Continuous and real-time monitoring of learners’ satisfaction: an application of Big Data in the e-learning domain

Purpose – Understanding and monitoring the learners’ satisfaction become key activities for designing effective and successful learning experiences. Sentiment analysis of informal discussions and streams of messages exchanged within forums and blogs, together with the clustering of formal feedback provided by the learners on their learning experiences are two fundamental knowledge sources for designing an integrated evaluation strategy of online courses. Indeed, merging the “sentiment” of learners measured along the entire course with the main evidences extracted from clustering the feedback that learners provide at the end of the course, can suggest new ways to improve the overall learning experience. Design/methodology/approach – A design science approach has been adopted to conceptualize and define the components of the model and the prototype. Design science supports a pragmatic research paradigm that calls for the creation of innovative prototypes and the experimentation of innovative solutions. The phases of problem identification, objectives definition, artifact development, solution demonstration, evaluation, and research communication have been realized accordingly. Originality/value – This paper explores the application of Big Data to the e-learning domain. Specifically, the study presents the design and the implementation of a prototypalmonitoring of learners’ satisfaction within an online course. The paper highlights an approach that generates value from unstructured data stored in the LMS, processing learners interactions in the social space (e.g. forum) during the course, together with the key topics raising from the answers they provide to questionnaire filled in at the end of the course. Practical implications – The outcomes of this work can provide practical insights to design more successful learning experiences, which rely on the usage of a learning management system. Indeed, by using real time analytics tools and clustering techniques, the prototype has been developed can provide mentors and learning managers with the knowledge to monitor in progress and at the end the individual learning experience, thus offering the opportunity to intervene efficiently and effectively.