Educational Technology and Related Education Conferences for June to December 2015

The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590This work was part of and supported by GoodBrother, COST Action 19121—Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living

Pick Up and Play: Understanding Tangibility for Cloud Media

ABSTRACT The transition from local and personally owned file-based media management to cloud-based streaming services such as Spotify and Netflix brings new opportunities for users, but also leaves gaps in their understanding and practice. In this paper we present findings from an interview study that explored early adopters' complex relationships with their collections which spanned physical, digital and cloud media. From this we entered a design process focussing on new material forms for cloud based media. Based on this we discuss our design and point to areas where, tangible or not, affordances from physical and digital media are available to be explored in the cloud. Looking in particular at the concepts of scarcity, gifting, and identity we outline possible reasons why, and why not, they could be incorporated into cloud media services

An automatic participant detection framework for event tracking on twitter

Topic Detection and Tracking (TDT) on Twitter emulates human identifying developments in events from a stream of tweets, but while event participants are important for humans to understand what happens during events, machines have no knowledge of them. Our evaluation on football matches and basketball games shows that identifying event participants from tweets is a difficult problem exacerbated by Twitter’s noise and bias. As a result, traditional Named Entity Recognition (NER) approaches struggle to identify participants from the pre-event Twitter stream. To overcome these challenges, we describe Automatic Participant Detection (APD) to detect an event’s participants before the event starts and improve the machine understanding of events. We propose a six-step framework to identify participants and present our implementation, which combines information from Twitter’s pre-event stream and Wikipedia. In spite of the difficulties associated with Twitter and NER in the challenging context of events, our approach manages to restrict noise and consistently detects the majority of the participants. By empowering machines with some of the knowledge that humans have about events, APD lays the foundation not just for improved TDT.peer-reviewe

Stress Detection Using Experience Sampling: A Systematic Mapping Study

Stress has been designated the "Health Epidemic of the 21st Century" by the World Health Organization and negatively affects the quality of individuals' lives by detracting most body systems. In today's world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees' performance and even their health by threatening them emotionally and physically. The main advantage of experience sampling is that evaluating instantaneous experiences causes less memory bias than traditional retroactive measures. Further, it allows the exploration of temporal relationships in subjective experiences. The objective of this paper is to structure, analyze, and characterize the state of the art of available literature in the field of surveillance of work stress via the experience sampling method. We used the formal research methodology of systematic mapping to conduct a breadth-first review. We found 358 papers between 2010 and 2021 that are classified with respect to focus, research type, and contribution type. The resulting research landscape summarizes the opportunities and challenges of utilizing the experience sampling method on stress detection for practitioners and academics. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This research was funded by Molde University College, Specialized Univ. Norway, through support of the Open Access fund.Scopus2-s2.0-8512936724