Ex-Ante and Ex-Post Feature Evaluation of Online Courses Using the Kano Model

Abstract

Evaluating the effectiveness of online courses and what makes or breaks e-learning pose several challenges. Assessing educational data is generally a multivariate problem of high dimensionality. The implementation is often costly, the experimentation setup is complex, and supervision needs technical expertise. This research proposes an ex-ante and ex-post comparison of online course features using the Kano method from customer satisfaction analysis. Undergraduate students were asked to fill out questionnaires before and after taking a fully functional online course to compare their perceived importance of e-learning features. Attitudes towards 12 features, including ease of use, multimedia inclusion, account settings, and other specific features were gathered. The questionnaire also included feedback on overall experience, general positive and negative elements, and a free-form field for comments and suggestions regarding online courses. The results of this experiment suggest a shift in how students perceive the importance of features associated with online courses after successful completion.