CHATBOT TO SUPPORT LEARNING AMONG NEWCOMERS IN CITIZEN SCIENCE

Abstract

Purpose: Citizen Science (CS) can be defined as the intentional involvement of the public as volunteers in scientific activities or research. Participation in CS can be described as a learning process, but long-term engagement of volunteers is hard to sustain. Earlier studies suggest further research on motivating engagement among new CS members as well as sustaining engagement among old-timers. As chatbots have become a rising topic in CS, the thesis aims to examine how this technology facilitates engagement among CS newcomers, Theory: The thesis adopts the Micro-learning concepts to argue that the chatbot’s interface amplifies the efficiency of learning nudges. The nudges are designed with multimodal learning resources (e.g., text and graphics) to focus on one learning goal at a time. Therefore, they have the potential to reduce information overload for newcomers when they start their participation in a CS project. Method: This study adopts Design Thinking as its methodology to collect data from 14 participants who are between 20 and 30 years old and residing in Southeast Asia over two iterations. During the iterations, usability and personality of the chatbot were examined for user engagement. The think-aloud technique and a questionnaire of usability were employed to collect data in the first iteration while the AttrakDiff framework was used to do that in the second iteration. Results: It has been found that chatbots have many potentials for motivating sustained engagement among newcomers to CS. A bot can engage CS volunteers in four ways by: (1) reducing information overload, (2) providing a responsive learning process, (3) guiding them on how to use the platform, and (4) establishing a rapport. In light of this, the thesis suggests design guidelines that could be considered when developing a CS chatbot for newcomers. The guidelines recommend four steps to follow when developing a CS chatbot, starting with understanding the user's background and going through the design factors on three levels. In addition, the guidelines presented here could be applied to other learning platforms such as Massive Open Online Courses (MOOCs) which also experience the similar challenges of low engagement.