Preservice Teachers’ Acceptance of Learning Management Software: An Application of the UTAUT2 Model

Raman, Arumugam; Don, Yahya

doi:10.5539/ies.v6n7p157

Cited by 228 publications

(208 citation statements)

References 12 publications

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“…This is considerably less than what, for example, Venkatesh et al (2012) claimed in their UTAUT2 model. However in their study on technology use of student teachers, Raman and Don (2013) found that the explanatory variables of the UTAUT2 model explained only 29.5% of the variance in use of technology (i.c., LMS use behavior). This means the IMBP might be a parsimonious way to get insight into the key concepts that influence technology use in teacher education.…”

Section: Discussionmentioning

confidence: 99%

“…related to technology use by US instructors in higher education classes (Lewis, Fretwell, Ryan, & Parham, 2013). Raman and Don (2013) applied an adapted version of UTAUT2 to preservice teachers' acceptance of Learning Managament Systems (LMS) and reported a direct effect of Facilitating conditions and indirect effects (via Behavioral intention) of Performance expectancy, Effort expectancy, Social influence, Facilitating conditions, and Hedonic motivation on preservice teachers' use of LMS. In general, these were weak to moderate effects, with the exception of Facilitating conditions, which had strong direct effect on technology use.…”

Section: Theoretical Models Of Technology In Educationmentioning

confidence: 99%

“…Recent research showed that a limited technology infrastructure and the absence of positive attitudes toward technology in education form the two main constraints of using technology in teacher education programs (Omoogun, Ephraim, & Omoogun, 2013). These findings align with the outcomes of other studies on technology use in teacher education presenting Facilitating conditions and Attitudes toward technology to be the most significant explanatory variables of technology use in teacher education (cf., Barton & Haydn, 2006;Drent & Meelissen, 2008;Raman & Don, 2013;Tondeur et al, 2012). As we were interested in actual behavior only, we examined direct effects from Facilitating conditions and the three key concepts Attitudes, Perceived norm and Self-efficacy on teacher educators' use of technology.…”

Section: Theoretical Models Of Technology In Educationmentioning

confidence: 99%

See 2 more Smart Citations

The Integrative Model of Behavior Prediction to Explain Technology Use in Post-graduate Teacher Education Programs in the Netherlands

Admiraal

Lockhorst

Smit

et al. 2013

IJHE

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This study examined technology in post-graduate teacher training programs in the Netherlands. A questionnaire was completed by 111 teacher educators from 12 Dutch universities with a post-graduate teacher training program. The general view of the use of technology in Dutch post-graduate teacher education was quite conventional. Basic technology such as computers, WiFi, electronic whiteboards, virtual learning environments and presentation software was commonly used, but more advanced or innovative technology was less common. In addition, the psychological Integrative Model of Behavior Prediction of Fishbein and Azjen was applied to explain differences between teacher educators in the use of both hardware and software in teacher education. Teacher educators' positive attitudes toward technology in education were significantly related to the extent to which hardware facilities were used to support teacher training pedagogy. Perceived norm largely explained differences in the extent to which software applications were used. However, both aspects were moderated by teacher educators' gender and teaching experience showing that female teacher educators and experienced teacher eduactors reported to use technology in teacher education more than other teacher educators. Soft-and hardware conditions and self-efficacy in technology did not add much explanatory power. Implications for technology use in post-graduate teacher training are formulated.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Models Of Technology In Educationmentioning

confidence: 99%

Section: Theoretical Models Of Technology In Educationmentioning

confidence: 99%

See 1 more Smart Citation

The Integrative Model of Behavior Prediction to Explain Technology Use in Post-graduate Teacher Education Programs in the Netherlands

Admiraal

Lockhorst

Smit

et al. 2013

IJHE

View full text Add to dashboard Cite

show abstract

“…It is defined as "consumers' cognitive tradeoff between the perceived benefits of the applications and the monetary cost for using them" ([10], p. 159). When the technology investigated is free available this construct was also excluded from the analysis in previous studies [37]. Nonetheless, if the job recommender is not free of charge this construct needs to be considered in a job recommender acceptance study.…”

Section: H2: Effort Expectancy Is Positively Associated With the Intementioning

confidence: 99%

Job Seekers' Acceptance of Job Recommender Systems: Results of an Empirical Study

Laumer

Gubler²,

Maier

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…E-learning systems, such as Blackboard, WebCT, Moodle, etc., have proven to be powerful platforms for management and progress in the education sector [31,32]. Teachers may use the system to develop web-based lecture notes and online quizzes and to monitor students' progress; students may use the system to download course materials, check course announcements or syllabi, submit assignments, take quizzes, and check course grades [31].…”

Section: Computer-assisted Language Learning (Call)mentioning

confidence: 99%

Learning Performance Enhancement Using Computer-Assisted Language Learning by Collaborative Learning Groups

Wang

Liao

2017

Symmetry

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Abstract:This study attempted to test whether the use of computer-assisted language learning (CALL) and innovative collaborative learning could be more effective than the use of traditional collaborative learning in improving students' English proficiencies. A true experimental design was used in the study. Four randomly-assigned groups participated in the study: a traditional collaborative learning group (TCLG, 34 students), an innovative collaborative learning group (ICLG, 31 students), a CALL traditional collaborative learning group (C ALL TCLG, 32 students), and a CALL innovative collaborative learning group (C ALL ICLG, 31 students). TOEIC (Test of English for International Communication) listening, reading, speaking, and writing pre-test and post-test assessments were given to all students at an interval of sixteen weeks. Multivariate analysis of covariance (MANCOVA), multivariate analysis of variance (MANOVA), and analysis of variance (ANOVA) were used to analyze the data. The results revealed that students who used CALL had significantly better learning performance than those who did not. Students in innovative collaborative learning had significantly better learning performances than those in traditional collaborative learning. Additionally, students using CALL innovative collaborative learning had better learning performances than those in CALL collaborative learning, those in innovative collaborative learning, and those in traditional collaborative learning.

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Preservice Teachers’ Acceptance of Learning Management Software: An Application of the UTAUT2 Model

Cited by 228 publications

References 12 publications

The Integrative Model of Behavior Prediction to Explain Technology Use in Post-graduate Teacher Education Programs in the Netherlands

The Integrative Model of Behavior Prediction to Explain Technology Use in Post-graduate Teacher Education Programs in the Netherlands

Job Seekers' Acceptance of Job Recommender Systems: Results of an Empirical Study

Learning Performance Enhancement Using Computer-Assisted Language Learning by Collaborative Learning Groups

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