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The purpose of this study is to evaluate the effect of learner–learner and learner–facilitator interactions on learner satisfaction and their substitutability.

A quantitative survey research focusing on 130 students was used to collect data. Stratified sampling was preferred for the study, with a Likert type research instrument being administered online.

Learner–learner and learner–facilitator interactions mediate the effect of e-learning on learner satisfaction. These sets of interactions act as independent mediators, each playing a pivotal role in enhancing learner satisfaction. The interactions are however not substitutable.

The findings of the study will provide insights to academic administrators, to ensure that the two sets of interactions contribute to an effective and conducive e-learning environment and ultimately learner satisfaction. Deliberate efforts should be made to design increased e-learning interactivity into the cyber learning environment for effective learning.

The findings result in a ranking of learner–learner and learner–facilitator interactions for increased learner satisfaction. The interactions cannot be substituted without affecting the level of learner satisfaction. The interactions play a complementarity role, and collectively, enrich the quality of e-learning. A collective deployment is recommended.

This study's purpose is to propose a research model based on the stimulus-organism-response (S-O-R) model to examine whether gamification and social interaction as environmental stimulus antecedents to learners' perceptions of presence can affect their continuance intention of massive open online courses (MOOCs).

Sample data for this study were collected from learners who had experience in taking gamified MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 353 usable questionnaires were analyzed using structural equation modeling (SEM).

This study demonstrated that learners' perceived gamification, learner–instructor interaction and learner–learner interaction in MOOCs positively affected their cognitive presence, teaching presence and social presence elicited by MOOCs, which jointly expounded their continuance intention of MOOCs. The results support all proposed hypotheses, and the research model accounts for 72.6% of the variance in learners' MOOCs continuance intention.

This study employs the S-O-R model as a theoretical umbrella to frame learners' MOOCs continuance intention as a series of the internal process, which is influenced by gamification and social interaction. Noteworthily, three psychological constructs of the community of inquiry model (i.e. cognitive presence, teaching presence and social presence) are adopted to represent learners' internal experiences of MOOCs usage. To date, hedonic/utilitarian concepts are more commonly adopted as organisms in prior studies based on the S-O-R model, whereas psychological constructs have received lesser attention. Hence, this study enriches the S-O-R model into an invaluable context.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to examine whether media richness (MR), human-system interaction (HSI) and human-human interaction (HHI) as technological feature antecedents to medical professionals’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs).

Sample data for this study were collected from medical professionals at six university-/medical university-affiliated hospitals in Taiwan. A total of 600 questionnaires were distributed, and 309 (51.5%) usable questionnaires were analyzed using structural equation modeling in this study.

This study certified that medical professionals’ perceived MR, HSI and HHI in MOOCs positively affected their emotional LE, cognitive LE and social LE elicited by MOOCs, which together explained their LP in MOOCs. The results support all proposed hypotheses and the research model accounts for 84.1% of the variance in medical professionals’ LP in MOOCs.

This study uses the S-O-R model as a theoretical base to construct medical professionals’ LP in MOOCs as a series of the psychological process, which is affected by MR and interaction (i.e. HSI and HHI). Noteworthily, three psychological constructs, emotional LE, cognitive LE and social LE, are adopted to represent medical professionals’ organisms of MOOCs adoption. To date, hedonic/utilitarian concepts are more commonly adopted as organisms in prior studies using the S-O-R model and psychological constructs have received lesser attention. Hence, this study enriches the S-O-R model into an invaluable context, and this study’s contribution on the application of capturing psychological constructs for completely explaining three types of technological features as external stimuli to medical professionals’ LP in MOOCs is well-documented.

The purpose of this study is to propose a hybrid model integrating the expectation-confirmation model with the views of cognitive absorption (CA) theory and updated DeLone and McLean information system success model to examine whether quality factors as antecedents to medical professionals’ beliefs can affect their continuance intention of the cloud-based e-learning system.

This study’s sampling frame was taken from among medical professionals working in hospitals with over 300 beds in Taiwan which had implemented the cloud-based learning management system (LMS) with a blend of asynchronous and synchronous technologies. Sample data for this study were collected from medical professionals at six hospitals in Taiwan. The data for this study were gathered by means of a paper-and-pencil survey, and each sample hospital that participated in this study was asked to identify a contact person who could distribute the survey questionnaires to medical professionals who had experience in using the cloud-based LMS in their learning. A total of 600 questionnaires were distributed, and 378 (63.0%) usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that medical professionals’ perceived learner–content interaction quality, learner–system interaction quality, service quality, cloud storage service quality and learner–human interaction quality all positively caused their perceived usefulness, confirmation and CA elicited by the cloud-based e-learning system, which jointly explained their satisfaction with the system, and resulted in their continuance intention of the system.

Several limitations and suggestions may open avenues for future research. First, the limitation of self-reported measures should be considered; future research may combine with qualitative data (e.g. semi-structured, narrative, in-depth interviews, focus group interviews and open-ended questions) to get more complete interpretations of medical professionals’ cloud-based e-learning continuance intention. Next, this study’s data were collected from hospitals in Taiwan only; given this study’s limited scope, future research may generalize this study’s sample to the respondents of other national cultural backgrounds and make cross-country comparisons to enhance the completeness of this study. Finally, this study’ results were based on cross-sectional data; future research may use a longitudinal analysis by taking into account the evolution of medical professionals’ cloud-based e-learning continuance intention over time.

This study fully evaluates interaction-related and cloud-related quality determinants through an understanding of medical professionals’ state of CA in explaining their cloud-based e-learning system continuance intention that is difficult to expound with only their utilitarian perception of the system. Hence, the results contribute to deep insights into an all-round quality evaluation in the field of medical professionals’ cloud-based e-learning continuance intention, and extrinsic and intrinsic motivators are both taken into consideration in this study’s theoretical development of medical professionals’ cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.

The purpose of this paper is to evaluate the relative effects of three facets or connectors argued to be vital for learners in successful e-learning outcomes in developing economies.

Data were collected through a survey involving 130 learners. A stratified sampling technique was employed. Regression analyses making use of linear, multiple and PROCESS macro in Statistical Package for the Social Sciences (SPSS) were used to analyze data.

Technological self-efficacy and social presence are the most important facets needed by participants for effective learning in higher education institutions in developing countries. Learning tools meant to enhance teaching and learning and also contribute to learner satisfaction.

The findings of the study provide insights to academic administrators to pay close attention to the three connectors in order to ensure quality learning. The findings guide higher learning institutions to adequately and selectively pay attention to the three connections. Deliberate efforts focusing on students' situations, opinions and concerns are vital for learner satisfaction in developing economies.

This study represents a first attempt to examine the effect of the “right connections” for effective learning in developing economies, using a quantitative approach. The findings bring into attention the role of assessing learner inputs and virtual environment in boosting the effectiveness of e-learning. The findings also result in a model that should lead to increased learner satisfaction through the implementation of right connections. The study “disputes” the relevance of a universal e-learning system.

The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).

Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance.

The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%).

This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

The purpose of this study is to propose the research model integrating the expectation-confirmation model with the views of learning engagement (LE) and extending DeLone and McLean information systems (IS) success model to examine whether quality determinants as antecedents to students' beliefs can influence students' continuance intention of massive open online courses (MOOCs).

Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 600 questionnaires were distributed, and 363 (60.5%) useable questionnaires were analyzed using structural equation modeling in this study.

This study proved that students' perceived knowledge quality, system quality, interface design quality, learner–instructor interaction quality, and collaboration quality all positively caused students' perceived usefulness, confirmation and LE in MOOCs, which jointly explained students' satisfaction with MOOCs and subsequently resulted in students' continuance intention of MOOCs.

This study fully evaluates IS-related and interaction-related quality determinants via an understanding of students' state of LE in explaining students' continuance intention of MOOCs that is difficult to expound with only their utilitarian perception of MOOCs. Hence, this study contributes to deep insights into an all-round quality evaluation in the field of MOOCs continuance intention and takes extrinsic and intrinsic motivators into account in the theoretical development of MOOCs continuance intention to acquire a more comprehensive and robust analysis.

This research aims to examine the impact of virtual learning platforms and instructor presence (IP) on learner satisfaction (LS). Further, this study examines the role of learner engagement (LE) in order to improve the LS.

This research uses both primary and secondary data sources to compile the research's findings. The primary source of data includes 610 responses from various higher education institutes in India. The collected data were analysed using the partial least square structural equation modelling (PLS-SEM) technique.

This research provides evidence that the theoretical model is accurate with the gathered data sample. In the model, online platform (OP) is an independent variable, whereas LS is a dependent variable, and IP and LE are the mediating variables. The outcomes demonstrated that OP has a positive impact on IP and LE. Also, the relationships between IP and LE, IP and LS and LE and LS are significantly positive. The mediation analysis validates the importance of the IP and LE for relationships.

This investigation presents a comprehensive model, which demonstrates the relationship between OP, IP, LE and LS. The study makes a unique reference to several theories in order to boost interaction and IP in virtual learning, the learner's learning experience can be enhanced. The model helps teachers and educational institutions formalise strategies to boost interaction and examine the institutions' pedagogy to enhance satisfaction.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether media richness and social interaction as environmental feature antecedents to nurses’ learning engagement (LE) can affect their continuance intention of massive open online courses (MOOCs) and task performance.

Sample data for this study were collected from nurses at five university-/medical university-affiliated hospitals in Taiwan. A total of 500 questionnaires were distributed, and 303 (60.6%) usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that nurses’ perceived media richness and social interaction in MOOCs positively influenced their behavioral LE and psychological LE elicited by MOOCs, which jointly caused their continuance intention of MOOCs and, in turn, enhance their task performance. The results support all proposed hypotheses and the research model, respectively, explains 84.3% and 63.7% of the variance in nurses’ continuance intention of MOOCs and task performance.

This study uses the S-O-R model as a theoretical base to frame nurses’ continuance intention of MOOCs and task performance as a series of the internal process, which is affected by environmental stimuli (i.e. media richness and social interaction) and organismic states. Noteworthily, while the S-O-R model has been extensively used in prior literature, little research uses this paradigm to expound nurses’ continuance intention of MOOCs in the work settings. Besides, there is a dearth of evidence on the antecedents of nurses’ task performance in the context of MOOCs. Hence, this study’s empirical evidence contributes significantly to the existing literature on bridging the gap of limited evaluation for the research on the impact of nurses’ MOOCs learning on their task performance in the work settings, which is very scarce in the S-O-R view.

This study aimed to investigate learning behaviors deeply in flipped classrooms. In addition, it is worth considering how to help learners through recognition technology with natural language processing (NLP) when learners have question and answer (Q&A). In addition, the Internet of Things (IoT) can be utilized to make the physical learning environment more comfortable and smarter.

The authors developed smart learning environment (SLE) with smart mechanisms supported by recognition technology, NLP and IoT to help learners and employed scaffolding to facilitate their group discussions. This study is an explanatory research to investigate graduate learners' learning behavior when they are collaborating with group members and interacting with the environment in flipped classroom using the proposed SLE.

The results revealed that learners who collaborated more while coediting had significant learning achievement, and NLP sufficiently addressed their questions. Physical conditions of the SLE were comfortable for learners. They perceived that SLE could facilitate group discussions with scaffolding.

This study suggests to utilize flipped classrooms with technologies, e.g. Google Slides integration, to help learners to do more collaboration and use smart mechanisms, e.g. Q&A with NLP, to make learners more interacting during the discussion process.

The proposed SLE can record and analyze smartly their collaboration meaningfully with group members and interact with the environment. Accordingly, researchers found that collaboration in flipped classrooms can help their learning achievement, and it is worth being widely promoted.