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.
Highlights
Student engagement refers to the extent to which students are interested or involved in their learning and how they are linked to other students, their classes, and their institutions [1]
= 1, the result of the gap statistic method, was excluded because it would make no change to the existing data, and the initial value of the K-means clustering technique starts from K = 2
Chatbots are conversational systems that interact with users using text or audio, such as Amazon Alexa, Siri on iPhone, Cortana, and Google Assistant
Summary
Student engagement refers to the extent to which students are interested or involved in their learning and how they are linked to other students, their classes, and their institutions [1]. Three dimensions of student engagement have been identified [2]: (1) behavioural engagement, represented by behavioural norms, such as attendance and involvement; (2) emotional engagement, represented by emotions, such as enjoyment, interest, and a sense of belonging; and, (3) cognitive engagement, represented by investing more time in learning beyond that required [2]. Student engagement has received significant attention in the literature since the 1990s [3], in terms of its value for learning and achievement [4]. The literature shows that higher education institutions (HEIs) are facing a critical problem with low-level student engagement. With the significant increase in the number of internet users and mobile phone owners, there has been great interest in employing these
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