With the extensive application of such emerging technologies as big data, cloud computing and artificial intelligence in the educational climate, the learning environment of contemporary college students has gradually upgraded from traditional learning environment to digital and intelligent learning environment. The sweeping outbreak of COVID-19 in early 2020 around the world has accelerated the digitalized transformation of education, thus profoundly affecting the operating mechanism of education and teaching. At the same time, driven by the demand of connotative development of higher education, universities are required to change the traditional classroom teaching characterized by "knowledge transfer" ,reconstruct classroom ecology which is committed to cultivating college students' high-level ability、accomplishment and solving unknown problems, and cultivate college students' autonomous learning ability, realizing the transformation of students' ability from acquiring basic subject knowledge to high-level ability and accomplishment such as cultivating innovative spirit. However, the performance of contemporary college students in technological adaptation and learning style adaptation in the digitalized learning environment falls short, that is, college students' adaptability of digitalized learning is poor. Therefore, exploring the measurement and influencing factors of college students' digitalized learning adaptability in the digitalized learning environment has become an important research topic in the process of digitalized transformation of higher education.
This study focuses on "Measurement and influencing factors of College students' Digital Learning adaptability in Digitalized Learning Environment". The main contents are as follows: (1) What are the components of college students' learning adaptability in the digitalized learning environment? (2) How to measure college students' digital learning adaptability, that is, to develop a scale of college students' digital learning adaptability? (3) What are the main influencing factors of college students' digital learning adaptability? (4) Based on the analysis of influencing factors, what is the logical relationship between influencing factors? How to improve the digital learning adaptability of college students?
In order to solve the abovementioned problems, this study carry out a series of work, and then draw the following conclusions:
(1) Determine the basic components of college students' digital learning adaptability in the digitalized learning environment. Based on the conceptual theory of learning adaptability and autonomous learning, determine the basic components of college students' digital learning adaptability by means of literature review, interviews and expert consultation. The basic components of college students' digital learning adaptability in the digitalized learning environment include four dimensions: internal drive of digital learning, digital learning ability, learning management and learning communication.
(2) Develop a scale of digital learning adaptability for college students in the digitalized learning environment. On the basis of determining the basic components of college students' digital learning adaptability, the measurement items are compiled, questionnaires are sent out two times. By means of pre-test analysis and formal analysis, the scale of digital learning adaptability of college students is determined, which contains 18 questions.
(3) Construct model of influencing factors of college students' digital learning adaptability. Based on the classical Unified Theory of Acceptance and Use of Technology (UTAUT) and Task Technology Fit (TTF), a model of influencing factors of college students' digital learning adaptability is constructed. Send out questionnaires, collect data, and make use of such statistical analyses as confirmatory factor analysis, convergence validity and discriminant validity analysis, model fit test, path analysis and intermediary analysis. The results are as follows: Variance explanatory power(R2)of such independent variables as Perceived Task Technology Fit(PTTF)、Usage Behavior(UB)、Perceived Behavioral Control(PBC) to dependent variable, i.e. College students' digital learning adaptability is 0.707, an excellent explanatory ability. Variance explanatory power(R2)of Perceived Behavioral Control(PBC)、Behavior Intention(BI) to Usage Behavior(UB) comes to 0.510,a relatively excellent explanatory ability. Variance explanatory power(R2)of Perceived Task Technology Fit(PTTF)、Perceived Usefulness(PU)、Perceived Ease of Use(PEOU)、Subjective Norm(SN)、Perceived Behavioral Control(PBC) to Behavior Intention(BI)) is 0.487,an acceptable explanatory ability. Variance explanatory power(R2)of Perceived Task Technology Fit(PTTF)、Perceived Ease of Use(PEOU) to Perceived Usefulness(PU) is 0.543,showing a a relatively good explanatory ability. In addition, based on the results of path analysis and intermediary effect analysis, some suggestions about how to improve college students' digital learning adaptability are put forward.
The innovational points of this study are as follows (1) Theoretical level. Explore the relevant concepts which defines college students' digital learning adaptability, compile a scale of college students' digital learning adaptability, and construct a model of influencing factors of college students' digital learning adaptability based on the classical theoretical model. Which enriches the research theme of digital transformation in higher education.(2) Practical level. The scale of college students' digital learning adaptability and the research on its influencing factors will have a foundational and positive influence on the practice of digital transformation of higher education. In addition, the application of high-order statistical analysis methods to study the influencing factors of college students' digital learning adaptability can demonstrate and explore more complex structural relationship among the influencing factors, which will deepen the research in this field, which is of positive significance to the practice of college students' digital learning.
1. 王运武, 李袁爽, 姜松雪& 李雪婷. (2022).疫情背景下高等教育数字化转型趋势——美国《2022地平线报告(教与学版)》解读与启示[J]. 中国教育信息化, 28(05), pp.13-20.
2. 黄荣怀. (2012).从数字学习环境到智慧学习环境——学习环境的变革与趋势[J]. 开放教育研究, 12(1), pp.75-84.
3. 李芳芳.柴玲玲. (2022).学习环境研究的可视化分析:技术赋能的学习变革[J]. 陇东学院学报, 33(1), pp.91-98.
4. 杨俊锋, 龚朝花, 余慧菊& Kinshuk. (2015).智慧学习环境的研究热点和发展趋势——对话ET&S主编Kinshuk(金沙克)教授[J]. 电化教育研究, 36(05), pp.85-88, 95.
5. 李葆萍, 江绍祥, 江丰光& 陈桄. (2014).智慧学习环境的研究现状和趋势——近十年国际期刊论文的内容分析[J]. 开放教育研究, 20(05), pp.111-119.
6. 别敦荣. (2019).大学课堂革命的主要任务、重点、难点和突破口[J]. 中国高教研究, 310(06), pp.1-7.
7. 王亚克& 胡小平. (2022).信息技术时代大学课堂革命的内涵与方向[J]. 教育与考试, 卷缺失(05), pp.57-63.
8. 韩锡斌, 陈香妤, 刁均峰& 周潜. (2022).高等教育教学数字化转型核心...析——基于学生和教师的视角[J]. 中国电化教育, 卷缺失(426), pp.37-42.
9. 黄孝章. (2021).高等教育数字化转型与教育教学模式改革研究[J]. 教育教学论坛, 卷缺失(42), pp.65-68.
10. 罗元元& 杨杏芳. (2020).论信息化时代高等教育的“数...大学”的颠覆性创新何以可能[J]. 北京教育·高教, 2020(8), pp.8-14.
11. 祝智庭.胡娇. (2022).教育数字化转型的实践逻辑与发展机遇[J]. 电化教育研究, 卷缺失(345), pp.5-15.
12. 于泽元& 那明明. (2021).人工智能时代学习方式变革与课程开发向度[J]. 教师教育学报, 8(4), pp.30-37.
13. 杨晓哲&叶露. (2022).新技术支持下义务教育的学习环境与方式变革[J]. 全球教育展望, 51(418), pp.60-67.
14. 陈栋. (2020).基于学习环境变革的泛在学习环境设计[J]. 现代信息科技, 4(5), pp.114-117.
15. 王牧华& 宋莉. (2018).当代学习环境研究的转向及启示[J]. 课程.教材.教法, 38(01), pp.60-66, 72.
16. 李林&王媛媛. (2020).大学生在线学习适应性及学习...于1698个问卷样本的数据[J]. 中国教育信息化, 28(519), pp.53-58.
17. 彭青华. (2022).网络环境下大学生英语学习适应性调查[J]. 赣南师范学院学报, 2022(5), .
18. 秦瑾若. (2019). 基于MOOC的大学生混合式学习适应性影响因素及干预研究. 博士. 陕西师范大学
19. Kim,J., Kwon,Y. & Cho,D. (2011). Investigating Factors That Influence Social Presence and Learning Outcomes in DistanceHigher Education. Computers & Education,57, pp.1512-1520.
20. Lepp,A., Barkley,J. & Karpinski,A. (2014). The Relationship Between Cell Phone Use, Academic Performance, Anxiety, and Satisfaction with Life in College Students. Computers in Human Behavior,31, pp.343-350.
21. Buket akkoyunlu,M. (2008). A Study of Student's Perceptions in a Blended Learning Environment Based on Different Learning Styles. Journal of Educational Technology & Society,11(1), pp.183-193.
22. 陈肖生. (2002).网络教育与学习适应性研究综述[J]. 中国远程教育, 卷缺失(03), pp.6-8, 13-79.
23. V autors,LorenzoGarciaAretio. (2018).The blended learning revolution in distance education[J]. Journal of Distance Education, 卷缺失(期缺失), pp.134-145.
24. Tali & Heiman等. (2016). Social Media Users and Their Social Adaptation Process in Virtual Environment: Is It Easier for Turkish Cypriots to Be Social but Virtual Beings?. Computers in Human Behavior,61, pp.472-477.
25. Silke & Vanslambrouck等. (2019). An In-depth Analysis of Adult Students in Blended Environments: Do They Regulate Their Learning in an ‘old School’ Way?. Computers & Education,128, pp.75-87.
26. Israel & Joseph,M. (2015). Effectiveness of Integrating Moocs in Traditional Classrooms for Undergraduate Students. International Review of Research in Open and Distributed Learning,16(5), pp.1-2.
27. Gyamfi & Adu,S. (2015). Students' Perception of Blended Learning Environment: a Case Study of the University of Education, Winneba, Kumasi-campus, Ghana. International Journal of Education and Development Using Information and Communication Technology,11(2), pp.1-2.
28. Nai & Li等. (2016).Modelling_and_Managing_Learner_Satisfaction__Use_of_Learner_Feedback_to_Enhance_Blended_and_Online_Learning_Experience[J]. Decision Sciences Journal of Innovative Education, 14(2), pp.216-242.
29. Wichadee,Saovapa. (2018).Significant predictors for effectiveness of blended learningin a language course[J]. The jalt call Journal, 14(1), pp.25-42.
30. Yang等. (2013).Motives for Using Facebook, Patterns of Facebook Activities,and Late Adolescents’ Social Adjustment to College[J]. Youth Adolescence, 42(2), pp.403-416.
31. Zhu等. (2011). How Does Internet Information Seeking Help Academic Performance? –the Moderating and Mediating Roles of Academic Self-efficacy. Computers & Education,57(5), pp.2476-2484.
32. Kim等. (2011). Investigating FactorsThat Influence Social Presence and Learning Outcomes in Distance Higher Education. Computers & Education,57(8), pp.1512-1520.Holstein等. (2016). The Characteristics of Successful Moocs in the Fields of Software, Science, and Management,according to Students' Perception. Interdisciplinary Journal of E-skills and Lifelong Learning,12(8), pp.1-20.
33. Baker w,SirykB. (1985).Measurement adjustment to college[J]. Journal of Counseling Psychology, 31(2), p.50.
34. Simon. (1995).Test of Reactions and Adaptation in College (TRACA New Measure of Learning Propensity for College Students[J]. Journal of Educational Psychology, 87(2), pp.293-306.
35. Sjinming. (2014).The study of self-regulated learning-related variables in web-based blended learning: with a focus on school adjustment behavior,Academic Burnout, Self-Determination, and Participation in e-Lcaming[J]. The journal of Yeolin Education, 22(2), pp.237-260.
36. 赵康. (2021).基于MOOC的混合学习适应性研究[J]. 湖北广播电视大学学报, 41(03), pp.36-41.
37. 杨彦军& 童慧. (2015).基于MOOC的混合教学中大学生学习适应性研究[J]. 中国信息技术教育, 卷缺失(21), pp.115-118.
38. 张成龙, 李丽娇& 李建凤. (2017).基于MOOCs的混合式学习适应性影响因素研究——以Y高校的实践为例[J]. 中国电化教育, 363(04), pp.60-66.
39. 何丽. (2022). 智慧教室环境中小学生学习适应性影响因素研究. 硕士. 贵州师范大学.
40. 王晓丽, 路宏& 贾巍. (2015).农村中小学教师远程学习适应性影响因素研究——以宁夏“国培计划”远程培训为例[J]. 电化教育研究, 36(04), pp.108-113.
41. 周步成. (1991). 学习适应性测验. 上海: 华东师范大学出版社.
42. 陈英豪, 林正文& 李坤祟. (1991). 学习适应性量表. 台北: 中国台北心理出版社.
43. 冯廷勇, 苏缇, 胡兴旺& 李红. (2006).大学生学习适应量表的编制[J]. 心理学报, 5(期缺失), pp.762-769.
44. 徐小军. (2004). 大学生学习适应性:结构、发展特点与影响因素研究. 硕士. 西南师范大学.
45. Amzalag,Meital, Elias,Nelly & Kali,Yael. (2015).Adoption of Online Network Tools by Minority Students: The Case of Students of Ethiopian Origin in Israel[J]. Interdisciplinary Journal of e-Skills and Lifelong Learning, 11(5), pp.291-312.
46. Hwang等. (2015).Employing self-assessment, journaling, and peer sharing to enhance learning from an online course[J]. Journal of Computing in Higher Education, 27(8), pp.114-133.
47. 仇焕青. (2010). 在校大学生网络学习适应性整合性干预模式探究. 硕士. 湖南大学.
48. 简婕& 解月光. (2011).试论学习环境及其数字化——一种教学论的视角[J]. 中国电化教育, 卷缺失(02), pp.14-18.
49. 中国社科院语言研究所词典研究室. (2017). 现代汉语词典第7版纪念版. 北京: 商务印书馆: p.1428.
50. 倪文杰. (2002). 现代汉语辞海第2卷. 北京: 中央名族大学出版社: p.1435.
51. George & Myerson. (2013). 达尔文与物种起源. 大连: 大连理工大学出版社: p.86.
52. T.胡森T.N.波斯尔斯维特. (2005). 教育大百科全书第03卷. 重庆: 西南大学出版社: p.174.
53. 林崇德、杨治良、黄希庭. (2003). 心理学大辞典下. 上海: 商务印书馆: p.1158.
54. 林崇德、杨治良、黄希庭. (2003). 心理学大辞典下. 上海: 商务印书馆: p.1159.
55. 田澜. (出版年缺失).我国中小学生学习适应性研究述评[J]. 刊名缺失, 卷缺失(期缺失), .
56. 任友群. (2002).建构主义学习理论的哲学社会学源流[J]. 全球教育展望, 31(11), pp.15-19.
57. 温彭年& 贾国英. (2002).建构主义理论与教学改革——建构主义学习理论综述[J]. 教育理论与实践, 12(05), pp.17-22.
58. Ponton,MichaelK & Rhea,NancyE. (2006).AUTONOMOUS_LEARNING_FROM_A_SOC[J]. New Horizons in Adult Education & Human Resource Development, 20(2), pp.38-49.
59. Fishbein,M.&AjzenI. (1975). Belief, attitude, intention, and behavior: An introduct ion to theory and research. Addison-Wesley: Reading, MA: pp.168-175.
60. Bandura,A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall: Englewood Cliffs, NJ:pp.88-96.
61. 朱颖颖& 宫承波. (2023).老年群体短视频用户体验要素模型探究[J]. 当代传播, 卷缺失(02), pp.92-98.
62. 关磊. (2020).高校数字图书馆网站用户持续使用意愿研究——基于用户体验、TAM和ECM的整合模型[J]. 图书馆工作与研究, 卷缺失(02), pp.48-59.
63. 吕宛青& 葛绪锋. (2020).高校学生对混合式教学接受意愿的实证研究——基于TAM和TPB的整合模型[J]. 云南大学学报(自然科学版), 42(S1), pp.97-105.
64. Simon. (1995).Test of Reactions and Adaptation in College (TRACA New Measure of Learning Propensity for College Students[J]. Journal of Educational Psychology, 87(2), pp.293-306.
65. Gerdes,H. & Mallinckrodt,B. (1994). Emotional, Social, and Academic Adjustment of College Students: a Longitudinal Study of Retention. Journal of Counseling & Development,72, pp.281-288.
66. 张万朋& 程钰琳. (2017).跨地域背景下学生学习适应性的影响因素分析——基于2015年秋季大陆赴台交换生的调查[J]. 教育与经济, 卷缺失(04), pp.89-96.
67. 黄荣怀. (2022).加快教育数字化转型推动学校高质量发展[J]. 人民教育, 卷缺失(Z3),pp.28-32.
68. 张琼& 谢凌凌. (2005).网络课程中的学习适应性研究:概念、机制和理论[J]. 当代教育论坛, 卷缺失(14), pp.100-101.
69. 管珏琪. (2017). 电子书包环境下中小学生的数字化学习力研究. 博士. 华东师范大学.
70. Wang,X., Wang,Z., Wang,Q., Chen,W. & Pi,Z. (2021). Supporting Digitally Enhanced Learning Through Measurement in Higher Education: Development and Validation of a University Students' Digital Competence Scale. Journal of Computer Assisted Learning,37(28), pp.1063-1076.
71. 刘影. (2021).基于混合式学习模式的高校学生学习方式变革研究[J]. 无线互联科技, 2021(21), pp.148-149.
72. 刘金海. (2020). 高中生数学深度学习测评工具的开发及应用研究[Z].
73. 威廉.威尔斯马& 史蒂芬.马尔斯. (2010). 教育研究方法导论. 北京: 教育科学出版社: pp.167-168.
74. 林崇德杨治良黄希庭. (2003). 心理学大辞典下. 上海: 商务印书馆: p.1156.
75. 于莹. (2015).多媒体网络辅助环境下英语学习适应性研究[J]. 北京邮电大学学报(社会科学版), 17(01), pp.107-113.
76. 曹雅涵, 蘇宥鋐, 賴泑伶, 陳碧茵& 黃國豪. (2017).探討先備知識對競爭遊戲式題庫練習之影響:以HTML5證照輔導為例[J]. 數位學習科技期刊, 11(1), pp.1-22.
77. Junfeng yang,A. (2021). Development and Validation of a Digital Learning Competence Scale: a Comprehensive Review. Sustainability,13(5593), pp.1-2.
78. Hair,AndersonTatham&Black. (1998). Multivariate data analysis (5th ed.). Englewood Cliffs, NJ: Prentice Hall: pp.233-239.
79. Hair. (2010). Multivariate data analysis (7th ed.). Englewood Cliffs, NJ: Prentice Hall: pp.408-420.
80. Hair. (2010). Multivariate data analysis (7th ed.). Englewood Cliffs, NJ: Prentice Hall: pp.408-420.
81. Kline,R.B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford: pp.146-151.
82. Aaker,D.A.&BagozziR.P. (1979).Unobservable variables in structural equation models with an application in industrial selling[J]. Journal of Marketing Research, 卷缺失(期缺失), pp.147-158.
83. Churchill,G.A. (1981).A paradigm for developing better measures of marketing constructs[J]. Journal of Marketing Research, 8(16), pp.64-73.
84. Segars,A.H. (1997).Assessing the unidimensionality of measurement: A paradigm and illustration within the context of information systems research[J]. Omega, 25(1), pp.107-121.
85. (2006). Confirmatory factor analysis for applied research (pp. ix–x). New York: Guilford.
86. Fornell,C. (1981). For Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research,18(1), pp.39-50.
87. Kline,R. (2011). Principles and practice of structural equation modeling (3nd ed.). New York: Guilford: pp.95-101.
88. Hair. (2010). Multivariate data analysis (7th ed.). Englewood Cliffs, NJ: Prentice Hall: pp.356-367.
89. 顾明远. (1998). 教育大辞典. 上海: 上海教育出版社: pp.153-154.
90. 王芊芊. (2022).大学生网络学习适应性影响因素研究[J]. 中国教育信息化, 28(519), pp.53-58.
91. 于海波. (2023).高职学生学习适应性的影响因素分析及提升策略[J]. 教育与职业, 14(1038), pp.108-112.
92. 张成龙. (2017).基于MOOCs的混合式学习...研究——以Y高校的实践为例[J]. 远程教育与网络教育, 2017(4), pp.60-66.
93. 米佳. (2021).基于网络学生在线学习适应性的调查分析[J]. 电脑与信息技术, 29(5), pp.80-84.
94. 彭青华. (2012).网络环境下大学生英语学习适应性调查[J]. 赣南师范学院学报, 2012(5), pp.90-93.
95. 陈霜李新房. (2023).数字时代藏族大学生数字化学习适应性的提升策略[J]. 西藏科技, 45(4), pp.35-41.
96. Wujy,ChenTY. (2018).Who is better adapted in learning online within the personal learning environment? Relating gender differences in cognitive attention networks to digital distraction[J]. MIS Quarterly, 128(8), pp.312-329.
97. S,Vanslambrouck. (2019).An in-depth analysis of adult students in blended environments: Do they regulate their learning in an old school way?[J]. Management Science, 128(1), pp.75-83.
98. 杨彦军& 童慧. (2015).基于MOOC的混合教学中大学生学习适应性研究[J]. 中国信息技术教育, 25(8), .
99. 韩啸. (2017).整合技术接受模型的荟萃分析:基于国内10年研究文献[J]. 情报杂志, 36(08), pp.150-155, 174.
100. 张立新& 秦丹. (2019).整合视角下教师采纳新技术的影响因素体系研究[J]. 远程教育杂志, 37(04), pp.106-112.
101. 孙元. (2010). 基于任务—技术匹配理论视角的整合性技术接受模型发展研究. 博士. 浙江大学.
102. Dale l. goodhue,&RonaldL.Thompson. (1995).Task-Technology Fit and Individual Performance[J]. MIS Quarterly, 19(2), pp.213-236.
103. Kieran mathiesona,MarkKeil1. (1998).Beyond the interface Ease of use and task technology fit[J]. Information&Management, 34(1998), pp.221-230.
104. Mark t.dishaw1,DIANEM.STRONG. (1998).Assessing software maintenance tool utilization using task technology fit and fitness-for-use models[J]. SOFTWARE MAINTENANCE:RESEARCH AND PRACTICE, 10(1998), p.151–179.
105. Mark t.dishaw,DianeM.Strong. (1999).Extending the technology acceptance model with task technology fit constructs[J]. Information&Management, 36(1999), pp.9-21.
106. 唐玉栋& 王庆国. (2022).基于TAM和TPB整合模型的峨眉武术线上学习影响研究[J]. 体育科技, 43(6), pp.125-129.
107. 井道龙. (2013). 基于TTF和TAM整合模型的移动学习研究. 硕士. 南京大学.
108. 张成. (2022).基于TTF/TAM整合模型...居民参与意愿的影响因素分析[J]. 职业与健康, 38(21), pp.2959-2963.
109. Kositanurit,B., Ngwenyama,O. & Osei-bryson,K. (2006). An Exploration of Factors That Impact Individual Performance in an Erp Environment: an Analysis Using Multiple Analytical Techniques. European Journal of Information Systems,15, pp.556-568.
110. Davis. (1989).Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS Quarterly, 13(3), pp.319-340.
111. Petter&straub. (2017).Specifying formative constructs in information systems research[J]. MIS Quarterly, 31(4), pp.623-656.
112. Pavlou,Fygenson. (2016).Understanding and prediction electronic commerce adoption: An extension of the theory of planned behavior[J]. MIS Quarterly, 30(1), pp.115-143.
113. Fishbein,Ajzen. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison -Wesley Pub. Co.: Mass.
114. Ajzen. (1991).The theory of planned behavior. Organizational Behavior and Human Decision Processes[J]. Journal of Applied Social Psychology, 50(2), pp.179-211.
115. Karahanna. (2006).Reconceptualizing compatibility beliefs in technology acceptance research[J]. MIS Quarterly, 30(4), pp.781-804.
116. Davis,F.D. (1989).Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS Quarterly, 13(3), pp.319-340.
117. Szajna. (1996).Empirical evaluation of the revised technology acceptance model[J]. Management Science, 42(1), pp.85-92.
118. Davis,Bagozzi. (1989).User acceptance of computer technology: A comparison of two theoretical models[J]. Management Science, 35(8), pp.982-1003.
119. Adams,D.A. (1992).Perceived usefulness, ease of use, and usage of information technology: A replication[J]. MIS Quarterly, 16(2), pp.227-247.
120. Davis. (1996).A critical assessment of potential measurement biases in the technology acceptance model: Three experiments.[J]. International, 45(1), pp.19-45.
121. Ajzen. (1991).The theory of planned behavior. Organizational Behavior and Human Decision Processes[J]. Journal of Experimental Social Psychology, 50(2), pp.179-211.
122. Venkatesh,V. (2000).Determinants of perceived easeof use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model[J]. Information Systems Research, 11(4), pp.342-365.
123. Venkatesh,V.DavisF.D. (2010).A model of the antecedents of perceived ease of use: Development and test[J]. Decision Sciences, 27(3), pp.451-481.
124. Taylor,S. (1995).Assessing IT usage: The role of prior experience.[J]. MIS Quarterly, 19(4), pp.561-570.
125. Goodhue,D.L.ThompsonR.L. (1995).Task-technology fit and individual performance[J]. MIS Quarterly, 19(2), pp.213-236.
126. Sarker,S.ValacichJ. (2005).Technology adoption by groups: A valence perspective.[J]. Journal of the Association for Information Systems, 6(2), pp.37-71.
127. Thompson,B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Englewood Cliffs: American Psychological Association: pp.88-89.
128. Bagozzi,R.P. (1983).Issues in the Application of Covariance Structure Analysis:A Further Comment[J]. Journal of Personality and Social Psychology, 41(4), pp.607-627.
129. Hair,Jr.J.F.AndersonR.E.TathamRL.&BlackW.C. (2009). Multivariate data analysis (7th ed.). Englewood Cliffs, NJ: Prentice Hall.
130. Chin,W.W. (1998).Commentary Issues and Opinion on Structural Equation Modeling[J]. MIS Quarterly, 22(1), .
131. Hooper,D. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods,6(1), pp.53-60.
132. Chin,W.W.&ToddP.A. (1995).On the use,usefulness, and ease of use of stuctural aquation modeling in mis research: a note of caution.(management information systems)[J]. MIS Quarterly,, 19(2), pp.237-246.
133. Joreskog,K. (1984). Analysis of Linear Structural Relationships By the Method of Maximum Likelihood,3th Ed.. Newbury Park,ca: Sage: pp.141-151.
134. Doll,W.J.XiaW.&TorkzadehG. (1994).A Confirmatory Factor Analysis of the End-User Computing Satisfaction Instrument[J]. Management Information Systems Quarterly, 18(4), pp.453-461.
135. Unesc. (2022). 图书题名缺失. Paris: Unesc: .
136. 冯廷勇& 李红. (2002).当代大学生学习适应的初步研究[J]. 心理学探新, 12(01),pp.44-48.
137. Livingstone,Sonia. (2004).Media Literacy and the Challenge of New Information and Communication Technologies[J]. The Communication Review, 7(1), pp.3-14.
138. 钟志贤, 王姝莉& 易凯谕. (2020).论公民媒介素养测评框架建构[J]. 电化教育研究, 2020(1), pp.19-36.
139. 翁克山. (2023).大学生数字素养与移动语言学习现状的匹配度研究[J]. 成都师范学院学报, 39(4), pp.98-107.
140. 黄荣怀, 陈庚, 张进宝& 王运武. (2010).论信息化学习方式及其数字资源形态[J]. 现代远程教育研究, 2010(6), pp.68-73.
141. Kline,R. (2011). Principles and Practice of Structural Equation Modeling (3nd Ed.). New York: Guilford.
142. Fornell,C.&LarckerD.F. (1981).Evaluating structural equation models with unobservable variables and measurement error[J]. Journal of marketing research, 18(1), pp.39-50.
143. Hair,Jr.J.F.AndersonR.E.TathamRL.&BlackW.C. (1998). Multivariate data analysis (5th ed.). Englewood Cliffs,NJ: Prentice Hall.
144. Al.,Hairet. (2009). Multivariate data analysis (7th ed.). Englewood Cliffs, NJ: Prentice Hall.