Exploring User Engagement and Incentives in Inquiry-based Active Learning and Knowledge-Sharing Platforms

PhD Thesis Proposal Defence


Title: "Exploring User Engagement and Incentives in Inquiry-based Active 
Learning and Knowledge-Sharing Platforms"

by

Mr. Reza HADI MOGAVI


Abstract:

In recent years, active learning and knowledge-sharing platforms (henceforth 
ACKs) have gained recognition as powerful educational tools enabling users to 
learn and practice myriad topics, such as programming and foreign language from 
any place and at any time. However, the high rate of user dropouts and low user 
engagement are impeding users' endeavors to learn, collaborate, and share their 
knowledge on these platforms. Although a vast body of extant studies has 
examined user engagement and incentives in several ACKs such as Connectivist 
MOOCs, significant research gaps remain unaddressed: (1) The research on some 
salient ACKs, including inquiry-based ACKs, remains sparse. The most prominent 
examples of inquiry-based ACKs include Question-based Learning platforms (QLs) 
such as Math Playground and Duolingo, as well as Question Answering websites 
(QAs) such as Stack Overflow and Ask Ubuntu; (2) there is a paucity of 
explicable dropout forecast models for inquiry-based ACKs that can determine 
the underlying reasons for users dropping out of these platforms; and (3) a 
lack of awareness about the reasons behind gamification failure in 
inquiry-based ACKs.

This thesis aims to address these research gaps by adopting a mixed 
quantitative and qualitative research approach. The thesis comprises two key 
segments investigating user engagement and incentives in QL and QA platforms, 
respectively. Each platform entails its unique design attributes and subtle 
nuances that, in turn, require a thorough investigation.

When examining QLs, we first characterize the engagement patterns (moods) of 
users over time in a large-scale QL typically used to impart training in 
computational and programming to undergraduate students (users). Subsequently, 
we present a novel hybrid dropout prediction model benefitting from the 
utilization of students’ engagement moods in order to enhance the accuracy of 
dropout predictions in QLs. According to our findings, users working on QLs 
exhibit collective preferences to answer questions premised on the engagement 
mood category with which they are associated. Any deviation from these 
collective preferences significantly enhances the probability of user dropouts. 
Gamification denotes a popular strategy to avoid or mitigate user dropouts in 
similar scenarios. Nevertheless, in the capacity of an external incentive, it 
is often fraught with its own share of problems. Within this thesis, our 
subsequent study adopts a qualitative research approach to explore one of the 
most pressing concerns in gamification, i.e., an adverse phenomenon alluded to 
as gamification misuse, in a large-scale gamified QL. We undertake careful 
thematic analysis to identify the most common factors underpinning gamification 
misuse, before classifying them into two groups: active and passive. To 
mitigate or prevent the occurrence of gamification misuse in their future 
designs of gamified learning platforms, we also provide gamified QLs with a 
practical set of suggestions.

In the process of studying QAs, we first investigate user dropouts in QAs 
through the lens of flow theory, a well-known psychological theory. The theory 
posits that users tend to be highly engaged in their experience when the tasks 
(assignments) encountered by them are congruent with their skill levels. 
Accordingly, we present a method of new task assignment that may help decrease 
user dropouts in QAs. We then explore promotional gamification schemes in QAs 
in a subsequent study. Promotional gamification refers to a temporary 
gamification scheme added atop an already gamified QA to increase user 
engagement for a short time-span (e.g., during the holiday season). This thesis 
demonstrates that the addition of more gamification schemes to a pre-existing 
gamified platform does not always increase user willingness or engagement to 
contribute more to QAs. On the contrary, it risks increased user dropouts and 
overjustification after the removal of additional gamification schemes.

Overall, this thesis provides unique insights to inform researchers' and 
practitioners' understanding of user engagement and incentives in inquiry-based 
ACKs, potentially enabling them to reduce users' dropout rates, improve their 
learning experiences, and obviate unnecessary mishaps such as gamification 
misuse and overjustification.


Date:			Wednesday, 23 November 2022

Time:                  	10:00am - 12:00noon

Venue: 			Room 5508
 			Lifts 25/26

Committee Members:	Prof. Pan Hui (Supervisor, EMIA)
 			Dr. Xiaojuan Ma (Supervisor)
 			Prof. Raymond Wong (Chairperson)
 			Prof. Chiew-Lan Tai


**** ALL are Welcome ****