.906

67

0.000

1.60294

1.3550

1.8508

MDF1

12.883

67

0.000

1.51471

1.2800

1.7494

MDF2

14.866

67

0.000

1.95588

1.6933

2.2185

MDF3

12.310

67

0.000

1.51471

1.2691

1.7603

EE1

13.047

67

0.000

1.32353

1.1211

1.5260

EE2

19.674

67

0.000

2.19118

1.9689

2.4135

EE3

7.548

67

0.000

0.92647

0.6815

1.1715

EE4

14.557

67

0.000

2.00000

1.7258

2.2742

EE5

13.615

67

0.000

1.79412

1.5311

2.0571

AF1

13.999

67

0.000

1.13235

0.9709

1.2938

AF2

13.460

67

0.000

1.29412

1.1022

1.4860

AF3

11.284

67

0.000

1.26471

1.0410

1.4884

AF4

11.515

67

0.000

1.60294

1.3251

1.8808

AF5

11.090

67

0.000

1.51471

1.2421

1.7873

2. Reliability Analysis

The gauge portion of this paper consists of four subscales. In order to effectively ensure the reliability of the scale, we also test the reliability of the four subscales in the analysis of the reliability of the total scale, as shown in Table 2 below.

Table 2

Total scale reliability analysis

Average of scales

Scale variance

Relevant nature of the total number of items corrected

Cronbach's Alpha

AF1

43.5735

129.144

0.677

0.921

AF2

43.4118

126.664

0.704

0.920

AF3

43.4412

124.489

0.703

0.919

AF4

43.1029

120.303

0.724

0.918

AF5

43.1912

119.470

0.776

0.917

ID1

42.6029

130.959

0.401

0.926

ID2

43.8824

128.643

0.501

0.924

ID3

43.3529

134.023

0.181

0.932

ID4

42.9118

125.455

0.578

0.922

ID5

43.1029

119.974

0.839

0.916

MDF1

43.1912

123.709

0.705

0.919

MDF2

42.7500

122.847

0.658

0.920

MDF3

43.1912

125.261

0.597

0.922

EE1

43.3824

128.031

0.588

0.922

EE2

42.5147

126.761

0.592

0.922

EE3

43.7794

125.936

0.568

0.922

EE4

42.7059

122.002

0.662

0.920

EE5

42.9118

121.604

0.712

0.919

As can be seen from the above table, the correlation coefficient between ID3(I was hesitant to use the APP. I am afraid that once the mistake will have an impact on normal learning.) and the remaining items was 0.181, the correlation coefficient between ID1(Among my peers around me, I am usually the first to try new information technologies.) and the remaining items was 0.401, The correlation coefficient is very low. After the item is removed from the scale of internal consistency Alpha coefficient value changes in terms of point of view, After ID3 is deleted, the Alpha coefficient of the mobile learning scale changed from 0.925 to 0.932. After the deletion of ID1, the Alpha coefficient of the mobile learning scale changed from 0.925 to 0.926. The remaining 17 items, the items deleted after the scale coefficients are smaller than 0.925. As can be seen from the table to correct problems related items and the total score, ID3, the homogeneity of entry ID1 and the rest of the title is not high, consider deleting it.

Alpha coefficient of 0.6 on behalf of the reliability of the scale is acceptable, 0.7 reliability rating scale above is better, More than 0.8 reliability scale on behalf of very good. Alpha coefficients of the four subscales in the reliability analysis table are 0.691, 0.796, 0.797 and 0.874, respectively. In addition, the alpha coefficient of the total scale is 0.925, indicating that the reliability of the influencing factors is good, as shown in Table 3.

Table 3

Reliability analysis

Variable

Item

Cronbach's Alpha

Cronbach's alpha in the subscale

Individual Differences(ID)

ID2

0.704

0.691

ID4

0.486

ID5

0.574

Mobile Device Features(MDF)

MDF1

0.662

0.796

MDF2

0.706

MDF3

0.792

External Environment(EE)

EE1

0.757

0.779

EE2

0.742

EE3

0.781

EE4

0.707

EE5

0.688

Application Function(AF)

AF1

0.865

0.874

AF2

0.855

AF3

0.845

AF4

0.842

AF5

0.825

3.Validity analysis

To test the construct validity of the scale, we conducted a factor analysis as shown in Table 4.

Table 4

KMO and Bartlett test

Project

Value

Kaiser-Meyer-Olkin measures sampling suitability

0.877

Bartlett's ball check

698.040

df

120

Significance

0.000

KMO is the Kaiser-Meyer-Olkin sampling suitability measure (values range from 0 to 1), when the KMO value is bigger (closer to 1), it means that there are more common factors among the variables, and the lower the net correlation coefficient between the variables, the more suitable for the factor analysis. The KMO value here is 0.877, the indicator statistic is greater than 0.80, and the property presented is «good», indicating that there are common factors between the variables and the variables are suitable for the factor analysis. As shown in Table 5.

Table 5

Rotation component matrix

Items

Ingredient

1

2

3

4

ID2

0.704

ID4

0.845

ID5

0.803

MDF1

0.879

MDF2

0.856

MDF3

0.795

EE1

0.668

EE2

0.717

EE3

0.596

EE4

0.806

EE5

0.843

AF1

0.780

AF2

0.808

AF3

0.831

AF4

0.833

AF5

0.871

The loading of component 1 on ID2, ID4, ID5 is greater than 0.7 and the first factor can be determined by ID2, ID4, ID5. Similarly, the second factor is determined by MDF1, MDF2 and MDF3. The third factor is determined by EE1, EE2, EE3, EE4 and EE5. The fourth factor is determined by AF1, AF2, AF3, AF4 and AF5, Table dimensions are basically the same, indicating that the scale has good structural validity.

4.Model verification

(1)Personal factor analysis

Through the path analysis of the gender of the individual factors, we found that the significance of the individual factors was 0.000, located in the range of significant differences, as shown in Table 6 below. From this we can judge that the personal factors of college liberal arts students have an impact on their mobile learning behavior. Gender coefficient value is positive, we can see that gender has a significant positive impact on mobile learning in liberal arts students in colleges and universities, and then validates the hypothesis of the influence of personal factors on mobile learning behavior.

Table 6

Regression Analysis of Personal Factors and Mobile Learning Behavior

Personal variable

Non-standardized coefficients

Standardized coefficients

T

Significance

B

Standard error

Beta

Equation (constant)

34.319

6.923

-

4.957

0.000

Gender

3.226

3.693

0.107

0.874

0.385

(2)Perception factor analysis

By studying the mobile learning behavior of college liberal arts students, it is found that most students think that blue ink cloud class APP is helpful for the course of «Ancient Chinese» and «Office Automation Practice». At present, this mode is also applied in teaching practice. Through the reliability analysis and validity analysis, it is proved that the research model of mobile learning behavior of college liberal arts students has strong fittingness.

Regression analysis of the observed factors, such as Table 7. Based on the model of mobile learning behavior of liberal arts students in colleges and universities, this paper analyzes the individual differences of mobile learning behavior, the characteristics of mobile devices, external environment factors, application functions, personal factors and mobile learning behaviors from the perspective of perceived ease of use and perceived usefulness Relationship.

Table 7

Regression coefficient analysis of influential factors

Regression coefficient

Individual Differences >>> Learning intention

0.873

Mobile device features>>>Learning intention

0.845

External environment >>> Learning intention

0.929

Application function >>> Learning intention

0.921

Learning intention >>> Learning behavior

0.986

Through the above measurements, we can draw the following conclusions:

①Individual differences positively influence learning intent

②Mobile device features positively influence learning intent

③The external environment positively affects the learning intent

④Application function positively affects learning intent

AppendixObservation variable dimension measurement

Variable

Serial number

Problem description

Individual Differences (ID)

ID1

Among my peers around me, I am usually the first to try new information technologies. (deleted)

ID2

If others use mobile phones and other mobile terminals to demonstrate how to use the APP, I can accomplish the same operation.

ID3

I was hesitant to use the APP. I am afraid that once the mistake will have an impact on normal learning. (deleted)

ID4

I think the APP help reduce plagiarism.

ID5

I think the learning effect of using the APP is trustworthy.

Mobile Device Features (MDF)

MDF1

The navigation design of the APP is conducive to more convenient reading.

MDF2

The APP makes my study more proactive.

MDF3

The APP is easy to use, you can not be limited by time and place restrictions.

External Environment (EE)

EE1

If my classmates, friends or teachers recommend me to use the APP for secondary study, I will try it..

EE2

Using the APP can improve my class performance.

EE3

I am able to use the APP on a variety of end devices.

EE4

Under the open network and plenty of internet traffic, I will do my own self-study using the APP.

EE5

When I encounter problems with the APP, I can get help from service providers or others.

Application Function (AF)

AF1

The issue of the APP is clear and understandable.

AF2

The issue of the APP is related to the syllabus.

AF3

A lot of information can be uploaded to the APP, which helps me to understand and study the course..

AF4

The APP's sign-in function has enhanced my learning autonomy.

AF5

The APP's function of homework assignments can enhance my learning motivation, improve learning efficiency, and be quick and easy.

References:

  1. Park, S. Y., Nam, M. W., & Cha, S.B. (2012). University Students' Behavioral Intention to Use Mobile Learning: Evaluating the Technology Acceptance Model [J]. British Journal of Educational Technology, 43(4), 592–605.
  2. Chang, S. C., & Tung, F. C. (2007). An Empirical Investigation of Students’ Behavioral Intentions to Use the Online Learning Course Websites [J]. British Journal of Educational Technology, 39(1):71–83.
  3. Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., &García-Peñalvo, F. J. (2017). Learning with Mobile Technologies-Students’ Behavior [J]. Computers in Human Behavior, 72:612–620.
  4. Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the Determinants and Age and Gender Differences in the Acceptance of Mobile Learning [J]. British Journal of Educational Technology, 40(1):92–118.
  5. Nikou, S. A., & Economides, A. A. (2017). Mobile-Based Assessment: Investigating the Factors that Influence Behavioral Intention to Use [J]. Computers & Education, 109:56–73.
Основные термины (генерируются автоматически): APP, KMO, MDF.


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