Search results

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.

Although MOOCs have become a pervasive online learning model, the problem of high dropout rates still persists. Gathering the reasons for the high dropout rate can help to improve the platform design and management of the MOOCs.

A total of 74 studies was extracted from the Web of Science and Scopus. Following the PRISMA (Preferred Reporting Items for systematic Reviews and Meta-Analyses) guidelines, the open-source program CiteSpace is employed to review and induce the studies on the antecedents of MOOC dropout.

The antecedents of the MOOC dropout rate are the psychological, social, personal, course-related, and time factors, and the unexpected hidden cost. Motivation and interaction, which have a decisive impact on the dropout rate of MOOCs, interact with each other. Interaction helps to strengthen the motivation, and appropriate course design enhances the degree of interaction.

From the perspective of a learner, the more knowledge and skills the learners acquire, the more likely they will complete the course. Possessing adequate foundational knowledge is one way to arrest the dropout rate. On the part of the MOOC platform, better course design eases the dropout rate. Further, the course duration and hidden cost in MOOCs contribute to the dropout rate.

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.

Founded in sociocultural theories of learning, the authors argue that engaging learners in collaborative knowledge building is critical. When responding to others’ ideas, research shows that learners in online settings more frequently focus on surface-level aspects of colleagues’ contributions – sharing, comparing and praising – rather than engaging in knowledge building. Collaborative, knowledge-building discourse includes generative interactional practices that feature disagreeing, negotiating meaning, testing and reflecting on co-constructed ideas, summarizing conversations and making metacognitive contributions to discussions. The purpose of this paper is to review studies that show evidence of key design features and pedagogical practices that support collaborative knowledge building by promoting generative interactional practices and particular patterns in interaction.

This conceptual paper presents pragmatic design and instructional guidelines for online course discussions. The purpose is to synthesize existing research and share a detailed framework for supporting generative discussion in asynchronous online work.

The authors review studies that show evidence of key design features and pedagogical practices that support collaborative knowledge building. Design features to promote generative discourse include using the asynchronous nature of online settings to have students work privately, share their work, discuss their work with the class and then revise; providing instructions/discussion criteria that scaffold knowledge building; and using appropriate digital tools that mediate interaction around content. The pedagogical practices that affect patterns of interaction include modeling generative discourse, promoting increased interactions by and between participants and using opportunistic grouping strategies.

The authors include examples from one of their existing online courses that include these design features and pedagogical practices and discuss results from their ongoing work regarding the generativity of learner interactions in this course.

This study aimed to investigate Jordanian university students' interaction, Internet self-efficacy, self-regulation and satisfaction regarding online education during the COVID-19 pandemic.

A correlational cross-sectional design was utilized using convenience sampling to include 702 undergraduate students from Jordanian universities using an online self-administered questionnaire. Descriptive statistics, T-tests, one-way ANOVA and multiple regression analyses were used to analyze the data.

The mean score of students' satisfaction was low (m = 45.14, SD = 25.62). Regarding student's interaction, learner-instructor interaction had the highest total mean score (m = 58.53, SD = 24.51), followed by learner-learner interaction (m = 47.50, SD = 22.64). Learner-content interaction had the lowest total mean score (m = 45.80, SD = 24.60). Significant differences in students' satisfaction were identified according to the level of education, university type and marital status. Significant predictors of students' satisfaction with online education were self-regulated learning, Internet self-efficacy, learner-content interaction, learner-learner interaction and the number of e-learning theoretical courses.

Online education is not well-established in developing countries. This study contributed to the limited knowledge of university students’ preparedness and satisfaction with online education during the early stage of COVID-19 pandemic.

Course redesign is a creative process that involves the four sets of considerations set out by the DIME model. In this chapter, we highlight key considerations related to design, interaction, media, and evaluation and describe the interconnections of the decisions within the model that make the process iterative. In addition, we suggest supplementary matters for your consideration. Specifically, we explore matters related to career and course management. Career considerations are strategic level concerns related to course redesign that have potentially long-term implications. Course management considerations are tactical level suggestions aimed at making your course implementation a success. Issues and suggestions are grounded in experience.

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.

Interactive activities are an important aspect of distributed learning situations, wherein online communities and learner motivational levels evolve and thrive. Through the thoughtful integration of interactive activities into the online learning process, learners and instructors gain considerable exposure to reciprocally favorable occurrences among learners, content, interface, instructor, community, and self. The thoughtful design and development of a distributed learning environment aids the use of interactive activities in moving beyond mere online interactions towards a more theoretically productive level of interactions. Within a theoretically productive level of interaction wherein the learners obtain information, develop conceptual frameworks through which the information is not only derived but becomes useful knowledge, develop higher‐level thinking skills, and continue to be internally motivated to continue with the course, the learners conceptualize a learning community which can be sorely lacking within distributed learning situations that do not integrate appropriate interactive activities.