Cloud-based Virtual Learning Environment Research Articles

Using cloud-based virtual learning environments to mitigate increasing disparity in urban-rural academic competence

• Cloud-based Virtual Learning Environment (C-VLE) can help rural students to be academically competitive with their urban counterparts. • This study examined C-VLE adoption and its impact on rural students’ academic performance. • This study extended the task-technology fit theory with channel expansion theory and C-VLE related situational constructs. • This study can assist the educational authorities in increasing the quality of education in rural and remote areas. • This study has significant insights for researchers from technology management, public policy, and educational technology. The unequal distribution of educational opportunities between urban and rural areas is still a significant challenge in China. As a result, students from urban areas can benefit from highly qualified teachers and enjoy state-of-the-art facilities in contrast with their rural counterparts. Consequently, students from urban areas are more likely to join higher education institutes in comparison with their rural counterparts. In such a scenario, a cloud-based virtual learning environment (C-VLE) is of great importance to provide quality education in rural areas. This study aims to predict C-VLE usage's impact on academic performance and gender differences in rural China. The respondents belonged to rural areas and experienced varying levels of such difficulties. This study extended the task-technology fit theory with the channel expansion theory. The study revealed that task-technology fit, individual-technology fit, perceived media richness, omnipresence, and interactivity positively influence C-VLE usage that leads elevating academic performance. As a result, the students from rural areas have a better competitive position than their urban counterparts. Surprisingly, gender did not play any moderating role. The study provides significant theoretical and practical implications for scholars, educational authorities, and policymakers.

An Early Intervention Technique for At-Risk Prediction of Higher Education Students in Cloud-based Virtual Learning Environment using Classification Algorithms during COVID-19

Higher Education is considered vital for societal development. It leads to many benefits including a prosperous career and financial security. Virtual learning through cloud platforms has become fashionable as it is expediency and flexible to students. New student learning models and prediction outcomes can be developed by using these platforms. The appliance of machine learning techniques in identifying students at-risk is a challenging and concerning factor in virtual learning environment. When there are few students, it is easy for identification, but it is impractical on larger number of students. This study included 530 higher education students from various regions in India and the outcomes generated from online survey data were analyzed. The main objective of this research is to predict early identification of students at-risk in cloud virtual learning environment by analyzing their demographic characteristics, previous academic achievement, learning behavior, device type, mode of access, connectivity, self-efficacy, cloud platform usage, readiness and effectiveness in participating online sessions using four machine learning algorithms namely K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Random Forest (RF). Predictive system helps to provide solutions to low performance students. It has been implemented on real data of students from higher education who perform various courses in virtual learning environment. Deep analysis is performed to estimate the at-risk students. The experimental results exhibited that random forest achieved higher accuracy of 88.61% compared to other algorithms.

Explaining Continuance Intention of a Cloud-Based Virtual Learning Environment with Psychological Ownership and the Technology Acceptance Model

Explaining Continuance Intention of a Cloud-Based Virtual Learning Environment with Psychological Ownership and the Technology Acceptance Model

Predicting teachers’ continuance in a virtual learning environment with psychological ownership and the TAM: a perspective from Malaysia

Psychological ownership (PO) is a sense of being psychologically tied to an object to the extent that it becomes part of the extended self. As technology becomes ubiquitous in daily lives, research have shown the potential of this concept to influence users’ behavior. Hence, this study incorporates PO and its antecedents with the beliefs of perceived ease of use (PEU) and perceived usefulness (PU) to predict teachers’ intention to continue using a cloud-based virtual learning environment (cVLE). A hypothesized model with direct and mediated relationships was tested with data obtained from 1068 practicing secondary school teachers using the cVLE. Results from structural equation modeling supported the model, which explained 56.3% of variance in continuance intention with sufficient predictive relevance (Stone–Geisser Q2 = 0.463). Results demonstrated that the tenets of PO hold true in the cVLE context, as a significant influence to PEU and PU, as well as a significant mediator in the hypothesized model. PEU was found to have a stronger effect than PU on continuance intention, implying its importance despite teachers having gained experience in using it and the cVLE considered as user-friendly.

Effects of psychological ownership on teachers\u2019 beliefs about a cloud-based virtual learning environment

Perceived usefulness and perceived ease of use constitute important belief factors when technology adoption decisions are made within a non-mandatory setting. This paper investigated the role played by psychological ownership in shaping teachers’ beliefs about using a cloud-based virtual learning environment (VLE). Psychological ownership is increasingly becoming a relevant phenomenon in technology adoption research, where people can feel psychologically attached to a particular technology. The study proposed that such phenomenon can also occur when using a VLE, and a hypothesised model with six constructs was tested with 629 Malaysian teachers from 21 schools. Results from structural equation modelling-partial least squares analysis found teachers’ experiences with the VLE significantly influenced psychological ownership, which in turn significantly predicted perceived usefulness and perceived ease of use of the VLE. Overall, the model possesses predictive relevance for the outcome predictors as indicated by Stone-Geisser’s Q2, and accounted for 61.6% of variance in perceived usefulness and 62.0% of variance in perceived ease of use. This study provides insights into the motivation behind teachers’ beliefs which are shaped by their experiences with the VLE. Implications for theory and practice were discussed based on the insights of the study.

MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning

Mobile learning in massive open online course (MOOC) evidently differs from its traditional ways as it relies more on collaborations and becomes more fragmented. We present a cloud-based virtual learning environment (VLE) which can organize learners into a better teamwork context and customize micro learning resources in order to meet personal demands in real time. Particularly, a smart micro learning environment was built by a newly designed Software as a Service (SaaS), namely Micro Learning as a Service (MLaaS). It aims to provide adaptive micro learning contents as well as learning path identifications customized for each individual learner. To personalize the micro learning, a dynamic learner model is constructed with regards to the internal and external factors that can affect learning experience and outcomes. Educational data mining (EDM) techniques are employed as the main method to understand learners’ behaviors and recognize learning resource features. A solution of learning path optimization is also proposed towards assembling a complete MOOC learning experience.

The drivers for cloud-based virtual learning environment

PurposeThe purpose of this paper is to verify the moderating effects of school category based on the self-determination theory (SDT) in cloud-based virtual learning environment (VLE) acceptance.Design/methodology/approachTwo phases of national surveys were conducted using a random sampling technique. The research model was tested empirically using 608 samples of primary and secondary school teachers.FindingsThe full model supports significant effects of autonomy, competence, and relatedness on trust, knowledge sharing attitude and behavioral intention except the autonomy-attitude and relatedness-behavioral intention relationships. In primary school, 61, 24.1, 52.1, and 41.1 percent of variances whereas, in secondary school, 57.4, 31.5, 48.6, and 37.5 percent of variances in instructional effectiveness, behavioral intention, knowledge sharing attitude, and trust in VLE website were explained. There are significant moderating effects of category of school in all causal relationships except the autonomy-knowledge sharing attitude relationship.Practical implicationsThere are several useful theoretical and practical implications for scholars and practitioners including Ministry of Education, FrogAsia, etc.Social implicationsThe findings may enhance education quality for societal change.Originality/valueUnlike most studies which focused on TAM, UTAUT, etc., this study investigated the effects of SDT on attitude in sharing knowledge, trust in website and instructional effectiveness. Instead of studying gender differences, this study examined the school category differences.

Predicting instructional effectiveness of cloud-based virtual learning environment

PurposeCloud computing technology is advancing and expanding at an explosive rate. These advancements have further extended the capabilities of the virtual learning environment (VLE) to provide accessibility anywhere, anytime where educational resources can be saved, modified, retrieved and shared on the cloud. The purpose of this paper is to examine the predictors of instructional effectiveness of cloud computing VLE by extending the Self Determination and Channel Expansion Theory with external constructs of VLE interactivity, content design, school support, trust in website, knowledge sharing attitude and demographic variables.Design/methodology/approachRandom sampling data were collected in two waves of nation-wide survey and analyzed with artificial neural network approach.FindingsSDT, CET, content design, interactivity, trust in website, school support and demographics significantly predict instructional effectiveness.Research limitations/implicationsThe study has provided a new paradigm shift from investigating the behavioral intention and continuance intention to the effectiveness of an information system. It advocates that quality of research may be improved by adhering to the basic research methodology starting from rigorous instrument development and validation to future research direction.Practical implicationsThe research provides implications to Ministry of Education, the VLE content and service providers, scholars and practitioners.Social implicationsThe findings of the study may further improve the quality of living of the society when the instructional effectiveness of the cloud-based VLE is further enhanced.Originality/valueExisting grid computing VLE studies have focussed on the acceptance of students and teachers and not its instructional effectiveness. Unlike existing studies that examined extrinsic motivational factors (e.g. TAM, UTAUT), this study uses intrinsic motivational factors (e.g. relatedness, competence and autonomy) as well as perceived media richness. Malaysia is the first nation to implement the VLE at a national scale and the findings from this study will provide a new insight on the determinants of instructional effectiveness of the VLE system.

Behavioural intention in cloud-based VLE: An extension to Channel Expansion Theory

Cloud computing has been a phenomenon nowadays. The ability to access, store/retrieve and share information anytime, anywhere on the cloud has contributed to the birth of the cloud-based virtual learning environment (c-VLE). Though there are studies conducted on the grid-based VLEs, however due to the significant differences between the two computing technologies; the findings from prior studies may not be germane in the c-VLE context. Unlike the previous studies which studied TAM, UTAUT and their extensions from the perspective of undergraduates and university instructors, this study examined the role of Channel Expansion Theory (CET), VLE related attributes (i.e. interactivity, content design, trust-in-website), school support and knowledge sharing attitude from the perspective of school teachers. Educational level and teaching experience were introduced as the control variables. Data were gathered in two waves of nation-wide surveys with random sampling. The findings revealed significant effects of CET, all VLE related attributes (except content design), school support and attitude towards knowledge sharing on behavioural intention (BI). BI was found to have direct effect on instructional effectiveness (IE). The model explained 68.9%, 62.5% and 28.9% variances in CET, BI and IE. The study provides theoretical and practical implications to the scholars and practitioners.

Understanding cloud-based VLE from the SDT and CET perspectives: Development and validation of a measurement instrument

With the emergence of the cloud computing technology, virtual learning environment (VLE) may play an imperative role in promoting instructional effectiveness of ubiquitous learning. However, the existing literature on VLE has been mostly based on the acceptance of VLE from the perspective of the undergraduate students. There is a dearth in studies on the VLE instructional effectiveness from the K-12 teachers’ perspective, the effects of Self Determination Theory and Channel Expansion Theory. Existing VLE instruments have not been rigorously validated and do not consider the importance of cultural differences. This research aims at creating and rigorously validating an instrument to study the cloud-based Frog VLE’s instructional effectiveness in the Malaysian cultural setting. The robustness of the instrument was validated using structural equation modeling, expert panel, Q-sort, pre-test, pilot-test and fieldwork study. This research may offer a parsimonious instrument to evaluate the instructional effectiveness of the Frog VLE for subsequent studies contributing to theory building in the IS literature.

Predicting the acceptance of cloud-based virtual learning environment: The roles of Self Determination and Channel Expansion Theory

The emergence of the cloud computing technology has further enhanced the capabilities of the cloud-based virtual learning environment (VLE) compared to the grid computing based VLE as teaching resources can be accessed, saved, retrieved and shared on the cloud any time any where without any limitation. Unlike existing VLE literature that examines extrinsic motivation (e.g. TAM; UTAUT) from the perspective of the learners in the context of the conventional grid-computing VLE; this study examine the intrinsic motivation from the teachers’ perspective in the context of the cloud-based VLE. So far, the influences of Self Determination Theory (i.e. relatedness, competence, autonomy) and Channel Expansion Theory (i.e. media richness) have been over-looked. In this study, the roles of SDT, CET, VLE content design and interactivity together with the trust-in-website, attitude toward knowledge sharing and school support are being examined. A sample of 1064 respondents was gathered using simple random sampling across the country and analyzed with PLS-SEM. The research model is able to predict intention to use with 65.96% variance explained. SDT, CET, VLE content design, Attitude toward knowledge sharing, trust-in-website, school support and education significantly effects intention to use VLE. This study provides theoretical and practical implications while contributing to the VLE literature.