Research On Information Technology Adoption And Learner Retention In Online Education

The emergence of new concepts of online learning such as Massive Open Online Course(MOOC) that come along with Information technology advancement has brought huge momentum to the development of online education. A hot domain for the internet industry, online education has not only brought huge commercial opportunities but also potential and fundamental transformation in learning mode. Amidst the fast development of online education, problems of two aspects are drawing increasing attention—“learners’ low acceptance of emerging online learning technologies” and “significant learner dropout.” Research shows that the early dropout rate of MOOC based on social media technologies and collaborative learning is fairly high, and many online education institutions have also been plagued by ongoing high rates of dropout. For online education institutions, the first problem influences the market promotion of the new online educational service, and the second means the loss of existent market shares, both of which lead to the increase of operational costs and the decrease of revenues.Studies of the above two problems mainly starting from the perspective of pedagogy or information management alone fail to apply the integrated theories of the two areas. Besides, there is a lack of systematic research based on behavioral phase division when seeing online learners’ suspension of learning as a simple learner dropout problem. Regarding learners’ participation in online learning as a complete life cycle, this paper explores three problems—“learning technology adoption, continued learning intention and dropout prediction” based on the three phases—“cause, process and result,” using pedagogical theories in addition to management theories and methods. The paper mainly includes the following aspects:Firstly, the research object—social media technology, an increasingly popular topic, is chosen based on the issue of technology adoption in online education. From the analysis of the features of online education based on social media, the construct that embodies its features is introduced to expand Technology Acceptance Model and set up an acceptance model of social media in online education. Empirical analysis provides the result that this model can effectively illustrate the social media acceptance behavior of online learners. Learners’ perceived knowledge accessibility influences their intention to use through perceived usefulness and perceived ease of use.Secondly, for the formation mechanism of online learners’ continued learning Intention, after a systematic analysis of dropout theory, theory of customer satisfaction and theory of information system continuance, two key constructs of dropout theory—academic integration and social integration—are integrated into Expectation-Confirmation Model of IS Continuance to set up the model of e-learners’ continuance intention toward e-learning. Results of empirical analyses show that learners’ social integration and academic integration, being external factors, influence their continued learning intention through perceived usefulness, expectation confirmation and satisfaction. Whether the external environment is favorable has a moderate effect on the decision making of learners’ continued learning.Thirdly, for learner dropout prediction, learner personal characteristics, learning performance and learning behavior are chosen as the input variables of the prediction model based on the conclusive analysis of abundant studies and the reality of the information system of online educational institutions. Various prediction models are set up on the three levels—“single and integrated models”, “considering learning behavior or not”, and “considering cost-benefit or not.” The prediction results are evaluated, compared and analyzed based on the related indexes of prediction precision and cost-benefit. The results show that the various prediction models constructed can all effectively predict dropout. Integrated model has better results than single models. Models considering learning behavior are more effective than those that do not. Models considering mis-classification costs are better in cost-benefit indexes than those that do not.Fourthly, the conclusions and results of the research on the above three aspects are contextually applied in the following situations. External resource databases and other three components, namely teacher, knowledge agent and knowledge mining, are introduced based on the LTSA systematic framework model, and then a construct of learning management system facing collaborative learning is proposed. A learner dropout monitoring systematic framework of a 4-database construction based on the data center of online educational institutions is put forward, and its operational procedures are analyzed. Strategies for online learner dropout are offered based on soft systems methodology through steps including expressing the problem situation, defining root, building conceptual models and comparing the models with the real world.Being different from existing similar studies that merely consider e-learners’ suspension as a simple loss of learners(or dropout), this dissertation concerns the whole process of learners’ participation in e-learning as a complete life cycle and studies two problems that are closely related to losses of learners in online education, which are IT adoption and learner retention. Moreover, the research findings are utilised for contextualized applications, thus forming a relatively complete theoretical and methodological system.