Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher Education

Raffaghelli, Juliana Elisa; Rodríguez, M. Elena; Bañeres, David

doi:10.1016/j.compedu.2022.104468

Cited by 87 publications

(59 citation statements)

References 47 publications

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“…Performance expectations and effort expectations in robotics education positively and directly affect teachers' acceptance (Hl, H2), i.e., the higher performance expectations (PE) or effort expectations (EE) that teachers place on robotics education, the stronger their acceptance of robotics education. This conclusion is not only consistent with the original UTAUT model, but also with earlier research findings (Almaiah et al, 2019 ; Raffaghelli et al, 2022 ). Analysis of the paths revealed that performance expectations (path coefficient of 0.117) had a slightly stronger effect on acceptance than effort expectations (path coefficient of 0.101), generally speaking, teachers' willingness to attempt to introduce a new teaching model into their actual classrooms will heavily consider whether the model contributes to their performance levels.…”

Section: Research Results and Analysissupporting

confidence: 92%

Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network algorithm

Wang

Chen²

2022

Front. Neurorobot.

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With the advancement of artificial intelligence, robotics education has been a significant way to enhance students' digital competency. In turn, the willingness of teachers to embrace robotics education is related to the effectiveness of robotics education implementation and the sustainability of robotics education. Two hundred and sixty-nine teachers who participated in the “virtual human education in primary and secondary schools in Guangdong and Henan” and the questionnaire were used as the subjects of study. UTAUT model and its corresponding scale were modified by deep learning algorithms to investigate and analyze teachers' acceptance of robotics education in four dimensions: performance expectations, effort expectations, community influence and enabling conditions. Findings show that 53.68% of the teachers were progressively exposed to robotics education in the last three years, which is related to the context of the rise of robotics education in schooling in recent years, where contributing conditions have a direct and significant impact on teachers' acceptance of robotics education. The correlation coefficients between teacher performance expectations, effort expectations, community influence, and enabling conditions and acceptance were 0.290 (p = 0.000<0.001), −0.144 (p = 0.048<0.05), 0.396 (p = 0.000<0.001), and 0.422 (p = 0.000<0.001) respectively, indicating that these four core dimensions both had a significant effect on acceptance. Optimization comparison results of deep learning models show that mDAE and AmDAE provide a substantial reduction in training time compared to existing noise-reducing autoencoder models. It is shown that time-complexity of the deep neural network algorithm is positively related to the number of layers of the model.

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Section: Research Results and Analysissupporting

confidence: 92%

Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network algorithm

Wang

Chen²

2022

Front. Neurorobot.

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“…These effects nevertheless become insignificant when observed under voluntary conditions. Based on various reports, SI was found to have a positive and significant effect on a person's and students' intentions to ordinarily and educationally use technology-based media, respectively [53,74,75]. This was in line with other previous studies, where SI affected students towards using video-based learning media [35].…”

Section: Social Influence (Si)supporting

confidence: 86%

Factors Influencing Students’ Continuous Intentions for Using Micro-Lectures in the Post-COVID-19 Period: A Modification of the UTAUT-2 Approach

Wijaya

Weinhandl

2022

Electronics

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Micro-lectures, i.e., short learning videos on a specific aspect of a topic, have become one of the most effective technology-based learning media approaches and were widely used during the COVID-19 pandemic. However, in the post-pandemic era starting from early 2022, as K-12 students have been allowed to resume going to school, it is necessary to evaluate students’ intentions to continuously use micro-lectures for learning mathematics. Therefore, this study aims to explore attitudes and continuous intentions of students towards the utilization of micro-lectures. To investigate students’ intentions of using micro-lectures, we utilized the unified theory of acceptance and use of technology (UTAUT-2). Data were collected from 321 junior high school students (14–17 years old) in Bandung, Indonesia, who used online classes and micro-lectures to learn mathematics during the pandemic. A structural equation model was also used to analyze the independent (performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation, and habit) and dependent (attitude and continuous intention) variables. Furthermore, online questionnaires were used to obtain data on students’ attitudes and continuous intention to utilize micro-lectures in the post-COVID-19 era. The results suggested that effort expectancy (EE) and hedonic motivation (HM) had a significant effect on attitudes, whose correlation with habit also influenced the continuous intention during this post-pandemic period. Despite these results, the habit variable was found to be the factor most influencing continuous intention. These results provide information to teachers, schools, and the government to continuously increase the use of micro-lectures based on improving student learning performances in the post-pandemic era.

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“…(4) Facilitating Conditions (FCs) Facilitating Conditions are the teacher's complete perception of the various technical support conditions needed for the successful use of information technology (Venkatesh et al, 2003). According to several previous reviews, significant positive effects of FCs were observed on UB and BI (Chao, 2019;Alowayr, 2021;Raffaghelli et al, 2022). Based on these results, this variable is the perception of frontline K-12 teachers in using IWB, regarding the convenience of use, equipment and technical support, and after-sale services, such as networking facilities and skills training.…”

Section: The Situation Of Interactive Whiteboard In Remote and Rural ...mentioning

confidence: 99%

Determinants of Behavioral Intention and Use of Interactive Whiteboard by K-12 Teachers in Remote and Rural Areas

Zhou

Wijaya

2022

Front. Psychol.

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Interactive Whiteboard (IWB) has recently been used to replace the TWB (traditional whiteboard), with many of its features being observed to help teachers in educational activities. This is based on effectively and efficiently increasing the teacher-student interaction. Therefore, this study aims to analyze the determinants of Behavioral Intention (BI) and the use of interactive whiteboards by K-12 teachers, in remote and rural Chinese areas. The Modified-Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model was used in this analysis, as a learning medium to deliver the subject matter to students. The sample and population were also the teachers in the Guangxi Zhuang Autonomous Region, China, where 171 voluntary respondents participated in this study. Furthermore, the obtained data were processed using a Structural Equation Model (SEM) approach, through the Smart-PLS software. The results showed that Habit and Hedonic Motivation had a significant influence on the Behavioral Intention (BI) of teachers, toward the utilization of IWB in remote and rural areas. Besides this, Facilitating Conditions (FCs) and BI also had a significant positive effect on Usage Behavior. Based on these results, important information was provided to school principals, local governments, and teachers for education quality improvement, regarding the patterns of increasing IWB utilization in remote and rural areas.

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Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher Education

Cited by 87 publications

References 47 publications

Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network algorithm

Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network algorithm

Factors Influencing Students’ Continuous Intentions for Using Micro-Lectures in the Post-COVID-19 Period: A Modification of the UTAUT-2 Approach

Determinants of Behavioral Intention and Use of Interactive Whiteboard by K-12 Teachers in Remote and Rural Areas

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