Abstract

E-learning facilitates both synchronous and asynchronous learning and it plays very important role in the teaching learning process. A large group of learners are engaged in the idea exchange independently by interacting with the members present in the learning management system. In order to generate meaningful learning outcome of the individual peer learners, the feedback review is very essential to extract the conceptual content which reflect the instantaneous learner’s behavior, emotions, capabilities, interestingness and difficulties and to fits them effectively. Collecting feedback in the form of numeric scale is very tough for both the learners and facilitators while specifying the rating, but it is too easy for the learners provide feedback in the form of text messages. The key challenge for analyzers is to extract the meaningful feedback content and dynamic rating of the learner’s feedback related to various conceptual contexts. We propose a novel method using multivariate predictive model for conceptual content analytics based on e-learners reviews using standard statistical model inverse regression. Finally the analysis is used in the prediction studies and to illustrate their effectiveness against the learner’s feedback.

Keywords:

E-learning , feedback , logistic regression , review analytics , text mining,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.