As the field of child-computer interaction (CCI) develops and forms an increasingly distinct identity, there is a need for reflection upon the state of the field, and its development thus far. This paper provides an overview of the thematic structure of the CCI field in order to support such reflection, expanding upon previous reviews through implementation of a correlated topic model, an automated, inductive content analysis method, in analysing 4,771 CCI research papers published between 2003 and 2021. Prominence of research topics, and their evolution, are explored. Results portray CCI as a vibrant and varied research landscape which has evolved dynamically over time, exhibiting increasing specialisation and emergence of distinct subfields, and progressing from a technology- to needs-driven agenda. This analysis contributes an extensive empirical mapping of the CCI research landscape, facilitating reflection upon the field and its development, and revealing gaps in extant literature and opportunities for future research.

The interest in artificial intelligence (AI) education is growing exponentially; nevertheless, how to learn about AI, particularly Natural Language Processing (NLP), has been a challenging problem for educators and researchers worldwide. This study used a graphical programming platform Snap! to facilitate learning by allowing learners to explore AI and its NLP techniques in class. Data from 18,452 logged events were collected and Lag Sequential Analysis (LSA) was used to examine how learners behaved and learned sequentially. Non-cognitive factors were used to group learners as detailed and subtle behavior sequences that did not occur by chance could be uncovered. The results showed that five groups of learners, that is Passive Learners, Performers, Adaptive Learners, Interested Learners, and Dedicated Learners. They presented varied learning behavior patterns, which should be considered further in designing personalized and intelligent learning platforms to support AI education.

Modelling healthcare providers' knowledge while they are gaining new concepts is an important step towards supporting self-regulated personalised learning at scale. This is especially important if we are to address health workforce skills development and enhance the subsequent quality of care patients receive in the Global South, where a huge skills gap exists. Rich data about healthcare providers' learning can be captured by their responses to close-ended problems within conjunctive solution space -such as clinical training scenarios for emergency care delivery- on smartphone-based learning interventions which are being proposed as a solution for reducing the healthcare skills gap in this context. Together with sequential data detailing a learner's progress while they are solving a learning task, this provides useful insights into their learning behaviour. Predicting learning or forgetting curves from representations of healthcare providers knowledge is a difficult task, but recent promising machine learning advances have produced techniques capable of learning knowledge representations and overcoming this challenge. In this study, we train a Long Short-Term Memory neural network for predicting learners' future performance and forgetting curves by feeding it sequence embeddings of learning task attempts from healthcare providers from Global South. From this training, the model captures nuanced representations of a healthcare provider's clinical knowledge and their patterns of learning behaviours, predicting their future performance with high accuracy. More significantly, by differentiating reduced performance based on spaced learning, the model can help provide timely warning that helps support healthcare providers to reinforce their self-regulated learning while providing a basis for personalised instructional support to aid improved clinical outcomes from their professional practices.

Game appropriation is currently not well conceptualized. What literature does exists (CitationGriffiths & Light, 2008; CitationLowood, 2005; CitationPostigo, 2008; CitationStalker, 2005) uses the term primarily to denote gamers' practices beyond the designers' original intentions, for instance, game content modifications. This article frames game appropriation in a different manner; unlike existing appropriation models, game appropriation is conceptualized as a motivational process underpinned by three primary factors: game design characteristics, social interaction, and the psychological characteristics of the gamer. The main contribution of this article is the development of the first model of game appropriation, the game appropriation model (GAM). GAM explains the process of digital games' incorporation into gamers' daily practices as well as the nature of their gameplay. Game appropriation recognizes the online–offline continuity; it contributes to understating gameplay as a long-term, dynamic activity, directly interrelated with a gamers' everyday life rather than a set of defined moments of participation.

Book synopsis: This publication covers papers presented at the Artificial Intelligence in Education conference 2009 (AIED). AIED2009 is part of an ongoing series of biennial international conferences for top quality research in intelligent systems and cognitive science for educational computing applications. The conference provides opportunities for the cross-fertilization of techniques from many fields that make up this interdisciplinary research area, including: artificial intelligence, computer science, cognitive and learning sciences, education, educational technology, psychology, philosophy, sociology, anthropology, linguistics and the many domain-specific areas for which AIED systems have been designed and evaluated. AIED2009 focuses on the theme 'Building learning systems that care: from knowledge representation to affective modelling'. The key research question is how to tackle the complex issues related to building learning systems that care, ranging from representing knowledge and context to modelling social, cognitive, metacognitive and affective dimensions.This requires multidisciplinary research that links theory and technology from artificial intelligence, cognitive science and computer science with theory and practice from education and the social sciences.

We argue that the One Laptop Per Child (OLPC) can be better understood by examining the general history of development communication and, specifically, through a historical debate between communication scholars Ithiel de Sola Pool and Herbert Schiller. Although originally conducted around broadcast media, the Pool-Schiller conversation identifies questions still relevant to contemporary information and communication for development (ICT4D) projects like the OLPC. Our analysis of their debate identifies five key questions we can apply to the OLPC or any given ICTD4D project: where does change happen? How does change happen? What obligations do designers and researchers have as change agents? What is the role of technology in change? What is the relationship between change, technology and international development? Equipped with this framework, we argue that one place to see OLPC's answers to these Pool-Schiller questions - and, thus, an understanding of OLPC development ideologies - can be found in a textual analysis of the OLPC software design guidelines. This preliminary analysis suggests that OLPC sees the child as the agent of change and the network as the mechanism of change.

Presents a method whereby an autonomous mobile robot automatically selects the most informative data from a set of images acquired a priori, using a statistical method termed information sampling. These data could be a single pixel or a number scattered throughout an image. This information is then used to build a topological map of the environment. Our sole input data are omnidirectional images obtained from a catadioptric panoramic camera. Experimental results show that by using only the best data the topological position of a robot, visually maneuvering through a simple indoor environment, can easily be determined.

Proposes a method for the visual-based navigation of a mobile robot in indoor environments, using a single omnidirectional (catadioptric) camera. The geometry of the catadioptric sensor and the method used to obtain a bird's eye (orthographic) view of the ground plane are presented. This representation significantly simplifies the solution to navigation problems, by eliminating any perspective effects. The nature of each navigation task is taken into account when designing the required navigation skills and environmental representations. We propose two main navigation modalities: topological navigation and visual path following. Topological navigation is used for traveling long distances and does not require knowledge of the exact position of the robot but rather, a qualitative position on the topological map. The navigation process combines appearance based methods and visual servoing upon some environmental features. Visual path following is required for local, very precise navigation, e.g., door traversal, docking. The robot is controlled to follow a prespecified path accurately, by tracking visual landmarks in bird's eye views of the ground plane. By clearly separating the nature of these navigation tasks, a simple and yet powerful navigation system is obtained.