This research aims to explore how to enhance student engagement in higher education institutions (HEIs) using a novel conversational system (chatbots). The study applies a design science research (DSR) methodology and is executed in three iterations: persona elicitation, survey and student engagement factor models (SEFMs), and chatbots interactions analysis. In the first iteration, two K-means clustering analyses are applied to student data, including engagement on campus and student interaction with a virtual learning environment (VLE). Performing the data analysis on the two datasets produces eight distinct personas. Keywords—chatbots; conversational system; design science research; higher education institutions; persona; The pragmatic findings from this study will make two contributions to the current literature. Firstly, machine learning is used to build data-driven personas using K-means clustering. Secondly, a persona template is designed for university students, which will support the construction of data-driven personas. The future work covers the second and third iterations. The second iteration will produce SEFMs, which will include the factors that affect student engagement, confirmed using structural equation modelling (SEM). The third iteration will create effective and usable chatbots that enhance student engagement. Future iterations will build tailored interaction models for these personas and evaluate them using chatbot technology.

This research aims to explore how to enhance student engagement in higher education institutions (HEIs) using a novel conversational system (chatbots). The study applies a design science research (DSR) methodology and is executed in three iterations: persona elicitation, survey and student en...