Personas Design for Conversational Systems in Education

• Main Authors: Fatima Ali Amer Jid Almahri, David Bell, Mahir Arzoky
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Informatics
• Subjects: chatbots; clustering; conversational system; data analysis; data-driven personas development method; <i>k</i>-means; machine learning; personas; personas design; student engagement
• Online Access: https://www.mdpi.com/2227-9709/6/4/46

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 <i>K</i>-means clustering technique. Data analysis is conducted using two datasets. Three methods are used to find the <i>K</i>-values: the elbow, gap statistic, and silhouette methods. Subsequently, the silhouette coefficient is used to find the optimal value of <i>K</i>. 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 <i>K</i>-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.