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Using the online educational game Battleship Numberline, we have collected over 8 million number line estimates from hundreds of thousands of players. Using random assignment, we evaluate the effects of various adaptive sequencing algorithms on player engagement and learning.
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... Feedforward has been applied to enhance learning in different contexts. Educational games deployed algorithms that ensured players always received positive responses to their actions [60] by proactively adapting the difficulty to their learning rate [85]. In classroom teaching, feedforward evaluation let educators make real-time adjustments [15] and allowed students to adapt their plans [74] accordingly to the current learning outcomes. ...
Media architecture exploits interactive technology to encourage passers-by to engage with an architectural environment. Whereas most media architecture installations focus on visual stimulation, we developed a permanent media facade that rhythmically knocks xylophone blocks embedded beneath 11 window sills, according to the human actions constantly traced via an overhead camera. In an attempt to overcome its apparent limitations in engaging passers-by more enduringly and purposefully, our study investigates the impact of feedforward learning, a constructive interaction method that instructs passers-by about the results of their actions. Based on a comparative (n=25) and a one-month in-the-wild (n=1877) study, we propose how feedforward learning could empower passers-by to understand the interaction of more abstract types of media architecture, and how particular quantitative indicators capturing this learning could predict how enduringly and purposefully a passer-might engage. We believe these contributions could inspire more creative integrations of non-visual modalities in future public interactive interventions.
... It is not uncommon to see the term used interchangeably with a variety of emotional states and other related aspects as well [4,3,5]. Some authors equate engagement with time spent on a task [28] or some other outcome that can be measured (e.g., the number of visitors or amount of replays) [29][30][31]. Others consider engagement a precursor to or 'initial stage' of immersion, while immersion, in turn, becomes conflated with aspects of presence (i.e., a sense of 'being' in the virtual environment) and absorption (i.e., loss of time and space) [4]. ...
Although games are frequently described as ‘engaging’, what this means exactly continues to be subject of debate in game literature. Engagement is often defined through related concepts like immersion and positive emotions. However, this neglects the fact that applied games aim to provide more than an entertaining experience, and that engagement with the applied purpose can exist separately from engagement with the game’s systems. To make this differentiation more apparent, this article introduces the Applied Games Engagement Model (AGEM), a theoretical model that distinguishes between an applied game’s systems and its non-entertainment purpose. It poses that game systems and purpose can overlap in varying amounts, both from game to game, and from moment to moment within a single game. The value of the model is in the explicit acknowledgement that the attention necessary for engaging with content is a limited resource, and that measures for engagement in applied games need to consider that not all engagement is purposeful. The article lays the conceptual foundation for the study of engagement in applied games, and provides a framework for how to design for an applied purpose. It illustrates its use in analysing applied games and their designs through three case studies.
... Systems that adapt to the user have been explored as a means of facilitating the flow state. This includes adaptive algorithms that determine when a player should be allowed to progress in a learning game (Lomas et al. 2012) and a model that adapts to the gameplayer, not by adapting the level of challenge but by changing the way the player perceives the results of his or her actions, with the aim of improving player confidence (Van Der Spek 2012). ...
Engaging users is a priority for designers of products and services of every kind. The need to understand users’ experiences has motivated a focus on user engagement across computer science. However, to date, there has been limited review of how Human--Computer Interaction and computer science research interprets and employs the concept. Questions persist concerning its conception, abstraction, and measurement. This article presents a systematic review of engagement spanning a corpus of 351 articles and 102 definitions. We map the current state of engagement research, including the diverse interpretation, theory, and measurement of the concept. We describe the ecology of engagement and strategies for the design of engaging experiences, discuss the value of the concept and its relationship to other terms, and present a set of guidelines and opportunities for future research.
Since the 1960s, researchers have been trying to optimize the sequencing of instructional activities using the tools of reinforcement learning (RL) and sequential decision making under uncertainty. Many researchers have realized that reinforcement learning provides a natural framework for optimal instructional sequencing given a particular model of student learning, and excitement towards this area of research is as alive now as it was over fifty years ago. But does RL actually help students learn? If so, when and where might we expect it to be most helpful? To help answer these questions, we review the variety of attempts to use RL for instructional sequencing. First, we present a historical narrative of this research area. We identify three waves of research, which gives us a sense of the various communities of researchers that have been interested in this problem and where the field is going. Second, we review all of the empirical research that has compared RL-induced instructional policies to baseline methods of sequencing. We find that over half of the studies found that RL-induced policies significantly outperform baselines. Moreover, we identify five clusters of studies with different characteristics and varying levels of success in using RL to help students learn. We find that reinforcement learning has been most successful in cases where it has been constrained with ideas and theories from cognitive psychology and the learning sciences. However, given that our theories and models are limited, we also find that it has been useful to complement this approach with running more robust offline analyses that do not rely heavily on the assumptions of one particular model. Given that many researchers are turning to deep reinforcement learning and big data to tackle instructional sequencing, we believe keeping these best practices in mind can help guide the way to the reward in using RL for instructional sequencing.
A systematic review was designed to address the question of "What is engagement and how has the term been used, defined and measured in the context of serious games?". The goal of the review was to collect, evaluate, and analyse literature related to the definition and measurement of engagement in serious games published between in 1970 to 2015 across a broad range of disciplines. A total of 1390 papers were initially identified from the search criteria. These were reduced to 107 papers that directly assessed engagement in a serious game. These selected papers were then analysed to examine the use of the term 'engagement' in the serious games, the genres of the games studied, the various definitions of engagement and the methods used to measure different aspects of engagement. Three distinct types of engagement, related to behaviour, affect and cognition are found in the studies, along with a broad range of evaluation methods including interviews, questionnaires, physiological approaches, in game metrics, and time and performance on task.
