Personalization is an upcoming trend in gamification research, with several researchers proposing that gamified systems should take personal characteristics into account. However, creating good gamified designs is effort intensive as it is and tailoring system interactions to each user will only add to this workload. We propose machine learning algorithm-based personalized content selection to address a part of this problem and present a process for creating personalized designs that allows automating a part of the implementation. The process is based on Deterding's 2015 framework for gameful design, the lens of intrinsic skill atoms, with additional steps for selecting a personalization strategy and algorithm creation. We then demonstrate the process by implementing personalized gamification for a computer-supported collaborative learning environment. For this demonstration, we use the gamification user type hexad for personalization and the heuristics for effective design of gamification for overall design. The result of the applied design process is a context-aware, personalized gamification ruleset for collaborative environments. Lastly, we present a method for translating gamification rulesets to machine-readable classifier algorithm using the CN2 rule inducer.
Motivation: Code camps and hackathons been used in education for almost two decades. These approaches are usually intensive and for most times quite practical events for solving some real-world problems with various educational objectives. The objectives and structures of these events differ depending on the role of the event in curricula. Problem statement: Both code camps and hackathons been implemented in various ways, with varying success levels. As expected the implementation of the event varies considerably depending on the objectives set for the event, but that then leads to the difficulty and problem setting to understand what organizing of these events actually mean. For educational context, curricula have also its role in defining the targeted skills and competencies the events has to consider too. Approach: We applied a systematic literature review (SLR) to look at the various definitions and modes of these events. Whether it is called "code camp", or "hackathon", or anything else with the same basic meaning, we want to find out what skills and competencies these events emphasize, how they are used in Computer Science (CS) and Software Engineering (SE) education and what are the general structures of the actual arranged events. Contribution: It is aim of this SLR to i) identify various possible ways of implementing these intensive events, and ii) reflect the results to the lessons we have learned of almost two decades of various intensive code camps and hackathons we have been organizing building and participating into. Based on the results, we claim that there is tremendous potential of using these events in education and in the curriculum than how it has been applied so far.
Participatory sensing (PS) and citizen science hold promises for a genuinely interactive and inclusive citizen engagement in meaningful and sustained collection of data about social and environmental phenomena. Yet the underlying motivations for public engagement in PS remain still unclear particularly regarding the role of gamification, for which HCI research findings are often inconclusive. This paper reports the findings of an experimental study specifically designed to further understand the effects of gamification on citizen engagement. Our study involved the development and implementation of two versions (gamified and non-gamified) of a mobile application designed to capture lake ice coverage data in the sub-arctic region. Emerging findings indicate a statistically significant effect of gamification on participants' engagement levels in PS. The motivation, approach and results of our study are outlined and implications of the findings for future PS design are reflected.
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