Personas Design for Conversational Systems in Education

Abstract

This research aims to explore how to enhance student engagement in higher education institutions (HEIs) while using a novel conversational system (chatbots). The principal research methodology for this study is design science research (DSR), which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This paper focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, specifically the K-means clustering technique. Data analysis is conducted using two datasets. Three methods are used to find the K-values: the elbow, gap statistic, and silhouette methods. Subsequently, the silhouette coefficient is used to find the optimal value of K. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically K-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations. It will cover building SEFMs, building tailored interaction models for these personas and then evaluating them using chatbot technology.