Purpose
By using a technology acceptance model (TAM) on survey results collected from two member schools of a Vietnamese educational institution, this study aims to uncover the key factors that affect students’ acceptance of e-learning during the Covid-19 period.
Design/methodology/approach
A bilingual questionnaire in English and Vietnamese was delivered. It was pre-tested on 30 participants before it was finalized. The authors first reviewed the measurement model and made adjustments to the theoretical TAM model. Then the adjusted TAM was used to investigate the relationships of the constructs in the model.
Findings
The results of the structural model show that computer self-efficacy (CSE) has a positive impact on perceived ease of use (PEOU). There is also a positive relationship between system interactivity (SI) and PEOU. Surprisingly, the authors documented that PEOU has no significant impact on students’ attitudes (ATT). The results show that SI can moderately affect ATT. Finally, it is noted that the social factor (SF) directly affects the student’s attitudes (ATT).
Research/limitations/implications
This study contains three limitations. First, as this study only focuses on undergraduate programs, readers should be careful in applying the findings and/or implications of this study to other education levels such as K-12, vocational training and postgraduate programs. Second, the findings are generated within the context of one type of e-learning, conducted via Google Meet. Therefore, future research is needed to provide further validation and comparison across other forms of e-learning. Finally, to further prevent the common bias problem, future research should use both five-point and seven-point Likert scales for the response options in the survey, as well as use negatively worded items. This will help prevent respondents from providing similar answers to all questions.
Originality/value
This study has both theoretical and practical implications. From a theoretical perspective, the study can provide a solid framework for similar studies. From a practical perspective, this study offers implications for governments and universities in the process of adopting e-learning, given that the Covid-19 pandemic is currently in its second and more dangerous wave.
Recent development in computing, sensing and crowd-sourced data have resulted in an explosion in the availability of quantitative information. The possibilities of analyzing this so-called Big Data to inform research and the decision-making process are virtually endless. In general, analyses have to be done across multiple data sets in order to bring out the most value of Big Data. A first important step is to identify temporal correlations between data sets. Given the characteristics of Big Data in terms of volume and velocity, techniques that identify correlations not only need to be fast and scalable, but also need to help users in ordering the correlations across temporal scales so that they can focus on important relationships. In this paper, we present AMIC (Adaptive Mutual Information-based Correlation), a method based on mutual information to identify correlations at multiple temporal scales in large time series. Discovered correlations are suggested to users in an order based on the strength of the relationships. Our method supports an adaptive streaming technique that minimizes duplicated computation and is implemented on top of Apache Spark for scalability. We also provide a comprehensive evaluation on the effectiveness and the scalability of AMIC using both synthetic and real-world data sets.
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